def test_intensity_only(self):
seq = K.PatchSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
grid_size=(2, 2),
)
assert not seq.is_intensity_only()
seq = K.PatchSequential(
K.ImageSequential(K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5)),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
grid_size=(2, 2),
)
assert seq.is_intensity_only()
def test_forward(self, shape, padding, patchwise_apply, same_on_batch,
keepdim, device, dtype):
seq = K.PatchSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
grid_size=(2, 2),
padding=padding,
patchwise_apply=patchwise_apply,
same_on_batch=same_on_batch,
keepdim=keepdim,
)
input = torch.randn(*shape, device=device, dtype=dtype)
trans = torch.randn(shape[0], 3, 3, device=device, dtype=dtype)
out = seq(input)
assert out.shape[-3:] == input.shape[-3:]
out = seq((input, trans))
assert out[0].shape[-3:] == input.shape[-3:]
assert out[1].shape == trans.shape
def test_forward(self, return_transform, random_apply, device, dtype):
inp = torch.randn(1, 3, 30, 30, device=device, dtype=dtype)
aug = K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
kornia.filters.MedianBlur((3, 3)),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0, return_transform=True),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0)
),
K.ImageSequential(
K.RandomAffine(360, p=1.0)
),
K.RandomAffine(360, p=1.0),
K.RandomMixUp(p=1.0),
return_transform=return_transform,
random_apply=random_apply,
)
out = aug(inp)
if aug.return_label:
out, _ = out
if isinstance(out, (tuple,)):
assert out[0].shape == inp.shape
else:
assert out.shape == inp.shape
aug.inverse(inp)
reproducibility_test(inp, aug)
def test_inverse_and_forward_return_transform(self, random_apply, device,
dtype):
inp = torch.randn(1, 3, 1000, 500, device=device, dtype=dtype)
bbox = torch.tensor([[[355, 10], [660, 10], [660, 250], [355, 250]]],
device=device,
dtype=dtype)
keypoints = torch.tensor([[[465, 115], [545, 116]]],
device=device,
dtype=dtype)
mask = bbox_to_mask(
torch.tensor([[[155, 0], [900, 0], [900, 400], [155, 400]]],
device=device,
dtype=dtype), 1000, 500)[:, None].float()
aug = K.AugmentationSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0, return_transform=True)),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0, return_transform=True),
K.RandomAffine(360, p=1.0, return_transform=True),
data_keys=["input", "mask", "bbox", "keypoints"],
random_apply=random_apply,
)
with pytest.raises(
Exception): # No parameters available for inversing.
aug.inverse(inp, mask, bbox, keypoints)
out = aug(inp, mask, bbox, keypoints)
assert out[0][0].shape == inp.shape
assert out[1].shape == mask.shape
assert out[2].shape == bbox.shape
assert out[3].shape == keypoints.shape
reproducibility_test((inp, mask, bbox, keypoints), aug)
def test_forward(self, shape, padding, patchwise_apply, same_on_batch,
keepdim, random_apply, device, dtype):
torch.manual_seed(11)
try: # skip wrong param settings.
seq = K.PatchSequential(
K.color.RgbToBgr(),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.RandomMixUp(p=1.0),
grid_size=(2, 2),
padding=padding,
patchwise_apply=patchwise_apply,
same_on_batch=same_on_batch,
keepdim=keepdim,
random_apply=random_apply,
)
# TODO: improve me and remove the exception.
except Exception:
return
input = torch.randn(*shape, device=device, dtype=dtype)
out = seq(input)
if seq.return_label:
out, _ = out
assert out.shape[-3:] == input.shape[-3:]
reproducibility_test(input, seq)
def test_individual_forward_and_inverse(self, device, dtype):
inp = torch.randn(1, 3, 1000, 500, device=device, dtype=dtype)
bbox = torch.tensor([[[355, 10], [660, 10], [660, 250], [355, 250]]], device=device, dtype=dtype)
keypoints = torch.tensor([[[465, 115], [545, 116]]], device=device, dtype=dtype)
mask = bbox_to_mask(
torch.tensor([[[155, 0], [900, 0], [900, 400], [155, 400]]], device=device, dtype=dtype), 1000, 500
)[:, None].float()
aug = K.AugmentationSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0, return_transform=True),
),
K.RandomAffine(360, p=1.0, return_transform=False),
data_keys=['input', 'mask', 'bbox', 'keypoints']
)
reproducibility_test((inp, mask, bbox, keypoints), aug)
aug = K.AugmentationSequential(K.RandomAffine(360, p=1.0, return_transform=True))
assert aug(inp, data_keys=['input'])[0].shape == inp.shape
aug = K.AugmentationSequential(K.RandomAffine(360, p=1.0, return_transform=False))
assert aug(inp, data_keys=['input']).shape == inp.shape
assert aug(mask, data_keys=['mask'], params=aug._params).shape == mask.shape
assert aug.inverse(inp, data_keys=['input']).shape == inp.shape
assert aug.inverse(bbox, data_keys=['bbox']).shape == bbox.shape
assert aug.inverse(keypoints, data_keys=['keypoints']).shape == keypoints.shape
assert aug.inverse(mask, data_keys=['mask']).shape == mask.shape
def test_forward_and_inverse(self, random_apply, return_transform, device, dtype):
inp = torch.randn(1, 3, 1000, 500, device=device, dtype=dtype)
bbox = torch.tensor([[[355, 10], [660, 10], [660, 250], [355, 250]]], device=device, dtype=dtype)
keypoints = torch.tensor([[[465, 115], [545, 116]]], device=device, dtype=dtype)
mask = bbox_to_mask(
torch.tensor([[[155, 0], [900, 0], [900, 400], [155, 400]]], device=device, dtype=dtype), 1000, 500
)[:, None].float()
aug = K.AugmentationSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0, return_transform=True),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0),
data_keys=["input", "mask", "bbox", "keypoints"],
random_apply=random_apply,
return_transform=return_transform,
)
out = aug(inp, mask, bbox, keypoints)
if return_transform and isinstance(out, (tuple, list)):
assert out[0][0].shape == inp.shape
else:
assert out[0].shape == inp.shape
assert out[1].shape == mask.shape
assert out[2].shape == bbox.shape
assert out[3].shape == keypoints.shape
reproducibility_test((inp, mask, bbox, keypoints), aug)
out_inv = aug.inverse(*out)
assert out_inv[0].shape == inp.shape
assert out_inv[1].shape == mask.shape
assert out_inv[2].shape == bbox.shape
assert out_inv[3].shape == keypoints.shape
def test_exception(self, error_param):
with pytest.raises(Exception): # AssertError and NotImplementedError
K.PatchSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
**error_param,
)
def test_construction(self, same_on_batch, return_transform, keepdim):
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0),
same_on_batch=same_on_batch,
return_transform=return_transform,
keepdim=keepdim,
)
def test_forward(self, random_apply, device, dtype):
inp = torch.randn(1, 3, 30, 30, device=device, dtype=dtype)
aug = K.ImageSequential(
K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
kornia.filters.MedianBlur((3, 3)),
K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
K.ImageSequential(K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0)),
K.ImageSequential(K.RandomAffine(360, p=1.0)),
K.RandomAffine(360, p=1.0),
K.RandomMixUp(p=1.0),
random_apply=random_apply,
)
out = aug(inp)
if aug.return_label:
out, _ = out
assert out.shape == inp.shape
aug.inverse(inp)
reproducibility_test(inp, aug)
def test_individual_forward_and_inverse(self, device, dtype):
inp = torch.randn(1, 3, 1000, 500, device=device, dtype=dtype)
bbox = torch.tensor([[[355, 10], [660, 10], [660, 250], [355, 250]]],
device=device,
dtype=dtype)
keypoints = torch.tensor([[[465, 115], [545, 116]]],
device=device,
dtype=dtype)
mask = bbox_to_mask(
torch.tensor([[[155, 0], [900, 0], [900, 400], [155, 400]]],
device=device,
dtype=dtype), 500, 1000)[:, None].float()
crop_size = (200, 200)
aug = K.AugmentationSequential(
K.ImageSequential(K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0)),
K.AugmentationSequential(K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0)),
K.RandomAffine(360, p=1.0),
K.RandomCrop(crop_size,
padding=1,
cropping_mode='resample',
fill=0),
data_keys=['input', 'mask', 'bbox', 'keypoints'],
)
reproducibility_test((inp, mask, bbox, keypoints), aug)
out = aug(inp, mask, bbox, keypoints)
assert out[0].shape == (*inp.shape[:2], *crop_size)
assert out[1].shape == (*mask.shape[:2], *crop_size)
assert out[2].shape == bbox.shape
assert out[3].shape == keypoints.shape
out_inv = aug.inverse(*out)
assert out_inv[0].shape == inp.shape
assert out_inv[1].shape == mask.shape
assert out_inv[2].shape == bbox.shape
assert out_inv[3].shape == keypoints.shape
aug = K.AugmentationSequential(K.RandomAffine(360, p=1.0))
assert aug(inp, data_keys=['input']).shape == inp.shape
aug = K.AugmentationSequential(K.RandomAffine(360, p=1.0))
assert aug(inp, data_keys=['input']).shape == inp.shape
assert aug(mask, data_keys=['mask'],
params=aug._params).shape == mask.shape
assert aug.inverse(inp, data_keys=['input']).shape == inp.shape
assert aug.inverse(bbox, data_keys=['bbox']).shape == bbox.shape
assert aug.inverse(keypoints,
data_keys=['keypoints']).shape == keypoints.shape
assert aug.inverse(mask, data_keys=['mask']).shape == mask.shape
def test_forward(self, return_transform, device, dtype):
inp = torch.randn(1, 3, 30, 30, device=device, dtype=dtype)
aug = K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
kornia.filters.MedianBlur((3, 3)),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0, return_transform=True),
K.RandomAffine(360, p=1.0),
return_transform=return_transform,
)
out = aug(inp)
if isinstance(out, (tuple, )):
assert out[0].shape == inp.shape
else:
assert out.shape == inp.shape
def test_forward(self, shape, padding, patchwise_apply, same_on_batch,
keepdim, random_apply, device, dtype):
try: # skip wrong param settings.
seq = K.PatchSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
K.RandomPerspective(0.2, p=0.5),
K.RandomSolarize(0.1, 0.1, p=0.5),
),
K.ColorJitter(0.1, 0.1, 0.1, 0.1),
grid_size=(2, 2),
padding=padding,
patchwise_apply=patchwise_apply,
same_on_batch=same_on_batch,
keepdim=keepdim,
random_apply=random_apply,
)
except:
return
input = torch.randn(*shape, device=device, dtype=dtype)
trans = torch.randn(shape[0], 3, 3, device=device, dtype=dtype)
out = seq(input)
assert out.shape[-3:] == input.shape[-3:]
out = seq((input, trans))
assert out[0].shape[-3:] == input.shape[-3:]
assert out[1].shape == trans.shape
reproducibility_test(input, seq)
def test_mixup(self, inp, random_apply, same_on_batch, device, dtype):
inp = torch.as_tensor(inp, device=device, dtype=dtype)
aug = K.AugmentationSequential(
K.ImageSequential(K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0)),
K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0),
K.RandomMixUp(p=1.0),
data_keys=["input"],
random_apply=random_apply,
same_on_batch=same_on_batch,
)
out = aug(inp)
if aug.return_label:
out, _ = out
assert out.shape[-3:] == inp.shape[-3:]
reproducibility_test(inp, aug)
def test_construction(self, same_on_batch, keepdim, random_apply):
aug = K.ImageSequential(
K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0),
K.RandomMixUp(p=1.0),
same_on_batch=same_on_batch,
keepdim=keepdim,
random_apply=random_apply,
)
c = 0
for a in aug.get_forward_sequence():
if isinstance(a, (MixAugmentationBase, )):
c += 1
assert c < 2
aug.same_on_batch = True
aug.keepdim = True
for m in aug.children():
assert m.same_on_batch is True, m.same_on_batch
assert m.keepdim is True, m.keepdim
def test_mixup(self, inp, return_transform, random_apply, same_on_batch,
device, dtype):
inp = torch.as_tensor(inp, device=device, dtype=dtype)
aug = K.AugmentationSequential(
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0, return_transform=True)),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0),
K.RandomMixUp(p=1.0),
data_keys=["input"],
random_apply=random_apply,
return_transform=return_transform,
same_on_batch=same_on_batch,
)
out = aug(inp)
if aug.return_label:
out, _ = out
if return_transform and isinstance(out, (tuple, list)):
out = out[0]
assert out.shape[-3:] == inp.shape[-3:]
reproducibility_test(inp, aug)
def test_forward_and_inverse_return_transform(self, random_apply, device,
dtype):
inp = torch.randn(1, 3, 1000, 500, device=device, dtype=dtype)
bbox = torch.tensor([[[355, 10], [660, 10], [660, 250], [355, 250]]],
device=device,
dtype=dtype)
keypoints = torch.tensor([[[465, 115], [545, 116]]],
device=device,
dtype=dtype)
mask = bbox_to_mask(
torch.tensor([[[155, 0], [900, 0], [900, 400], [155, 400]]],
device=device,
dtype=dtype), 1000, 500)[:, None].float()
aug = K.AugmentationSequential(
K.ImageSequential(K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0)),
K.AugmentationSequential(K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0)),
K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0),
data_keys=["input", "mask", "bbox", "keypoints"],
random_apply=random_apply,
)
out = aug(inp, mask, bbox, keypoints)
assert out[0].shape == inp.shape
assert out[1].shape == mask.shape
assert out[2].shape == bbox.shape
assert out[3].shape == keypoints.shape
reproducibility_test((inp, mask, bbox, keypoints), aug)
# TODO(jian): we sometimes throw the following error
# AttributeError: 'tuple' object has no attribute 'shape'
out_inv = aug.inverse(*out)
assert out_inv[0].shape == inp.shape
assert out_inv[1].shape == mask.shape
assert out_inv[2].shape == bbox.shape
assert out_inv[3].shape == keypoints.shape
class TestVideoSequential:
@pytest.mark.parametrize('shape', [(3, 4), (2, 3, 4), (2, 3, 5, 6),
(2, 3, 4, 5, 6, 7)])
@pytest.mark.parametrize('data_format', ["BCTHW", "BTCHW"])
def test_exception(self, shape, data_format, device, dtype):
aug_list = K.VideoSequential(K.ColorJitter(0.1, 0.1, 0.1, 0.1),
data_format=data_format,
same_on_frame=True)
with pytest.raises(AssertionError):
img = torch.randn(*shape, device=device, dtype=dtype)
aug_list(img)
@pytest.mark.parametrize(
'augmentation',
[
K.RandomAffine(360, p=1.0),
K.CenterCrop((3, 3), p=1.0),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomCrop((5, 5), p=1.0),
K.RandomErasing(p=1.0),
K.RandomGrayscale(p=1.0),
K.RandomHorizontalFlip(p=1.0),
K.RandomVerticalFlip(p=1.0),
K.RandomPerspective(p=1.0),
K.RandomResizedCrop((5, 5), p=1.0),
K.RandomRotation(360.0, p=1.0),
K.RandomSolarize(p=1.0),
K.RandomPosterize(p=1.0),
K.RandomSharpness(p=1.0),
K.RandomEqualize(p=1.0),
K.RandomMotionBlur(3, 35.0, 0.5, p=1.0),
K.Normalize(torch.tensor([0.5, 0.5, 0.5]),
torch.tensor([0.5, 0.5, 0.5]),
p=1.0),
K.Denormalize(torch.tensor([0.5, 0.5, 0.5]),
torch.tensor([0.5, 0.5, 0.5]),
p=1.0),
],
)
@pytest.mark.parametrize('data_format', ["BCTHW", "BTCHW"])
def test_augmentation(self, augmentation, data_format, device, dtype):
input = torch.randint(255, (1, 3, 3, 5, 6), device=device,
dtype=dtype).repeat(2, 1, 1, 1, 1) / 255.0
torch.manual_seed(21)
aug_list = K.VideoSequential(augmentation,
data_format=data_format,
same_on_frame=True)
reproducibility_test(input, aug_list)
@pytest.mark.parametrize(
'augmentations',
[
[
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0)
],
[
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0)
],
[K.RandomAffine(360, p=1.0),
kornia.color.BgrToRgb()],
[
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.0),
K.RandomAffine(360, p=0.0)
],
[K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.0)],
[K.RandomAffine(360, p=0.0)],
[
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, p=1.0),
K.RandomMixUp(p=1.0)
],
],
)
@pytest.mark.parametrize('data_format', ["BCTHW", "BTCHW"])
@pytest.mark.parametrize('random_apply',
[1, (1, 1), (1, ), 10, True, False])
def test_same_on_frame(self, augmentations, data_format, random_apply,
device, dtype):
aug_list = K.VideoSequential(*augmentations,
data_format=data_format,
same_on_frame=True,
random_apply=random_apply)
if data_format == 'BCTHW':
input = torch.randn(2, 3, 1, 5, 6, device=device,
dtype=dtype).repeat(1, 1, 4, 1, 1)
output = aug_list(input)
if aug_list.return_label:
output, _ = output
assert (output[:, :, 0] == output[:, :, 1]).all()
assert (output[:, :, 1] == output[:, :, 2]).all()
assert (output[:, :, 2] == output[:, :, 3]).all()
if data_format == 'BTCHW':
input = torch.randn(2, 1, 3, 5, 6, device=device,
dtype=dtype).repeat(1, 4, 1, 1, 1)
output = aug_list(input)
if aug_list.return_label:
output, _ = output
assert (output[:, 0] == output[:, 1]).all()
assert (output[:, 1] == output[:, 2]).all()
assert (output[:, 2] == output[:, 3]).all()
reproducibility_test(input, aug_list)
@pytest.mark.parametrize(
'augmentations',
[
[K.RandomAffine(360, p=1.0)],
[K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0)],
[
K.RandomAffine(360, p=0.0),
K.ImageSequential(K.RandomAffine(360, p=0.0))
],
],
)
@pytest.mark.parametrize('data_format', ["BCTHW", "BTCHW"])
def test_against_sequential(self, augmentations, data_format, device,
dtype):
aug_list_1 = K.VideoSequential(*augmentations,
data_format=data_format,
same_on_frame=False)
aug_list_2 = torch.nn.Sequential(*augmentations)
if data_format == 'BCTHW':
input = torch.randn(2, 3, 1, 5, 6, device=device,
dtype=dtype).repeat(1, 1, 4, 1, 1)
if data_format == 'BTCHW':
input = torch.randn(2, 1, 3, 5, 6, device=device,
dtype=dtype).repeat(1, 4, 1, 1, 1)
torch.manual_seed(0)
output_1 = aug_list_1(input)
torch.manual_seed(0)
if data_format == 'BCTHW':
input = input.transpose(1, 2)
output_2 = aug_list_2(input.reshape(-1, 3, 5, 6))
output_2 = output_2.view(2, 4, 3, 5, 6)
if data_format == 'BCTHW':
output_2 = output_2.transpose(1, 2)
assert (output_1 == output_2).all(), dict(aug_list_1._params)
@pytest.mark.jit
@pytest.mark.skip(reason="turn off due to Union Type")
def test_jit(self, device, dtype):
B, C, D, H, W = 2, 3, 5, 4, 4
img = torch.ones(B, C, D, H, W, device=device, dtype=dtype)
op = K.VideoSequential(K.ColorJitter(0.1, 0.1, 0.1, 0.1),
same_on_frame=True)
op_jit = torch.jit.script(op)
assert_close(op(img), op_jit(img))
def test_exception(self, random_apply_weights, device, dtype):
inp = torch.randn(1, 3, 30, 30, device=device, dtype=dtype)
with pytest.raises(Exception): # AssertError and NotImplementedError
K.ImageSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
random_apply_weights=random_apply_weights).inverse(inp)
