Add output_format do video datasets and readers

torchvision/datasets/hmdb51.py

@@ -37,11 +37,13 @@ class HMDB51(VisionDataset):
             otherwise from the ``test`` split.
         transform (callable, optional): A function/transform that takes in a TxHxWxC video
             and returns a transformed version.
+        output_format (str, optional): The format of the output video tensors (before transforms).
+            Can be either "THWC" (default) or "TCHW".
 
     Returns:
         tuple: A 3-tuple with the following entries:
 
-            - video (Tensor[T, H, W, C]): The `T` video frames
+            - video (Tensor[T, H, W, C] or Tensor[T, C, H, W]): The `T` video frames
             - audio(Tensor[K, L]): the audio frames, where `K` is the number of channels
               and `L` is the number of points
             - label (int): class of the video clip
@@ -71,6 +73,7 @@ def __init__(
         _video_height: int = 0,
         _video_min_dimension: int = 0,
         _audio_samples: int = 0,
+        output_format: str = "THWC",
     ) -> None:
         super().__init__(root)
         if fold not in (1, 2, 3):
@@ -96,6 +99,7 @@ def __init__(
             _video_height=_video_height,
             _video_min_dimension=_video_min_dimension,
             _audio_samples=_audio_samples,
+            output_format=output_format,
         )
         # we bookkeep the full version of video clips because we want to be able
         # to return the meta data of full version rather than the subset version of

torchvision/datasets/kinetics.py

@@ -62,11 +62,14 @@ class Kinetics(VisionDataset):
         download (bool): Download the official version of the dataset to root folder.
         num_workers (int): Use multiple workers for VideoClips creation
         num_download_workers (int): Use multiprocessing in order to speed up download.
+        output_format (str, optional): The format of the output video tensors (before transforms).
+            Can be either "THWC" or "TCHW" (default).
+            Note that in most other utils and datasets, the default is actually "THWC".
 
     Returns:
         tuple: A 3-tuple with the following entries:
 
-            - video (Tensor[T, C, H, W]): the `T` video frames in torch.uint8 tensor
+            - video (Tensor[T, C, H, W] or Tensor[T, H, W, C]): the `T` video frames in torch.uint8 tensor
             - audio(Tensor[K, L]): the audio frames, where `K` is the number of channels
               and `L` is the number of points in torch.float tensor
             - label (int): class of the video clip
@@ -106,6 +109,7 @@ def __init__(
         _audio_samples: int = 0,
         _audio_channels: int = 0,
         _legacy: bool = False,
+        output_format: str = "TCHW",
     ) -> None:
 
         # TODO: support test
@@ -115,10 +119,12 @@ def __init__(
 
         self.root = root
         self._legacy = _legacy
+
         if _legacy:
             print("Using legacy structure")
             self.split_folder = root
             self.split = "unknown"
+            output_format = "THWC"
             if download:
                 raise ValueError("Cannot download the videos using legacy_structure.")
         else:
@@ -145,6 +151,7 @@ def __init__(
             _video_min_dimension=_video_min_dimension,
             _audio_samples=_audio_samples,
             _audio_channels=_audio_channels,
+            output_format=output_format,
         )
         self.transform = transform
 
@@ -233,9 +240,6 @@ def __len__(self) -> int:
 
     def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, int]:
         video, audio, info, video_idx = self.video_clips.get_clip(idx)
-        if not self._legacy:
-            # [T,H,W,C] --> [T,C,H,W]
-            video = video.permute(0, 3, 1, 2)
         label = self.samples[video_idx][1]
 
         if self.transform is not None:
@@ -308,7 +312,7 @@ def __init__(
         warnings.warn(
             "The Kinetics400 class is deprecated since 0.12 and will be removed in 0.14."
             "Please use Kinetics(..., num_classes='400') instead."
-            "Note that Kinetics(..., num_classes='400') returns video in a more logical Tensor[T, C, H, W] format."
+            "Note that Kinetics(..., num_classes='400') returns video in a Tensor[T, C, H, W] format."
         )
         if any(value is not None for value in (num_classes, split, download, num_download_workers)):
             raise RuntimeError(

torchvision/datasets/ucf101.py

@@ -38,11 +38,13 @@ class UCF101(VisionDataset):
             otherwise from the ``test`` split.
         transform (callable, optional): A function/transform that  takes in a TxHxWxC video
             and returns a transformed version.
+        output_format (str, optional): The format of the output video tensors (before transforms).
+            Can be either "THWC" (default) or "TCHW".
 
     Returns:
         tuple: A 3-tuple with the following entries:
 
-            - video (Tensor[T, H, W, C]): the `T` video frames
+            - video (Tensor[T, H, W, C] or Tensor[T, C, H, W]): The `T` video frames
             -  audio(Tensor[K, L]): the audio frames, where `K` is the number of channels
                and `L` is the number of points
             - label (int): class of the video clip
@@ -64,6 +66,7 @@ def __init__(
         _video_height: int = 0,
         _video_min_dimension: int = 0,
         _audio_samples: int = 0,
+        output_format: str = "THWC",
     ) -> None:
         super().__init__(root)
         if not 1 <= fold <= 3:
@@ -87,6 +90,7 @@ def __init__(
             _video_height=_video_height,
             _video_min_dimension=_video_min_dimension,
             _audio_samples=_audio_samples,
+            output_format=output_format,
         )
         # we bookkeep the full version of video clips because we want to be able
         # to return the meta data of full version rather than the subset version of

torchvision/datasets/video_utils.py

@@ -99,6 +99,7 @@ class VideoClips:
             on the resampled video
         num_workers (int): how many subprocesses to use for data loading.
             0 means that the data will be loaded in the main process. (default: 0)
+        output_format (str): The format of the output video tensors. Can be either "THWC" (default) or "TCHW".
     """
 
     def __init__(
@@ -115,6 +116,7 @@ def __init__(
         _video_max_dimension: int = 0,
         _audio_samples: int = 0,
         _audio_channels: int = 0,
+        output_format: str = "THWC",
     ) -> None:
 
         self.video_paths = video_paths
@@ -127,6 +129,9 @@ def __init__(
         self._video_max_dimension = _video_max_dimension
         self._audio_samples = _audio_samples
         self._audio_channels = _audio_channels
+        self.output_format = output_format.upper()
+        if self.output_format not in ("THWC", "TCHW"):
+            raise ValueError(f"output_format should be either 'THWC' or 'TCHW', got {output_format}.")
 
         if _precomputed_metadata is None:
             self._compute_frame_pts()
@@ -366,6 +371,11 @@ def get_clip(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any]
             video = video[resampling_idx]
             info["video_fps"] = self.frame_rate
         assert len(video) == self.num_frames, f"{video.shape} x {self.num_frames}"
+
+        if self.output_format == "TCHW":
+            # [T,H,W,C] --> [T,C,H,W]
+            video = video.permute(0, 3, 1, 2)
+
         return video, audio, info, video_idx
 
     def __getstate__(self) -> Dict[str, Any]:

torchvision/io/video.py

@@ -239,6 +239,7 @@ def read_video(
     start_pts: Union[float, Fraction] = 0,
     end_pts: Optional[Union[float, Fraction]] = None,
     pts_unit: str = "pts",
+    output_format: str = "THWC",
 ) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any]]:
     """
     Reads a video from a file, returning both the video frames as well as
@@ -252,15 +253,20 @@ def read_video(
             The end presentation time
         pts_unit (str, optional): unit in which start_pts and end_pts values will be interpreted,
             either 'pts' or 'sec'. Defaults to 'pts'.
+        output_format (str, optional): The format of the output video tensors. Can be either "THWC" (default) or "TCHW".
 
     Returns:
-        vframes (Tensor[T, H, W, C]): the `T` video frames
+        vframes (Tensor[T, H, W, C] or Tensor[T, C, H, W]): the `T` video frames
         aframes (Tensor[K, L]): the audio frames, where `K` is the number of channels and `L` is the number of points
         info (Dict): metadata for the video and audio. Can contain the fields video_fps (float) and audio_fps (int)
     """
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(read_video)
 
+    output_format = output_format.upper()
+    if output_format not in ("THWC", "TCHW"):
+        raise ValueError(f"output_format should be either 'THWC' or 'TCHW', got {output_format}.")
+
     from torchvision import get_video_backend
 
     if not os.path.exists(filename):
@@ -334,6 +340,10 @@ def read_video(
     else:
         aframes = torch.empty((1, 0), dtype=torch.float32)
 
+    if output_format == "TCHW":
+        # [T,H,W,C] --> [T,C,H,W]
+        vframes = vframes.permute(0, 3, 1, 2)
+
     return vframes, aframes, info