def main(*args):
flags = tf.flags.FLAGS
opt = Config()
for key in flags:
opt.setdefault(key, flags.get_flag_value(key, None))
check_args(opt)
data_config_file = Path(opt.data_config)
if not data_config_file.exists():
raise RuntimeError("dataset config file doesn't exist!")
for _suffix in ('json', 'yaml'): # for compatibility
# apply a 2-stage (or master-slave) configuration, master can be
# override by slave
model_config_root = Path(f'parameters/root.{_suffix}')
if opt.p:
model_config_file = Path(opt.p)
else:
model_config_file = Path(f'parameters/{opt.model}.{_suffix}')
if model_config_root.exists():
opt.update(Config(str(model_config_root)))
if model_config_file.exists():
opt.update(Config(str(model_config_file)))
model_params = opt.get(opt.model)
opt.update(model_params)
model = get_model(opt.model)(**model_params)
root = '{}/{}'.format(opt.save_dir, model.name)
if opt.comment:
root += '_' + opt.comment
opt.root = root
verbosity = tf.logging.DEBUG if opt.v else tf.logging.INFO
# map model to trainer, ~~manually~~ automatically, by setting `_trainer`
# attribute in models
trainer = model.trainer
train_data, test_data, infer_data = fetch_datasets(data_config_file, opt)
train_config, test_config, infer_config = init_loader_config(opt)
test_config.subdir = test_data.name
infer_config.subdir = 'infer'
# start fitting!
dump(opt)
with trainer(model, root, verbosity) as t:
# prepare loader
loader = partial(QuickLoader, n_threads=opt.threads)
train_loader = loader(train_data, 'train', train_config,
augmentation=True)
val_loader = loader(train_data, 'val', train_config, crop='center',
steps_per_epoch=1)
test_loader = loader(test_data, 'test', test_config)
infer_loader = loader(infer_data, 'infer', infer_config)
# fit
t.fit([train_loader, val_loader], train_config)
# validate
t.benchmark(test_loader, test_config)
# do inference
t.infer(infer_loader, infer_config)
if opt.export:
t.export(opt.root + '/exported', opt.freeze)
def main(*args):
flags = tf.flags.FLAGS
flags.mark_as_parsed()
opt = Config()
for key in flags:
opt.setdefault(key, flags.get_flag_value(key, None))
check_args(opt)
data_config_file = Path(opt.data_config)
if not data_config_file.exists():
raise RuntimeError("dataset config file doesn't exist!")
for _suffix in ('json', 'yaml'):
# apply a 2-stage (or master-slave) configuration, master can be override by slave
model_config_root = Path('parameters/{}.{}'.format('root', _suffix))
model_config_file = Path('parameters/{}.{}'.format(opt.model, _suffix))
if model_config_root.exists():
opt.update(Config(str(model_config_root)))
if model_config_file.exists():
opt.update(Config(str(model_config_file)))
model_params = opt.get(opt.model)
opt.update(model_params)
model = get_model(opt.model)(**model_params)
root = '{}/{}_sc{}_c{}'.format(opt.save_dir, model.name, opt.scale, opt.channel)
if opt.comment:
root += '_' + opt.comment
opt.root = root
verbosity = tf.logging.DEBUG if opt.v else tf.logging.INFO
# map model to trainer, manually
if opt.model == 'zssr':
trainer = ZSSR
elif opt.model == 'frvsr':
trainer = FRVSR
else:
trainer = VSR
train_data, test_data, infer_data = fetch_datasets(data_config_file, opt)
train_config, test_config, infer_config = init_loader_config(opt)
test_config.subdir = test_data.name
# start fitting!
with trainer(model, root, verbosity) as t:
# prepare loader
loader = partial(QuickLoader, n_threads=opt.threads)
train_loader = loader(train_data, 'train', train_config, augmentation=True)
val_loader = loader(train_data, 'val', train_config, augmentation=True, crop='center', steps_per_epoch=1)
test_loader = loader(test_data, 'test', test_config)
infer_loader = loader(infer_data, 'infer', infer_config)
# fit
t.fit([train_loader, val_loader], train_config)
# validate
t.benchmark(test_loader, test_config)
# do inference
t.infer(infer_loader, infer_config)
if opt.export:
t.export(opt.root)