Adding support of Video to remaining Transforms and Kernels (#6724)

* Adding support of Video to missed Transforms and Kernels

* Fixing Grayscale Transform.

* Fixing FiveCrop and TenCrop Transforms.

* Fix Linter

* Fix more kernels.

* Add `five_crop_video` and `ten_crop_video` kernels

* Added a TODO.

* Missed Video isinstance

* nits

* Fix bug on AugMix

* Nits and TODOs.

* Reapply Philip's recommendation

* Fix mypy and JIT

* Fixing test

torchvision/prototype/features/__init__.py

@@ -13,4 +13,14 @@ from ._image import (
 )
 from ._label import Label, OneHotLabel
 from ._mask import Mask
-from ._video import ImageOrVideoType, ImageOrVideoTypeJIT, TensorImageOrVideoType, TensorImageOrVideoTypeJIT, Video
+from ._video import (
+    ImageOrVideoType,
+    ImageOrVideoTypeJIT,
+    LegacyVideoType,
+    LegacyVideoTypeJIT,
+    TensorImageOrVideoType,
+    TensorImageOrVideoTypeJIT,
+    Video,
+    VideoType,
+    VideoTypeJIT,
+)

torchvision/prototype/features/_video.py

@@ -238,6 +238,7 @@ LegacyVideoTypeJIT = torch.Tensor
 TensorVideoType = Union[torch.Tensor, Video]
 TensorVideoTypeJIT = torch.Tensor
 
+# TODO: decide if we should do definitions for both Images and Videos or use unions in the methods
 ImageOrVideoType = Union[ImageType, VideoType]
 ImageOrVideoTypeJIT = Union[ImageTypeJIT, VideoTypeJIT]
 TensorImageOrVideoType = Union[TensorImageType, TensorVideoType]

torchvision/prototype/transforms/_augment.py

@@ -99,6 +99,7 @@ class RandomErasing(_RandomApplyTransform):
         return inpt
 
 
+# TODO: Add support for Video: https://github.com/pytorch/vision/issues/6731
 class _BaseMixupCutmix(_RandomApplyTransform):
     def __init__(self, alpha: float, p: float = 0.5) -> None:
         super().__init__(p=p)

torchvision/prototype/transforms/_auto_augment.py

@@ -483,7 +483,8 @@ class AugMix(_AutoAugmentBase):
         augmentation_space = self._AUGMENTATION_SPACE if self.all_ops else self._PARTIAL_AUGMENTATION_SPACE
 
         orig_dims = list(image_or_video.shape)
-        batch = image_or_video.view([1] * max(4 - image_or_video.ndim, 0) + orig_dims)
+        expected_dim = 5 if isinstance(orig_image_or_video, features.Video) else 4
+        batch = image_or_video.view([1] * max(expected_dim - image_or_video.ndim, 0) + orig_dims)
         batch_dims = [batch.size(0)] + [1] * (batch.ndim - 1)
 
         # Sample the beta weights for combining the original and augmented image or video. To get Beta, we use a
@@ -520,7 +521,7 @@ class AugMix(_AutoAugmentBase):
         mix = mix.view(orig_dims).to(dtype=image_or_video.dtype)
 
         if isinstance(orig_image_or_video, (features.Image, features.Video)):
-            mix = type(orig_image_or_video).wrap_like(orig_image_or_video, mix)  # type: ignore[arg-type]
+            mix = orig_image_or_video.wrap_like(orig_image_or_video, mix)  # type: ignore[arg-type]
         elif isinstance(orig_image_or_video, PIL.Image.Image):
             mix = F.to_image_pil(mix)
 

torchvision/prototype/transforms/_color.py

@@ -119,7 +119,7 @@ class RandomPhotometricDistort(Transform):
         output = inpt[..., permutation, :, :]
 
         if isinstance(inpt, (features.Image, features.Video)):
-            output = type(inpt).wrap_like(inpt, output, color_space=features.ColorSpace.OTHER)  # type: ignore[arg-type]
+            output = inpt.wrap_like(inpt, output, color_space=features.ColorSpace.OTHER)  # type: ignore[arg-type]
 
         elif isinstance(inpt, PIL.Image.Image):
             output = F.to_image_pil(output)

torchvision/prototype/transforms/_deprecated.py

@@ -29,7 +29,7 @@ class ToTensor(Transform):
 
 
 class Grayscale(Transform):
-    _transformed_types = (features.Image, PIL.Image.Image, features.is_simple_tensor)
+    _transformed_types = (features.Image, PIL.Image.Image, features.is_simple_tensor, features.Video)
 
     def __init__(self, num_output_channels: Literal[1, 3] = 1) -> None:
         deprecation_msg = (
@@ -52,15 +52,15 @@ class Grayscale(Transform):
         super().__init__()
         self.num_output_channels = num_output_channels
 
-    def _transform(self, inpt: features.ImageType, params: Dict[str, Any]) -> features.ImageType:
+    def _transform(self, inpt: features.ImageOrVideoType, params: Dict[str, Any]) -> features.ImageOrVideoType:
         output = _F.rgb_to_grayscale(inpt, num_output_channels=self.num_output_channels)
-        if isinstance(inpt, features.Image):
-            output = features.Image.wrap_like(inpt, output, color_space=features.ColorSpace.GRAY)
+        if isinstance(inpt, (features.Image, features.Video)):
+            output = inpt.wrap_like(inpt, output, color_space=features.ColorSpace.GRAY)  # type: ignore[arg-type]
         return output
 
 
 class RandomGrayscale(_RandomApplyTransform):
-    _transformed_types = (features.Image, PIL.Image.Image, features.is_simple_tensor)
+    _transformed_types = (features.Image, PIL.Image.Image, features.is_simple_tensor, features.Video)
 
     def __init__(self, p: float = 0.1) -> None:
         warnings.warn(
@@ -81,8 +81,8 @@ class RandomGrayscale(_RandomApplyTransform):
         num_input_channels, _, _ = query_chw(sample)
         return dict(num_input_channels=num_input_channels)
 
-    def _transform(self, inpt: features.ImageType, params: Dict[str, Any]) -> features.ImageType:
+    def _transform(self, inpt: features.ImageOrVideoType, params: Dict[str, Any]) -> features.ImageOrVideoType:
         output = _F.rgb_to_grayscale(inpt, num_output_channels=params["num_input_channels"])
-        if isinstance(inpt, features.Image):
-            output = features.Image.wrap_like(inpt, output, color_space=features.ColorSpace.GRAY)
+        if isinstance(inpt, (features.Image, features.Video)):
+            output = inpt.wrap_like(inpt, output, color_space=features.ColorSpace.GRAY)  # type: ignore[arg-type]
         return output

torchvision/prototype/transforms/_geometry.py

@@ -155,12 +155,13 @@ class FiveCrop(Transform):
     """
     Example:
         >>> class BatchMultiCrop(transforms.Transform):
-        ...     def forward(self, sample: Tuple[Tuple[features.Image, ...], features.Label]):
-        ...         images, labels = sample
-        ...         batch_size = len(images)
-        ...         images = features.Image.wrap_like(images[0], torch.stack(images))
+        ...     def forward(self, sample: Tuple[Tuple[Union[features.Image, features.Video], ...], features.Label]):
+        ...         images_or_videos, labels = sample
+        ...         batch_size = len(images_or_videos)
+        ...         image_or_video = images_or_videos[0]
+        ...         images_or_videos = image_or_video.wrap_like(image_or_video, torch.stack(images_or_videos))
         ...         labels = features.Label.wrap_like(labels, labels.repeat(batch_size))
-        ...         return images, labels
+        ...         return images_or_videos, labels
         ...
         >>> image = features.Image(torch.rand(3, 256, 256))
         >>> label = features.Label(0)
@@ -172,15 +173,21 @@ class FiveCrop(Transform):
         torch.Size([5])
     """
 
-    _transformed_types = (features.Image, PIL.Image.Image, features.is_simple_tensor)
+    _transformed_types = (features.Image, PIL.Image.Image, features.is_simple_tensor, features.Video)
 
     def __init__(self, size: Union[int, Sequence[int]]) -> None:
         super().__init__()
         self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
 
     def _transform(
-        self, inpt: features.ImageType, params: Dict[str, Any]
-    ) -> Tuple[features.ImageType, features.ImageType, features.ImageType, features.ImageType, features.ImageType]:
+        self, inpt: features.ImageOrVideoType, params: Dict[str, Any]
+    ) -> Tuple[
+        features.ImageOrVideoType,
+        features.ImageOrVideoType,
+        features.ImageOrVideoType,
+        features.ImageOrVideoType,
+        features.ImageOrVideoType,
+    ]:
         return F.five_crop(inpt, self.size)
 
     def forward(self, *inputs: Any) -> Any:
@@ -194,14 +201,14 @@ class TenCrop(Transform):
     See :class:`~torchvision.prototype.transforms.FiveCrop` for an example.
     """
 
-    _transformed_types = (features.Image, PIL.Image.Image, features.is_simple_tensor)
+    _transformed_types = (features.Image, PIL.Image.Image, features.is_simple_tensor, features.Video)
 
     def __init__(self, size: Union[int, Sequence[int]], vertical_flip: bool = False) -> None:
         super().__init__()
         self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
         self.vertical_flip = vertical_flip
 
-    def _transform(self, inpt: features.ImageType, params: Dict[str, Any]) -> List[features.ImageType]:
+    def _transform(self, inpt: features.ImageOrVideoType, params: Dict[str, Any]) -> List[features.ImageOrVideoType]:
         return F.ten_crop(inpt, self.size, vertical_flip=self.vertical_flip)
 
     def forward(self, *inputs: Any) -> Any:

torchvision/prototype/transforms/_meta.py

@@ -22,18 +22,18 @@ class ConvertBoundingBoxFormat(Transform):
 
 
 class ConvertImageDtype(Transform):
-    _transformed_types = (features.is_simple_tensor, features.Image)
+    _transformed_types = (features.is_simple_tensor, features.Image, features.Video)
 
     def __init__(self, dtype: torch.dtype = torch.float32) -> None:
         super().__init__()
         self.dtype = dtype
 
-    def _transform(self, inpt: features.TensorImageType, params: Dict[str, Any]) -> features.TensorImageType:
+    def _transform(
+        self, inpt: features.TensorImageOrVideoType, params: Dict[str, Any]
+    ) -> features.TensorImageOrVideoType:
         output = F.convert_image_dtype(inpt, dtype=self.dtype)
         return (
-            output
-            if features.is_simple_tensor(inpt)
-            else features.Image.wrap_like(inpt, output)  # type: ignore[arg-type]
+            output if features.is_simple_tensor(inpt) else type(inpt).wrap_like(inpt, output)  # type: ignore[attr-defined]
         )
 
 

torchvision/prototype/transforms/_misc.py

@@ -140,6 +140,7 @@ class GaussianBlur(Transform):
         return F.gaussian_blur(inpt, self.kernel_size, **params)
 
 
+# TODO: Enhance as described at https://github.com/pytorch/vision/issues/6697
 class ToDtype(Lambda):
     def __init__(self, dtype: torch.dtype, *types: Type) -> None:
         self.dtype = dtype

torchvision/prototype/transforms/functional/__init__.py

@@ -96,6 +96,7 @@ from ._geometry import (
     five_crop,
     five_crop_image_pil,
     five_crop_image_tensor,
+    five_crop_video,
     hflip,  # TODO: Consider moving all pure alias definitions at the bottom of the file
     horizontal_flip,
     horizontal_flip_bounding_box,
@@ -136,6 +137,7 @@ from ._geometry import (
     ten_crop,
     ten_crop_image_pil,
     ten_crop_image_tensor,
+    ten_crop_video,
     vertical_flip,
     vertical_flip_bounding_box,
     vertical_flip_image_pil,

torchvision/prototype/transforms/functional/_augment.py

@@ -35,7 +35,7 @@ def erase(
     if isinstance(inpt, torch.Tensor):
         output = erase_image_tensor(inpt, i=i, j=j, h=h, w=w, v=v, inplace=inplace)
         if not torch.jit.is_scripting() and isinstance(inpt, (features.Image, features.Video)):
-            output = type(inpt).wrap_like(inpt, output)  # type: ignore[arg-type]
+            output = inpt.wrap_like(inpt, output)  # type: ignore[arg-type]
         return output
     else:  # isinstance(inpt, PIL.Image.Image):
         return erase_image_pil(inpt, i=i, j=j, h=h, w=w, v=v, inplace=inplace)

torchvision/prototype/transforms/functional/_deprecated.py

 import warnings
-from typing import Any, List
+from typing import Any, List, Union
 
 import PIL.Image
 import torch
@@ -22,10 +22,13 @@ def to_grayscale(inpt: PIL.Image.Image, num_output_channels: int = 1) -> PIL.Ima
     return _F.to_grayscale(inpt, num_output_channels=num_output_channels)
 
 
-def rgb_to_grayscale(inpt: features.LegacyImageTypeJIT, num_output_channels: int = 1) -> features.LegacyImageTypeJIT:
+def rgb_to_grayscale(
+    inpt: Union[features.LegacyImageTypeJIT, features.LegacyVideoTypeJIT], num_output_channels: int = 1
+) -> Union[features.LegacyImageTypeJIT, features.LegacyVideoTypeJIT]:
     old_color_space = (
         features._image._from_tensor_shape(inpt.shape)  # type: ignore[arg-type]
-        if isinstance(inpt, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(inpt, features.Image))
+        if isinstance(inpt, torch.Tensor)
+        and (torch.jit.is_scripting() or not isinstance(inpt, (features.Image, features.Video)))
         else None
     )
 
@@ -56,7 +59,7 @@ def to_tensor(inpt: Any) -> torch.Tensor:
     return _F.to_tensor(inpt)
 
 
-def get_image_size(inpt: features.ImageTypeJIT) -> List[int]:
+def get_image_size(inpt: features.ImageOrVideoTypeJIT) -> List[int]:
     warnings.warn(
         "The function `get_image_size(...)` is deprecated and will be removed in a future release. "
         "Instead, please use `get_spatial_size(...)` which returns `[h, w]` instead of `[w, h]`."

torchvision/prototype/transforms/functional/_geometry.py

@@ -1376,16 +1376,27 @@ def five_crop_image_pil(
     return tl, tr, bl, br, center
 
 
+def five_crop_video(
+    video: torch.Tensor, size: List[int]
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    return five_crop_image_tensor(video, size)
+
+
 def five_crop(
-    inpt: features.ImageTypeJIT, size: List[int]
+    inpt: features.ImageOrVideoTypeJIT, size: List[int]
 ) -> Tuple[
-    features.ImageTypeJIT, features.ImageTypeJIT, features.ImageTypeJIT, features.ImageTypeJIT, features.ImageTypeJIT
+    features.ImageOrVideoTypeJIT,
+    features.ImageOrVideoTypeJIT,
+    features.ImageOrVideoTypeJIT,
+    features.ImageOrVideoTypeJIT,
+    features.ImageOrVideoTypeJIT,
 ]:
-    # TODO: consider breaking BC here to return List[features.ImageTypeJIT] to align this op with `ten_crop`
+    # TODO: consider breaking BC here to return List[features.ImageOrVideoTypeJIT] to align this op with `ten_crop`
     if isinstance(inpt, torch.Tensor):
         output = five_crop_image_tensor(inpt, size)
-        if not torch.jit.is_scripting() and isinstance(inpt, features.Image):
-            output = tuple(features.Image.wrap_like(inpt, item) for item in output)  # type: ignore[assignment]
+        if not torch.jit.is_scripting() and isinstance(inpt, (features.Image, features.Video)):
+            tmp = tuple(inpt.wrap_like(inpt, item) for item in output)  # type: ignore[arg-type]
+            output = tmp  # type: ignore[assignment]
         return output
     else:  # isinstance(inpt, PIL.Image.Image):
         return five_crop_image_pil(inpt, size)
@@ -1418,11 +1429,17 @@ def ten_crop_image_pil(image: PIL.Image.Image, size: List[int], vertical_flip: b
     return [tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip]
 
 
-def ten_crop(inpt: features.ImageTypeJIT, size: List[int], vertical_flip: bool = False) -> List[features.ImageTypeJIT]:
+def ten_crop_video(video: torch.Tensor, size: List[int], vertical_flip: bool = False) -> List[torch.Tensor]:
+    return ten_crop_image_tensor(video, size, vertical_flip=vertical_flip)
+
+
+def ten_crop(
+    inpt: features.ImageOrVideoTypeJIT, size: List[int], vertical_flip: bool = False
+) -> List[features.ImageOrVideoTypeJIT]:
     if isinstance(inpt, torch.Tensor):
         output = ten_crop_image_tensor(inpt, size, vertical_flip=vertical_flip)
-        if not torch.jit.is_scripting() and isinstance(inpt, features.Image):
-            output = [features.Image.wrap_like(inpt, item) for item in output]
+        if not torch.jit.is_scripting() and isinstance(inpt, (features.Image, features.Video)):
+            output = [inpt.wrap_like(inpt, item) for item in output]  # type: ignore[arg-type]
         return output
     else:  # isinstance(inpt, PIL.Image.Image):
         return ten_crop_image_pil(inpt, size, vertical_flip=vertical_flip)

torchvision/prototype/transforms/functional/_meta.py

@@ -55,6 +55,10 @@ def get_spatial_size_image_pil(image: PIL.Image.Image) -> List[int]:
     return [height, width]
 
 
+# TODO: Should we have get_spatial_size_video here? How about masks/bbox etc? What is the criterion for deciding when
+# a kernel will be created?
+
+
 def get_spatial_size(inpt: features.InputTypeJIT) -> List[int]:
     if isinstance(inpt, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(inpt, features._Feature)):
         return get_spatial_size_image_tensor(inpt)
@@ -246,7 +250,7 @@ def convert_color_space(
     ):
         if old_color_space is None:
             raise RuntimeError(
-                "In order to convert the color space of simple tensor images, "
+                "In order to convert the color space of simple tensors, "
                 "the `old_color_space=...` parameter needs to be passed."
             )
         return convert_color_space_image_tensor(