port tests for transforms.ColorJitter

test/test_transforms_v2_consistency.py

@@ -228,23 +228,6 @@ def __init__(
         # Use default tolerances of `torch.testing.assert_close`
         closeness_kwargs=dict(rtol=None, atol=None),
     ),
-    ConsistencyConfig(
-        v2_transforms.ColorJitter,
-        legacy_transforms.ColorJitter,
-        [
-            ArgsKwargs(),
-            ArgsKwargs(brightness=0.1),
-            ArgsKwargs(brightness=(0.2, 0.3)),
-            ArgsKwargs(contrast=0.4),
-            ArgsKwargs(contrast=(0.5, 0.6)),
-            ArgsKwargs(saturation=0.7),
-            ArgsKwargs(saturation=(0.8, 0.9)),
-            ArgsKwargs(hue=0.3),
-            ArgsKwargs(hue=(-0.1, 0.2)),
-            ArgsKwargs(brightness=0.1, contrast=0.4, saturation=0.5, hue=0.3),
-        ],
-        closeness_kwargs={"atol": 1e-5, "rtol": 1e-5},
-    ),
     ConsistencyConfig(
         v2_transforms.PILToTensor,
         legacy_transforms.PILToTensor,
@@ -453,49 +436,6 @@ def test_call_consistency(config, args_kwargs):
     )
 
 
-get_params_parametrization = pytest.mark.parametrize(
-    ("config", "get_params_args_kwargs"),
-    [
-        pytest.param(
-            next(config for config in CONSISTENCY_CONFIGS if config.prototype_cls is transform_cls),
-            get_params_args_kwargs,
-            id=transform_cls.__name__,
-        )
-        for transform_cls, get_params_args_kwargs in [
-            (v2_transforms.ColorJitter, ArgsKwargs(brightness=None, contrast=None, saturation=None, hue=None)),
-            (v2_transforms.AutoAugment, ArgsKwargs(5)),
-        ]
-    ],
-)
-
-
-@get_params_parametrization
-def test_get_params_alias(config, get_params_args_kwargs):
-    assert config.prototype_cls.get_params is config.legacy_cls.get_params
-
-    if not config.args_kwargs:
-        return
-    args, kwargs = config.args_kwargs[0]
-    legacy_transform = config.legacy_cls(*args, **kwargs)
-    prototype_transform = config.prototype_cls(*args, **kwargs)
-
-    assert prototype_transform.get_params is legacy_transform.get_params
-
-
-@get_params_parametrization
-def test_get_params_jit(config, get_params_args_kwargs):
-    get_params_args, get_params_kwargs = get_params_args_kwargs
-
-    torch.jit.script(config.prototype_cls.get_params)(*get_params_args, **get_params_kwargs)
-
-    if not config.args_kwargs:
-        return
-    args, kwargs = config.args_kwargs[0]
-    transform = config.prototype_cls(*args, **kwargs)
-
-    torch.jit.script(transform.get_params)(*get_params_args, **get_params_kwargs)
-
-
 @pytest.mark.parametrize(
     ("config", "args_kwargs"),
     [

test/test_transforms_v2_refactored.py

@@ -3881,3 +3881,67 @@ def test_correctness_perspective_bounding_boxes(self, startpoints, endpoints, fo
         )
 
         assert_close(actual, expected, rtol=0, atol=1)
+
+
+class TestColorJitter:
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image_pil, make_image, make_video],
+    )
+    @pytest.mark.parametrize("dtype", [torch.uint8, torch.float32])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_transform(self, make_input, dtype, device):
+        if make_input is make_image_pil and not (dtype is torch.uint8 and device == "cpu"):
+            pytest.skip(
+                "PIL image tests with parametrization other than dtype=torch.uint8 and device='cpu' "
+                "will degenerate to that anyway."
+            )
+
+        check_transform(
+            transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.25),
+            make_input(dtype=dtype, device=device),
+        )
+
+    def test_transform_noop(self):
+        input = make_image()
+        input_version = input._version
+
+        transform = transforms.ColorJitter()
+        output = transform(input)
+
+        assert output is input
+        assert output.data_ptr() == input.data_ptr()
+        assert output._version == input_version
+
+    def test_transform_error(self):
+        with pytest.raises(ValueError, match="must be non negative"):
+            transforms.ColorJitter(brightness=-1)
+
+        for brightness in [object(), [1, 2, 3]]:
+            with pytest.raises(TypeError, match="single number or a sequence with length 2"):
+                transforms.ColorJitter(brightness=brightness)
+
+        with pytest.raises(ValueError, match="values should be between"):
+            transforms.ColorJitter(brightness=(-1, 0.5))
+
+        with pytest.raises(ValueError, match="values should be between"):
+            transforms.ColorJitter(hue=1)
+
+    @pytest.mark.parametrize("brightness", [None, 0.1, (0.2, 0.3)])
+    @pytest.mark.parametrize("contrast", [None, 0.4, (0.5, 0.6)])
+    @pytest.mark.parametrize("saturation", [None, 0.7, (0.8, 0.9)])
+    @pytest.mark.parametrize("hue", [None, 0.3, (-0.1, 0.2)])
+    def test_transform_correctness(self, brightness, contrast, saturation, hue):
+        image = make_image(dtype=torch.uint8, device="cpu")
+
+        transform = transforms.ColorJitter(brightness=brightness, contrast=contrast, saturation=saturation, hue=hue)
+
+        with freeze_rng_state():
+            torch.manual_seed(0)
+            actual = transform(image)
+
+            torch.manual_seed(0)
+            expected = F.to_image(transform(F.to_pil_image(image)))
+
+        mae = (actual.float() - expected.float()).abs().mean()
+        assert mae < 2