feat: add functional center crop on mask (#5961)

* feat: add functional center crop on mask

* test: add correctness center crop with random segmentation mask

* test: improvements

* test: improvements

* Apply suggestions from code review

Co-authored-by: Philip Meier <[email protected]>

Co-authored-by: Federico Pozzi <[email protected]>
Co-authored-by: vfdev <[email protected]>
Co-authored-by: Philip Meier <[email protected]>

Author: 4 people

test/test_prototype_transforms_functional.py

@@ -10,11 +10,11 @@
 from torch import jit
 from torch.nn.functional import one_hot
 from torchvision.prototype import features
+from torchvision.prototype.transforms.functional._geometry import _center_crop_compute_padding
 from torchvision.prototype.transforms.functional._meta import convert_bounding_box_format
 from torchvision.transforms.functional import _get_perspective_coeffs
 from torchvision.transforms.functional_tensor import _max_value as get_max_value
 
-
 make_tensor = functools.partial(torch.testing.make_tensor, device="cpu")
 
 
@@ -421,6 +421,14 @@ def center_crop_bounding_box():
         )
 
 
+def center_crop_segmentation_mask():
+    for mask, output_size in itertools.product(
+        make_segmentation_masks(image_sizes=((16, 16), (7, 33), (31, 9))),
+        [[4, 3], [42, 70], [4]],  # crop sizes < image sizes, crop_sizes > image sizes, single crop size
+    ):
+        yield SampleInput(mask, output_size)
+
+
 @pytest.mark.parametrize(
     "kernel",
     [
@@ -1337,3 +1345,26 @@ def _compute_expected_bbox(bbox, output_size_):
         else:
             expected_bboxes = expected_bboxes[0]
         torch.testing.assert_close(output_boxes, expected_bboxes)
+
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize("output_size", [[4, 2], [4], [7, 6]])
+def test_correctness_center_crop_segmentation_mask(device, output_size):
+    def _compute_expected_segmentation_mask(mask, output_size):
+        crop_height, crop_width = output_size if len(output_size) > 1 else [output_size[0], output_size[0]]
+
+        _, image_height, image_width = mask.shape
+        if crop_width > image_height or crop_height > image_width:
+            padding = _center_crop_compute_padding(crop_height, crop_width, image_height, image_width)
+            mask = F.pad_image_tensor(mask, padding, fill=0)
+
+        left = round((image_width - crop_width) * 0.5)
+        top = round((image_height - crop_height) * 0.5)
+
+        return mask[:, top : top + crop_height, left : left + crop_width]
+
+    mask = torch.randint(0, 2, size=(1, 6, 6), dtype=torch.long, device=device)
+    actual = F.center_crop_segmentation_mask(mask, output_size)
+
+    expected = _compute_expected_segmentation_mask(mask, output_size)
+    torch.testing.assert_close(expected, actual)

torchvision/prototype/transforms/functional/__init__.py

@@ -46,6 +46,7 @@
     resize_image_pil,
     resize_segmentation_mask,
     center_crop_bounding_box,
+    center_crop_segmentation_mask,
     center_crop_image_tensor,
     center_crop_image_pil,
     resized_crop_bounding_box,

torchvision/prototype/transforms/functional/_geometry.py

@@ -630,6 +630,10 @@ def center_crop_bounding_box(
     return crop_bounding_box(bounding_box, format, top=crop_top, left=crop_left)
 
 
+def center_crop_segmentation_mask(segmentation_mask: torch.Tensor, output_size: List[int]) -> torch.Tensor:
+    return center_crop_image_tensor(img=segmentation_mask, output_size=output_size)
+
+
 def resized_crop_image_tensor(
     img: torch.Tensor,
     top: int,