Add video support on MixUp and CutMix

test/test_prototype_transforms.py

@@ -112,9 +112,12 @@ def test_common(self, transform, input):
             (
                 transform,
                 [
-                    dict(image=image, one_hot_label=one_hot_label)
-                    for image, one_hot_label in itertools.product(
-                        make_images(extra_dims=BATCH_EXTRA_DIMS, dtypes=[torch.float]),
+                    dict(inpt=inpt, one_hot_label=one_hot_label)
+                    for inpt, one_hot_label in itertools.product(
+                        itertools.chain(
+                            make_images(extra_dims=BATCH_EXTRA_DIMS, dtypes=[torch.float]),
+                            make_videos(extra_dims=BATCH_EXTRA_DIMS, dtypes=[torch.float]),
+                        ),
                         make_one_hot_labels(extra_dims=BATCH_EXTRA_DIMS, dtypes=[torch.float]),
                     )
                 ],

torchvision/prototype/transforms/_augment.py

@@ -107,8 +107,11 @@ def __init__(self, alpha: float, p: float = 0.5) -> None:
         self._dist = torch.distributions.Beta(torch.tensor([alpha]), torch.tensor([alpha]))
 
     def forward(self, *inputs: Any) -> Any:
-        if not (has_any(inputs, features.Image, features.is_simple_tensor) and has_any(inputs, features.OneHotLabel)):
-            raise TypeError(f"{type(self).__name__}() is only defined for tensor images and one-hot labels.")
+        if not (
+            has_any(inputs, features.Image, features.Video, features.is_simple_tensor)
+            and has_any(inputs, features.OneHotLabel)
+        ):
+            raise TypeError(f"{type(self).__name__}() is only defined for tensor images/videos and one-hot labels.")
         if has_any(inputs, PIL.Image.Image, features.BoundingBox, features.Mask, features.Label):
             raise TypeError(
                 f"{type(self).__name__}() does not support PIL images, bounding boxes, masks and plain labels."
@@ -119,7 +122,7 @@ def _mixup_onehotlabel(self, inpt: features.OneHotLabel, lam: float) -> features
         if inpt.ndim < 2:
             raise ValueError("Need a batch of one hot labels")
         output = inpt.clone()
-        output = output.roll(1, -2).mul_(1 - lam).add_(output.mul_(lam))
+        output = output.roll(1, 0).mul_(1.0 - lam).add_(output.mul_(lam))
         return features.OneHotLabel.wrap_like(inpt, output)
 
 
@@ -129,14 +132,15 @@ def _get_params(self, sample: Any) -> Dict[str, Any]:
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         lam = params["lam"]
-        if isinstance(inpt, features.Image) or features.is_simple_tensor(inpt):
-            if inpt.ndim < 4:
-                raise ValueError("Need a batch of images")
+        if isinstance(inpt, (features.Image, features.Video)) or features.is_simple_tensor(inpt):
+            expected_ndim = 5 if isinstance(inpt, features.Video) else 4
+            if inpt.ndim < expected_ndim:
+                raise ValueError("The transform expects a batched input")
             output = inpt.clone()
-            output = output.roll(1, -4).mul_(1 - lam).add_(output.mul_(lam))
+            output = output.roll(1, 0).mul_(1.0 - lam).add_(output.mul_(lam))  # type: ignore[arg-type]
 
-            if isinstance(inpt, features.Image):
-                output = features.Image.wrap_like(inpt, output)
+            if isinstance(inpt, (features.Image, features.Video)):
+                output = type(inpt).wrap_like(inpt, output)
 
             return output
         elif isinstance(inpt, features.OneHotLabel):
@@ -169,17 +173,18 @@ def _get_params(self, sample: Any) -> Dict[str, Any]:
         return dict(box=box, lam_adjusted=lam_adjusted)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
-        if isinstance(inpt, features.Image) or features.is_simple_tensor(inpt):
+        if isinstance(inpt, (features.Image, features.Video)) or features.is_simple_tensor(inpt):
             box = params["box"]
-            if inpt.ndim < 4:
-                raise ValueError("Need a batch of images")
+            expected_ndim = 5 if isinstance(inpt, features.Video) else 4
+            if inpt.ndim < expected_ndim:
+                raise ValueError("The transform expects a batched input")
             x1, y1, x2, y2 = box
-            image_rolled = inpt.roll(1, -4)
+            rolled = inpt.roll(1, 0)
             output = inpt.clone()
-            output[..., y1:y2, x1:x2] = image_rolled[..., y1:y2, x1:x2]
+            output[..., y1:y2, x1:x2] = rolled[..., y1:y2, x1:x2]  # type: ignore[arg-type]
 
-            if isinstance(inpt, features.Image):
-                output = features.Image.wrap_like(inpt, output)
+            if isinstance(inpt, (features.Image, features.Video)):
+                output = inpt.wrap_like(inpt, output)  # type: ignore[arg-type]
 
             return output
         elif isinstance(inpt, features.OneHotLabel):

torchvision/prototype/transforms/_auto_augment.py

@@ -483,8 +483,8 @@ def forward(self, *inputs: Any) -> Any:
         augmentation_space = self._AUGMENTATION_SPACE if self.all_ops else self._PARTIAL_AUGMENTATION_SPACE
 
         orig_dims = list(image_or_video.shape)
-        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)
+        expected_ndim = 5 if isinstance(orig_image_or_video, features.Video) else 4
+        batch = image_or_video.view([1] * max(expected_ndim - 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