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merged 5 commits into from
Aug 30, 2022
Merged

Expand prototype transforms consistency tests to most random tests #6522

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261e7f7
add checks for pure random apply transformations
pmeier Aug 30, 2022
71190ff
add tests for some non-deterministic transforms used in classificatio…
pmeier Aug 30, 2022
50125bb
add consistency checks for most transforms
pmeier Aug 30, 2022
db939a9
fix RandomAffine, RandomPerspective, and GaussianBlur
pmeier Aug 30, 2022
05e4c39
fix tests
pmeier Aug 30, 2022
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11 changes: 6 additions & 5 deletions test/test_prototype_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -555,10 +555,10 @@ def test__get_params(self, degrees, translate, scale, shear, mocker):
if translate is not None:
w_max = int(round(translate[0] * w))
h_max = int(round(translate[1] * h))
assert -w_max <= params["translations"][0] <= w_max
assert -h_max <= params["translations"][1] <= h_max
assert -w_max <= params["translate"][0] <= w_max
assert -h_max <= params["translate"][1] <= h_max
else:
assert params["translations"] == (0, 0)
assert params["translate"] == (0, 0)

if scale is not None:
assert scale[0] <= params["scale"] <= scale[1]
Expand Down Expand Up @@ -759,7 +759,8 @@ def test__transform(self, kernel_size, sigma, mocker):
if isinstance(kernel_size, (tuple, list)):
assert transform.kernel_size == kernel_size
else:
assert transform.kernel_size == (kernel_size, kernel_size)
kernel_size = (kernel_size, kernel_size)
assert transform.kernel_size == kernel_size

if isinstance(sigma, (tuple, list)):
assert transform.sigma == sigma
Expand All @@ -779,7 +780,7 @@ def test__transform(self, kernel_size, sigma, mocker):
torch.manual_seed(12)
params = transform._get_params(inpt)

fn.assert_called_once_with(inpt, **params)
fn.assert_called_once_with(inpt, kernel_size, **params)


class TestRandomColorOp:
Expand Down
238 changes: 226 additions & 12 deletions test/test_prototype_transforms_consistency.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,6 +137,7 @@ def parametrization(self):
[
ArgsKwargs(18),
ArgsKwargs((18, 13)),
ArgsKwargs(18, vertical_flip=True),
],
make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, sizes=[(20, 19)]),
),
Expand Down Expand Up @@ -215,6 +216,214 @@ def parametrization(self):
# images given that the transform does nothing but call it anyway.
supports_pil=False,
),
ConsistencyConfig(
prototype_transforms.RandomHorizontalFlip,
legacy_transforms.RandomHorizontalFlip,
[
ArgsKwargs(p=0),
ArgsKwargs(p=1),
],
),
ConsistencyConfig(
prototype_transforms.RandomVerticalFlip,
legacy_transforms.RandomVerticalFlip,
[
ArgsKwargs(p=0),
ArgsKwargs(p=1),
],
),
ConsistencyConfig(
prototype_transforms.RandomEqualize,
legacy_transforms.RandomEqualize,
[
ArgsKwargs(p=0),
ArgsKwargs(p=1),
],
make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, dtypes=[torch.uint8]),
),
ConsistencyConfig(
prototype_transforms.RandomInvert,
legacy_transforms.RandomInvert,
[
ArgsKwargs(p=0),
ArgsKwargs(p=1),
],
),
ConsistencyConfig(
prototype_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(
prototype_transforms.RandomSolarize,
legacy_transforms.RandomSolarize,
[
ArgsKwargs(p=0, threshold=0.5),
ArgsKwargs(p=1, threshold=0.3),
ArgsKwargs(p=1, threshold=0.99),
],
),
ConsistencyConfig(
prototype_transforms.RandomAutocontrast,
legacy_transforms.RandomAutocontrast,
[
ArgsKwargs(p=0),
ArgsKwargs(p=1),
],
),
ConsistencyConfig(
prototype_transforms.RandomAdjustSharpness,
legacy_transforms.RandomAdjustSharpness,
[
ArgsKwargs(p=0, sharpness_factor=0.5),
ArgsKwargs(p=1, sharpness_factor=0.3),
ArgsKwargs(p=1, sharpness_factor=0.99),
],
),
ConsistencyConfig(
prototype_transforms.RandomGrayscale,
legacy_transforms.RandomGrayscale,
[
ArgsKwargs(p=0),
ArgsKwargs(p=1),
],
),
ConsistencyConfig(
prototype_transforms.RandomResizedCrop,
legacy_transforms.RandomResizedCrop,
[
ArgsKwargs(16),
ArgsKwargs(17, scale=(0.3, 0.7)),
ArgsKwargs(25, ratio=(0.5, 1.5)),
ArgsKwargs((31, 28), interpolation=prototype_transforms.InterpolationMode.NEAREST),
ArgsKwargs((33, 26), interpolation=prototype_transforms.InterpolationMode.BICUBIC),
ArgsKwargs((29, 32), antialias=False),
ArgsKwargs((28, 31), antialias=True),
],
),
ConsistencyConfig(
prototype_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(
prototype_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=0.7, hue=0.3),
],
),
ConsistencyConfig(
prototype_transforms.ElasticTransform,
legacy_transforms.ElasticTransform,
[
ArgsKwargs(),
ArgsKwargs(alpha=20.0),
ArgsKwargs(alpha=(15.3, 27.2)),
ArgsKwargs(sigma=3.0),
ArgsKwargs(sigma=(2.5, 3.9)),
ArgsKwargs(interpolation=prototype_transforms.InterpolationMode.NEAREST),
ArgsKwargs(interpolation=prototype_transforms.InterpolationMode.BICUBIC),
ArgsKwargs(fill=1),
],
# ElasticTransform needs larger images to avoid the needed internal padding being larger than the actual image
make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, sizes=[(163, 163), (72, 333), (313, 95)]),
),
ConsistencyConfig(
prototype_transforms.GaussianBlur,
legacy_transforms.GaussianBlur,
[
ArgsKwargs(kernel_size=3),
ArgsKwargs(kernel_size=(1, 5)),
ArgsKwargs(kernel_size=3, sigma=0.7),
ArgsKwargs(kernel_size=5, sigma=(0.3, 1.4)),
],
),
ConsistencyConfig(
prototype_transforms.RandomAffine,
legacy_transforms.RandomAffine,
[
ArgsKwargs(degrees=30.0),
ArgsKwargs(degrees=(-20.0, 10.0)),
ArgsKwargs(degrees=0.0, translate=(0.4, 0.6)),
ArgsKwargs(degrees=0.0, scale=(0.3, 0.8)),
ArgsKwargs(degrees=0.0, shear=13),
ArgsKwargs(degrees=0.0, shear=(8, 17)),
ArgsKwargs(degrees=0.0, shear=(4, 5, 4, 13)),
ArgsKwargs(degrees=(-20.0, 10.0), translate=(0.4, 0.6), scale=(0.3, 0.8), shear=(4, 5, 4, 13)),
ArgsKwargs(degrees=30.0, interpolation=prototype_transforms.InterpolationMode.NEAREST),
ArgsKwargs(degrees=30.0, fill=1),
ArgsKwargs(degrees=30.0, fill=(2, 3, 4)),
ArgsKwargs(degrees=30.0, center=(0, 0)),
],
),
ConsistencyConfig(
prototype_transforms.RandomCrop,
legacy_transforms.RandomCrop,
[
ArgsKwargs(12),
ArgsKwargs((15, 17)),
ArgsKwargs(11, padding=1),
ArgsKwargs((8, 13), padding=(2, 3)),
ArgsKwargs((14, 9), padding=(0, 2, 1, 0)),
ArgsKwargs(36, pad_if_needed=True),
ArgsKwargs((7, 8), fill=1),
ArgsKwargs(5, fill=(1, 2, 3)),
ArgsKwargs(12),
ArgsKwargs(15, padding=2, padding_mode="edge"),
ArgsKwargs(17, padding=(1, 0), padding_mode="reflect"),
ArgsKwargs(8, padding=(3, 0, 0, 1), padding_mode="symmetric"),
],
make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, sizes=[(26, 26), (18, 33), (29, 22)]),
),
ConsistencyConfig(
prototype_transforms.RandomPerspective,
legacy_transforms.RandomPerspective,
[
ArgsKwargs(p=0),
ArgsKwargs(p=1),
ArgsKwargs(p=1, distortion_scale=0.3),
ArgsKwargs(p=1, distortion_scale=0.2, interpolation=prototype_transforms.InterpolationMode.NEAREST),
ArgsKwargs(p=1, distortion_scale=0.1, fill=1),
ArgsKwargs(p=1, distortion_scale=0.4, fill=(1, 2, 3)),
],
),
ConsistencyConfig(
prototype_transforms.RandomRotation,
legacy_transforms.RandomRotation,
[
ArgsKwargs(degrees=30.0),
ArgsKwargs(degrees=(-20.0, 10.0)),
ArgsKwargs(degrees=30.0, interpolation=prototype_transforms.InterpolationMode.BILINEAR),
ArgsKwargs(degrees=30.0, expand=True),
ArgsKwargs(degrees=30.0, center=(0, 0)),
ArgsKwargs(degrees=30.0, fill=1),
ArgsKwargs(degrees=30.0, fill=(1, 2, 3)),
],
),
]


Expand All @@ -227,23 +436,23 @@ def test_automatic_coverage_deterministic():
and not issubclass(obj, enum.Enum)
and name
not in {
"Compose",
# This framework is based on the assumption that the input image can always be a tensor and optionally a
# PIL image. The transforms below require a non-tensor input and thus have to be tested manually.
# PIL image, but the transforms below require a non-tensor input.
"PILToTensor",
"ToTensor",
# Transform containers cannot be tested without other tranforms
"Compose",
"RandomApply",
"RandomChoice",
"RandomOrder",
# If the random parameter generation in the legacy and prototype transform is the same, setting the seed
# should be sufficient. In that case, the transforms below should be tested automatically.
"AugMix",
"AutoAugment",
"RandAugment",
"TrivialAugmentWide",
}
}
# filter out random transformations
legacy = {name for name in legacy if "Random" not in name} - {
"AugMix",
"TrivialAugmentWide",
"GaussianBlur",
"RandAugment",
"AutoAugment",
"ColorJitter",
"ElasticTransform",
}

prototype = {config.legacy_cls.__name__ for config in CONSISTENCY_CONFIGS}

Expand Down Expand Up @@ -285,6 +494,7 @@ def test_consistency(prototype_transform_cls, legacy_transform_cls, args_kwargs,
image_repr = f"[{tuple(image.shape)}, {str(image.dtype).rsplit('.')[-1]}]"

try:
torch.manual_seed(0)
output_legacy_tensor = legacy(image_tensor)
except Exception as exc:
raise pytest.UsageError(
Expand All @@ -294,6 +504,7 @@ def test_consistency(prototype_transform_cls, legacy_transform_cls, args_kwargs,
) from exc

try:
torch.manual_seed(0)
output_prototype_tensor = prototype(image_tensor)
except Exception as exc:
raise AssertionError(
Expand All @@ -309,6 +520,7 @@ def test_consistency(prototype_transform_cls, legacy_transform_cls, args_kwargs,
)

try:
torch.manual_seed(0)
output_prototype_image = prototype(image)
except Exception as exc:
raise AssertionError(
Expand All @@ -325,6 +537,7 @@ def test_consistency(prototype_transform_cls, legacy_transform_cls, args_kwargs,

if image_pil is not None:
try:
torch.manual_seed(0)
output_legacy_pil = legacy(image_pil)
except Exception as exc:
raise pytest.UsageError(
Expand All @@ -334,6 +547,7 @@ def test_consistency(prototype_transform_cls, legacy_transform_cls, args_kwargs,
) from exc

try:
torch.manual_seed(0)
output_prototype_pil = prototype(image_pil)
except Exception as exc:
raise AssertionError(
Expand Down
8 changes: 4 additions & 4 deletions torchvision/prototype/transforms/_geometry.py
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Original file line number Diff line number Diff line change
Expand Up @@ -373,9 +373,9 @@ def _get_params(self, sample: Any) -> Dict[str, Any]:
max_dy = float(self.translate[1] * height)
tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))
ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))
translations = (tx, ty)
translate = (tx, ty)
else:
translations = (0, 0)
translate = (0, 0)

if self.scale is not None:
scale = float(torch.empty(1).uniform_(self.scale[0], self.scale[1]).item())
Expand All @@ -389,7 +389,7 @@ def _get_params(self, sample: Any) -> Dict[str, Any]:
shear_y = float(torch.empty(1).uniform_(self.shear[2], self.shear[3]).item())

shear = (shear_x, shear_y)
return dict(angle=angle, translations=translations, scale=scale, shear=shear)
return dict(angle=angle, translate=translate, scale=scale, shear=shear)
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Nice catch! Seems like I just copied the name from stable RandomAffine.get_params but unfortunately previous tests didn't revealed that.


def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return F.affine(
Expand Down Expand Up @@ -489,7 +489,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
class RandomPerspective(_RandomApplyTransform):
def __init__(
self,
distortion_scale: float,
distortion_scale: float = 0.5,
fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
p: float = 0.5,
Expand Down
2 changes: 1 addition & 1 deletion torchvision/prototype/transforms/_misc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -136,7 +136,7 @@ def _get_params(self, sample: Any) -> Dict[str, Any]:
return dict(sigma=[sigma, sigma])

def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return F.gaussian_blur(inpt, **params)
return F.gaussian_blur(inpt, self.kernel_size, **params)


class ToDtype(Lambda):
Expand Down