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NicolasHugfacebook-github-bot
NicolasHug
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[fbsync] expand has_any and has_all to also accept check callables (#6447)
Summary: * expand has_any and has_all to also accept check callables * add test and fix has_all * add support for simple tensor images to CutMix, MixUp and RandomIoUCrop * remove TODO * remove pythonic syntax sugar * simplify * use concreate examples in test rather than abstract ones * simplify further Reviewed By: datumbox Differential Revision: D39013675 fbshipit-source-id: 6cd68d471b7cf94192284cdf7948c87ed570e6af
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‎test/test_prototype_transforms_utils.py

Lines changed: 83 additions & 0 deletions
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@@ -0,0 +1,83 @@
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import PIL.Image
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import pytest
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import torch
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from test_prototype_transforms_functional import make_bounding_box, make_image, make_segmentation_mask
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from torchvision.prototype import features
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from torchvision.prototype.transforms._utils import has_all, has_any, is_simple_tensor
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from torchvision.prototype.transforms.functional import to_image_pil
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IMAGE = make_image(color_space=features.ColorSpace.RGB)
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BOUNDING_BOX = make_bounding_box(format=features.BoundingBoxFormat.XYXY, image_size=IMAGE.image_size)
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SEGMENTATION_MASK = make_segmentation_mask(size=IMAGE.image_size)
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@pytest.mark.parametrize(
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("sample", "types", "expected"),
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[
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.Image,), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.BoundingBox,), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.SegmentationMask,), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.Image, features.BoundingBox), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.Image, features.SegmentationMask), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.BoundingBox, features.SegmentationMask), True),
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((SEGMENTATION_MASK,), (features.Image, features.BoundingBox), False),
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((BOUNDING_BOX,), (features.Image, features.SegmentationMask), False),
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((IMAGE,), (features.BoundingBox, features.SegmentationMask), False),
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(
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(IMAGE, BOUNDING_BOX, SEGMENTATION_MASK),
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(features.Image, features.BoundingBox, features.SegmentationMask),
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True,
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),
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((), (features.Image, features.BoundingBox, features.SegmentationMask), False),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (lambda obj: isinstance(obj, features.Image),), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (lambda _: False,), False),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (lambda _: True,), True),
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((IMAGE,), (features.Image, PIL.Image.Image, is_simple_tensor), True),
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((torch.Tensor(IMAGE),), (features.Image, PIL.Image.Image, is_simple_tensor), True),
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((to_image_pil(IMAGE),), (features.Image, PIL.Image.Image, is_simple_tensor), True),
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],
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)
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def test_has_any(sample, types, expected):
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assert has_any(sample, *types) is expected
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@pytest.mark.parametrize(
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("sample", "types", "expected"),
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[
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.Image,), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.BoundingBox,), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.SegmentationMask,), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.Image, features.BoundingBox), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.Image, features.SegmentationMask), True),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (features.BoundingBox, features.SegmentationMask), True),
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(
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(IMAGE, BOUNDING_BOX, SEGMENTATION_MASK),
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(features.Image, features.BoundingBox, features.SegmentationMask),
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True,
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),
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((BOUNDING_BOX, SEGMENTATION_MASK), (features.Image, features.BoundingBox), False),
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((BOUNDING_BOX, SEGMENTATION_MASK), (features.Image, features.SegmentationMask), False),
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((IMAGE, SEGMENTATION_MASK), (features.BoundingBox, features.SegmentationMask), False),
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(
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(IMAGE, BOUNDING_BOX, SEGMENTATION_MASK),
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(features.Image, features.BoundingBox, features.SegmentationMask),
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True,
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),
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((BOUNDING_BOX, SEGMENTATION_MASK), (features.Image, features.BoundingBox, features.SegmentationMask), False),
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((IMAGE, SEGMENTATION_MASK), (features.Image, features.BoundingBox, features.SegmentationMask), False),
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((IMAGE, BOUNDING_BOX), (features.Image, features.BoundingBox, features.SegmentationMask), False),
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(
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(IMAGE, BOUNDING_BOX, SEGMENTATION_MASK),
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(lambda obj: isinstance(obj, (features.Image, features.BoundingBox, features.SegmentationMask)),),
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True,
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),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (lambda _: False,), False),
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((IMAGE, BOUNDING_BOX, SEGMENTATION_MASK), (lambda _: True,), True),
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],
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)
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def test_has_all(sample, types, expected):
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assert has_all(sample, *types) is expected

‎torchvision/prototype/transforms/_augment.py

Lines changed: 4 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -9,7 +9,7 @@
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from torchvision.prototype.transforms import functional as F
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from ._transform import _RandomApplyTransform
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from ._utils import get_image_dimensions, has_all, has_any, is_simple_tensor, query_image
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from ._utils import get_image_dimensions, has_any, is_simple_tensor, query_image
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class RandomErasing(_RandomApplyTransform):
@@ -105,7 +105,9 @@ def __init__(self, *, alpha: float, p: float = 0.5) -> None:
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def forward(self, *inpts: Any) -> Any:
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sample = inpts if len(inpts) > 1 else inpts[0]
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if not has_all(sample, features.Image, features.OneHotLabel):
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if not (
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has_any(sample, features.Image, PIL.Image.Image, is_simple_tensor) and has_any(sample, features.OneHotLabel)
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):
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raise TypeError(f"{type(self).__name__}() is only defined for Image's *and* OneHotLabel's.")
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if has_any(sample, features.BoundingBox, features.SegmentationMask, features.Label):
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raise TypeError(

‎torchvision/prototype/transforms/_geometry.py

Lines changed: 1 addition & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -719,10 +719,9 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
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def forward(self, *inputs: Any) -> Any:
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sample = inputs if len(inputs) > 1 else inputs[0]
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# TODO: Allow image to be a torch.Tensor
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if not (
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has_all(sample, features.BoundingBox)
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and has_any(sample, PIL.Image.Image, features.Image)
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and has_any(sample, PIL.Image.Image, features.Image, is_simple_tensor)
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and has_any(sample, features.Label, features.OneHotLabel)
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):
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raise TypeError(

‎torchvision/prototype/transforms/_utils.py

Lines changed: 15 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
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from typing import Any, Tuple, Type, Union
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from typing import Any, Callable, Tuple, Type, Union
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import PIL.Image
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import torch
@@ -39,14 +39,24 @@ def get_image_dimensions(image: Union[PIL.Image.Image, torch.Tensor, features.Im
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return channels, height, width
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def has_any(sample: Any, *types: Type) -> bool:
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def has_any(sample: Any, *types_or_checks: Union[Type, Callable[[Any], bool]]) -> bool:
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flat_sample, _ = tree_flatten(sample)
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return any(issubclass(type(obj), types) for obj in flat_sample)
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for type_or_check in types_or_checks:
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for obj in flat_sample:
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if isinstance(obj, type_or_check) if isinstance(type_or_check, type) else type_or_check(obj):
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return True
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return False
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def has_all(sample: Any, *types: Type) -> bool:
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def has_all(sample: Any, *types_or_checks: Union[Type, Callable[[Any], bool]]) -> bool:
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flat_sample, _ = tree_flatten(sample)
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return not bool(set(types) - set([type(obj) for obj in flat_sample]))
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for type_or_check in types_or_checks:
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for obj in flat_sample:
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if isinstance(obj, type_or_check) if isinstance(type_or_check, type) else type_or_check(obj):
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break
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else:
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return False
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return True
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def is_simple_tensor(inpt: Any) -> bool:

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