def from_config(config):
config = deepcopy(config)
files_a, files_b = map(
lambda x: sorted(glob(config[x], recursive=True)),
('files_a', 'files_b'))
transform_fn = aug.get_transforms(size=config['size'],
scope=config['scope'],
crop=config['crop'])
normalize_fn = aug.get_normalize()
corrupt_fn = aug.get_corrupt_function(config['corrupt'])
hash_fn = hash_from_paths
# ToDo: add more hash functions
verbose = config.get('verbose', True)
data = subsample(data=zip(files_a, files_b),
bounds=config.get('bounds', (0, 1)),
hash_fn=hash_fn,
verbose=verbose)
files_a, files_b = map(list, zip(*data))
return PairedDataset(files_a=files_a,
files_b=files_b,
preload=config['preload'],
preload_size=config['preload_size'],
corrupt_fn=corrupt_fn,
normalize_fn=normalize_fn,
transform_fn=transform_fn,
verbose=verbose)
def test_aug(self):
for scope in ('geometric', 'weak'):
for crop in ('random', 'center'):
aug_pipeline = get_transforms(80, scope=scope, crop=crop)
a, b = self.make_images()
a, b = aug_pipeline(a, b)
np.testing.assert_allclose(a, b)
def main(config_path='config/config.yaml'):
with open(config_path, 'r') as f:
config = yaml.load(f, Loader=yaml.SafeLoader)
batch_size = config.pop('batch_size')
get_dataloader = partial(
DataLoader,
batch_size=batch_size,
num_workers=0 if os.environ.get('DEBUG') else cpu_count(),
shuffle=True,
drop_last=True)
datasets = (NoPairDataset(
"datasets/tilt", get_transforms(256),
get_corrupt_function(config=config['train']['corrupt'])),
NoPairDataset("datasets/tilt", get_transforms(256)))
train, val = map(get_dataloader, datasets)
trainer = Trainer(config, train=train, val=val)
trainer.train()
def from_config(config):
config = deepcopy(config)
files = glob(
f'{config.get("data_dir", "/home/arseny/datasets/idrnd_train")}/**/*.png',
recursive=True)
transform_fn = aug.get_transforms(size=config['size'],
scope=config['scope'],
crop=config['crop'])
normalize_fn = aug.get_normalize()
corrupt_fn = aug.get_corrupt_function(config['corrupt'])
soften_fn = create_soften_fn(config.get('soften', 1))
def hash_fn(x: str, salt: str = '') -> str:
x = os.path.basename(x)
label, video, frame = x.split('_')
return sha1(f'{label}_{video}_{salt}'.encode()).hexdigest()
verbose = config.get('verbose', True)
n_fold = config['n_fold']
total_folds = 10
test = subsample(data=files,
bounds=(1 / total_folds * n_fold,
1 / total_folds * (n_fold + 1)),
hash_fn=hash_fn,
verbose=verbose,
salt='validation')
if config['test']:
data = test
else:
files = set(files) - set(test)
data = subsample(data=files,
bounds=config.get('bounds', (0, 1)),
hash_fn=hash_fn,
verbose=verbose,
salt=config['salt'])
return IdRndDataset(files=tuple(data),
preload=config['preload'],
preload_size=config['preload_size'],
corrupt_fn=corrupt_fn,
normalize_fn=normalize_fn,
transform_fn=transform_fn,
soften_fn=soften_fn,
mixup=config['mixup'],
verbose=verbose)
def from_config(config):
config = deepcopy(config)
img_path = config.get('img_dir')
labels_path = config.get('labels_path')
imgs, labels = zip(*parse_labels(img_path, labels_path))
transform_fn = aug.get_transforms(size=config['size'],
crop=config['crop'])
normalize_fn = aug.get_normalize()
corrupt_fn = aug.get_corrupt_function(config['corrupt'])
verbose = config.get('verbose', True)
return TigerDataset(imgs=imgs,
labels=labels,
size=config['size'],
corrupt_fn=corrupt_fn,
normalize_fn=normalize_fn,
transform_fn=transform_fn,
verbose=verbose)
def from_config(config):
config = deepcopy(config)
files_a, files_b = [], []
if config['phase'] == "train":
files_a += glob("data/TRAIN/GOPRO_Large/*/blur/*.png")
files_b += glob("data/TRAIN/GOPRO_Large/*/sharp/*.png")
files_a += glob("data/TRAIN/GOPRO_Large/*/blur_gamma/*.png")
files_b += glob("data/TRAIN/GOPRO_Large/*/sharp/*.png")
files_a += glob("data/TRAIN/quantitative_datasets/*/input/*.jpg")
files_b += glob("data/TRAIN/quantitative_datasets/*/GT/*.jpg")
files_a += glob("data/TRAIN/REDS_train/train_blur/*/*.png")
files_b += glob("data/TRAIN/REDS_train/train_sharp/*/*.png")
for i in range(4):
files_a += sorted(
glob(
"data/TRAIN/synthetic_dataset/uniform/*0{}.png".format(
i + 1)))
files_b += sorted(
glob("data/TRAIN/synthetic_dataset/ground_truth/*.png"))
files_a += sorted(
glob("data/TRAIN/synthetic_dataset/nonuniform/*0{}.png".
format(i + 1)))
files_b += sorted(
glob("data/TRAIN/synthetic_dataset/ground_truth/*.png"))
elif config['phase'] == "val":
files_a += glob("data/TEST/GOPRO_Large/*/blur/*.png")
files_b += glob("data/TEST/GOPRO_Large/*/sharp/*.png")
# files_a += glob("data/TEST/GOPRO_Large/*/blur_gamma/*.png")
# files_b += glob("data/TEST/GOPRO_Large/*/sharp/*.png")
# files_a, files_b = map(lambda x: sorted(glob(config[x], recursive=True)), ('files_a', 'files_b'))
transform_fn = aug.get_transforms(size=config['size'],
scope=config['scope'],
crop=config['crop'])
normalize_fn = aug.get_normalize()
corrupt_fn = aug.get_corrupt_function(config['corrupt'])
hash_fn = hash_from_paths
# ToDo: add more hash functions
verbose = config.get('verbose', True)
data = subsample(data=zip(files_a, files_b),
bounds=config.get('bounds', (0, 1)),
hash_fn=hash_fn,
verbose=verbose)
files_a, files_b = map(list, zip(*data))
return PairedDataset(files_a=files_a,
files_b=files_b,
preload=config['preload'],
preload_size=config['preload_size'],
corrupt_fn=corrupt_fn,
normalize_fn=normalize_fn,
transform_fn=transform_fn,
verbose=verbose)
