import yaml
from utils import Parser, Trainer
from utils import save_data
if __name__ == '__main__':
parser = Parser()
parser.create_parser()
pargs = parser.parser.parse_args()
if pargs.config is not None:
with open(pargs.config, 'r') as f:
default_arg = yaml.load(f, Loader=yaml.FullLoader)
key = vars(pargs).keys()
for k in default_arg.keys():
if k not in key:
print('WRONG ARG: {}'.format(k))
assert (k in key)
parser.parser.set_defaults(**default_arg)
args = parser.parser.parse_args()
mode = args.mode
if 'data_preprocess' == mode:
save_data(args.data_preprocess_args['window_size'],
args.data_preprocess_args['overlap'],
args.data_preprocess_args['label_path'])
elif 'train' == mode:
trainer = Trainer(args.train_args)
trainer.train()
else:
pass
elif self.args.phase == 'test':
if self.args.weights is None:
raise ValueError('Please appoint --weights.')
self.args.print_log = False
self.print_log('Model: {}.'.format(self.args.model))
self.print_log('Weights: {}.'.format(self.args.weights))
self.eval(
epoch=0, save_score=self.args.save_score)
self.print_log('Done.\n')
if __name__ == '__main__':
p = Parser()
p.create_parser()
pargs = p.parser.parse_args()
if pargs.config is not None:
with open(pargs.config, 'r') as f:
default_arg = yaml.load(f, Loader=yaml.FullLoader)
key = vars(pargs).keys()
for k in default_arg.keys():
if k not in key:
print('WRONG ARG: {}'.format(k))
assert (k in key)
p.parser.set_defaults(**default_arg)
args = p.parser.parse_args()
p.dump_args(args, args.work_dir)
