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Fixed uncaught warnings in tests v2 #7330

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Merged
merged 2 commits into from
Feb 24, 2023
Merged

Fixed uncaught warnings in tests v2 #7330

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dba46d3
Fixed uncaught warnings in tests
vfdev-5 Feb 24, 2023
3e0f37c
Merge branch 'main' into tests-fix-warnings
NicolasHug Feb 24, 2023
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41 changes: 27 additions & 14 deletions test/test_transforms_v2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -136,14 +136,14 @@ class TestSmoke:
(transforms.RandomCrop([16, 16], pad_if_needed=True), None),
(transforms.RandomHorizontalFlip(p=1.0), None),
(transforms.RandomPerspective(p=1.0), None),
(transforms.RandomResize(min_size=10, max_size=20), None),
(transforms.RandomResizedCrop([16, 16]), None),
(transforms.RandomResize(min_size=10, max_size=20, antialias=True), None),
(transforms.RandomResizedCrop([16, 16], antialias=True), None),
(transforms.RandomRotation(degrees=30), None),
(transforms.RandomShortestSize(min_size=10), None),
(transforms.RandomShortestSize(min_size=10, antialias=True), None),
(transforms.RandomVerticalFlip(p=1.0), None),
(transforms.RandomZoomOut(p=1.0), None),
(transforms.Resize([16, 16], antialias=True), None),
(transforms.ScaleJitter((16, 16), scale_range=(0.8, 1.2)), None),
(transforms.ScaleJitter((16, 16), scale_range=(0.8, 1.2), antialias=True), None),
(transforms.ClampBoundingBox(), None),
(transforms.ConvertBoundingBoxFormat(datapoints.BoundingBoxFormat.CXCYWH), None),
(transforms.ConvertDtype(), None),
Expand Down Expand Up @@ -1514,7 +1514,7 @@ class TestRandomShortestSize:
def test__get_params(self, min_size, max_size, mocker):
spatial_size = (3, 10)

transform = transforms.RandomShortestSize(min_size=min_size, max_size=max_size)
transform = transforms.RandomShortestSize(min_size=min_size, max_size=max_size, antialias=True)

sample = mocker.MagicMock(spec=datapoints.Image, num_channels=3, spatial_size=spatial_size)
params = transform._get_params([sample])
Expand Down Expand Up @@ -1595,7 +1595,7 @@ def test__get_params(self):
min_size = 3
max_size = 6

transform = transforms.RandomResize(min_size=min_size, max_size=max_size)
transform = transforms.RandomResize(min_size=min_size, max_size=max_size, antialias=True)

for _ in range(10):
params = transform._get_params([])
Expand Down Expand Up @@ -1791,15 +1791,21 @@ def test_classif_preset(image_type, label_type, dataset_return_type, to_tensor):
else:
sample = image, label

if to_tensor is transforms.ToTensor:
with pytest.warns(UserWarning, match="deprecated and will be removed"):
to_tensor = to_tensor()
else:
to_tensor = to_tensor()

Comment on lines +1794 to +1799
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super nit, just fyi for those kind of cases we can use something like

cm = pytest.warns(...) if ... else contextlib.nullcontext()
with cm:
    to_tensor = to_tensor()

t = transforms.Compose(
[
transforms.RandomResizedCrop((224, 224)),
transforms.RandomResizedCrop((224, 224), antialias=True),
transforms.RandomHorizontalFlip(p=1),
transforms.RandAugment(),
transforms.TrivialAugmentWide(),
transforms.AugMix(),
transforms.AutoAugment(),
to_tensor(),
to_tensor,
# TODO: ConvertImageDtype is a pass-through on PIL images, is that
# intended? This results in a failure if we convert to tensor after
# it, because the image would still be uint8 which make Normalize
Expand Down Expand Up @@ -1830,10 +1836,17 @@ def test_classif_preset(image_type, label_type, dataset_return_type, to_tensor):
@pytest.mark.parametrize("sanitize", (True, False))
def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize):
torch.manual_seed(0)

if to_tensor is transforms.ToTensor:
with pytest.warns(UserWarning, match="deprecated and will be removed"):
to_tensor = to_tensor()
else:
to_tensor = to_tensor()

if data_augmentation == "hflip":
t = [
transforms.RandomHorizontalFlip(p=1),
to_tensor(),
to_tensor,
transforms.ConvertImageDtype(torch.float),
]
elif data_augmentation == "lsj":
Expand All @@ -1847,7 +1860,7 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize):
# ),
transforms.RandomCrop((1024, 1024), pad_if_needed=True),
transforms.RandomHorizontalFlip(p=1),
to_tensor(),
to_tensor,
transforms.ConvertImageDtype(torch.float),
]
elif data_augmentation == "multiscale":
Expand All @@ -1856,7 +1869,7 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize):
min_size=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), max_size=1333, antialias=True
),
transforms.RandomHorizontalFlip(p=1),
to_tensor(),
to_tensor,
transforms.ConvertImageDtype(torch.float),
]
elif data_augmentation == "ssd":
Expand All @@ -1865,14 +1878,14 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize):
transforms.RandomZoomOut(fill=defaultdict(lambda: (123.0, 117.0, 104.0), {datapoints.Mask: 0})),
transforms.RandomIoUCrop(),
transforms.RandomHorizontalFlip(p=1),
to_tensor(),
to_tensor,
transforms.ConvertImageDtype(torch.float),
]
elif data_augmentation == "ssdlite":
t = [
transforms.RandomIoUCrop(),
transforms.RandomHorizontalFlip(p=1),
to_tensor(),
to_tensor,
transforms.ConvertImageDtype(torch.float),
]
if sanitize:
Expand Down Expand Up @@ -1907,7 +1920,7 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize):

out = t(sample)

if to_tensor is transforms.ToTensor and image_type is not datapoints.Image:
if isinstance(to_tensor, transforms.ToTensor) and image_type is not datapoints.Image:
assert is_simple_tensor(out["image"])
else:
assert isinstance(out["image"], datapoints.Image)
Expand Down