def make_vanilla_tensor_images(*args, **kwargs):
for image in make_images(*args, **kwargs):
if image.ndim > 3:
continue
yield image.data
class TestSmoke:
@parametrize_from_transforms(
transforms.RandomErasing(p=1.0),
transforms.HorizontalFlip(),
transforms.Resize([16, 16]),
transforms.CenterCrop([16, 16]),
transforms.ConvertImageDtype(),
)
def test_common(self, transform, input):
transform(input)
@parametrize([(
transform,
[
dict(
image=features.Image.new_like(image,
image.unsqueeze(0),
dtype=torch.float),
one_hot_label=features.OneHotLabel.new_like(
one_hot_label,
one_hot_label.unsqueeze(0),
dtype=torch.float),
) for image, one_hot_label in itertools.product(
make_images(), make_one_hot_labels())
],
) for transform in [
transforms.RandomMixup(alpha=1.0),
transforms.RandomCutmix(alpha=1.0),
]])
def test_mixup_cutmix(self, transform, input):
transform(input)
@parametrize([(
transform,
itertools.chain.from_iterable(
fn(
color_spaces=[
features.ColorSpace.GRAY,
features.ColorSpace.RGB,
],
dtypes=[torch.uint8],
extra_dims=[(4, )],
) for fn in [
make_images,
make_vanilla_tensor_images,
make_pil_images,
]),
) 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=[features.ColorSpace.RGB],
dtypes=[torch.float32]) for fn in [
make_images,
make_vanilla_tensor_images,
]),
),
])
def test_normalize(self, transform, input):
transform(input)
@parametrize([(
transforms.RandomResizedCrop([16, 16]),
itertools.chain(
make_images(extra_dims=[(4, )]),
make_vanilla_tensor_images(),
make_pil_images(),
),
)])
def test_random_resized_crop(self, transform, input):
transform(input)
@parametrize([(
transforms.ConvertImageColorSpace(color_space=new_color_space,
old_color_space=old_color_space),
itertools.chain.from_iterable([
fn(color_spaces=[old_color_space]) for fn in (
make_images,
make_vanilla_tensor_images,
make_pil_images,
)
]),
) for old_color_space, new_color_space in itertools.product(
[
features.ColorSpace.GRAY,
features.ColorSpace.GRAY_ALPHA,
features.ColorSpace.RGB,
features.ColorSpace.RGB_ALPHA,
],
repeat=2,
)])
def test_convert_image_color_space(self, transform, input):
transform(input)