[proto] Added functional rotate_segmentation_mask op

test/test_prototype_transforms_functional.py

@@ -266,15 +266,15 @@ def affine_bounding_box():
 
 @register_kernel_info_from_sample_inputs_fn
 def affine_segmentation_mask():
-    for image, angle, translate, scale, shear in itertools.product(
+    for mask, angle, translate, scale, shear in itertools.product(
         make_segmentation_masks(extra_dims=((), (4,))),
         [-87, 15, 90],  # angle
         [5, -5],  # translate
         [0.77, 1.27],  # scale
         [0, 12],  # shear
     ):
         yield SampleInput(
-            image,
+            mask,
             angle=angle,
             translate=(translate, translate),
             scale=scale,
@@ -285,8 +285,12 @@ def affine_segmentation_mask():
 @register_kernel_info_from_sample_inputs_fn
 def rotate_bounding_box():
     for bounding_box, angle, expand, center in itertools.product(
-        make_bounding_boxes(), [-87, 15, 90], [True, False], [None, [12, 23]]  # angle  # expand  # center
+        make_bounding_boxes(), [-87, 15, 90], [True, False], [None, [12, 23]]
     ):
+        if center is not None and expand:
+            # Skip warning: The provided center argument is ignored if expand is True
+            continue
+
         yield SampleInput(
             bounding_box,
             format=bounding_box.format,
@@ -297,6 +301,26 @@ def rotate_bounding_box():
         )
 
 
+@register_kernel_info_from_sample_inputs_fn
+def rotate_segmentation_mask():
+    for mask, angle, expand, center in itertools.product(
+        make_segmentation_masks(extra_dims=((), (4,))),
+        [-87, 15, 90],  # angle
+        [True, False],  # expand
+        [None, [12, 23]],  # center
+    ):
+        if center is not None and expand:
+            # Skip warning: The provided center argument is ignored if expand is True
+            continue
+
+        yield SampleInput(
+            mask,
+            angle=angle,
+            expand=expand,
+            center=center,
+        )
+
+
 @pytest.mark.parametrize(
     "kernel",
     [
@@ -411,8 +435,9 @@ def _compute_expected_bbox(bbox, angle_, translate_, scale_, shear_, center_):
             center=center,
         )
 
-        if center is None:
-            center = [s // 2 for s in bboxes_image_size[::-1]]
+        center_ = center
+        if center_ is None:
+            center_ = [s * 0.5 for s in bboxes_image_size[::-1]]
 
         if bboxes.ndim < 2:
             bboxes = [bboxes]
@@ -421,7 +446,7 @@ def _compute_expected_bbox(bbox, angle_, translate_, scale_, shear_, center_):
         for bbox in bboxes:
             bbox = features.BoundingBox(bbox, format=bboxes_format, image_size=bboxes_image_size)
             expected_bboxes.append(
-                _compute_expected_bbox(bbox, angle, (translate, translate), scale, (shear, shear), center)
+                _compute_expected_bbox(bbox, angle, (translate, translate), scale, (shear, shear), center_)
             )
         if len(expected_bboxes) > 1:
             expected_bboxes = torch.stack(expected_bboxes)
@@ -510,8 +535,10 @@ def _compute_expected_mask(mask, angle_, translate_, scale_, shear_, center_):
             shear=(shear, shear),
             center=center,
         )
-        if center is None:
-            center = [s // 2 for s in mask.shape[-2:][::-1]]
+
+        center_ = center
+        if center_ is None:
+            center_ = [s * 0.5 for s in mask.shape[-2:][::-1]]
 
         if mask.ndim < 4:
             masks = [mask]
@@ -520,7 +547,7 @@ def _compute_expected_mask(mask, angle_, translate_, scale_, shear_, center_):
 
         expected_masks = []
         for mask in masks:
-            expected_mask = _compute_expected_mask(mask, angle, (translate, translate), scale, (shear, shear), center)
+            expected_mask = _compute_expected_mask(mask, angle, (translate, translate), scale, (shear, shear), center_)
             expected_masks.append(expected_mask)
         if len(expected_masks) > 1:
             expected_masks = torch.stack(expected_masks)
@@ -550,8 +577,7 @@ def test_correctness_affine_segmentation_mask_on_fixed_input(device):
 
 
 @pytest.mark.parametrize("angle", range(-90, 90, 56))
-@pytest.mark.parametrize("expand", [True, False])
-@pytest.mark.parametrize("center", [None, (12, 14)])
+@pytest.mark.parametrize("expand, center", [(True, None), (False, None), (False, (12, 14))])
 def test_correctness_rotate_bounding_box(angle, expand, center):
     def _compute_expected_bbox(bbox, angle_, expand_, center_):
         affine_matrix = _compute_affine_matrix(angle_, [0.0, 0.0], 1.0, [0.0, 0.0], center_)
@@ -620,16 +646,17 @@ def _compute_expected_bbox(bbox, angle_, expand_, center_):
             center=center,
         )
 
-        if center is None:
-            center = [s // 2 for s in bboxes_image_size[::-1]]
+        center_ = center
+        if center_ is None:
+            center_ = [s * 0.5 for s in bboxes_image_size[::-1]]
 
         if bboxes.ndim < 2:
             bboxes = [bboxes]
 
         expected_bboxes = []
         for bbox in bboxes:
             bbox = features.BoundingBox(bbox, format=bboxes_format, image_size=bboxes_image_size)
-            expected_bboxes.append(_compute_expected_bbox(bbox, -angle, expand, center))
+            expected_bboxes.append(_compute_expected_bbox(bbox, -angle, expand, center_))
         if len(expected_bboxes) > 1:
             expected_bboxes = torch.stack(expected_bboxes)
         else:
@@ -638,7 +665,7 @@ def _compute_expected_bbox(bbox, angle_, expand_, center_):
 
 
 @pytest.mark.parametrize("device", cpu_and_gpu())
-@pytest.mark.parametrize("expand", [False])  # expand=True does not match D2, analysis in progress
+@pytest.mark.parametrize("expand", [False])  # expand=True does not match D2
 def test_correctness_rotate_bounding_box_on_fixed_input(device, expand):
     # Check transformation against known expected output
     image_size = (64, 64)
@@ -689,3 +716,91 @@ def test_correctness_rotate_bounding_box_on_fixed_input(device, expand):
     )
 
     torch.testing.assert_close(output_boxes.tolist(), expected_bboxes)
+
+
+@pytest.mark.parametrize("angle", range(-90, 90, 37))
+@pytest.mark.parametrize("expand, center", [(True, None), (False, None), (False, (12, 14))])
+def test_correctness_rotate_segmentation_mask(angle, expand, center):
+    def _compute_expected_mask(mask, angle_, expand_, center_):
+        assert mask.ndim == 3 and mask.shape[0] == 1
+        image_size = mask.shape[-2:]
+        affine_matrix = _compute_affine_matrix(angle_, [0.0, 0.0], 1.0, [0.0, 0.0], center_)
+        inv_affine_matrix = np.linalg.inv(affine_matrix)
+
+        if expand_:
+            # Pillow implementation on how to perform expand:
+            # https://github.com/python-pillow/Pillow/blob/11de3318867e4398057373ee9f12dcb33db7335c/src/PIL/Image.py#L2054-L2069
+            height, width = image_size
+            points = np.array(
+                [
+                    [0.0, 0.0, 1.0],
+                    [0.0, 1.0 * height, 1.0],
+                    [1.0 * width, 1.0 * height, 1.0],
+                    [1.0 * width, 0.0, 1.0],
+                ]
+            )
+            new_points = points @ inv_affine_matrix.T
+            min_vals = np.min(new_points, axis=0)[:2]
+            max_vals = np.max(new_points, axis=0)[:2]
+            cmax = np.ceil(np.trunc(max_vals * 1e4) * 1e-4)
+            cmin = np.floor(np.trunc((min_vals + 1e-8) * 1e4) * 1e-4)
+            new_width, new_height = (cmax - cmin).astype("int32").tolist()
+            tr = np.array([-(new_width - width) / 2.0, -(new_height - height) / 2.0, 1.0]) @ inv_affine_matrix.T
+
+            inv_affine_matrix[:2, 2] = tr[:2]
+            image_size = [new_height, new_width]
+
+        inv_affine_matrix = inv_affine_matrix[:2, :]
+        expected_mask = torch.zeros(1, *image_size, dtype=mask.dtype)
+
+        for out_y in range(expected_mask.shape[1]):
+            for out_x in range(expected_mask.shape[2]):
+                output_pt = np.array([out_x + 0.5, out_y + 0.5, 1.0])
+                input_pt = np.floor(np.dot(inv_affine_matrix, output_pt)).astype(np.int32)
+                in_x, in_y = input_pt[:2]
+                if 0 <= in_x < mask.shape[2] and 0 <= in_y < mask.shape[1]:
+                    expected_mask[0, out_y, out_x] = mask[0, in_y, in_x]
+        return expected_mask.to(mask.device)
+
+    for mask in make_segmentation_masks(extra_dims=((), (4,))):
+        output_mask = F.rotate_segmentation_mask(
+            mask,
+            angle=angle,
+            expand=expand,
+            center=center,
+        )
+
+        center_ = center
+        if center_ is None:
+            center_ = [s * 0.5 for s in mask.shape[-2:][::-1]]
+
+        if mask.ndim < 4:
+            masks = [mask]
+        else:
+            masks = [m for m in mask]
+
+        expected_masks = []
+        for mask in masks:
+            expected_mask = _compute_expected_mask(mask, -angle, expand, center_)
+            expected_masks.append(expected_mask)
+        if len(expected_masks) > 1:
+            expected_masks = torch.stack(expected_masks)
+        else:
+            expected_masks = expected_masks[0]
+        torch.testing.assert_close(output_mask, expected_masks)
+
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+def test_correctness_rotate_segmentation_mask_on_fixed_input(device):
+    # Check transformation against known expected output and CPU/CUDA devices
+
+    # Create a fixed input segmentation mask with 2 square masks
+    # in top-left, bottom-left corners
+    mask = torch.zeros(1, 32, 32, dtype=torch.long, device=device)
+    mask[0, 2:10, 2:10] = 1
+    mask[0, 32 - 9 : 32 - 3, 3:9] = 2
+
+    # Rotate 90 degrees
+    expected_mask = torch.rot90(mask, k=1, dims=(-2, -1))
+    out_mask = F.rotate_segmentation_mask(mask, 90, expand=False)
+    torch.testing.assert_close(out_mask, expected_mask)

torchvision/prototype/transforms/functional/__init__.py

@@ -56,6 +56,7 @@
     rotate_bounding_box,
     rotate_image_tensor,
     rotate_image_pil,
+    rotate_segmentation_mask,
     pad_image_tensor,
     pad_image_pil,
     pad_bounding_box,

torchvision/prototype/transforms/functional/_geometry.py

@@ -324,7 +324,7 @@ def rotate_image_tensor(
     center_f = [0.0, 0.0]
     if center is not None:
         if expand:
-            warnings.warn("The provided center argument is ignored if expand is True")
+            warnings.warn("The provided center argument has no effect on the result if expand is True")
         else:
             _, height, width = get_dimensions_image_tensor(img)
             # Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center.
@@ -345,7 +345,7 @@ def rotate_image_pil(
     center: Optional[List[float]] = None,
 ) -> PIL.Image.Image:
     if center is not None and expand:
-        warnings.warn("The provided center argument is ignored if expand is True")
+        warnings.warn("The provided center argument has no effect on the result if expand is True")
         center = None
 
     return _FP.rotate(
@@ -361,6 +361,10 @@ def rotate_bounding_box(
     expand: bool = False,
     center: Optional[List[float]] = None,
 ) -> torch.Tensor:
+    if center is not None and expand:
+        warnings.warn("The provided center argument has no effect on the result if expand is True")
+        center = None
+
     original_shape = bounding_box.shape
     bounding_box = convert_bounding_box_format(
         bounding_box, old_format=format, new_format=features.BoundingBoxFormat.XYXY
@@ -373,6 +377,21 @@ def rotate_bounding_box(
     ).view(original_shape)
 
 
+def rotate_segmentation_mask(
+    img: torch.Tensor,
+    angle: float,
+    expand: bool = False,
+    center: Optional[List[float]] = None,
+) -> torch.Tensor:
+    return rotate_image_tensor(
+        img,
+        angle=angle,
+        expand=expand,
+        interpolation=InterpolationMode.NEAREST,
+        center=center,
+    )
+
+
 pad_image_tensor = _FT.pad
 pad_image_pil = _FP.pad