port tests for F.erase and transforms.RandomErasing

test/test_transforms_v2.py

@@ -602,53 +602,6 @@ def test__get_params(self):
         assert (-alpha / h <= displacement[0, ..., 1]).all() and (displacement[0, ..., 1] <= alpha / h).all()
 
 
-class TestRandomErasing:
-    def test_assertions(self):
-        with pytest.raises(TypeError, match="Argument value should be either a number or str or a sequence"):
-            transforms.RandomErasing(value={})
-
-        with pytest.raises(ValueError, match="If value is str, it should be 'random'"):
-            transforms.RandomErasing(value="abc")
-
-        with pytest.raises(TypeError, match="Scale should be a sequence"):
-            transforms.RandomErasing(scale=123)
-
-        with pytest.raises(TypeError, match="Ratio should be a sequence"):
-            transforms.RandomErasing(ratio=123)
-
-        with pytest.raises(ValueError, match="Scale should be between 0 and 1"):
-            transforms.RandomErasing(scale=[-1, 2])
-
-        image = make_image((24, 32))
-
-        transform = transforms.RandomErasing(value=[1, 2, 3, 4])
-
-        with pytest.raises(ValueError, match="If value is a sequence, it should have either a single value"):
-            transform._get_params([image])
-
-    @pytest.mark.parametrize("value", [5.0, [1, 2, 3], "random"])
-    def test__get_params(self, value):
-        image = make_image((24, 32))
-        num_channels, height, width = F.get_dimensions(image)
-
-        transform = transforms.RandomErasing(value=value)
-        params = transform._get_params([image])
-
-        v = params["v"]
-        h, w = params["h"], params["w"]
-        i, j = params["i"], params["j"]
-        assert isinstance(v, torch.Tensor)
-        if value == "random":
-            assert v.shape == (num_channels, h, w)
-        elif isinstance(value, (int, float)):
-            assert v.shape == (1, 1, 1)
-        elif isinstance(value, (list, tuple)):
-            assert v.shape == (num_channels, 1, 1)
-
-        assert 0 <= i <= height - h
-        assert 0 <= j <= width - w
-
-
 class TestTransform:
     @pytest.mark.parametrize(
         "inpt_type",

test/test_transforms_v2_consistency.py

@@ -276,20 +276,6 @@ def __init__(
         ],
         closeness_kwargs=dict(rtol=0, atol=21),
     ),
-    ConsistencyConfig(
-        v2_transforms.RandomErasing,
-        legacy_transforms.RandomErasing,
-        [
-            ArgsKwargs(p=0),
-            ArgsKwargs(p=1),
-            ArgsKwargs(p=1, scale=(0.3, 0.7)),
-            ArgsKwargs(p=1, ratio=(0.5, 1.5)),
-            ArgsKwargs(p=1, value=1),
-            ArgsKwargs(p=1, value=(1, 2, 3)),
-            ArgsKwargs(p=1, value="random"),
-        ],
-        supports_pil=False,
-    ),
     ConsistencyConfig(
         v2_transforms.ColorJitter,
         legacy_transforms.ColorJitter,
@@ -570,7 +556,6 @@ def test_call_consistency(config, args_kwargs):
         )
         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.RandomErasing, 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.RandomCrop, ArgsKwargs(make_image(size=(61, 47)), output_size=(19, 25))),

test/test_transforms_v2_refactored.py

@@ -2485,3 +2485,145 @@ def test_correctness(self):
                 assert isinstance(out_value, torch.Tensor) and not isinstance(out_value, datapoints.Datapoint)
             else:
                 assert isinstance(out_value, type(input_value))
+
+
+class TestErase:
+    INPUT_SIZE = (17, 11)
+    FUNCTIONAL_KWARGS = dict(
+        zip("ijhwv", [2, 2, 10, 8, torch.tensor(0.0, dtype=torch.float32, device="cpu").reshape(-1, 1, 1)])
+    )
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_kernel_image(self, dtype, device):
+        check_kernel(F.erase_image, make_image(self.INPUT_SIZE, dtype=dtype, device=device), **self.FUNCTIONAL_KWARGS)
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_kernel_image_inplace(self, dtype, device):
+        input = make_image(self.INPUT_SIZE, dtype=dtype, device=device)
+        input_version = input._version
+
+        output_out_of_place = F.erase_image(input, **self.FUNCTIONAL_KWARGS)
+        assert output_out_of_place.data_ptr() != input.data_ptr()
+
+        output_inplace = F.erase_image(input, **self.FUNCTIONAL_KWARGS, inplace=True)
+        assert output_inplace.data_ptr() == input.data_ptr()
+        assert output_inplace._version > input_version
+
+        assert_equal(output_inplace, output_out_of_place)
+
+    def test_kernel_video(self):
+        check_kernel(F.erase_video, make_video(self.INPUT_SIZE), **self.FUNCTIONAL_KWARGS)
+
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image_pil, make_image, make_video],
+    )
+    def test_functional(self, make_input):
+        check_functional(F.erase, make_input(), **self.FUNCTIONAL_KWARGS)
+
+    @pytest.mark.parametrize(
+        ("kernel", "input_type"),
+        [
+            (F.erase_image, torch.Tensor),
+            (F._erase_image_pil, PIL.Image.Image),
+            (F.erase_image, datapoints.Image),
+            (F.erase_video, datapoints.Video),
+        ],
+    )
+    def test_functional_signature(self, kernel, input_type):
+        check_functional_kernel_signature_match(F.erase, kernel=kernel, input_type=input_type)
+
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image_pil, make_image, make_video],
+    )
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_transform(self, make_input, device):
+        check_transform(transforms.RandomErasing(p=1), make_input(device=device))
+
+    def _reference_erase_image(self, image, *, i, j, h, w, v):
+        mask = torch.zeros_like(image, dtype=torch.bool)
+        mask[..., i : i + h, j : j + w] = True
+
+        # The broadcasting and type casting logic is handled automagically in the kernel through indexing
+        value = torch.broadcast_to(v, (*image.shape[:-2], h, w)).to(image)
+
+        erased_image = torch.empty_like(image)
+        erased_image[mask] = value.flatten()
+        erased_image[~mask] = image[~mask]
+
+        return erased_image
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_functional_image_correctness(self, dtype, device):
+        image = make_image(dtype=dtype, device=device)
+
+        actual = F.erase(image, **self.FUNCTIONAL_KWARGS)
+        expected = self._reference_erase_image(image, **self.FUNCTIONAL_KWARGS)
+
+        assert_equal(actual, expected)
+
+    @param_value_parametrization(
+        scale=[(0.1, 0.2), [0.0, 1.0]],
+        ratio=[(0.3, 0.7), [0.1, 5.0]],
+        value=[0, 0.5, (0, 1, 0), [-0.2, 0.0, 1.3], "random"],
+    )
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    @pytest.mark.parametrize("seed", list(range(5)))
+    def test_transform_image_correctness(self, param, value, dtype, device, seed):
+        transform = transforms.RandomErasing(**{param: value}, p=1)
+
+        image = make_image(dtype=dtype, device=device)
+
+        with freeze_rng_state():
+            torch.manual_seed(seed)
+            # This emulates the random apply check that happens before _get_params is called
+            torch.rand(1)
+            params = transform._get_params([image])
+
+            torch.manual_seed(seed)
+            actual = transform(image)
+
+        expected = self._reference_erase_image(image, **params)
+
+        assert_equal(actual, expected)
+
+    def test_transform_errors(self):
+        with pytest.raises(TypeError, match="Argument value should be either a number or str or a sequence"):
+            transforms.RandomErasing(value={})
+
+        with pytest.raises(ValueError, match="If value is str, it should be 'random'"):
+            transforms.RandomErasing(value="abc")
+
+        with pytest.raises(TypeError, match="Scale should be a sequence"):
+            transforms.RandomErasing(scale=123)
+
+        with pytest.raises(TypeError, match="Ratio should be a sequence"):
+            transforms.RandomErasing(ratio=123)
+
+        with pytest.raises(ValueError, match="Scale should be between 0 and 1"):
+            transforms.RandomErasing(scale=[-1, 2])
+
+        transform = transforms.RandomErasing(value=[1, 2, 3, 4])
+
+        with pytest.raises(ValueError, match="If value is a sequence, it should have either a single value"):
+            transform._get_params([make_image()])
+
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_bounding_boxes, make_detection_mask, make_segmentation_mask],
+    )
+    def test_transform_passthrough(self, make_input):
+        transform = transforms.RandomErasing(p=1)
+
+        input = make_input(self.INPUT_SIZE)
+
+        with pytest.warns(UserWarning, match="currently passing through inputs of type"):
+            # RandomErasing requires an image or video to be present
+            _, output = transform(make_image(self.INPUT_SIZE), input)
+
+        assert output is input

test/transforms_v2_dispatcher_infos.py

@@ -279,17 +279,6 @@ def fill_sequence_needs_broadcast(args_kwargs):
         },
         pil_kernel_info=PILKernelInfo(F._adjust_sharpness_image_pil, kernel_name="adjust_sharpness_image_pil"),
     ),
-    DispatcherInfo(
-        F.erase,
-        kernels={
-            datapoints.Image: F.erase_image,
-            datapoints.Video: F.erase_video,
-        },
-        pil_kernel_info=PILKernelInfo(F._erase_image_pil),
-        test_marks=[
-            skip_dispatch_datapoint,
-        ],
-    ),
     DispatcherInfo(
         F.adjust_contrast,
         kernels={

test/transforms_v2_kernel_infos.py

@@ -1222,36 +1222,6 @@ def sample_inputs_adjust_sharpness_video():
 )
 
 
-def sample_inputs_erase_image_tensor():
-    for image_loader in make_image_loaders(sizes=[DEFAULT_PORTRAIT_SPATIAL_SIZE]):
-        # FIXME: make the parameters more diverse
-        h, w = 6, 7
-        v = torch.rand(image_loader.num_channels, h, w)
-        yield ArgsKwargs(image_loader, i=1, j=2, h=h, w=w, v=v)
-
-
-def sample_inputs_erase_video():
-    for video_loader in make_video_loaders(sizes=[DEFAULT_PORTRAIT_SPATIAL_SIZE], num_frames=[3]):
-        # FIXME: make the parameters more diverse
-        h, w = 6, 7
-        v = torch.rand(video_loader.num_channels, h, w)
-        yield ArgsKwargs(video_loader, i=1, j=2, h=h, w=w, v=v)
-
-
-KERNEL_INFOS.extend(
-    [
-        KernelInfo(
-            F.erase_image,
-            kernel_name="erase_image_tensor",
-            sample_inputs_fn=sample_inputs_erase_image_tensor,
-        ),
-        KernelInfo(
-            F.erase_video,
-            sample_inputs_fn=sample_inputs_erase_video,
-        ),
-    ]
-)
-
 _ADJUST_CONTRAST_FACTORS = [0.1, 0.5]