def get_wrist_fracture_transformation(crop_size): return transforms.Compose([ SplitDataToFunction(wrap_img_target_solt), slc.Stream([ slt.RandomFlip(p=1, axis=1), slt.RandomProjection(affine_transforms=slc.Stream([ slt.RandomScale(range_x=(0.8, 1.2), p=1), slt.RandomShear(range_x=(-0.1, 0.1), p=0.5), slt.RandomShear(range_y=(-0.1, 0.1), p=0.5), slt.RandomRotate(rotation_range=(-10, 10), p=1), ]), v_range=(1e-5, 5e-4), p=0.8), slt.PadTransform(pad_to=(256, 256), padding='z'), slt.CropTransform(crop_size, crop_mode='r'), slc.SelectiveStream([ slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=0.5, blur_type='m', k_size=(11, )), ]), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), ]), slt.ImageGammaCorrection(p=1, gamma_range=(0.5, 1.5)), ]), DataToFunction(solt_to_img_target), ApplyByIndex(transforms.ToTensor(), 0) ])
def get_landmark_transform(config): return transforms.Compose([ # WrapImageLandmarksSOLT(), slc.Stream([ slt.RandomFlip(p=0.5, axis=1), slt.RandomScale(range_x=(0.8, 1.2), p=1), slt.RandomRotate(rotation_range=(-180, 180), p=0.2), slt.RandomProjection(affine_transforms=slc.Stream([ slt.RandomScale(range_x=(0.8, 1.3), p=1), slt.RandomRotate(rotation_range=(-180, 180), p=1), slt.RandomShear(range_x=(-0.1, 0.1), range_y=(0, 0), p=0.5), slt.RandomShear(range_y=(-0.1, 0.1), range_x=(0, 0), p=0.5), ]), v_range=(1e-5, 2e-3), p=0.8), slt.PadTransform(int(config.dataset.crop_size * 1.4), padding='z'), slt.CropTransform(config.dataset.crop_size, crop_mode='r'), slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=1, blur_type='g', k_size=(3, 5)), slt.ImageBlur(p=1, blur_type='m', k_size=(3, 5)), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), slc.Stream([ slt.ImageSaltAndPepper(p=1, gain_range=0.05), slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), ]), slc.Stream([ slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), slt.ImageSaltAndPepper(p=1, gain_range=0.01), ]), slc.Stream() ]), slt.ImageGammaCorrection(p=1, gamma_range=(0.5, 1.5)) ]), SOLTtoHourGlassGSinput(downsample=4, sigma=3), ApplyTransformByIndex(transform=dwutils.npg2tens, ids=[0, 1]), ])
def get_landmark_transform_kneel(config): cutout = slt.ImageCutOut( cutout_size=(int(config.dataset.cutout * config.dataset.augs.crop.crop_x), int(config.dataset.cutout * config.dataset.augs.crop.crop_y)), p=0.5) ppl = transforms.Compose([ slc.Stream(), slc.SelectiveStream( [ slc.Stream([ slt.RandomFlip(p=0.5, axis=1), slt.RandomProjection(affine_transforms=slc.Stream([ slt.RandomScale(range_x=(0.9, 1.1), p=1), slt.RandomRotate(rotation_range=(-90, 90), p=1), slt.RandomShear( range_x=(-0.1, 0.1), range_y=(-0.1, 0.1), p=0.5), slt.RandomShear( range_x=(-0.1, 0.1), range_y=(-0.1, 0.1), p=0.5), ]), v_range=(1e-5, 2e-3), p=0.5), # slt.RandomScale(range_x=(0.5, 2.5), p=0.5), ]), slc.Stream() ], probs=[0.7, 0.3]), slc.Stream([ slt.PadTransform( (config.dataset.augs.pad.pad_x, config.dataset.augs.pad.pad_y), padding='z'), slt.CropTransform((config.dataset.augs.crop.crop_x, config.dataset.augs.crop.crop_y), crop_mode='r'), ]), slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=1, blur_type='g', k_size=(3, 5)), slt.ImageBlur(p=1, blur_type='m', k_size=(3, 5)), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), slc.Stream([ slt.ImageSaltAndPepper(p=1, gain_range=0.05), slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), ]), slc.Stream([ slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), slt.ImageSaltAndPepper(p=1, gain_range=0.01), ]), slc.Stream() ], n=1), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), cutout if config.dataset.use_cutout else slc.Stream(), DataToFunction(solt_to_img_target), ApplyByIndex(transforms.ToTensor(), 0) ]) return ppl
def init_mnist_transforms(): train_trf = Compose([ wrap2solt, slc.Stream([ slt.ResizeTransform(resize_to=(64, 64), interpolation='bilinear'), slt.RandomScale(range_x=(0.9, 1.1), same=False, p=0.5), slt.RandomShear(range_x=(-0.05, 0.05), p=0.5), slt.RandomRotate(rotation_range=(-10, 10), p=0.5), # slt.RandomRotate(rotation_range=(-5, 5), p=0.5), slt.PadTransform(pad_to=70), slt.CropTransform(crop_size=64, crop_mode='r'), slt.ImageAdditiveGaussianNoise(p=1.0) ]), unpack_solt, ApplyTransform(Normalize((0.5, ), (0.5, ))) ]) test_trf = Compose([ wrap2solt, slt.ResizeTransform(resize_to=(64, 64), interpolation='bilinear'), # slt.PadTransform(pad_to=64), unpack_solt, ApplyTransform(Normalize((0.5, ), (0.5, ))), ]) return train_trf, test_trf
def init_mnist_cifar_transforms(n_channels=1, stage='train'): if n_channels == 1: norm_mean_std = Normalize((0.1307, ), (0.3081, )) elif n_channels == 3: norm_mean_std = Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261)) else: raise ValueError("Not support channels of {}".format(n_channels)) train_trf = Compose([ wrap2solt, slc.Stream([ slt.RandomScale(range_x=(0.9, 1.1), same=False, p=0.5), slt.RandomShear(range_x=(-0.05, 0.05), p=0.5), slt.RandomRotate(rotation_range=(-5, 5), p=0.5), slt.PadTransform(pad_to=34), slt.CropTransform(crop_size=32, crop_mode='r') ]), unpack_solt, ApplyTransform(norm_mean_std) ]) if stage == 'train': return train_trf test_trf = Compose([ wrap2solt, slt.PadTransform(pad_to=32), unpack_solt, ApplyTransform(norm_mean_std) ]) return test_trf
def test_fusion_happens(): ppl = slc.Stream([ slt.RandomScale((0.5, 1.5), (0.5, 1.5), p=1), slt.RandomRotate((-50, 50), padding='z', p=1), slt.RandomShear((-0.5, 0.5), (-0.5, 0.5), padding='z', p=1), slt.RandomFlip(p=1, axis=1), ]) st = ppl.optimize_stack(ppl.transforms) assert len(st) == 2
def init_transforms(nc=1): if nc == 1: norm_mean_std = Normalize((0.1307, ), (0.3081, )) elif nc == 3: norm_mean_std = Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261)) else: raise ValueError("Not support channels of {}".format(nc)) train_trf = Compose([ wrap2solt, slc.Stream([ slt.ResizeTransform(resize_to=(32, 32), interpolation='bilinear'), slt.RandomScale(range_x=(0.9, 1.1), same=False, p=0.5), slt.RandomShear(range_x=(-0.05, 0.05), p=0.5), slt.RandomRotate(rotation_range=(-10, 10), p=0.5), # slt.RandomRotate(rotation_range=(-5, 5), p=0.5), slt.PadTransform(pad_to=36), slt.CropTransform(crop_size=32, crop_mode='r'), slt.ImageAdditiveGaussianNoise(p=1.0) ]), unpack_solt, ApplyTransform(norm_mean_std) ]) test_trf = Compose([ wrap2solt, slt.ResizeTransform(resize_to=(32, 32), interpolation='bilinear'), unpack_solt, ApplyTransform(norm_mean_std) ]) def custom_augment(img): tr = Compose([ wrap2solt, slc.Stream([ slt.ResizeTransform(resize_to=(32, 32), interpolation='bilinear'), slt.RandomScale(range_x=(0.9, 1.1), same=False, p=0.5), slt.RandomShear(range_x=(-0.05, 0.05), p=0.5), slt.RandomRotate(rotation_range=(-10, 10), p=0.5), # slt.RandomRotate(rotation_range=(-5, 5), p=0.5), slt.PadTransform(pad_to=36), slt.CropTransform(crop_size=32, crop_mode='r'), slt.ImageAdditiveGaussianNoise(p=1.0) ]), unpack_solt, ApplyTransform(norm_mean_std) ]) img_tr, _ = tr((img, 0)) return img_tr return train_trf, test_trf, custom_augment
def init_augs(): kvs = GlobalKVS() args = kvs['args'] cutout = slt.ImageCutOut(cutout_size=(int(args.cutout * args.crop_x), int(args.cutout * args.crop_y)), p=0.5) # plus-minus 1.3 pixels jitter = slt.KeypointsJitter(dx_range=(-0.003, 0.003), dy_range=(-0.003, 0.003)) ppl = tvt.Compose([ jitter if args.use_target_jitter else slc.Stream(), slc.SelectiveStream([ slc.Stream([ slt.RandomFlip(p=0.5, axis=1), slt.RandomProjection(affine_transforms=slc.Stream([ slt.RandomScale(range_x=(0.8, 1.3), p=1), slt.RandomRotate(rotation_range=(-90, 90), p=1), slt.RandomShear( range_x=(-0.1, 0.1), range_y=(-0.1, 0.1), p=0.5), ]), v_range=(1e-5, 2e-3), p=0.5), slt.RandomScale(range_x=(0.5, 2.5), p=0.5), ]), slc.Stream() ], probs=[0.7, 0.3]), slc.Stream([ slt.PadTransform((args.pad_x, args.pad_y), padding='z'), slt.CropTransform((args.crop_x, args.crop_y), crop_mode='r'), ]), slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=1, blur_type='g', k_size=(3, 5)), slt.ImageBlur(p=1, blur_type='m', k_size=(3, 5)), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), slc.Stream([ slt.ImageSaltAndPepper(p=1, gain_range=0.05), slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), ]), slc.Stream([ slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), slt.ImageSaltAndPepper(p=1, gain_range=0.01), ]), slc.Stream() ], n=1), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), cutout if args.use_cutout else slc.Stream(), partial(solt2torchhm, downsample=None, sigma=None), ]) kvs.update('train_trf', ppl)
def test_stream_settings(): ppl = slc.Stream([ slt.RandomRotate((45, 45), interpolation='bicubic', padding='z', p=1), slt.RandomRotate((45, 45), padding='r', p=1), slt.RandomRotate((45, 45), interpolation='bicubic', padding='z', p=1), slt.RandomShear(0.1, 0.1, interpolation='bilinear', padding='z'), ], interpolation='nearest', padding='z') for trf in ppl.transforms: assert trf.interpolation[0] == 'nearest' assert trf.padding[0] == 'z'
def get_landmark_transform_kneel(config): cutout = slt.ImageCutOut(cutout_size=(int(config.dataset.cutout * config.dataset.augs.crop.crop_x), int(config.dataset.cutout * config.dataset.augs.crop.crop_y)), p=0.5) # plus-minus 1.3 pixels jitter = slt.KeypointsJitter(dx_range=(-0.003, 0.003), dy_range=(-0.003, 0.003)) ppl = transforms.Compose([ ColorPaddingWithSide(p=0.05, pad_size=10, side=SIDES.RANDOM, color=(50,100)), TriangularMask(p=0.025, arm_lengths=(100, 50), side=SIDES.RANDOM, color=(50,100)), TriangularMask(p=0.025, arm_lengths=(50, 100), side=SIDES.RANDOM, color=(50,100)), LowVisibilityTransform(p=0.05, alpha=0.15, bgcolor=(50,100)), SubSampleUpScale(p=0.01), jitter if config.dataset.augs.use_target_jitter else slc.Stream(), slc.SelectiveStream([ slc.Stream([ slt.RandomFlip(p=0.5, axis=1), slt.RandomProjection(affine_transforms=slc.Stream([ slt.RandomScale(range_x=(0.9, 1.1), p=1), slt.RandomRotate(rotation_range=(-90, 90), p=1), slt.RandomShear(range_x=(-0.1, 0.1), range_y=(-0.1, 0.1), p=0.5), ]), v_range=(1e-5, 2e-3), p=0.5), # slt.RandomScale(range_x=(0.5, 2.5), p=0.5), ]), slc.Stream() ], probs=[0.7, 0.3]), slc.Stream([ slt.PadTransform((config.dataset.augs.pad.pad_x, config.dataset.augs.pad.pad_y), padding='z'), slt.CropTransform((config.dataset.augs.crop.crop_x, config.dataset.augs.crop.crop_y), crop_mode='r'), ]), slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=1, blur_type='g', k_size=(3, 5)), slt.ImageBlur(p=1, blur_type='m', k_size=(3, 5)), slt.ImageAdditiveGaussianNoise(p=1, gain_range=0.5), slc.Stream([ slt.ImageSaltAndPepper(p=1, gain_range=0.05), slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), ]), slc.Stream([ slt.ImageBlur(p=0.5, blur_type='m', k_size=(3, 5)), slt.ImageSaltAndPepper(p=1, gain_range=0.01), ]), slc.Stream() ], n=1), slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)), cutout if config.dataset.use_cutout else slc.Stream(), partial(solt2torchhm, downsample=None, sigma=None), ]) return ppl
def test_random_proj_and_selective_stream(img_5x5): img = img_5x5 dc = sld.DataContainer((img, ), 'I') ppl = slt.RandomProjection(slc.SelectiveStream([ slt.RandomRotate(rotation_range=(90, 90), p=0), slt.RandomScale(range_y=(0, 0.1), same=True, p=0), slt.RandomShear(range_y=(-0.1, 0.1), p=0), ], n=3), v_range=(0, 0)) dc_res = ppl(dc) assert np.array_equal(dc.data, dc_res.data)
def custom_augment(img): tr = Compose([ wrap2solt, slc.Stream([ slt.ResizeTransform(resize_to=(32, 32), interpolation='bilinear'), slt.RandomScale(range_x=(0.9, 1.1), same=False, p=0.5), slt.RandomShear(range_x=(-0.05, 0.05), p=0.5), slt.RandomRotate(rotation_range=(-10, 10), p=0.5), # slt.RandomRotate(rotation_range=(-5, 5), p=0.5), slt.PadTransform(pad_to=36), slt.CropTransform(crop_size=32, crop_mode='r'), slt.ImageAdditiveGaussianNoise(p=1.0) ]), unpack_solt, ApplyTransform(norm_mean_std) ]) img_tr, _ = tr((img, 0)) return img_tr
def test_stream_settings_strict(): ppl = slc.Stream([ slt.RandomRotate((45, 45), interpolation='bicubic', padding='z', p=1), slt.RandomRotate((45, 45), padding='r', p=1), slt.RandomRotate((45, 45), interpolation=('bicubic', 'strict'), padding=('r', 'strict'), p=1), slt.RandomShear(0.1, 0.1, interpolation='bilinear', padding='z'), ], interpolation='nearest', padding='z') for idx, trf in enumerate(ppl.transforms): if idx == 2: assert trf.interpolation[0] == 'bicubic' assert trf.padding[0] == 'r' else: assert trf.interpolation[0] == 'nearest' assert trf.padding[0] == 'z'
def test_shear_range_none(): trf = slt.RandomShear(None, None) assert trf.shear_range_x == (0, 0) assert trf.shear_range_y == (0, 0)
def train_test_transforms(conf, mean=None, std=None): trf = conf['training'] prob = trf['transform_probability'] crop_size = tuple(trf['crop_size']) # Training transforms if trf['uCT']: train_transforms = [ slt.RandomProjection(slc.Stream([ slt.RandomRotate(rotation_range=tuple(trf['rotation_range']), p=prob), slt.RandomScale(range_x=tuple(trf['scale_range']), range_y=tuple(trf['scale_range']), same=False, p=prob), slt.RandomShear(range_x=tuple(trf['shear_range']), range_y=tuple(trf['shear_range']), p=prob), slt.RandomTranslate(range_x=trf['translation_range'], range_y=trf['translation_range'], p=prob) ]), v_range=tuple(trf['v_range'])), # Spatial slt.RandomFlip(p=prob), slt.PadTransform(pad_to=crop_size), slt.CropTransform(crop_mode='r', crop_size=crop_size), # Intensity #slt.ImageGammaCorrection(gamma_range=tuple(trf['gamma_range']), p=prob), #slt.ImageRandomHSV(h_range=tuple(trf['hsv_range']), # s_range=tuple(trf['hsv_range']), # v_range=tuple(trf['hsv_range']), p=prob), # Brightness/contrast slc.SelectiveStream([ slt.ImageRandomBrightness(brightness_range=tuple( trf['brightness_range']), p=prob), slt.ImageRandomContrast(contrast_range=trf['contrast_range'], p=prob) ]), # Noise slc.SelectiveStream([ slt.ImageSaltAndPepper(p=prob, gain_range=trf['gain_range_sp']), slt.ImageAdditiveGaussianNoise( p=prob, gain_range=trf['gain_range_gn']), slc.SelectiveStream([ slt.ImageBlur(p=prob, blur_type='g', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])), slt.ImageBlur(p=prob, blur_type='m', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])) ]) ]) ] else: train_transforms = [ # Projection slt.RandomProjection( slc.Stream([ slt.RandomRotate(rotation_range=tuple( trf['rotation_range']), p=prob), slt.RandomScale(range_x=tuple(trf['scale_range']), range_y=tuple(trf['scale_range']), same=False, p=prob), #slt.RandomShear(range_x=tuple(trf['shear_range']), # range_y=tuple(trf['shear_range']), p=prob), #slt.RandomTranslate(range_x=trf['translation_range'], range_y=trf['translation_range'], p=prob) ]), v_range=tuple(trf['v_range'])), # Spatial slt.RandomFlip(p=prob), slt.PadTransform(pad_to=crop_size[1]), slt.CropTransform(crop_mode='r', crop_size=crop_size), # Intensity # Add an empty stream #slc.SelectiveStream([]), slc.SelectiveStream([ slt.ImageGammaCorrection(gamma_range=tuple(trf['gamma_range']), p=prob), slt.ImageRandomHSV(h_range=tuple(trf['hsv_range']), s_range=tuple(trf['hsv_range']), v_range=tuple(trf['hsv_range']), p=prob) ]), slc.SelectiveStream([ slt.ImageRandomBrightness(brightness_range=tuple( trf['brightness_range']), p=prob), slt.ImageRandomContrast(contrast_range=trf['contrast_range'], p=prob) ]), slc.SelectiveStream([ slt.ImageSaltAndPepper(p=prob, gain_range=trf['gain_range_sp']), slt.ImageAdditiveGaussianNoise( p=prob, gain_range=trf['gain_range_gn']), slc.SelectiveStream([ slt.ImageBlur(p=prob, blur_type='g', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])), slt.ImageBlur(p=prob, blur_type='m', k_size=(3, 7, 11), gaussian_sigma=tuple(trf['sigma'])) ]) ]) ] train_trf = [ wrap_solt, #slc.Stream(train_transforms), slc.Stream([ slt.PadTransform(pad_to=crop_size[1]), slt.CropTransform(crop_mode='r', crop_size=crop_size) ]), unwrap_solt, ApplyTransform(numpy2tens, (0, 1, 2)) ] # Validation transforms val_trf = [ wrap_solt, slc.Stream([ slt.PadTransform(pad_to=crop_size[1]), slt.CropTransform(crop_mode='r', crop_size=crop_size) ]), unwrap_solt, ApplyTransform(numpy2tens, idx=(0, 1, 2)) ] # Test transforms test_trf = [unwrap_solt, ApplyTransform(numpy2tens, idx=(0, 1, 2))] # Use normalize_channel_wise if mean and std not calculated if mean is not None and std is not None: train_trf.append( ApplyTransform(partial(normalize_channel_wise, mean=mean, std=std))) if mean is not None and std is not None: val_trf.append( ApplyTransform(partial(normalize_channel_wise, mean=mean, std=std))) # Compose transforms train_trf_cmp = Compose(train_trf) val_trf_cmp = Compose(val_trf) test_trf_cmp = Compose(test_trf) return { 'train': train_trf_cmp, 'val': val_trf_cmp, 'test': test_trf_cmp, 'train_list': train_trf, 'val_list': val_trf, 'test_list': test_trf }
left_top = (bbox[0], bbox[3]) right_top = (bbox[2], bbox[3]) kpts = sld.KeyPoints( np.vstack((left_top, right_top, right_bottom, left_bottom)), entry['height'], entry['width']) print(kpts.data) pass dc = sld.DataContainer((data, kpts, 0), 'IPL') stream = slc.Stream([ slt.RandomProjection(slc.Stream([ slt.RandomScale(range_x=(0.8, 1.1), p=1), slt.RandomRotate(rotation_range=(-90, 90), p=1), slt.RandomShear(range_x=(-0.2, 0.2), range_y=None, p=0.7), ]), v_range=(1e-6, 3e-4), p=1), # Various cropping and padding tricks slt.PadTransform(1000, 'z'), slt.CropTransform(1000, crop_mode='c'), slt.CropTransform(950, crop_mode='r'), slt.PadTransform(1000, 'z'), # Intensity augmentations slt.ImageGammaCorrection(p=1, gamma_range=(0.5, 3)), slc.SelectiveStream([ slc.SelectiveStream([ slt.ImageSaltAndPepper(p=1, gain_range=0.01), slt.ImageBlur(p=0.5, blur_type='m', k_size=(11, )), ]),