Fix typos

packages/msrtc_rans/include/msrtc_rans/EntropyCoder.h

@@ -3,7 +3,7 @@
 
 /**
  * @file msrtc_rans/entropy_coder.h
- * @brief Defines msrtc_rans::EntropyEncoder and msrtc_rans::EntropyDecoder - entropy encoder and decoder using rANS alogirthm
+ * @brief Defines msrtc_rans::EntropyEncoder and msrtc_rans::EntropyDecoder - entropy encoder and decoder using rANS algorithm
  */
 
 #pragma once

packages/msrtc_rans/private/include/msrtc_rans/rans.h

@@ -3,7 +3,7 @@
 
 /**
  * @file msrtc_rans/rans.h
- * @brief Defines raw primitives of rANS alogirthm
+ * @brief Defines raw primitives of rANS algorithm
  */
 
 #pragma once
@@ -25,7 +25,7 @@ using rans_freq_t = uint32_t;
 
 namespace details {
 
-// 64-bit numbers multiplication outputing high 64-bit
+// 64-bit numbers multiplication outputting high 64-bit
 #if defined(_MSC_VER)
 static inline uint64_t Mul64Hi(uint64_t a, uint64_t b)
 {
@@ -80,7 +80,7 @@ struct RansEncSymbol {
     RansEncSymbol(freq_t start, freq_t freq, freq_t scale_bits);
 };
 
-// Raw rANS alogirthm encoder
+// Raw rANS algorithm encoder
 // Sink need to define:
 //  - operator(unit_t) for writing next unit
 template <typename StateType, typename UnitType, typename Sink>
@@ -291,7 +291,7 @@ struct RansDecSymbol {
     RansDecSymbol(freq_t start, freq_t freq) : m_freq(freq), m_start(start) { assert(freq > 0); }
 };
 
-// Raw rANS alogirthm decoder
+// Raw rANS algorithm decoder
 // Source needs to define:
 //  - operator(unit_t&) for reading next unit
 //  - OnOK() for reporting advancing to next symbol

packages/msrtc_rans/python/cpp/PyWrapper.h

@@ -34,7 +34,7 @@
 
 namespace PyWrapper {
 
-// Marker to contruct a reference to new object
+// Marker to construct a reference to new object
 struct NewRef {};
 
 // Simplistic implementation of reference counting pointer to python object

packages/msrtc_rans/python/src/msrtc/rans/_msrtc_rans.pyi

@@ -83,10 +83,10 @@ class RansDecoderStream:
 
 EntropyEncoder = Any
 """
-Entropy encoder implmentation
+Entropy encoder implementation
 """
 
 EntropyDecoder = Any
 """
-Entropy decoder implmentation
+Entropy decoder implementation
 """

video/compare_rd_video.py

@@ -307,7 +307,7 @@ def __init__(
 
         base_method_name = args.base_method
         assert len(args.log_paths) % 2 == 0, (
-            "log paths shoud include both the method name and the corresponding log path"
+            "log paths should include both the method name and the corresponding log path"
         )
         log_paths = {}
         for i in range(len(args.log_paths) // 2):

video/src/metrics/msssim.py

@@ -29,7 +29,7 @@ def __init__(self, *, channels=1, data_range=1):
         C2 = (0.03 * data_range) ** 2
         self.register_buffer("_C", torch.asarray((C1, C2), dtype=torch.float))
 
-        # scale weigths
+        # scale weights
         self.register_buffer("_weight5", torch.asarray((0.0448, 0.2856, 0.3001, 0.2363, 0.1333), dtype=torch.float))
         # scale weights for small images according to HM implementation
         self.register_buffer("_weight4", torch.asarray((0.0517, 0.3295, 0.3462, 0.2726), dtype=torch.float))

video/src/models/entropy_models.py

@@ -377,7 +377,7 @@ def _cdf2(inputs):
     def get_laplace_prob(values, scales):
         # noinspection PyUnusedLocal
         def _cdf(inputs):
-            # this is the original function of cdf, but we only care diffence of cdf
+            # this is the original function of cdf, but we only care difference of cdf
             return 0.5 + 0.5 * torch.sign(inputs) * (1.0 - torch.exp(-torch.abs(inputs)))
 
         def _cdf2(inputs):

video/src/utils/app.py

@@ -1298,7 +1298,7 @@ def optimizer_step(self):
 
             min_grad_scale = self.get_config_by_path("train.min_grad_scale", default=0, expected_type=numbers.Number)
             if min_grad_scale > 0:
-                # sync scale value to garantee all ranks skip or process batch
+                # sync scale value to guarantee all ranks skip or process batch
                 if self.world_size > 1:
                     grad_scale_tensor = torch.tensor(grad_scale, dtype=torch.float64, device=self.device)
                     torch.distributed.broadcast(grad_scale_tensor, src=0)

video/src/utils/common.py

@@ -242,7 +242,7 @@ def dump_json(obj, fid, float_digits=-1, **kwargs):
     # noinspection PyShadowingNames
     def inner(*args, **kwargs):
         args = list(args)
-        # fifth argument is float formater which we will replace
+        # fifth argument is float formatter which we will replace
         args[4] = lambda o: format(o, ".%df" % float_digits)
         return of(*args, **kwargs)
 
@@ -279,7 +279,7 @@ def configure_logging(
 
 def multiprocessing_init():
     """
-    Multiprocessing intitialization
+    Multiprocessing initialization
     """
     import torch.multiprocessing as mp
 

video/src/utils/distributed/operations.py

@@ -70,7 +70,7 @@ def run_distributed(
 ):
     if task_processor is not None:
         if list_processor is not None:
-            raise ValueError("Can not have both task and list processors sepcified")
+            raise ValueError("Can not have both task and list processors specified")
 
         list_processor = functools.partial(run_task_list, task_processor=task_processor)
 

video/src/utils/encoder_tester.py

@@ -1456,7 +1456,7 @@ def _build_task_list(self, testset_desc_filename: str, *, testset_root: Optional
                 elif decoder_format == "mp4":
                     decoder_ext = ".mp4"
                 else:
-                    raise ValueError(f"Unsuppoted decoder_format {decoder_format}")
+                    raise ValueError(f"Unsupported decoder_format {decoder_format}")
 
             for seq_name, seq_desc in dataset_desc["sequences"].items():
                 intra_period = params.intra_period

video/src/utils/video_writer.py

@@ -58,7 +58,7 @@ def __exit__(self, exc_type, exc_val, exc_tb):
 class FrameFormatAdapter:
     def __init__(self, dst_format, bit_depth=8):
         if dst_format not in ("420", "444", "rgb"):
-            raise ValueError(f"Unspported dst format: {dst_format}")
+            raise ValueError(f"Unsupported dst format: {dst_format}")
         self.dst_format = dst_format
 
         if 8 < bit_depth <= 16:

video/train_video.py

@@ -169,7 +169,7 @@ def _initialize_pretrained_part(self, model, part_name, config_path, module_name
         if pretrain_path is None:
             return False
 
-        logging.info(f"loading {part_name} weigths from {pretrain_path}")
+        logging.info(f"loading {part_name} weights from {pretrain_path}")
         state = get_state_dict(pretrain_path)
         for prefix in getattr(model, module_names_attr):
             module = getattr(model, prefix)
@@ -662,7 +662,7 @@ def _create_optic_flow_loss(self):
                 self.get_config_by_path("train.optic_flow_loss_path"), default_source="checkpoints_mount"
             )
 
-            logging.info(f"loading optic flow loss weigths from {optic_flow_path}")
+            logging.info(f"loading optic flow loss weights from {optic_flow_path}")
 
             from src.models.spynet import ME_Spynet