diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py index e9d1dfc0517..a47cf3bd408 100644 --- a/test/test_transforms_v2.py +++ b/test/test_transforms_v2.py @@ -29,7 +29,7 @@ from torch.utils._pytree import tree_flatten, tree_unflatten from torchvision import datapoints from torchvision.ops.boxes import box_iou -from torchvision.transforms.functional import InterpolationMode, pil_to_tensor, to_pil_image +from torchvision.transforms.functional import InterpolationMode, to_pil_image from torchvision.transforms.v2 import functional as F from torchvision.transforms.v2.utils import check_type, is_simple_tensor, query_chw @@ -406,59 +406,6 @@ def was_applied(output, inpt): assert transform.was_applied(output, input) -@pytest.mark.parametrize("p", [0.0, 1.0]) -class TestRandomVerticalFlip: - def input_expected_image_tensor(self, p, dtype=torch.float32): - input = torch.tensor([[[1, 1], [0, 0]], [[1, 1], [0, 0]]], dtype=dtype) - expected = torch.tensor([[[0, 0], [1, 1]], [[0, 0], [1, 1]]], dtype=dtype) - - return input, expected if p == 1 else input - - def test_simple_tensor(self, p): - input, expected = self.input_expected_image_tensor(p) - transform = transforms.RandomVerticalFlip(p=p) - - actual = transform(input) - - assert_equal(expected, actual) - - def test_pil_image(self, p): - input, expected = self.input_expected_image_tensor(p, dtype=torch.uint8) - transform = transforms.RandomVerticalFlip(p=p) - - actual = transform(to_pil_image(input)) - - assert_equal(expected, pil_to_tensor(actual)) - - def test_datapoints_image(self, p): - input, expected = self.input_expected_image_tensor(p) - transform = transforms.RandomVerticalFlip(p=p) - - actual = transform(datapoints.Image(input)) - - assert_equal(datapoints.Image(expected), actual) - - def test_datapoints_mask(self, p): - input, expected = self.input_expected_image_tensor(p) - transform = transforms.RandomVerticalFlip(p=p) - - actual = transform(datapoints.Mask(input)) - - assert_equal(datapoints.Mask(expected), actual) - - def test_datapoints_bounding_box(self, p): - input = datapoints.BoundingBox([0, 0, 5, 5], format=datapoints.BoundingBoxFormat.XYXY, spatial_size=(10, 10)) - transform = transforms.RandomVerticalFlip(p=p) - - actual = transform(input) - - expected_image_tensor = torch.tensor([0, 5, 5, 10]) if p == 1.0 else input - expected = datapoints.BoundingBox.wrap_like(input, expected_image_tensor) - assert_equal(expected, actual) - assert actual.format == expected.format - assert actual.spatial_size == expected.spatial_size - - class TestPad: def test_assertions(self): with pytest.raises(TypeError, match="Got inappropriate padding arg"): diff --git a/test/test_transforms_v2_refactored.py b/test/test_transforms_v2_refactored.py index 0db4824d584..b737a7f0102 100644 --- a/test/test_transforms_v2_refactored.py +++ b/test/test_transforms_v2_refactored.py @@ -842,7 +842,7 @@ def _reference_horizontal_flip_bounding_box(self, bounding_box): "fn", [F.horizontal_flip, transform_cls_to_functional(transforms.RandomHorizontalFlip, p=1)] ) def test_bounding_box_correctness(self, format, fn): - bounding_box = self._make_input(datapoints.BoundingBox) + bounding_box = self._make_input(datapoints.BoundingBox, format=format) actual = fn(bounding_box) expected = self._reference_horizontal_flip_bounding_box(bounding_box) @@ -1025,12 +1025,10 @@ def test_kernel_bounding_box(self, param, value, format, dtype, device): @pytest.mark.parametrize("mask_type", ["segmentation", "detection"]) def test_kernel_mask(self, mask_type): - check_kernel( - F.affine_mask, self._make_input(datapoints.Mask, mask_type=mask_type), **self._MINIMAL_AFFINE_KWARGS - ) + self._check_kernel(F.affine_mask, self._make_input(datapoints.Mask, mask_type=mask_type)) def test_kernel_video(self): - check_kernel(F.affine_video, self._make_input(datapoints.Video), **self._MINIMAL_AFFINE_KWARGS) + self._check_kernel(F.affine_video, self._make_input(datapoints.Video)) @pytest.mark.parametrize( ("input_type", "kernel"), @@ -1301,3 +1299,143 @@ def test_transform_negative_shear_error(self): def test_transform_unknown_fill_error(self): with pytest.raises(TypeError, match="Got inappropriate fill arg"): transforms.RandomAffine(degrees=0, fill="fill") + + +class TestVerticalFlip: + def _make_input(self, input_type, *, dtype=None, device="cpu", spatial_size=(17, 11), **kwargs): + if input_type in {torch.Tensor, PIL.Image.Image, datapoints.Image}: + input = make_image(size=spatial_size, dtype=dtype or torch.uint8, device=device, **kwargs) + if input_type is torch.Tensor: + input = input.as_subclass(torch.Tensor) + elif input_type is PIL.Image.Image: + input = F.to_image_pil(input) + elif input_type is datapoints.BoundingBox: + kwargs.setdefault("format", datapoints.BoundingBoxFormat.XYXY) + input = make_bounding_box( + dtype=dtype or torch.float32, + device=device, + spatial_size=spatial_size, + **kwargs, + ) + elif input_type is datapoints.Mask: + input = make_segmentation_mask(size=spatial_size, dtype=dtype or torch.uint8, device=device, **kwargs) + elif input_type is datapoints.Video: + input = make_video(size=spatial_size, dtype=dtype or torch.uint8, device=device, **kwargs) + + return input + + @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8]) + @pytest.mark.parametrize("device", cpu_and_cuda()) + def test_kernel_image_tensor(self, dtype, device): + check_kernel(F.vertical_flip_image_tensor, self._make_input(torch.Tensor, dtype=dtype, device=device)) + + @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat)) + @pytest.mark.parametrize("dtype", [torch.float32, torch.int64]) + @pytest.mark.parametrize("device", cpu_and_cuda()) + def test_kernel_bounding_box(self, format, dtype, device): + bounding_box = self._make_input(datapoints.BoundingBox, dtype=dtype, device=device, format=format) + check_kernel( + F.vertical_flip_bounding_box, + bounding_box, + format=format, + spatial_size=bounding_box.spatial_size, + ) + + @pytest.mark.parametrize( + "dtype_and_make_mask", [(torch.uint8, make_segmentation_mask), (torch.bool, make_detection_mask)] + ) + def test_kernel_mask(self, dtype_and_make_mask): + dtype, make_mask = dtype_and_make_mask + check_kernel(F.vertical_flip_mask, make_mask(dtype=dtype)) + + def test_kernel_video(self): + check_kernel(F.vertical_flip_video, self._make_input(datapoints.Video)) + + @pytest.mark.parametrize( + ("input_type", "kernel"), + [ + (torch.Tensor, F.vertical_flip_image_tensor), + (PIL.Image.Image, F.vertical_flip_image_pil), + (datapoints.Image, F.vertical_flip_image_tensor), + (datapoints.BoundingBox, F.vertical_flip_bounding_box), + (datapoints.Mask, F.vertical_flip_mask), + (datapoints.Video, F.vertical_flip_video), + ], + ) + def test_dispatcher(self, kernel, input_type): + check_dispatcher(F.vertical_flip, kernel, self._make_input(input_type)) + + @pytest.mark.parametrize( + ("input_type", "kernel"), + [ + (torch.Tensor, F.vertical_flip_image_tensor), + (PIL.Image.Image, F.vertical_flip_image_pil), + (datapoints.Image, F.vertical_flip_image_tensor), + (datapoints.BoundingBox, F.vertical_flip_bounding_box), + (datapoints.Mask, F.vertical_flip_mask), + (datapoints.Video, F.vertical_flip_video), + ], + ) + def test_dispatcher_signature(self, kernel, input_type): + check_dispatcher_signatures_match(F.vertical_flip, kernel=kernel, input_type=input_type) + + @pytest.mark.parametrize( + "input_type", + [torch.Tensor, PIL.Image.Image, datapoints.Image, datapoints.BoundingBox, datapoints.Mask, datapoints.Video], + ) + @pytest.mark.parametrize("device", cpu_and_cuda()) + def test_transform(self, input_type, device): + input = self._make_input(input_type, device=device) + + check_transform(transforms.RandomVerticalFlip, input, p=1) + + @pytest.mark.parametrize("fn", [F.vertical_flip, transform_cls_to_functional(transforms.RandomVerticalFlip, p=1)]) + def test_image_correctness(self, fn): + image = self._make_input(torch.Tensor, dtype=torch.uint8, device="cpu") + + actual = fn(image) + expected = F.to_image_tensor(F.vertical_flip(F.to_image_pil(image))) + + torch.testing.assert_close(actual, expected) + + def _reference_vertical_flip_bounding_box(self, bounding_box): + affine_matrix = np.array( + [ + [1, 0, 0], + [0, -1, bounding_box.spatial_size[0]], + ], + dtype="float64" if bounding_box.dtype == torch.float64 else "float32", + ) + + expected_bboxes = reference_affine_bounding_box_helper( + bounding_box, + format=bounding_box.format, + spatial_size=bounding_box.spatial_size, + affine_matrix=affine_matrix, + ) + + return datapoints.BoundingBox.wrap_like(bounding_box, expected_bboxes) + + @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat)) + @pytest.mark.parametrize("fn", [F.vertical_flip, transform_cls_to_functional(transforms.RandomVerticalFlip, p=1)]) + def test_bounding_box_correctness(self, format, fn): + bounding_box = self._make_input(datapoints.BoundingBox, format=format) + + actual = fn(bounding_box) + expected = self._reference_vertical_flip_bounding_box(bounding_box) + + torch.testing.assert_close(actual, expected) + + @pytest.mark.parametrize( + "input_type", + [torch.Tensor, PIL.Image.Image, datapoints.Image, datapoints.BoundingBox, datapoints.Mask, datapoints.Video], + ) + @pytest.mark.parametrize("device", cpu_and_cuda()) + def test_transform_noop(self, input_type, device): + input = self._make_input(input_type, device=device) + + transform = transforms.RandomVerticalFlip(p=0) + + output = transform(input) + + assert_equal(output, input) diff --git a/test/transforms_v2_dispatcher_infos.py b/test/transforms_v2_dispatcher_infos.py index b217e1638c7..6b13ad33861 100644 --- a/test/transforms_v2_dispatcher_infos.py +++ b/test/transforms_v2_dispatcher_infos.py @@ -138,16 +138,6 @@ def fill_sequence_needs_broadcast(args_kwargs): DISPATCHER_INFOS = [ - DispatcherInfo( - F.vertical_flip, - kernels={ - datapoints.Image: F.vertical_flip_image_tensor, - datapoints.Video: F.vertical_flip_video, - datapoints.BoundingBox: F.vertical_flip_bounding_box, - datapoints.Mask: F.vertical_flip_mask, - }, - pil_kernel_info=PILKernelInfo(F.vertical_flip_image_pil, kernel_name="vertical_flip_image_pil"), - ), DispatcherInfo( F.rotate, kernels={ diff --git a/test/transforms_v2_kernel_infos.py b/test/transforms_v2_kernel_infos.py index 0daae8aeec8..b28b514fa38 100644 --- a/test/transforms_v2_kernel_infos.py +++ b/test/transforms_v2_kernel_infos.py @@ -264,87 +264,6 @@ def reference_inputs_convert_format_bounding_box(): ) -def sample_inputs_vertical_flip_image_tensor(): - for image_loader in make_image_loaders(sizes=["random"], dtypes=[torch.float32]): - yield ArgsKwargs(image_loader) - - -def reference_inputs_vertical_flip_image_tensor(): - for image_loader in make_image_loaders(extra_dims=[()], dtypes=[torch.uint8]): - yield ArgsKwargs(image_loader) - - -def sample_inputs_vertical_flip_bounding_box(): - for bounding_box_loader in make_bounding_box_loaders( - formats=[datapoints.BoundingBoxFormat.XYXY], dtypes=[torch.float32] - ): - yield ArgsKwargs( - bounding_box_loader, format=bounding_box_loader.format, spatial_size=bounding_box_loader.spatial_size - ) - - -def sample_inputs_vertical_flip_mask(): - for image_loader in make_mask_loaders(sizes=["random"], dtypes=[torch.uint8]): - yield ArgsKwargs(image_loader) - - -def sample_inputs_vertical_flip_video(): - for video_loader in make_video_loaders(sizes=["random"], num_frames=["random"]): - yield ArgsKwargs(video_loader) - - -def reference_vertical_flip_bounding_box(bounding_box, *, format, spatial_size): - affine_matrix = np.array( - [ - [1, 0, 0], - [0, -1, spatial_size[0]], - ], - dtype="float64" if bounding_box.dtype == torch.float64 else "float32", - ) - - expected_bboxes = reference_affine_bounding_box_helper( - bounding_box, format=format, spatial_size=spatial_size, affine_matrix=affine_matrix - ) - - return expected_bboxes - - -def reference_inputs_vertical_flip_bounding_box(): - for bounding_box_loader in make_bounding_box_loaders(extra_dims=[()]): - yield ArgsKwargs( - bounding_box_loader, - format=bounding_box_loader.format, - spatial_size=bounding_box_loader.spatial_size, - ) - - -KERNEL_INFOS.extend( - [ - KernelInfo( - F.vertical_flip_image_tensor, - kernel_name="vertical_flip_image_tensor", - sample_inputs_fn=sample_inputs_vertical_flip_image_tensor, - reference_fn=pil_reference_wrapper(F.vertical_flip_image_pil), - reference_inputs_fn=reference_inputs_vertical_flip_image_tensor, - float32_vs_uint8=True, - ), - KernelInfo( - F.vertical_flip_bounding_box, - sample_inputs_fn=sample_inputs_vertical_flip_bounding_box, - reference_fn=reference_vertical_flip_bounding_box, - reference_inputs_fn=reference_inputs_vertical_flip_bounding_box, - ), - KernelInfo( - F.vertical_flip_mask, - sample_inputs_fn=sample_inputs_vertical_flip_mask, - ), - KernelInfo( - F.vertical_flip_video, - sample_inputs_fn=sample_inputs_vertical_flip_video, - ), - ] -) - _ROTATE_ANGLES = [-87, 15, 90] diff --git a/torchvision/transforms/v2/functional/_geometry.py b/torchvision/transforms/v2/functional/_geometry.py index b56205e6123..1d298ff914d 100644 --- a/torchvision/transforms/v2/functional/_geometry.py +++ b/torchvision/transforms/v2/functional/_geometry.py @@ -93,7 +93,8 @@ def vertical_flip_image_tensor(image: torch.Tensor) -> torch.Tensor: return image.flip(-2) -vertical_flip_image_pil = _FP.vflip +def vertical_flip_image_pil(image: PIL.Image) -> PIL.Image: + return _FP.vflip(image) def vertical_flip_mask(mask: torch.Tensor) -> torch.Tensor: