Port remaining transforms tests

test/test_transforms_v2.py

@@ -272,57 +272,6 @@ def test_common(self, transform, adapter, container_type, image_or_video, de_ser
             )
             assert transforms.SanitizeBoundingBoxes()(sample)["boxes"].shape == (0, 4)
 
-    @parametrize(
-        [
-            (
-                transform,
-                itertools.chain.from_iterable(
-                    fn(
-                        color_spaces=[
-                            "GRAY",
-                            "RGB",
-                        ],
-                        dtypes=[torch.uint8],
-                        extra_dims=[(), (4,)],
-                        **(dict(num_frames=[3]) if fn is make_videos else dict()),
-                    )
-                    for fn in [
-                        make_images,
-                        make_vanilla_tensor_images,
-                        make_pil_images,
-                        make_videos,
-                    ]
-                ),
-            )
-            for transform in (
-                transforms.RandAugment(),
-                transforms.TrivialAugmentWide(),
-                transforms.AutoAugment(),
-                transforms.AugMix(),
-            )
-        ]
-    )
-    def test_auto_augment(self, transform, input):
-        transform(input)
-
-    @parametrize(
-        [
-            (
-                transforms.Normalize(mean=[0.0, 0.0, 0.0], std=[1.0, 1.0, 1.0]),
-                itertools.chain.from_iterable(
-                    fn(color_spaces=["RGB"], dtypes=[torch.float32])
-                    for fn in [
-                        make_images,
-                        make_vanilla_tensor_images,
-                        make_videos,
-                    ]
-                ),
-            ),
-        ]
-    )
-    def test_normalize(self, transform, input):
-        transform(input)
-
 
 @pytest.mark.parametrize(
     "flat_inputs",
@@ -385,40 +334,6 @@ def was_applied(output, inpt):
         assert transform.was_applied(output, input)
 
 
-class TestElasticTransform:
-    def test_assertions(self):
-
-        with pytest.raises(TypeError, match="alpha should be a number or a sequence of numbers"):
-            transforms.ElasticTransform({})
-
-        with pytest.raises(ValueError, match="alpha is a sequence its length should be 1 or 2"):
-            transforms.ElasticTransform([1.0, 2.0, 3.0])
-
-        with pytest.raises(TypeError, match="sigma should be a number or a sequence of numbers"):
-            transforms.ElasticTransform(1.0, {})
-
-        with pytest.raises(ValueError, match="sigma is a sequence its length should be 1 or 2"):
-            transforms.ElasticTransform(1.0, [1.0, 2.0, 3.0])
-
-        with pytest.raises(TypeError, match="Got inappropriate fill arg"):
-            transforms.ElasticTransform(1.0, 2.0, fill="abc")
-
-    def test__get_params(self):
-        alpha = 2.0
-        sigma = 3.0
-        transform = transforms.ElasticTransform(alpha, sigma)
-
-        h, w = size = (24, 32)
-        image = make_image(size)
-
-        params = transform._get_params([image])
-
-        displacement = params["displacement"]
-        assert displacement.shape == (1, h, w, 2)
-        assert (-alpha / w <= displacement[0, ..., 0]).all() and (displacement[0, ..., 0] <= alpha / w).all()
-        assert (-alpha / h <= displacement[0, ..., 1]).all() and (displacement[0, ..., 1] <= alpha / h).all()
-
-
 class TestTransform:
     @pytest.mark.parametrize(
         "inpt_type",
@@ -705,25 +620,6 @@ def test__get_params(self):
             assert min_size <= size < max_size
 
 
-class TestUniformTemporalSubsample:
-    @pytest.mark.parametrize(
-        "inpt",
-        [
-            torch.zeros(10, 3, 8, 8),
-            torch.zeros(1, 10, 3, 8, 8),
-            tv_tensors.Video(torch.zeros(1, 10, 3, 8, 8)),
-        ],
-    )
-    def test__transform(self, inpt):
-        num_samples = 5
-        transform = transforms.UniformTemporalSubsample(num_samples)
-
-        output = transform(inpt)
-        assert type(output) is type(inpt)
-        assert output.shape[-4] == num_samples
-        assert output.dtype == inpt.dtype
-
-
 @pytest.mark.parametrize("image_type", (PIL.Image, torch.Tensor, tv_tensors.Image))
 @pytest.mark.parametrize("label_type", (torch.Tensor, int))
 @pytest.mark.parametrize("dataset_return_type", (dict, tuple))

test/test_transforms_v2_consistency.py

@@ -72,34 +72,6 @@ def __init__(
 LINEAR_TRANSFORMATION_MATRIX = torch.rand([LINEAR_TRANSFORMATION_MEAN.numel()] * 2)
 
 CONSISTENCY_CONFIGS = [
-    ConsistencyConfig(
-        v2_transforms.Normalize,
-        legacy_transforms.Normalize,
-        [
-            ArgsKwargs(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
-        ],
-        supports_pil=False,
-        make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, dtypes=[torch.float]),
-    ),
-    ConsistencyConfig(
-        v2_transforms.FiveCrop,
-        legacy_transforms.FiveCrop,
-        [
-            ArgsKwargs(18),
-            ArgsKwargs((18, 13)),
-        ],
-        make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, sizes=[(20, 19)]),
-    ),
-    ConsistencyConfig(
-        v2_transforms.TenCrop,
-        legacy_transforms.TenCrop,
-        [
-            ArgsKwargs(18),
-            ArgsKwargs((18, 13)),
-            ArgsKwargs(18, vertical_flip=True),
-        ],
-        make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, sizes=[(20, 19)]),
-    ),
     *[
         ConsistencyConfig(
             v2_transforms.LinearTransformation,
@@ -147,65 +119,6 @@ def __init__(
         # images given that the transform does nothing but call it anyway.
         supports_pil=False,
     ),
-    ConsistencyConfig(
-        v2_transforms.RandomEqualize,
-        legacy_transforms.RandomEqualize,
-        [
-            ArgsKwargs(p=0),
-            ArgsKwargs(p=1),
-        ],
-        make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, dtypes=[torch.uint8]),
-    ),
-    ConsistencyConfig(
-        v2_transforms.RandomInvert,
-        legacy_transforms.RandomInvert,
-        [
-            ArgsKwargs(p=0),
-            ArgsKwargs(p=1),
-        ],
-    ),
-    ConsistencyConfig(
-        v2_transforms.RandomPosterize,
-        legacy_transforms.RandomPosterize,
-        [
-            ArgsKwargs(p=0, bits=5),
-            ArgsKwargs(p=1, bits=1),
-            ArgsKwargs(p=1, bits=3),
-        ],
-        make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, dtypes=[torch.uint8]),
-    ),
-    ConsistencyConfig(
-        v2_transforms.RandomSolarize,
-        legacy_transforms.RandomSolarize,
-        [
-            ArgsKwargs(p=0, threshold=0.5),
-            ArgsKwargs(p=1, threshold=0.3),
-            ArgsKwargs(p=1, threshold=0.99),
-        ],
-    ),
-    *[
-        ConsistencyConfig(
-            v2_transforms.RandomAutocontrast,
-            legacy_transforms.RandomAutocontrast,
-            [
-                ArgsKwargs(p=0),
-                ArgsKwargs(p=1),
-            ],
-            make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, dtypes=[dt]),
-            closeness_kwargs=ckw,
-        )
-        for dt, ckw in [(torch.uint8, dict(atol=1, rtol=0)), (torch.float32, dict(rtol=None, atol=None))]
-    ],
-    ConsistencyConfig(
-        v2_transforms.RandomAdjustSharpness,
-        legacy_transforms.RandomAdjustSharpness,
-        [
-            ArgsKwargs(p=0, sharpness_factor=0.5),
-            ArgsKwargs(p=1, sharpness_factor=0.2),
-            ArgsKwargs(p=1, sharpness_factor=0.99),
-        ],
-        closeness_kwargs={"atol": 1e-6, "rtol": 1e-6},
-    ),
     ConsistencyConfig(
         v2_transforms.PILToTensor,
         legacy_transforms.PILToTensor,
@@ -230,22 +143,6 @@ def __init__(
         v2_transforms.RandomOrder,
         legacy_transforms.RandomOrder,
     ),
-    ConsistencyConfig(
-        v2_transforms.AugMix,
-        legacy_transforms.AugMix,
-    ),
-    ConsistencyConfig(
-        v2_transforms.AutoAugment,
-        legacy_transforms.AutoAugment,
-    ),
-    ConsistencyConfig(
-        v2_transforms.RandAugment,
-        legacy_transforms.RandAugment,
-    ),
-    ConsistencyConfig(
-        v2_transforms.TrivialAugmentWide,
-        legacy_transforms.TrivialAugmentWide,
-    ),
 ]
 
 
@@ -753,36 +650,9 @@ def test_common(self, t_ref, t, data_kwargs):
         (legacy_F.pil_to_tensor, {}),
         (legacy_F.convert_image_dtype, {}),
         (legacy_F.to_pil_image, {}),
-        (legacy_F.normalize, {}),
-        (legacy_F.resize, {"interpolation"}),
-        (legacy_F.pad, {"padding", "fill"}),
-        (legacy_F.crop, {}),
-        (legacy_F.center_crop, {}),
-        (legacy_F.resized_crop, {"interpolation"}),
-        (legacy_F.hflip, {}),
-        (legacy_F.perspective, {"startpoints", "endpoints", "fill", "interpolation"}),
-        (legacy_F.vflip, {}),
-        (legacy_F.five_crop, {}),
-        (legacy_F.ten_crop, {}),
-        (legacy_F.adjust_brightness, {}),
-        (legacy_F.adjust_contrast, {}),
-        (legacy_F.adjust_saturation, {}),
-        (legacy_F.adjust_hue, {}),
-        (legacy_F.adjust_gamma, {}),
-        (legacy_F.rotate, {"center", "fill", "interpolation"}),
-        (legacy_F.affine, {"angle", "translate", "center", "fill", "interpolation"}),
         (legacy_F.to_grayscale, {}),
         (legacy_F.rgb_to_grayscale, {}),
         (legacy_F.to_tensor, {}),
-        (legacy_F.erase, {}),
-        (legacy_F.gaussian_blur, {}),
-        (legacy_F.invert, {}),
-        (legacy_F.posterize, {}),
-        (legacy_F.solarize, {}),
-        (legacy_F.adjust_sharpness, {}),
-        (legacy_F.autocontrast, {}),
-        (legacy_F.equalize, {}),
-        (legacy_F.elastic_transform, {"fill", "interpolation"}),
     ],
 )
 def test_dispatcher_signature_consistency(legacy_dispatcher, name_only_params):