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import pytest
import torch
import kornia
import kornia.augmentation as K
from kornia.augmentation.base import MixAugmentationBase
from kornia.constants import BorderType
from kornia.geometry.bbox import bbox_to_mask
from kornia.testing import assert_close
def reproducibility_test(input, seq):
if isinstance(input, (tuple, list)):
output_1 = seq(*input)
output_2 = seq(*input, params=seq._params)
else:
output_1 = seq(input)
output_2 = seq(input, params=seq._params)
if isinstance(output_1, (tuple, list)) and isinstance(output_2, (tuple, list)):
[
assert_close(o1, o2)
for o1, o2 in zip(output_1, output_2)
if isinstance(o1, (torch.Tensor,)) and isinstance(o2, (torch.Tensor,))
]
elif isinstance(output_1, (tuple, list)) and isinstance(output_2, (torch.Tensor,)):
assert_close(output_1[0], output_2)
elif isinstance(output_2, (tuple, list)) and isinstance(output_1, (torch.Tensor,)):
assert_close(output_1, output_2[0])
elif isinstance(output_2, (torch.Tensor,)) and isinstance(output_1, (torch.Tensor,)):
assert_close(output_1, output_2, msg=f"{seq._params}")
else:
assert False, ("cannot compare", type(output_1), type(output_2))
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))
class TestSequential:
def test_exception(self, 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)).inverse(inp)
@pytest.mark.parametrize('same_on_batch', [True, False, None])
@pytest.mark.parametrize("return_transform", [True, False, None])
@pytest.mark.parametrize("keepdim", [True, False, None])
@pytest.mark.parametrize('random_apply', [1, (2, 2), (1, 2), (2,), 20, True, False])
def test_construction(self, same_on_batch, return_transform, keepdim, random_apply):
aug = K.ImageSequential(
K.ColorJitter(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,
return_transform=return_transform,
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.return_transform = True
aug.keepdim = True
for m in aug.children():
assert m.same_on_batch is True, m.same_on_batch
if not isinstance(m, (MixAugmentationBase,)):
assert m.return_transform is True, m.return_transform
assert m.keepdim is True, m.keepdim
@pytest.mark.parametrize("return_transform", [True, False, None])
@pytest.mark.parametrize('random_apply', [1, (2, 2), (1, 2), (2,), 10, True, False])
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)
class TestAugmentationSequential:
@pytest.mark.parametrize(
'data_keys', ["input", ["mask", "input"], ["input", "bbox_yxyx"], [0, 10], [BorderType.REFLECT]]
)
@pytest.mark.parametrize("augmentation_list", [K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0)])
def test_exception(self, augmentation_list, data_keys, device, dtype):
with pytest.raises(Exception): # AssertError and NotImplementedError
K.AugmentationSequential(augmentation_list, data_keys=data_keys)
@pytest.mark.parametrize('return_transform', [True, False])
@pytest.mark.parametrize('same_on_batch', [True, False])
@pytest.mark.parametrize('random_apply', [1, (2, 2), (1, 2), (2,), 10, True, False])
@pytest.mark.parametrize('inp', [torch.randn(1, 3, 1000, 500), torch.randn(3, 1000, 500)])
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_video(self, device, dtype):
input = torch.randn(2, 3, 5, 6, device=device, dtype=dtype)[None]
bbox = torch.tensor([[[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]]], device=device, dtype=dtype).expand(
2, -1, -1
)[None]
points = torch.tensor([[[1.0, 1.0]]], device=device, dtype=dtype).expand(2, -1, -1)[None]
aug_list = K.AugmentationSequential(
K.VideoSequential(
kornia.augmentation.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0), kornia.augmentation.RandomAffine(360, p=1.0)
),
data_keys=["input", "mask", "bbox", "keypoints"],
)
out = aug_list(input, input, bbox, points)
assert out[0].shape == input.shape
assert out[1].shape == input.shape
assert out[2].shape == bbox.shape
assert out[3].shape == points.shape
out_inv = aug_list.inverse(*out)
assert out_inv[0].shape == input.shape
assert out_inv[1].shape == input.shape
assert out_inv[2].shape == bbox.shape
assert out_inv[3].shape == points.shape
def test_random_flips(self, device, dtype):
inp = torch.randn(1, 3, 510, 1020, device=device, dtype=dtype)
bbox = torch.tensor([[[355, 10], [660, 10], [660, 250], [355, 250]]], device=device, dtype=dtype)
expected_bbox_vertical_flip = torch.tensor(
[[[355, 499], [660, 499], [660, 259], [355, 259]]], device=device, dtype=dtype
)
expected_bbox_horizontal_flip = torch.tensor(
[[[664, 10], [359, 10], [359, 250], [664, 250]]], device=device, dtype=dtype
)
aug_ver = K.AugmentationSequential(
K.RandomVerticalFlip(p=1.0), data_keys=["input", "bbox"], return_transform=False, same_on_batch=False
)
aug_hor = K.AugmentationSequential(
K.RandomHorizontalFlip(p=1.0), data_keys=["input", "bbox"], return_transform=False, same_on_batch=False
)
out_ver = aug_ver(inp, bbox)
out_hor = aug_hor(inp, bbox)
assert_close(out_ver[1], expected_bbox_vertical_flip)
assert_close(out_hor[1], expected_bbox_horizontal_flip)
@pytest.mark.parametrize('random_apply', [1, (2, 2), (1, 2), (2,), 10, True, False])
@pytest.mark.parametrize('return_transform', [True, False])
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_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
@pytest.mark.parametrize('random_apply', [2, (1, 1), (2,), 10, True, False])
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.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,
)
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)
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
@pytest.mark.parametrize('random_apply', [1, (2, 2), (1, 2), (2,), 10, True, False])
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)
@pytest.mark.jit
@pytest.mark.skip(reason="turn off due to Union Type")
def test_jit(self, device, dtype):
B, C, H, W = 2, 3, 4, 4
img = torch.ones(B, C, H, W, device=device, dtype=dtype)
op = K.AugmentationSequential(
K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0), K.RandomAffine(360, p=1.0), same_on_batch=True
)
op_jit = torch.jit.script(op)
assert_close(op(img), op_jit(img))
class TestPatchSequential:
@pytest.mark.parametrize(
'error_param',
[
{"random_apply": False, "patchwise_apply": True, "grid_size": (2, 3)},
{"random_apply": 2, "patchwise_apply": True},
{"random_apply": (2, 3), "patchwise_apply": True},
],
)
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,
)
@pytest.mark.parametrize('shape', [(2, 3, 24, 24)])
@pytest.mark.parametrize('padding', ["same", "valid"])
@pytest.mark.parametrize('patchwise_apply', [True, False])
@pytest.mark.parametrize('same_on_batch', [True, False, None])
@pytest.mark.parametrize('keepdim', [True, False, None])
@pytest.mark.parametrize('random_apply', [1, (2, 2), (1, 2), (2,), 10, True, False])
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_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()
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