port tests for F.resized_crop and RandomResizedCrop

test/test_transforms_v2.py

@@ -328,22 +328,6 @@ def test_auto_augment(self, transform, input):
     def test_normalize(self, transform, input):
         transform(input)
 
-    @parametrize(
-        [
-            (
-                transforms.RandomResizedCrop([16, 16], antialias=True),
-                itertools.chain(
-                    make_images(extra_dims=[(4,)]),
-                    make_vanilla_tensor_images(),
-                    make_pil_images(),
-                    make_videos(extra_dims=[()]),
-                ),
-            )
-        ]
-    )
-    def test_random_resized_crop(self, transform, input):
-        transform(input)
-
 
 @pytest.mark.parametrize(
     "flat_inputs",

test/test_transforms_v2_consistency.py

@@ -252,30 +252,6 @@ def __init__(
         # Use default tolerances of `torch.testing.assert_close`
         closeness_kwargs=dict(rtol=None, atol=None),
     ),
-    ConsistencyConfig(
-        v2_transforms.RandomResizedCrop,
-        legacy_transforms.RandomResizedCrop,
-        [
-            ArgsKwargs(16),
-            ArgsKwargs(17, scale=(0.3, 0.7)),
-            ArgsKwargs(25, ratio=(0.5, 1.5)),
-            ArgsKwargs((31, 28), interpolation=v2_transforms.InterpolationMode.NEAREST),
-            ArgsKwargs((31, 28), interpolation=PIL.Image.NEAREST),
-            ArgsKwargs((29, 32), antialias=False),
-            ArgsKwargs((28, 31), antialias=True),
-        ],
-        # atol=1 due to Resize v2 is using native uint8 interpolate path for bilinear and nearest modes
-        closeness_kwargs=dict(rtol=0, atol=1),
-    ),
-    ConsistencyConfig(
-        v2_transforms.RandomResizedCrop,
-        legacy_transforms.RandomResizedCrop,
-        [
-            ArgsKwargs((33, 26), interpolation=v2_transforms.InterpolationMode.BICUBIC, antialias=True),
-            ArgsKwargs((33, 26), interpolation=PIL.Image.BICUBIC, antialias=True),
-        ],
-        closeness_kwargs=dict(rtol=0, atol=21),
-    ),
     ConsistencyConfig(
         v2_transforms.ColorJitter,
         legacy_transforms.ColorJitter,
@@ -535,7 +511,6 @@ def test_call_consistency(config, args_kwargs):
             id=transform_cls.__name__,
         )
         for transform_cls, get_params_args_kwargs in [
-            (v2_transforms.RandomResizedCrop, ArgsKwargs(make_image(), scale=[0.3, 0.7], ratio=[0.5, 1.5])),
             (v2_transforms.ColorJitter, ArgsKwargs(brightness=None, contrast=None, saturation=None, hue=None)),
             (v2_transforms.GaussianBlur, ArgsKwargs(0.3, 1.4)),
             (v2_transforms.RandomPerspective, ArgsKwargs(23, 17, 0.5)),

test/test_transforms_v2_functional.py

@@ -1,5 +1,4 @@
 import inspect
-import math
 import os
 import re
 
@@ -526,88 +525,6 @@ def test_tv_tensor_explicit_metadata(self, metadata):
 #  `transforms_v2_kernel_infos.py`
 
 
-def _compute_affine_matrix(angle_, translate_, scale_, shear_, center_):
-    rot = math.radians(angle_)
-    cx, cy = center_
-    tx, ty = translate_
-    sx, sy = [math.radians(sh_) for sh_ in shear_]
-
-    c_matrix = np.array([[1, 0, cx], [0, 1, cy], [0, 0, 1]])
-    t_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]])
-    c_matrix_inv = np.linalg.inv(c_matrix)
-    rs_matrix = np.array(
-        [
-            [scale_ * math.cos(rot), -scale_ * math.sin(rot), 0],
-            [scale_ * math.sin(rot), scale_ * math.cos(rot), 0],
-            [0, 0, 1],
-        ]
-    )
-    shear_x_matrix = np.array([[1, -math.tan(sx), 0], [0, 1, 0], [0, 0, 1]])
-    shear_y_matrix = np.array([[1, 0, 0], [-math.tan(sy), 1, 0], [0, 0, 1]])
-    rss_matrix = np.matmul(rs_matrix, np.matmul(shear_y_matrix, shear_x_matrix))
-    true_matrix = np.matmul(t_matrix, np.matmul(c_matrix, np.matmul(rss_matrix, c_matrix_inv)))
-    return true_matrix
-
-
-@pytest.mark.parametrize("device", cpu_and_cuda())
-def test_correctness_vertical_flip_segmentation_mask_on_fixed_input(device):
-    mask = torch.zeros((3, 3, 3), dtype=torch.long, device=device)
-    mask[:, 0, :] = 1
-
-    out_mask = F.vertical_flip_mask(mask)
-
-    expected_mask = torch.zeros((3, 3, 3), dtype=torch.long, device=device)
-    expected_mask[:, -1, :] = 1
-    torch.testing.assert_close(out_mask, expected_mask)
-
-
-@pytest.mark.parametrize("device", cpu_and_cuda())
-@pytest.mark.parametrize(
-    "format",
-    [tv_tensors.BoundingBoxFormat.XYXY, tv_tensors.BoundingBoxFormat.XYWH, tv_tensors.BoundingBoxFormat.CXCYWH],
-)
-@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_bounding_boxes(device, format, top, left, height, width, size):
-    def _compute_expected_bbox(bbox, top_, left_, height_, width_, size_):
-        # bbox should be xyxy
-        bbox[0] = (bbox[0] - left_) * size_[1] / width_
-        bbox[1] = (bbox[1] - top_) * size_[0] / height_
-        bbox[2] = (bbox[2] - left_) * size_[1] / width_
-        bbox[3] = (bbox[3] - top_) * size_[0] / height_
-        return bbox
-
-    format = tv_tensors.BoundingBoxFormat.XYXY
-    canvas_size = (100, 100)
-    in_boxes = [
-        [10.0, 10.0, 20.0, 20.0],
-        [5.0, 10.0, 15.0, 20.0],
-    ]
-    expected_bboxes = []
-    for in_box in in_boxes:
-        expected_bboxes.append(_compute_expected_bbox(list(in_box), top, left, height, width, size))
-    expected_bboxes = torch.tensor(expected_bboxes, device=device)
-
-    in_boxes = tv_tensors.BoundingBoxes(
-        in_boxes, format=tv_tensors.BoundingBoxFormat.XYXY, canvas_size=canvas_size, device=device
-    )
-    if format != tv_tensors.BoundingBoxFormat.XYXY:
-        in_boxes = convert_bounding_box_format(in_boxes, tv_tensors.BoundingBoxFormat.XYXY, format)
-
-    output_boxes, output_canvas_size = F.resized_crop_bounding_boxes(in_boxes, format, top, left, height, width, size)
-
-    if format != tv_tensors.BoundingBoxFormat.XYXY:
-        output_boxes = convert_bounding_box_format(output_boxes, format, tv_tensors.BoundingBoxFormat.XYXY)
-
-    torch.testing.assert_close(output_boxes, expected_bboxes)
-    torch.testing.assert_close(output_canvas_size, size)
-
-
 def _parse_padding(padding):
     if isinstance(padding, int):
         return [padding] * 4

test/test_transforms_v2_refactored.py

@@ -3110,3 +3110,136 @@ def test_errors(self):
             F.convert_bounding_box_format(
                 input_tv_tensor, old_format=input_tv_tensor.format, new_format=input_tv_tensor.format
             )
+
+
+class TestResizedCrop:
+    INPUT_SIZE = (17, 11)
+    CROP_KWARGS = dict(top=2, left=2, height=5, width=7)
+    OUTPUT_SIZE = (19, 32)
+
+    @pytest.mark.parametrize(
+        ("kernel", "make_input"),
+        [
+            (F.resized_crop_image, make_image),
+            (F.resized_crop_bounding_boxes, make_bounding_boxes),
+            (F.resized_crop_mask, make_segmentation_mask),
+            (F.resized_crop_mask, make_detection_mask),
+            (F.resized_crop_video, make_video),
+        ],
+    )
+    def test_kernel(self, kernel, make_input):
+        input = make_input(self.INPUT_SIZE)
+        if isinstance(input, tv_tensors.BoundingBoxes):
+            extra_kwargs = dict(format=input.format)
+        elif isinstance(input, tv_tensors.Mask):
+            extra_kwargs = dict()
+        else:
+            extra_kwargs = dict(antialias=True)
+
+        check_kernel(kernel, input, **self.CROP_KWARGS, size=self.OUTPUT_SIZE, **extra_kwargs)
+
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image_pil, make_image, make_bounding_boxes, make_segmentation_mask, make_video],
+    )
+    def test_functional(self, make_input):
+        check_functional(
+            F.resized_crop, make_input(self.INPUT_SIZE), **self.CROP_KWARGS, size=self.OUTPUT_SIZE, antialias=True
+        )
+
+    @pytest.mark.parametrize(
+        ("kernel", "input_type"),
+        [
+            (F.resized_crop_image, torch.Tensor),
+            (F._resized_crop_image_pil, PIL.Image.Image),
+            (F.resized_crop_image, tv_tensors.Image),
+            (F.resized_crop_bounding_boxes, tv_tensors.BoundingBoxes),
+            (F.resized_crop_mask, tv_tensors.Mask),
+            (F.resized_crop_video, tv_tensors.Video),
+        ],
+    )
+    def test_functional_signature(self, kernel, input_type):
+        check_functional_kernel_signature_match(F.resized_crop, kernel=kernel, input_type=input_type)
+
+    @param_value_parametrization(
+        scale=[(0.1, 0.2), [0.0, 1.0]],
+        ratio=[(0.3, 0.7), [0.1, 5.0]],
+    )
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image_pil, make_image, make_bounding_boxes, make_segmentation_mask, make_video],
+    )
+    def test_transform(self, param, value, make_input):
+        check_transform(
+            transforms.RandomResizedCrop(size=self.OUTPUT_SIZE, **{param: value}, antialias=True),
+            make_input(self.INPUT_SIZE),
+            check_v1_compatibility=dict(rtol=0, atol=1),
+        )
+
+    # `InterpolationMode.NEAREST` is modeled after the buggy `INTER_NEAREST` interpolation of CV2.
+    # The PIL equivalent of `InterpolationMode.NEAREST` is `InterpolationMode.NEAREST_EXACT`
+    @pytest.mark.parametrize("interpolation", set(INTERPOLATION_MODES) - {transforms.InterpolationMode.NEAREST})
+    def test_functional_image_correctness(self, interpolation):
+        image = make_image(self.INPUT_SIZE, dtype=torch.uint8)
+
+        actual = F.resized_crop(
+            image, **self.CROP_KWARGS, size=self.OUTPUT_SIZE, interpolation=interpolation, antialias=True
+        )
+        expected = F.to_image(
+            F.resized_crop(
+                F.to_pil_image(image), **self.CROP_KWARGS, size=self.OUTPUT_SIZE, interpolation=interpolation
+            )
+        )
+
+        torch.testing.assert_close(actual, expected, atol=1, rtol=0)
+
+    def _reference_resized_crop_bounding_boxes(self, bounding_boxes, *, top, left, height, width, size):
+        new_height, new_width = size
+
+        crop_affine_matrix = np.array(
+            [
+                [1, 0, -left],
+                [0, 1, -top],
+                [0, 0, 1],
+            ],
+        )
+        resize_affine_matrix = np.array(
+            [
+                [new_width / width, 0, 0],
+                [0, new_height / height, 0],
+                [0, 0, 1],
+            ],
+        )
+        affine_matrix = (resize_affine_matrix @ crop_affine_matrix)[:2, :]
+
+        return reference_affine_bounding_boxes_helper(
+            bounding_boxes,
+            affine_matrix=affine_matrix,
+            new_canvas_size=size,
+        )
+
+    @pytest.mark.parametrize("format", list(tv_tensors.BoundingBoxFormat))
+    def test_functional_bounding_boxes_correctness(self, format):
+        bounding_boxes = make_bounding_boxes(self.INPUT_SIZE, format=format)
+
+        actual = F.resized_crop(bounding_boxes, **self.CROP_KWARGS, size=self.OUTPUT_SIZE)
+        expected = self._reference_resized_crop_bounding_boxes(
+            bounding_boxes, **self.CROP_KWARGS, size=self.OUTPUT_SIZE
+        )
+
+        assert_equal(actual, expected)
+        assert_equal(F.get_size(actual), F.get_size(expected))
+
+    def test_transform_errors_warnings(self):
+        with pytest.raises(ValueError, match="provide only two dimensions"):
+            transforms.RandomResizedCrop(size=(1, 2, 3))
+
+        with pytest.raises(TypeError, match="Scale should be a sequence"):
+            transforms.RandomResizedCrop(size=self.INPUT_SIZE, scale=123)
+
+        with pytest.raises(TypeError, match="Ratio should be a sequence"):
+            transforms.RandomResizedCrop(size=self.INPUT_SIZE, ratio=123)
+
+        for param in ["scale", "ratio"]:
+            with pytest.warns(match="Scale and ratio should be of kind"):
+                transforms.RandomResizedCrop(size=self.INPUT_SIZE, **{param: [1, 0]})

test/transforms_v2_dispatcher_infos.py

@@ -111,16 +111,6 @@ def xfail_jit_python_scalar_arg(name, *, reason=None):
 
 
 DISPATCHER_INFOS = [
-    DispatcherInfo(
-        F.resized_crop,
-        kernels={
-            tv_tensors.Image: F.resized_crop_image,
-            tv_tensors.Video: F.resized_crop_video,
-            tv_tensors.BoundingBoxes: F.resized_crop_bounding_boxes,
-            tv_tensors.Mask: F.resized_crop_mask,
-        },
-        pil_kernel_info=PILKernelInfo(F._resized_crop_image_pil),
-    ),
     DispatcherInfo(
         F.pad,
         kernels={