enable arbitrary batch size for all prototype kernels (#6726)

* enable arbitrary batch size for all prototype kernels

* put back perspective dispatcher

test/prototype_transforms_dispatcher_infos.py

@@ -138,12 +138,6 @@ def xfail_all_tests(*, reason, condition):
     ]
 
 
-xfails_degenerate_or_multi_batch_dims = xfail_all_tests(
-    reason="See https://github.com/pytorch/vision/issues/6670 for details.",
-    condition=lambda args_kwargs: len(args_kwargs.args[0].shape) > 4 or not all(args_kwargs.args[0].shape[:-3]),
-)
-
-
 DISPATCHER_INFOS = [
     DispatcherInfo(
         F.horizontal_flip,
@@ -260,7 +254,6 @@ DISPATCHER_INFOS = [
         pil_kernel_info=PILKernelInfo(F.perspective_image_pil),
         test_marks=[
             xfail_dispatch_pil_if_fill_sequence_needs_broadcast,
-            *xfails_degenerate_or_multi_batch_dims,
         ],
     ),
     DispatcherInfo(
@@ -271,7 +264,6 @@ DISPATCHER_INFOS = [
             features.Mask: F.elastic_mask,
         },
         pil_kernel_info=PILKernelInfo(F.elastic_image_pil),
-        test_marks=xfails_degenerate_or_multi_batch_dims,
     ),
     DispatcherInfo(
         F.center_crop,
@@ -294,7 +286,6 @@ DISPATCHER_INFOS = [
         test_marks=[
             xfail_jit_python_scalar_arg("kernel_size"),
             xfail_jit_python_scalar_arg("sigma"),
-            *xfails_degenerate_or_multi_batch_dims,
         ],
     ),
     DispatcherInfo(

test/prototype_transforms_kernel_infos.py

@@ -156,12 +156,6 @@ def xfail_all_tests(*, reason, condition):
     ]
 
 
-xfails_image_degenerate_or_multi_batch_dims = xfail_all_tests(
-    reason="See https://github.com/pytorch/vision/issues/6670 for details.",
-    condition=lambda args_kwargs: len(args_kwargs.args[0].shape) > 4 or not all(args_kwargs.args[0].shape[:-3]),
-)
-
-
 KERNEL_INFOS = []
 
 
@@ -1156,7 +1150,6 @@ KERNEL_INFOS.extend(
             reference_fn=pil_reference_wrapper(F.perspective_image_pil),
             reference_inputs_fn=reference_inputs_perspective_image_tensor,
             closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
-            test_marks=xfails_image_degenerate_or_multi_batch_dims,
         ),
         KernelInfo(
             F.perspective_bounding_box,
@@ -1168,7 +1161,6 @@ KERNEL_INFOS.extend(
             reference_fn=pil_reference_wrapper(F.perspective_image_pil),
             reference_inputs_fn=reference_inputs_perspective_mask,
             closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
-            test_marks=xfails_image_degenerate_or_multi_batch_dims,
         ),
         KernelInfo(
             F.perspective_video,
@@ -1239,7 +1231,6 @@ KERNEL_INFOS.extend(
             reference_fn=pil_reference_wrapper(F.elastic_image_pil),
             reference_inputs_fn=reference_inputs_elastic_image_tensor,
             closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
-            test_marks=xfails_image_degenerate_or_multi_batch_dims,
         ),
         KernelInfo(
             F.elastic_bounding_box,
@@ -1251,7 +1242,6 @@ KERNEL_INFOS.extend(
             reference_fn=pil_reference_wrapper(F.elastic_image_pil),
             reference_inputs_fn=reference_inputs_elastic_mask,
             closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
-            test_marks=xfails_image_degenerate_or_multi_batch_dims,
         ),
         KernelInfo(
             F.elastic_video,
@@ -1379,7 +1369,6 @@ KERNEL_INFOS.extend(
             test_marks=[
                 xfail_jit_python_scalar_arg("kernel_size"),
                 xfail_jit_python_scalar_arg("sigma"),
-                *xfails_image_degenerate_or_multi_batch_dims,
             ],
         ),
         KernelInfo(

torchvision/prototype/transforms/functional/_geometry.py

@@ -882,7 +882,23 @@ def perspective_image_tensor(
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     fill: features.FillTypeJIT = None,
 ) -> torch.Tensor:
-    return _FT.perspective(image, perspective_coeffs, interpolation=interpolation.value, fill=fill)
+    if image.numel() == 0:
+        return image
+
+    shape = image.shape
+
+    if image.ndim > 4:
+        image = image.view((-1,) + shape[-3:])
+        needs_unsquash = True
+    else:
+        needs_unsquash = False
+
+    output = _FT.perspective(image, perspective_coeffs, interpolation=interpolation.value, fill=fill)
+
+    if needs_unsquash:
+        output = output.view(shape)
+
+    return output
 
 
 @torch.jit.unused
@@ -1007,25 +1023,7 @@ def perspective_video(
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     fill: features.FillTypeJIT = None,
 ) -> torch.Tensor:
-    # TODO: this is a temporary workaround until the image kernel supports arbitrary batch sizes. Remove this when
-    #  https://github.com/pytorch/vision/issues/6670 is resolved.
-    if video.numel() == 0:
-        return video
-
-    shape = video.shape
-
-    if video.ndim > 4:
-        video = video.view((-1,) + shape[-3:])
-        needs_unsquash = True
-    else:
-        needs_unsquash = False
-
-    output = perspective_image_tensor(video, perspective_coeffs, interpolation=interpolation, fill=fill)
-
-    if needs_unsquash:
-        output = output.view(shape)
-
-    return output
+    return perspective_image_tensor(video, perspective_coeffs, interpolation=interpolation, fill=fill)
 
 
 def perspective(
@@ -1048,7 +1046,23 @@ def elastic_image_tensor(
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     fill: features.FillTypeJIT = None,
 ) -> torch.Tensor:
-    return _FT.elastic_transform(image, displacement, interpolation=interpolation.value, fill=fill)
+    if image.numel() == 0:
+        return image
+
+    shape = image.shape
+
+    if image.ndim > 4:
+        image = image.view((-1,) + shape[-3:])
+        needs_unsquash = True
+    else:
+        needs_unsquash = False
+
+    output = _FT.elastic_transform(image, displacement, interpolation=interpolation.value, fill=fill)
+
+    if needs_unsquash:
+        output = output.view(shape)
+
+    return output
 
 
 @torch.jit.unused
@@ -1128,25 +1142,7 @@ def elastic_video(
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     fill: features.FillTypeJIT = None,
 ) -> torch.Tensor:
-    # TODO: this is a temporary workaround until the image kernel supports arbitrary batch sizes. Remove this when
-    #  https://github.com/pytorch/vision/issues/6670 is resolved.
-    if video.numel() == 0:
-        return video
-
-    shape = video.shape
-
-    if video.ndim > 4:
-        video = video.view((-1,) + shape[-3:])
-        needs_unsquash = True
-    else:
-        needs_unsquash = False
-
-    output = elastic_image_tensor(video, displacement, interpolation=interpolation, fill=fill)
-
-    if needs_unsquash:
-        output = output.view(shape)
-
-    return output
+    return elastic_image_tensor(video, displacement, interpolation=interpolation, fill=fill)
 
 
 def elastic(

torchvision/prototype/transforms/functional/_misc.py

@@ -56,7 +56,23 @@ def gaussian_blur_image_tensor(
         if s <= 0.0:
             raise ValueError(f"sigma should have positive values. Got {sigma}")
 
-    return _FT.gaussian_blur(image, kernel_size, sigma)
+    if image.numel() == 0:
+        return image
+
+    shape = image.shape
+
+    if image.ndim > 4:
+        image = image.view((-1,) + shape[-3:])
+        needs_unsquash = True
+    else:
+        needs_unsquash = False
+
+    output = _FT.gaussian_blur(image, kernel_size, sigma)
+
+    if needs_unsquash:
+        output = output.view(shape)
+
+    return output
 
 
 @torch.jit.unused
@@ -71,25 +87,7 @@ def gaussian_blur_image_pil(
 def gaussian_blur_video(
     video: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None
 ) -> torch.Tensor:
-    # TODO: this is a temporary workaround until the image kernel supports arbitrary batch sizes. Remove this when
-    #  https://github.com/pytorch/vision/issues/6670 is resolved.
-    if video.numel() == 0:
-        return video
-
-    shape = video.shape
-
-    if video.ndim > 4:
-        video = video.view((-1,) + shape[-3:])
-        needs_unsquash = True
-    else:
-        needs_unsquash = False
-
-    output = gaussian_blur_image_tensor(video, kernel_size, sigma)
-
-    if needs_unsquash:
-        output = output.view(shape)
-
-    return output
+    return gaussian_blur_image_tensor(video, kernel_size, sigma)
 
 
 def gaussian_blur(