Add vertical_flip_bounding_boxes

Test Plan:
Run unit tests: `pytest test/test_transforms_v2.py -vvv -k "TestVerticalFlip and test_kernel_bounding_boxes"`

Author: AntoineSimoulin

test/test_transforms_v2.py

@@ -1531,7 +1531,7 @@ class TestVerticalFlip:
     def test_kernel_image(self, dtype, device):
         check_kernel(F.vertical_flip_image, make_image(dtype=dtype, device=device))
 
-    @pytest.mark.parametrize("format", SUPPORTED_BOX_FORMATS)
+    @pytest.mark.parametrize("format", list(tv_tensors.BoundingBoxFormat))
     @pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
     @pytest.mark.parametrize("device", cpu_and_cuda())
     def test_kernel_bounding_boxes(self, format, dtype, device):
@@ -1588,23 +1588,25 @@ def test_image_correctness(self, fn):
 
         torch.testing.assert_close(actual, expected)
 
-    def _reference_vertical_flip_bounding_boxes(self, bounding_boxes):
+    def _reference_vertical_flip_bounding_boxes(self, bounding_boxes, format):
         affine_matrix = np.array(
             [
                 [1, 0, 0],
                 [0, -1, bounding_boxes.canvas_size[0]],
             ],
         )
 
+        if tv_tensors.is_rotated_bounding_format(format):
+            return reference_affine_rotated_bounding_boxes_helper(bounding_boxes, affine_matrix=affine_matrix)
         return reference_affine_bounding_boxes_helper(bounding_boxes, affine_matrix=affine_matrix)
 
-    @pytest.mark.parametrize("format", SUPPORTED_BOX_FORMATS)
+    @pytest.mark.parametrize("format", list(tv_tensors.BoundingBoxFormat))
     @pytest.mark.parametrize("fn", [F.vertical_flip, transform_cls_to_functional(transforms.RandomVerticalFlip, p=1)])
     def test_bounding_boxes_correctness(self, format, fn):
         bounding_boxes = make_bounding_boxes(format=format)
 
         actual = fn(bounding_boxes)
-        expected = self._reference_vertical_flip_bounding_boxes(bounding_boxes)
+        expected = self._reference_vertical_flip_bounding_boxes(bounding_boxes, format)
 
         torch.testing.assert_close(actual, expected)
 

torchvision/transforms/v2/functional/_geometry.py

@@ -146,14 +146,32 @@ def vertical_flip_bounding_boxes(
 ) -> torch.Tensor:
     shape = bounding_boxes.shape
 
-    bounding_boxes = bounding_boxes.clone().reshape(-1, 4)
+    if tv_tensors.is_rotated_bounding_format(format):
+        bounding_boxes = (
+            bounding_boxes.clone().reshape(-1, 5)
+            if format != tv_tensors.BoundingBoxFormat.XYXYXYXY
+            else bounding_boxes.clone().reshape(-1, 8)
+        )
+    else:
+        bounding_boxes = bounding_boxes.clone().reshape(-1, 4)
 
     if format == tv_tensors.BoundingBoxFormat.XYXY:
         bounding_boxes[:, [1, 3]] = bounding_boxes[:, [3, 1]].sub_(canvas_size[0]).neg_()
     elif format == tv_tensors.BoundingBoxFormat.XYWH:
         bounding_boxes[:, 1].add_(bounding_boxes[:, 3]).sub_(canvas_size[0]).neg_()
-    else:  # format == tv_tensors.BoundingBoxFormat.CXCYWH:
+    elif format == tv_tensors.BoundingBoxFormat.CXCYWH:
         bounding_boxes[:, 1].sub_(canvas_size[0]).neg_()
+    elif format == tv_tensors.BoundingBoxFormat.XYXYXYXY:
+        bounding_boxes[:, 1::2].sub_(canvas_size[0]).neg_()
+        bounding_boxes = bounding_boxes[:, [0, 1, 6, 7, 4, 5, 2, 3]]
+    elif format == tv_tensors.BoundingBoxFormat.XYWHR:
+        bounding_boxes[:, 1].sub_(canvas_size[0]).neg_()
+        bounding_boxes = bounding_boxes[:, [0, 1, 3, 2, 4]]
+        bounding_boxes[:, -1].sub_(90).neg_()
+    else:  # format == tv_tensors.BoundingBoxFormat.CXCYWHR:
+        bounding_boxes[:, 1].sub_(canvas_size[0]).neg_()
+        bounding_boxes = bounding_boxes[:, [0, 1, 3, 2, 4]]
+        bounding_boxes[:, -1].sub_(90).neg_()
 
     return bounding_boxes.reshape(shape)