def test_sequential():
transforms = [Mock(side_effect=lambda **kw: kw) for _ in range(10)]
augmentation = Sequential(transforms, p=1)
image = np.ones((8, 8))
augmentation(image=image)
assert len([transform for transform in transforms
if transform.called]) == len(transforms)
def test_deterministic_sequential():
aug = ReplayCompose([Sequential([HorizontalFlip(), Blur()])], p=1)
for _ in range(10):
image = (np.random.random((8, 8)) * 255).astype(np.uint8)
image2 = np.copy(image)
data = aug(image=image)
assert "replay" in data
data2 = ReplayCompose.replay(data["replay"], image=image2)
assert np.array_equal(data["image"], data2["image"])
def test_single_transform_compose(
compose_transform: typing.Tuple[typing.Type[BaseCompose], dict],
inner_transform: typing.Tuple[typing.Union[typing.Type[BaseCompose], typing.Type[BasicTransform]], dict],
):
compose_cls, compose_kwargs = compose_transform
cls, kwargs = inner_transform
transform = cls(transforms=[], **kwargs) if issubclass(cls, BaseCompose) else cls(**kwargs)
with pytest.warns(UserWarning):
res_transform = compose_cls(transforms=transform, **compose_kwargs) # type: ignore
assert isinstance(res_transform.transforms, list)
@pytest.mark.parametrize(
"transforms",
[OneOf([Sequential([HorizontalFlip(p=1)])], p=1), SomeOf([Sequential([HorizontalFlip(p=1)])], n=1, p=1)],
)
def test_choice_inner_compositions(transforms):
"""Check that the inner composition is selected without errors."""
image = np.empty([10, 10, 3], dtype=np.uint8)
transforms(image=image)
@pytest.mark.parametrize(
"transforms",
[
Compose([ChannelShuffle(p=1)], p=1),
Compose([ChannelShuffle(p=0)], p=0),
],
)
def test_contiguous_output(transforms):