Merge pull request #1 from bhack/patch-2

Add dummy test

.git-blame-ignore-revs

@@ -7,3 +7,5 @@
 d367a01a18a3ae6bee13d8be3b63fd6a581ea46f
 # Upgrade usort to 1.0.2 and black to 22.3.0 (#5106) 
 6ca9c76adb6daf2695d603ad623a9cf1c4f4806f
+# Fix unnecessary exploded black formatting (#7709)
+a335d916db0694770e8152f41e19195de3134523

test/test_functional_tensor.py

@@ -293,24 +293,8 @@ def test_translations(self, device, height, width, dt, t, fn):
             (33, (5, -4), 1.0, [0.0, 0.0], [0, 0, 0]),
             (45, [-5, 4], 1.2, [0.0, 0.0], (1, 2, 3)),
             (33, (-4, -8), 2.0, [0.0, 0.0], [255, 255, 255]),
-            (
-                85,
-                (10, -10),
-                0.7,
-                [0.0, 0.0],
-                [
-                    1,
-                ],
-            ),
-            (
-                0,
-                [0, 0],
-                1.0,
-                [
-                    35.0,
-                ],
-                (2.0,),
-            ),
+            (85, (10, -10), 0.7, [0.0, 0.0], [1]),
+            (0, [0, 0], 1.0, [35.0], (2.0,)),
             (-25, [0, 0], 1.2, [0.0, 15.0], None),
             (-45, [-10, 0], 0.7, [2.0, 5.0], None),
             (-45, [-10, -10], 1.2, [4.0, 5.0], None),
@@ -392,19 +376,7 @@ def _get_data_dims_and_points_for_perspective():
 @pytest.mark.parametrize("device", cpu_and_cuda())
 @pytest.mark.parametrize("dims_and_points", _get_data_dims_and_points_for_perspective())
 @pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
-@pytest.mark.parametrize(
-    "fill",
-    (
-        None,
-        [0, 0, 0],
-        [1, 2, 3],
-        [255, 255, 255],
-        [
-            1,
-        ],
-        (2.0,),
-    ),
-)
+@pytest.mark.parametrize("fill", (None, [0, 0, 0], [1, 2, 3], [255, 255, 255], [1], (2.0,)))
 @pytest.mark.parametrize("fn", [F.perspective, torch.jit.script(F.perspective)])
 def test_perspective_pil_vs_tensor(device, dims_and_points, dt, fill, fn):
 
@@ -475,19 +447,7 @@ def test_perspective_interpolation_type():
 
 @pytest.mark.parametrize("device", cpu_and_cuda())
 @pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
-@pytest.mark.parametrize(
-    "size",
-    [
-        32,
-        26,
-        [
-            32,
-        ],
-        [32, 32],
-        (32, 32),
-        [26, 35],
-    ],
-)
+@pytest.mark.parametrize("size", [32, 26, [32], [32, 32], (32, 32), [26, 35]])
 @pytest.mark.parametrize("max_size", [None, 34, 40, 1000])
 @pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC, NEAREST, NEAREST_EXACT])
 def test_resize(device, dt, size, max_size, interpolation):

test/test_ops.py

@@ -11,7 +11,7 @@
 import torch.fx
 import torch.nn.functional as F
 from common_utils import assert_equal, cpu_and_cuda, needs_cuda
-from PIL import Image
+from PIL import Image, ImageDraw
 from torch import nn, Tensor
 from torch.autograd import gradcheck
 from torch.nn.modules.utils import _pair
@@ -621,6 +621,32 @@ def test_is_leaf_node(self, device):
         assert len(graph_node_names[0]) == len(graph_node_names[1])
         assert len(graph_node_names[0]) == 1 + op_obj.n_inputs
 
+class TestMasksToBoundaries(ABC):
+
+    @pytest.mark.parametrize("device", ['cpu', 'cuda'])  
+    def test_masks_to_boundaries(self, device):
+        # Create masks
+        mask = torch.zeros(4, 32, 32, dtype=torch.bool)
+        mask[0, 1:10, 1:10] = True
+        mask[0, 12:20, 12:20] = True
+        mask[0, 15:18, 20:32] = True
+        mask[1, 15:23, 15:23] = True
+        mask[1, 22:33, 22:33] = True
+        mask[2, 1:5, 22:30] = True
+        mask[2, 5:14, 25:27] = True
+        pil_img = Image.new("L", (32, 32))
+        draw = ImageDraw.Draw(pil_img)
+        draw.ellipse([2, 7, 26, 26], fill=1, outline=1, width=1)
+        mask[3, ...] = torch.from_numpy(np.asarray(pil_img))
+        mask = mask.to(device)
+        dilation_ratio = 0.02
+        boundaries = ops.masks_to_boundaries(mask, dilation_ratio)
+        # Generate expected output
+        # TODO: How we generate handle the expected output?
+        # replace with actual code to generate expected output
+        expected_boundaries = torch.zeros_like(mask)  
+        torch.testing.assert_close(expected_boundaries, boundaries)
+
 
 class TestNMS:
     def _reference_nms(self, boxes, scores, iou_threshold):

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
 
@@ -406,112 +406,6 @@ def was_applied(output, inpt):
         assert transform.was_applied(output, input)
 
 
-@pytest.mark.parametrize("p", [0.0, 1.0])
-class TestRandomHorizontalFlip:
-    def input_expected_image_tensor(self, p, dtype=torch.float32):
-        input = torch.tensor([[[0, 1], [0, 1]], [[1, 0], [1, 0]]], dtype=dtype)
-        expected = torch.tensor([[[1, 0], [1, 0]], [[0, 1], [0, 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.RandomHorizontalFlip(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.RandomHorizontalFlip(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.RandomHorizontalFlip(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.RandomHorizontalFlip(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.RandomHorizontalFlip(p=p)
-
-        actual = transform(input)
-
-        expected_image_tensor = torch.tensor([5, 0, 10, 5]) 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
-
-
-@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"):
@@ -721,130 +615,6 @@ def test_boundingbox_spatial_size(self, angle, expand):
         assert out_img.spatial_size == out_bbox.spatial_size
 
 
-class TestRandomAffine:
-    def test_assertions(self):
-        with pytest.raises(ValueError, match="is a single number, it must be positive"):
-            transforms.RandomAffine(-0.7)
-
-        for d in [[-0.7], [-0.7, 0, 0.7]]:
-            with pytest.raises(ValueError, match="degrees should be a sequence of length 2"):
-                transforms.RandomAffine(d)
-
-        with pytest.raises(TypeError, match="Got inappropriate fill arg"):
-            transforms.RandomAffine(12, fill="abc")
-
-        with pytest.raises(TypeError, match="Got inappropriate fill arg"):
-            transforms.RandomAffine(12, fill="abc")
-
-        for kwargs in [
-            {"center": 12},
-            {"translate": 12},
-            {"scale": 12},
-        ]:
-            with pytest.raises(TypeError, match="should be a sequence of length"):
-                transforms.RandomAffine(12, **kwargs)
-
-        for kwargs in [{"center": [1, 2, 3]}, {"translate": [1, 2, 3]}, {"scale": [1, 2, 3]}]:
-            with pytest.raises(ValueError, match="should be a sequence of length"):
-                transforms.RandomAffine(12, **kwargs)
-
-        with pytest.raises(ValueError, match="translation values should be between 0 and 1"):
-            transforms.RandomAffine(12, translate=[-1.0, 2.0])
-
-        with pytest.raises(ValueError, match="scale values should be positive"):
-            transforms.RandomAffine(12, scale=[-1.0, 2.0])
-
-        with pytest.raises(ValueError, match="is a single number, it must be positive"):
-            transforms.RandomAffine(12, shear=-10)
-
-        for s in [[-0.7], [-0.7, 0, 0.7]]:
-            with pytest.raises(ValueError, match="shear should be a sequence of length 2"):
-                transforms.RandomAffine(12, shear=s)
-
-    @pytest.mark.parametrize("degrees", [23, [0, 45], (0, 45)])
-    @pytest.mark.parametrize("translate", [None, [0.1, 0.2]])
-    @pytest.mark.parametrize("scale", [None, [0.7, 1.2]])
-    @pytest.mark.parametrize("shear", [None, 2.0, [5.0, 15.0], [1.0, 2.0, 3.0, 4.0]])
-    def test__get_params(self, degrees, translate, scale, shear, mocker):
-        image = mocker.MagicMock(spec=datapoints.Image)
-        image.num_channels = 3
-        image.spatial_size = (24, 32)
-        h, w = image.spatial_size
-
-        transform = transforms.RandomAffine(degrees, translate=translate, scale=scale, shear=shear)
-        params = transform._get_params([image])
-
-        if not isinstance(degrees, (list, tuple)):
-            assert -degrees <= params["angle"] <= degrees
-        else:
-            assert degrees[0] <= params["angle"] <= degrees[1]
-
-        if translate is not None:
-            w_max = int(round(translate[0] * w))
-            h_max = int(round(translate[1] * h))
-            assert -w_max <= params["translate"][0] <= w_max
-            assert -h_max <= params["translate"][1] <= h_max
-        else:
-            assert params["translate"] == (0, 0)
-
-        if scale is not None:
-            assert scale[0] <= params["scale"] <= scale[1]
-        else:
-            assert params["scale"] == 1.0
-
-        if shear is not None:
-            if isinstance(shear, float):
-                assert -shear <= params["shear"][0] <= shear
-                assert params["shear"][1] == 0.0
-            elif len(shear) == 2:
-                assert shear[0] <= params["shear"][0] <= shear[1]
-                assert params["shear"][1] == 0.0
-            else:
-                assert shear[0] <= params["shear"][0] <= shear[1]
-                assert shear[2] <= params["shear"][1] <= shear[3]
-        else:
-            assert params["shear"] == (0, 0)
-
-    @pytest.mark.parametrize("degrees", [23, [0, 45], (0, 45)])
-    @pytest.mark.parametrize("translate", [None, [0.1, 0.2]])
-    @pytest.mark.parametrize("scale", [None, [0.7, 1.2]])
-    @pytest.mark.parametrize("shear", [None, 2.0, [5.0, 15.0], [1.0, 2.0, 3.0, 4.0]])
-    @pytest.mark.parametrize("fill", [0, [1, 2, 3], (2, 3, 4)])
-    @pytest.mark.parametrize("center", [None, [2.0, 3.0]])
-    def test__transform(self, degrees, translate, scale, shear, fill, center, mocker):
-        interpolation = InterpolationMode.BILINEAR
-        transform = transforms.RandomAffine(
-            degrees,
-            translate=translate,
-            scale=scale,
-            shear=shear,
-            interpolation=interpolation,
-            fill=fill,
-            center=center,
-        )
-
-        if isinstance(degrees, (tuple, list)):
-            assert transform.degrees == [float(degrees[0]), float(degrees[1])]
-        else:
-            assert transform.degrees == [float(-degrees), float(degrees)]
-
-        fn = mocker.patch("torchvision.transforms.v2.functional.affine")
-        inpt = mocker.MagicMock(spec=datapoints.Image)
-        inpt.num_channels = 3
-        inpt.spatial_size = (24, 32)
-
-        # vfdev-5, Feature Request: let's store params as Transform attribute
-        # This could be also helpful for users
-        # Otherwise, we can mock transform._get_params
-        torch.manual_seed(12)
-        _ = transform(inpt)
-        torch.manual_seed(12)
-        params = transform._get_params([inpt])
-
-        fill = transforms._utils._convert_fill_arg(fill)
-        fn.assert_called_once_with(inpt, **params, interpolation=interpolation, fill=fill, center=center)
-
-
 class TestRandomCrop:
     def test_assertions(self):
         with pytest.raises(ValueError, match="Please provide only two dimensions"):