feat: add functional pad on segmentation mask

test/test_prototype_transforms_functional.py

@@ -352,6 +352,32 @@ def vertical_flip_segmentation_mask():
         yield SampleInput(mask)
 
 
+@register_kernel_info_from_sample_inputs_fn
+def pad_segmentation_mask():
+    for mask, padding, fill, padding_mode in itertools.product(
+        make_segmentation_masks(),
+        [[1], [1, 1], [1, 1, 2, 2]],  # padding
+        [0, 1],  # fill
+        ["constant", "symmetric", "edge"],  # padding mode,
+    ):
+        if padding_mode == "symmetric" and mask.ndim not in [3, 4]:
+            continue
+        if padding_mode == "edge" and fill != 0:
+            continue
+        if (
+            padding_mode == "edge"
+            and len(padding) == 2
+            and mask.ndim not in [2, 3]
+            or len(padding) == 4
+            and mask.ndim not in [4, 3]
+            or len(padding) == 1
+        ):
+            continue
+        if padding_mode == "edge" and mask.ndim not in [2, 3, 4, 5]:
+            continue
+        yield SampleInput(mask, padding=padding, fill=fill, padding_mode=padding_mode)
+
+
 @pytest.mark.parametrize(
     "kernel",
     [
@@ -933,3 +959,15 @@ def test_correctness_vertical_flip_segmentation_mask_on_fixed_input(device):
     expected_mask = torch.zeros((3, 3, 3), dtype=torch.long, device=device)
     expected_mask[:, -1, :] = 1
     torch.testing.assert_close(out_mask, expected_mask)
+
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+def test_correctness_pad_segmentation_mask_on_fixed_input(device):
+    mask = torch.ones((1, 3, 3), dtype=torch.long, device=device)
+    mask[:, 1, 1] = 0
+
+    out_mask = F.pad_segmentation_mask(mask, padding=[1, 1, 1, 1], fill=1)
+
+    expected_mask = torch.ones((1, 3 + 1 + 1, 3 + 1 + 1), dtype=torch.long, device=device)
+    expected_mask[:, 2, 2] = 0
+    torch.testing.assert_close(out_mask, expected_mask)

torchvision/prototype/transforms/functional/__init__.py

@@ -60,6 +60,7 @@
     pad_bounding_box,
     pad_image_tensor,
     pad_image_pil,
+    pad_segmentation_mask,
     crop_bounding_box,
     crop_image_tensor,
     crop_image_pil,

torchvision/prototype/transforms/functional/_geometry.py

@@ -396,6 +396,12 @@ def rotate_segmentation_mask(
 pad_image_pil = _FP.pad
 
 
+def pad_segmentation_mask(
+    segmentation_mask: torch.Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant"
+) -> torch.Tensor:
+    return pad_image_tensor(img=segmentation_mask, padding=padding, fill=fill, padding_mode=padding_mode)
+
+
 def pad_bounding_box(
     bounding_box: torch.Tensor, padding: List[int], format: features.BoundingBoxFormat
 ) -> torch.Tensor: