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()