def init_data_processing():
kvs = GlobalKVS()
train_augs = init_train_augs()
dataset = OAProgressionDataset(dataset=kvs['args'].dataset_root, split=kvs['metadata'], trf=train_augs)
mean_vector, std_vector = init_mean_std(snapshots_dir=kvs['args'].snapshots,
dataset=dataset, batch_size=kvs['args'].bs,
n_threads=kvs['args'].n_threads)
print(colored('====> ', 'red') + 'Mean:', mean_vector)
print(colored('====> ', 'red') + 'Std:', std_vector)
norm_trf = tv_transforms.Normalize(mean_vector.tolist(), std_vector.tolist())
train_trf = tv_transforms.Compose([
train_augs,
partial(apply_by_index, transform=norm_trf, idx=0)
])
val_trf = tv_transforms.Compose([
img_labels2solt,
slc.Stream([
slt.ResizeTransform((310, 310)),
slt.CropTransform(crop_size=(300, 300), crop_mode='c'),
slt.ImageColorTransform(mode='gs2rgb'),
], interpolation='bicubic'),
unpack_solt_data,
partial(apply_by_index, transform=tv_transforms.ToTensor(), idx=0),
partial(apply_by_index, transform=norm_trf, idx=0)
])
kvs.update('train_trf', train_trf)
kvs.update('val_trf', val_trf)
kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['snapshot_name'], 'session.pkl'))
def init_loader(metadata, args, snapshots_root):
mean_vector, std_vector = session.init_mean_std(snapshots_root, None, None, None)
norm_trf = tv_transforms.Normalize(mean_vector.tolist(), std_vector.tolist())
tta_trf = tv_transforms.Compose([
img_labels2solt,
slc.Stream([
slt.PadTransform(pad_to=(700, 700), padding='z'),
slt.CropTransform(crop_size=(700, 700), crop_mode='c'),
slt.ResizeTransform(resize_to=(310, 310), interpolation='bicubic'),
slt.ImageColorTransform(mode='gs2rgb'),
], interpolation='bicubic'),
unpack_solt_data,
partial(apply_by_index, transform=tv_transforms.ToTensor(), idx=0),
partial(apply_by_index, transform=norm_trf, idx=0),
partial(apply_by_index, transform=partial(five_crop, size=300), idx=0),
])
dataset = OAProgressionDataset(dataset=args.dataset_root,
split=metadata, trf=tta_trf)
loader = DataLoader(dataset,
batch_size=args.bs,
sampler=SequentialSampler(dataset),
num_workers=args.n_threads)
return loader
def init_train_augs():
trf = transforms.Compose([
img_labels2solt,
slc.Stream([
slt.PadTransform(pad_to=(700, 700)),
slt.CropTransform(crop_size=(700, 700), crop_mode='c'),
slt.ResizeTransform((310, 310)),
slt.ImageAdditiveGaussianNoise(p=0.5, gain_range=0.3),
slt.RandomRotate(p=1, rotation_range=(-10, 10)),
slt.CropTransform(crop_size=(300, 300), crop_mode='r'),
slt.ImageGammaCorrection(p=0.5, gamma_range=(0.5, 1.5)),
slt.ImageColorTransform(mode='gs2rgb')
], interpolation='bicubic', padding='z'),
unpack_solt_data,
partial(apply_by_index, transform=transforms.ToTensor(), idx=0),
])
return trf
def test_image_color_conversion_raises_error(mode, mask_3x4):
trf = slt.ImageColorTransform(mode=mode)
dc = sld.DataContainer(mask_3x4.squeeze(), 'I')
with pytest.raises(ValueError):
trf(dc)
def test_hsv_returns_expected_results(mode, img, expected):
trf = slt.ImageColorTransform(mode=mode)
dc = sld.DataContainer(img, 'I')
dc_res = trf(dc)
np.testing.assert_array_equal(expected, dc_res.data[0])
def __init__(self):
self.augmentor_op = Operations.Greyscale(probability=1)
self.imgaug_transform = iaa.Grayscale(alpha=1.0)
self.solt_stream = slc.Stream([slt.ImageColorTransform(mode="rgb2gs")])
