Adding an article for Faster R-CNN

NEWS.md

@@ -7,6 +7,8 @@
 ## New features
 
 * Added `target_transform_resize` to manage bounding_box resizing in parallel to `transform_resize` for images (#337).
+* Added article showcasing `model_fasterrcnn_resnet50_fpn()` with visualization utilities `draw_bounding_boxes()` and `vision_make_grid()` (@DerrickUnleashed, #301).
+
 
 ## Bug fixes and improvements
 
@@ -26,7 +28,9 @@
 * Added article showcasing `model_fcn_resnet50()` with visualization utilities `draw_segmentation_masks()` and `vision_make_grid()` (@DerrickUnleashed, #281).
 * Added collection dataset catalog with `search_collection()`, `get_collection_catalog()`, and `list_collection_datasets()` functions for discovering and exploring collections (#271, @ANAMASGARD).
 * Added `target_transform_coco_masks()` and `target_transform_trimap_masks()` transformation functions for explicit segmentation mask generation (@ANAMASGARD).
-* Added support for `connectivity` argument for drawing lines between keypoints in `draw_keypoints()` (@DerrickUnleashed, #303).
+
+* Added support for `connectivity` argument for drawing lines between keypoints in `draw_keypoints()` (@DerrickUnleashed #303)
+
 
 ## New models
 

R/models-faster_rcnn.R

@@ -975,31 +975,63 @@ mobilenet_v3_320_fpn_backbone <- function(pretrained = TRUE) {
 #' # ResNet backbone requires image normalization
 #' input <- image  %>%
 #'   transform_normalize(norm_mean, norm_std)
-#' batch_normalized <- input$unsqueeze(1)    # Add batch dimension (1, 3, H, W)
+#' batch <- input$unsqueeze(1)    # Add batch dimension (1, 3, H, W)
+#'
+#' # ResNet-50 FPN
+#' model <- model_fasterrcnn_resnet50_fpn(pretrained = TRUE, score_thresh = 0.5,
+#'                                         nms_thresh = 0.8, detections_per_img = 3)
+#' model$eval()
+#' pred <- model(batch)$detections[[1]]
+#' num_boxes <- as.integer(pred$boxes$size()[1])
+#' keep <- seq_len(min(5, num_boxes))
+#' boxes <- pred$boxes[keep, ]$view(c(-1, 4))
+#' labels <- coco_classes(as.integer(pred$labels[keep]))
+#' if (num_boxes > 0) {
+#'   boxed <- draw_bounding_boxes(image, boxes, labels = labels)
+#'   tensor_image_browse(boxed)
+#' }
 #'
 #' # ResNet-50 FPN V2
 #' model <- model_fasterrcnn_resnet50_fpn_v2(pretrained = TRUE, , detections_per_img = 5 )
 #' model$eval()
-#' torch::with_no_grad({pred <- model(batch_normalized)$detections[[1]]})
-#' labels <- coco_classes(as.integer(pred$labels))
+#' pred <- model(batch)$detections[[1]]
+#' num_boxes <- as.integer(pred$boxes$size()[1])
+#' keep <- seq_len(min(5, num_boxes))
+#' boxes <- pred$boxes[keep, ]$view(c(-1, 4))
+#' labels <- coco_classes(as.integer(pred$labels[keep]))
+#' if (num_boxes > 0) {
+#'   boxed <- draw_bounding_boxes(img, boxes, labels = labels)
+#'   tensor_image_browse(boxed)
+#' }
 #'
-#' # Visualize boxes
-#' labels <- coco_classes(as.integer(pred$labels))
-#' boxed <- draw_bounding_boxes(image, pred$boxes, labels = labels)
-#' tensor_image_browse(boxed)
+#' # MobileNet V3 Large FPN
+#' model <- model_fasterrcnn_mobilenet_v3_large_fpn(pretrained = TRUE)
+#' model$eval()
+#' pred <- model(batch)$detections[[1]]
+#' num_boxes <- as.integer(pred$boxes$size()[1])
+#' keep <- seq_len(min(5, num_boxes))
+#' boxes <- pred$boxes[keep, ]$view(c(-1, 4))
+#' labels <- coco_classes(as.integer(pred$labels[keep]))
+#' if (num_boxes > 0) {
+#'   boxed <- draw_bounding_boxes(image, boxes, labels = labels)
+#'   tensor_image_browse(boxed)
+#' }
 #'
 #' # MobileNet V3 Large 320 FPN
 #' batch <- image$unsqueeze(1)    # Add batch dimension (1, 3, H, W)
 #' model <- model_fasterrcnn_mobilenet_v3_large_320_fpn(
 #'   pretrained = TRUE, score_thresh = 0.02, nms_thresh = 0.8, detections_per_img = 5
 #' )
 #' model$eval()
-#' torch::with_no_grad({pred <- model(batch)$detections[[1]]})
-#'
-#' # Visualize boxes
-#' labels <- coco_classes(as.integer(pred$labels))
-#' boxed <- draw_bounding_boxes(image, pred$boxes, labels = labels)
-#' tensor_image_browse(boxed)
+#' pred <- model(batch)$detections[[1]]
+#' num_boxes <- as.integer(pred$boxes$size()[1])
+#' keep <- seq_len(min(5, num_boxes))
+#' boxes <- pred$boxes[keep, ]$view(c(-1, 4))
+#' labels <- coco_classes(as.integer(pred$labels[keep]))]
+#' if (num_boxes > 0) {
+#'   boxed <- draw_bounding_boxes(image, boxes, labels = labels)
+#'   tensor_image_browse(boxed)
+#' }
 #' }
 #'
 #' @family object_detection_model

_pkgdown.yml

@@ -31,6 +31,8 @@ navbar:
         href: articles/examples/fcnresnet.html
       - text: keypoints
         href: articles/examples/keypoints.html
+      - text: fasterrcnn
+        href: articles/examples/fasterrcnn.html
 
 reference:
 - title: Transforms

vignettes/examples/faster-rcnn.R

@@ -0,0 +1,81 @@
+# Loading Images ---------------------------------------------------
+library(torchvision)
+library(torch)
+
+url1 <- "https://raw.githubusercontent.com/pytorch/vision/main/gallery/assets/dog1.jpg"
+url2 <- "https://raw.githubusercontent.com/pytorch/vision/main/gallery/assets/dog2.jpg"
+
+dog1 <- base_loader(url1) %>% transform_to_tensor()
+dog2 <- base_loader(url2) %>% transform_to_tensor()
+
+
+# Visualizing a grid of images -------------------------------------
+
+
+dogs <- torch_stack(list(dog1, dog2))
+grid <- vision_make_grid(dogs, scale = TRUE, num_rows = 2)
+tensor_image_browse(grid)
+
+
+# Preprocessing the data -------------------------------------------
+
+
+norm_mean <- c(0.485, 0.456, 0.406)
+norm_std  <- c(0.229, 0.224, 0.225)
+
+dog1_prep <- dog1 %>%
+  transform_resize(c(800, 800)) %>%
+  transform_normalize(mean = norm_mean, std = norm_std) %>%
+  torch_tensor(dtype = torch_float32())
+dog2_prep <- dog2 %>%
+  transform_resize(c(800, 800)) %>%
+  transform_normalize(mean = norm_mean, std = norm_std) %>%
+  torch_tensor(dtype = torch_float32())
+
+# make batch (2,3,800,800)
+dog_batch <- torch_stack(list(dog1_prep, dog2_prep))
+
+
+# Loading Model ----------------------------------------------------
+
+
+model <- model_fasterrcnn_resnet50_fpn(
+  pretrained = TRUE,
+  score_thresh = 0.5,
+  nms_thresh = 0.8,
+  detections_per_img = 2
+)
+model$eval()
+
+# run model
+output <- model(dog_batch)
+
+
+# Processing the Output --------------------------------------------
+
+pred1 <- output$detections[[1]]
+pred2 <- output$detections[[2]]
+
+pred1$boxes
+pred1$labels
+pred1$scores
+
+
+# Visualizing the Output -------------------------------------------
+
+
+boxed1 <- draw_bounding_boxes(
+  dog1 %>% transform_resize(c(800, 800)),
+  boxes = pred1$boxes,
+  labels = coco_classes(as.integer(pred1$labels))
+)
+
+boxed2 <- draw_bounding_boxes(
+  dog2 %>% transform_resize(c(800, 800)),
+  boxes = pred2$boxes,
+  labels = coco_classes(as.integer(pred2$labels))
+)
+
+boxed <- torch_stack(list(boxed1, boxed2))
+grid <- vision_make_grid(boxed, scale = FALSE, num_rows = 2)
+tensor_image_browse(grid)

vignettes/examples/faster-rcnn.Rmd

@@ -0,0 +1,9 @@
+---
+title: "faster_rcnn"
+type: docs
+---
+
+```{r, echo = FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
+```