def main(config):
from torch.backends import cudnn
# For fast training
cudnn.benchmark = True
data_loader = get_loader(
config.mode_data,
config.image_size,
config.batch_size,
config.dataset_fake,
config.mode,
num_workers=config.num_workers,
all_attr=config.ALL_ATTR,
c_dim=config.c_dim)
from misc.scores import set_score
if set_score(config):
return
if config.mode == 'train':
from train import Train
Train(config, data_loader)
from test import Test
test = Test(config, data_loader)
test(dataset=config.dataset_real)
elif config.mode == 'test':
from test import Test
test = Test(config, data_loader)
if config.DEMO_PATH:
test.DEMO(config.DEMO_PATH)
else:
test(dataset=config.dataset_real)
