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import PIL.Image
import pytest
import torch
import torchvision.transforms.v2._utils
from common_utils import DEFAULT_SIZE, make_bounding_boxes, make_detection_masks, make_image
from torchvision import tv_tensors
from torchvision.transforms.v2._utils import has_all, has_any
from torchvision.transforms.v2.functional import to_pil_image
IMAGE = make_image(DEFAULT_SIZE, color_space="RGB")
BOUNDING_BOX = make_bounding_boxes(DEFAULT_SIZE, format=tv_tensors.BoundingBoxFormat.XYXY)
MASK = make_detection_masks(DEFAULT_SIZE)
@pytest.mark.parametrize(
("sample", "types", "expected"),
[
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.Image,), True),
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.BoundingBoxes,), True),
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.Mask,), True),
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.Image, tv_tensors.BoundingBoxes), True),
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.Image, tv_tensors.Mask), True),
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.BoundingBoxes, tv_tensors.Mask), True),
((MASK,), (tv_tensors.Image, tv_tensors.BoundingBoxes), False),
((BOUNDING_BOX,), (tv_tensors.Image, tv_tensors.Mask), False),
((IMAGE,), (tv_tensors.BoundingBoxes, tv_tensors.Mask), False),
(
(IMAGE, BOUNDING_BOX, MASK),
(tv_tensors.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask),
True,
),
((), (tv_tensors.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask), False),
((IMAGE, BOUNDING_BOX, MASK), (lambda obj: isinstance(obj, tv_tensors.Image),), True),
((IMAGE, BOUNDING_BOX, MASK), (lambda _: False,), False),
((IMAGE, BOUNDING_BOX, MASK), (lambda _: True,), True),
((IMAGE,), (tv_tensors.Image, PIL.Image.Image, torchvision.transforms.v2._utils.is_pure_tensor), True),
(
(torch.Tensor(IMAGE),),
(tv_tensors.Image, PIL.Image.Image, torchvision.transforms.v2._utils.is_pure_tensor),
True,
),
(
(to_pil_image(IMAGE),),
(tv_tensors.Image, PIL.Image.Image, torchvision.transforms.v2._utils.is_pure_tensor),
True,
),
],
)
def test_has_any(sample, types, expected):
assert has_any(sample, *types) is expected
@pytest.mark.parametrize(
("sample", "types", "expected"),
[
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.Image,), True),
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.BoundingBoxes,), True),
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.Mask,), True),
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.Image, tv_tensors.BoundingBoxes), True),
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.Image, tv_tensors.Mask), True),
((IMAGE, BOUNDING_BOX, MASK), (tv_tensors.BoundingBoxes, tv_tensors.Mask), True),
(
(IMAGE, BOUNDING_BOX, MASK),
(tv_tensors.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask),
True,
),
((BOUNDING_BOX, MASK), (tv_tensors.Image, tv_tensors.BoundingBoxes), False),
((BOUNDING_BOX, MASK), (tv_tensors.Image, tv_tensors.Mask), False),
((IMAGE, MASK), (tv_tensors.BoundingBoxes, tv_tensors.Mask), False),
(
(IMAGE, BOUNDING_BOX, MASK),
(tv_tensors.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask),
True,
),
((BOUNDING_BOX, MASK), (tv_tensors.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask), False),
((IMAGE, MASK), (tv_tensors.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask), False),
((IMAGE, BOUNDING_BOX), (tv_tensors.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask), False),
(
(IMAGE, BOUNDING_BOX, MASK),
(lambda obj: isinstance(obj, (tv_tensors.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask)),),
True,
),
((IMAGE, BOUNDING_BOX, MASK), (lambda _: False,), False),
((IMAGE, BOUNDING_BOX, MASK), (lambda _: True,), True),
],
)
def test_has_all(sample, types, expected):
assert has_all(sample, *types) is expected
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