Added resized_crop_segmentation_mask op

test/test_prototype_transforms_functional.py

@@ -362,6 +362,14 @@ def resized_crop_bounding_box():
         )
 
 
+@register_kernel_info_from_sample_inputs_fn
+def resized_crop_segmentation_mask():
+    for mask, top, left, height, width, size in itertools.product(
+        make_segmentation_masks(), [-8, 0, 9], [-8, 0, 9], [12, 20], [12, 20], [(32, 32), (16, 18)]
+    ):
+        yield SampleInput(mask, top=top, left=left, height=height, width=width, size=size)
+
+
 @pytest.mark.parametrize(
     "kernel",
     [
@@ -998,3 +1006,28 @@ def _compute_expected(bbox, top_, left_, height_, width_, size_):
         output_boxes = convert_bounding_box_format(output_boxes, format, features.BoundingBoxFormat.XYXY)
 
     torch.testing.assert_close(output_boxes, expected_bboxes)
+
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize(
+    "top, left, height, width, size",
+    [
+        [0, 0, 30, 30, (60, 60)],
+        [5, 5, 35, 45, (32, 34)],
+    ],
+)
+def test_correctness_resized_crop_segmentation_mask(device, top, left, height, width, size):
+    def _compute_expected(mask, top_, left_, height_, width_, size_):
+        output = mask.clone()
+        output = output[:, top_ : top_ + height_, left_ : left_ + width_]
+        output = torch.nn.functional.interpolate(output[None, :].float(), size=size_, mode="nearest")
+        output = output[0, :].long()
+        return output
+
+    in_mask = torch.zeros(1, 100, 100, dtype=torch.long, device=device)
+    in_mask[0, 10:20, 10:20] = 1
+    in_mask[0, 5:15, 12:23] = 2
+
+    expected_mask = _compute_expected(in_mask, top, left, height, width, size)
+    output_mask = F.resized_crop_segmentation_mask(in_mask, top, left, height, width, size)
+    torch.testing.assert_close(output_mask, expected_mask)

torchvision/prototype/transforms/functional/__init__.py

@@ -47,9 +47,10 @@
     resize_segmentation_mask,
     center_crop_image_tensor,
     center_crop_image_pil,
+    resized_crop_bounding_box,
     resized_crop_image_tensor,
     resized_crop_image_pil,
-    resized_crop_bounding_box,
+    resized_crop_segmentation_mask,
     affine_bounding_box,
     affine_image_tensor,
     affine_image_pil,

torchvision/prototype/transforms/functional/_geometry.py

@@ -555,6 +555,18 @@ def resized_crop_bounding_box(
     return resize_bounding_box(bounding_box, size, (height, width))
 
 
+def resized_crop_segmentation_mask(
+    mask: torch.Tensor,
+    top: int,
+    left: int,
+    height: int,
+    width: int,
+    size: List[int],
+) -> torch.Tensor:
+    mask = crop_segmentation_mask(mask, top, left, height, width)
+    return resize_segmentation_mask(mask, size)
+
+
 def _parse_five_crop_size(size: List[int]) -> List[int]:
     if isinstance(size, numbers.Number):
         size = [int(size), int(size)]