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.clone(), bbox.clone()) out_hor = aug_hor(inp.clone(), bbox.clone()) assert_close(out_ver[1], expected_bbox_vertical_flip) assert_close(out_hor[1], expected_bbox_horizontal_flip)
def get_train_augmentation_transforms( p_flip_vertical=0.5, p_flip_horizontal=0.5, max_rotation=10.0, max_zoom=1.1, max_warp=0.2, p_affine=0.75, max_lighting=0.2, p_lighting=0.75, ): """ Build a set of pytorch image Transforms to use during training: p_flip_vertical: probability of a vertical flip p_flip_horizontal: probability of a horizontal flip max_rotation: maximum rotation angle in degrees max_zoom: maximum zoom level max_warp: perspective warping scale (from 0.0 to 1.0) p_affine: probility of rotation, zoom and perspective warping max_lighting: maximum scaling of brightness and contrast """ return [ kaug.RandomVerticalFlip(p=p_flip_vertical), kaug.RandomHorizontalFlip(p=p_flip_horizontal), kaug.RandomAffine(p=p_affine, degrees=max_rotation, scale=(1.0, max_zoom)), kaug.RandomPerspective(p=p_affine, distortion_scale=max_warp), kaug.ColorJitter(p=p_lighting, brightness=max_lighting, contrast=max_lighting), # TODO: the kornia transforms work on batches and so by default they # add a batch dimension. Until I work out how to apply transform by # batches (and only while training) I will just keep this here to # remove the batch dimension again GetItemTransform(), ]
def generate_kornia_transforms(image_size=224, resize=256, mean=[], std=[], include_jitter=False): mean=torch.tensor(mean) if mean else torch.tensor([0.5, 0.5, 0.5]) std=torch.tensor(std) if std else torch.tensor([0.1, 0.1, 0.1]) if torch.cuda.is_available(): mean=mean.cuda() std=std.cuda() train_transforms=[G.Resize((resize,resize))] if include_jitter: train_transforms.append(K.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1)) train_transforms.extend([K.RandomHorizontalFlip(p=0.5), K.RandomVerticalFlip(p=0.5), K.RandomRotation(90), K.RandomResizedCrop((image_size,image_size)), K.Normalize(mean,std) ]) val_transforms=[G.Resize((resize,resize)), K.CenterCrop((image_size,image_size)), K.Normalize(mean,std) ] transforms=dict(train=nn.Sequential(*train_transforms), val=nn.Sequential(*val_transforms)) if torch.cuda.is_available(): for k in transforms: transforms[k]=transforms[k].cuda() return transforms
def default_aug(image_size: Tuple[int, int] = (360, 360)) -> nn.Module: return nn.Sequential( aug.ColorJitter(contrast=0.1, brightness=0.1, saturation=0.1, p=0.8), aug.RandomVerticalFlip(), aug.RandomHorizontalFlip(), RandomApply(filters.GaussianBlur2d((3, 3), (0.5, 0.5)), p=0.1), aug.RandomResizedCrop(size=image_size, scale=(0.5, 1)), aug.Normalize( mean=torch.tensor([0.485, 0.456, 0.406]), std=torch.tensor([0.229, 0.224, 0.225]), ), )
def __init__(self, viz: bool = False): super().__init__() self.viz = viz '''self.geometric = [ K.augmentation.RandomAffine(60., p=0.75), ]''' self.augmentations = nn.Sequential( augmentation.RandomRotation(degrees=30.), augmentation.RandomPerspective(distortion_scale=0.4), augmentation.RandomResizedCrop((224, 224)), augmentation.RandomHorizontalFlip(p=0.5), augmentation.RandomVerticalFlip(p=0.5), # K.augmentation.GaussianBlur((3, 3), (0.1, 2.0), p=1.0), # K.augmentation.ColorJitter(0.01, 0.01, 0.01, 0.01, p=0.25), ) self.denorm = augmentation.Denormalize(Tensor(DATASET_IMAGE_MEAN), Tensor(DATASET_IMAGE_STD))
def __init__(self, utes, mask, ct, length, opt): super(TrainDataset, self).__init__() self.utes = utes self.label = ct self.mask = mask self.length = length self.num_vols = utes.shape[0] self.batch_size = opt.trainBatchSize self.spatial = nn.Sequential( ka.RandomAffine(45, translate=(0.1, 0.1), scale=(0.85, 1.15), shear=(0.1, 0.1), same_on_batch=True), ka.RandomVerticalFlip(same_on_batch=True), ka.RandomHorizontalFlip(same_on_batch=True)) self.dim = 2 self.counter = 0
def __init__(self, probability: float = 0.1): self._probability = probability self._operation = aug.RandomVerticalFlip(p=probability)
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 get_gpu_transforms(augs: DictConfig, mode: str = '2d') -> dict: """Makes GPU augmentations from the augs section of a configuration. Parameters ---------- augs : DictConfig Augmentation parameters mode : str, optional If '2d', stacks clip in channels. If 3d, returns 5-D tensor, by default '2d' Returns ------- xform : dict keys: ['train', 'val', 'test']. Values: a nn.Sequential with Kornia augmentations. Example: auged_images = xform['train'](images) """ # input is a tensor of shape N x C x F x H x W train_transforms = [ToFloat()] val_transforms = [ToFloat()] kornia_transforms = [] if augs.LR > 0: kornia_transforms.append(K.RandomHorizontalFlip(p=augs.LR, same_on_batch=False, return_transform=False)) if augs.UD > 0: kornia_transforms.append(K.RandomVerticalFlip(p=augs.UD, same_on_batch=False, return_transform=False)) if augs.degrees > 0: kornia_transforms.append(K.RandomRotation(augs.degrees)) if augs.brightness > 0 or augs.contrast > 0 or augs.saturation > 0 or augs.hue > 0: kornia_transforms.append(K.ColorJitter(brightness=augs.brightness, contrast=augs.contrast, saturation=augs.saturation, hue=augs.hue, p=augs.color_p, same_on_batch=False, return_transform=False)) if augs.grayscale > 0: kornia_transforms.append(K.RandomGrayscale(p=augs.grayscale)) norm = NormalizeVideo(mean=augs.normalization.mean, std=augs.normalization.std) # kornia_transforms.append(norm) kornia_transforms = VideoSequential(*kornia_transforms, data_format='BCTHW', same_on_frame=True) train_transforms = [ToFloat(), kornia_transforms, norm] val_transforms = [ToFloat(), norm] denormalize = [] if mode == '2d': train_transforms.append(StackClipInChannels()) val_transforms.append(StackClipInChannels()) denormalize.append(UnstackClip()) denormalize.append(DenormalizeVideo(mean=augs.normalization.mean, std=augs.normalization.std)) train_transforms = nn.Sequential(*train_transforms) val_transforms = nn.Sequential(*val_transforms) denormalize = nn.Sequential(*denormalize) gpu_transforms = dict(train=train_transforms, val=val_transforms, test=val_transforms, denormalize=denormalize) log.info('GPU transforms: {}'.format(gpu_transforms)) return gpu_transforms
def __init__( self, net, image_size, hidden_layer=-2, project_hidden=True, project_dim=128, augment_both=True, use_nt_xent_loss=False, augment_fn=None, use_bilinear=False, use_momentum=False, momentum_value=0.999, key_encoder=None, temperature=0.1, batch_size=128, ): super().__init__() self.net = OutputHiddenLayer(net, layer=hidden_layer) DEFAULT_AUG = nn.Sequential( # RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8), # augs.RandomGrayscale(p=0.2), augs.RandomHorizontalFlip(), augs.RandomVerticalFlip(), augs.RandomSolarize(), augs.RandomPosterize(), augs.RandomSharpness(), augs.RandomEqualize(), augs.RandomRotation(degrees=8.0), RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1), augs.RandomResizedCrop((image_size, image_size), p=0.1), ) self.b = batch_size self.h = image_size self.w = image_size self.augment = default(augment_fn, DEFAULT_AUG) self.augment_both = augment_both self.temperature = temperature self.use_nt_xent_loss = use_nt_xent_loss self.project_hidden = project_hidden self.projection = None self.project_dim = project_dim self.use_bilinear = use_bilinear self.bilinear_w = None self.use_momentum = use_momentum self.ema_updater = EMA(momentum_value) self.key_encoder = key_encoder # for accumulating queries and keys across calls self.queries = None self.keys = None random_data = ( ( torch.randn(1, 3, image_size, image_size), torch.randn(1, 3, image_size, image_size), torch.randn(1, 3, image_size, image_size), ), torch.tensor([1]), ) # send a mock image tensor to instantiate parameters self.forward(random_data)