Added crop_segmentation_mask op

test/test_prototype_transforms_functional.py

@@ -332,6 +332,20 @@ def crop_bounding_box():
         )
 
 
+@register_kernel_info_from_sample_inputs_fn
+def crop_segmentation_mask():
+    for mask, top, left, height, width in itertools.product(
+        make_segmentation_masks(), [-8, 0, 9], [-8, 0, 9], [12, 20], [12, 20]
+    ):
+        yield SampleInput(
+            mask,
+            top=top,
+            left=left,
+            height=height,
+            width=width,
+        )
+
+
 @pytest.mark.parametrize(
     "kernel",
     [
@@ -860,3 +874,44 @@ def test_correctness_crop_bounding_box(device, top, left, height, width, expecte
     )
 
     torch.testing.assert_close(output_boxes.tolist(), expected_bboxes)
+
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize(
+    "top, left, height, width",
+    [
+        [4, 6, 30, 40],
+        [-8, 6, 70, 40],
+        [-8, -6, 70, 8],
+    ],
+)
+def test_correctness_crop_segmentation_mask(device, top, left, height, width):
+    def _compute_expected_mask(mask, top_, left_, height_, width_):
+        h, w = mask.shape[-2], mask.shape[-1]
+        if top_ >= 0 and left_ >= 0 and top_ + height_ < h and left_ + width_ < w:
+            expected = mask[..., top_ : top_ + height_, left_ : left_ + width_]
+        else:
+            # Create output mask
+            expected_shape = mask.shape[:-2] + (height_, width_)
+            expected = torch.zeros(expected_shape, device=mask.device, dtype=mask.dtype)
+
+            out_y1 = abs(top_) if top_ < 0 else 0
+            out_y2 = h - top_ if top_ + height_ >= h else height_
+            out_x1 = abs(left_) if left_ < 0 else 0
+            out_x2 = w - left_ if left_ + width_ >= w else width_
+
+            in_y1 = 0 if top_ < 0 else top_
+            in_y2 = h if top_ + height_ >= h else top_ + height_
+            in_x1 = 0 if left_ < 0 else left_
+            in_x2 = w if left_ + width_ >= w else left_ + width_
+            # Paste input mask into output
+            expected[..., out_y1:out_y2, out_x1:out_x2] = mask[..., in_y1:in_y2, in_x1:in_x2]
+
+        return expected
+
+    for mask in make_segmentation_masks():
+        if mask.device != torch.device(device):
+            mask = mask.to(device)
+        output_mask = F.crop_segmentation_mask(mask, top, left, height, width)
+        expected_mask = _compute_expected_mask(mask, top, left, height, width)
+        torch.testing.assert_close(output_mask, expected_mask)

torchvision/prototype/transforms/functional/__init__.py

@@ -63,6 +63,7 @@
     crop_bounding_box,
     crop_image_tensor,
     crop_image_pil,
+    crop_segmentation_mask,
     perspective_image_tensor,
     perspective_image_pil,
     vertical_flip_image_tensor,

torchvision/prototype/transforms/functional/_geometry.py

@@ -440,6 +440,10 @@ def crop_bounding_box(
     ).view(shape)
 
 
+def crop_segmentation_mask(img: torch.Tensor, top: int, left: int, height: int, width: int) -> torch.Tensor:
+    return crop_image_tensor(img, top, left, height, width)
+
+
 def perspective_image_tensor(
     img: torch.Tensor,
     perspective_coeffs: List[float],