update to new setup from PR reviews

Author: justincdavis

test/test_transforms_v2.py

@@ -4728,7 +4728,9 @@ def test_kernel_video(self):
             make_segmentation_mask,
             make_video,
             make_keypoints,
-            make_image_cvcuda,
+            pytest.param(
+                make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="test requires CVCUDA")
+            ),
         ],
     )
     def test_functional(self, make_input):
@@ -4769,7 +4771,9 @@ def test_functional_signature(self, kernel, input_type):
             make_segmentation_mask,
             make_video,
             make_keypoints,
-            make_image_cvcuda,
+            pytest.param(
+                make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="test requires CVCUDA")
+            ),
         ],
     )
     def test_transform(self, make_input):
@@ -4794,26 +4798,15 @@ def test_transform_errors(self):
         with pytest.raises(ValueError, match="Padding mode should be either"):
             transforms.Pad(12, padding_mode="abc")
 
-    @pytest.mark.parametrize("padding", CORRECTNESS_PADDINGS)
     @pytest.mark.parametrize(
-        ("padding_mode", "fill"),
+        "make_input",
         [
-            *[("constant", fill) for fill in CORRECTNESS_FILLS],
-            *[(padding_mode, None) for padding_mode in ["symmetric", "edge", "reflect"]],
+            make_image,
+            pytest.param(
+                make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="test requires CVCUDA")
+            ),
         ],
     )
-    @pytest.mark.parametrize("fn", [F.pad, transform_cls_to_functional(transforms.Pad)])
-    def test_image_correctness(self, padding, padding_mode, fill, fn):
-        image = make_image(dtype=torch.uint8, device="cpu")
-
-        fill = adapt_fill(fill, dtype=torch.uint8)
-
-        actual = fn(image, padding=padding, padding_mode=padding_mode, fill=fill)
-        expected = F.to_image(F.pad(F.to_pil_image(image), padding=padding, padding_mode=padding_mode, fill=fill))
-
-        assert_equal(actual, expected)
-
-    @pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="test requires CVCUDA")
     @pytest.mark.parametrize("padding", CORRECTNESS_PADDINGS)
     @pytest.mark.parametrize(
         ("padding_mode", "fill"),
@@ -4823,15 +4816,19 @@ def test_image_correctness(self, padding, padding_mode, fill, fn):
         ],
     )
     @pytest.mark.parametrize("fn", [F.pad, transform_cls_to_functional(transforms.Pad)])
-    def test_cvcuda_correctness(self, padding, padding_mode, fill, fn):
-        image = make_image_cvcuda(dtype=torch.uint8, device="cuda")
+    def test_image_correctness(self, make_input, padding, padding_mode, fill, fn):
+        image = make_input(dtype=torch.uint8, device="cpu")
 
         fill = adapt_fill(fill, dtype=torch.uint8)
 
         actual = fn(image, padding=padding, padding_mode=padding_mode, fill=fill)
-        expected = F.pad(F.cvcuda_to_tensor(image), padding=padding, padding_mode=padding_mode, fill=fill)
 
-        assert_equal(F.cvcuda_to_tensor(actual), expected)
+        if make_input is make_image_cvcuda:
+            image = cvcuda_to_pil_compatible_tensor(image)
+
+        expected = F.to_image(F.pad(F.to_pil_image(image), padding=padding, padding_mode=padding_mode, fill=fill))
+
+        assert_equal(actual, expected)
 
     def _reference_pad_bounding_boxes(self, bounding_boxes, *, padding):
         if isinstance(padding, int):

torchvision/transforms/v2/_geometry.py

@@ -26,6 +26,7 @@
     get_bounding_boxes,
     has_all,
     has_any,
+    is_cvcuda_tensor,
     is_pure_tensor,
     query_size,
 )
@@ -465,6 +466,8 @@ class Pad(Transform):
 
     _v1_transform_cls = _transforms.Pad
 
+    _transformed_types = Transform._transformed_types + (is_cvcuda_tensor,)
+
     def _extract_params_for_v1_transform(self) -> dict[str, Any]:
         params = super()._extract_params_for_v1_transform()
 

torchvision/transforms/v2/functional/_geometry.py

@@ -1682,7 +1682,7 @@ def _pad_with_vector_fill(
 _pad_image_pil = _register_kernel_internal(pad, PIL.Image.Image)(_FP.pad)
 
 
-if _CVCUDA_AVAILABLE:
+if CVCUDA_AVAILABLE:
     _pad_mode_to_cvcuda = {
         "constant": cvcuda.Border.CONSTANT,
         "reflect": cvcuda.Border.REFLECT101,
@@ -1721,7 +1721,7 @@ def _pad_cvcuda(
     )
 
 
-if _CVCUDA_AVAILABLE:
+if CVCUDA_AVAILABLE:
     _register_kernel_internal(pad, _import_cvcuda().Tensor)(_pad_cvcuda)