argparser = argparse.ArgumentParser(
description='train yolo-v3 network')
argparser.add_argument(
'-c',
'--config',
default="configs/svhn.json",
help='config file')
if __name__ == '__main__':
args = argparser.parse_args()
config_parser = ConfigParser(args.config)
# 1. create generator
train_generator, valid_generator = config_parser.create_generator()
# 2. create model
model = config_parser.create_model()
# 3. training
learning_rate, save_dname, n_epoches = config_parser.get_train_params()
train_fn(model,
train_generator,
valid_generator,
learning_rate=learning_rate,
save_dname=save_dname,
num_epoches=n_epoches)
argparser = argparse.ArgumentParser(description='train yolo-v3 network')
argparser.add_argument('-c',
'--config',
default="configs/test.json",
help='config file')
if __name__ == '__main__':
args = argparser.parse_args()
# config = './configs/svhn.json'
config = args.config
config_parser = ConfigParser(config)
# 1. create generator
split_train_valid = config_parser.split_train_val()
train_generator, valid_generator = config_parser.create_generator(
split_train_valid=split_train_valid)
# 2. create model
model = config_parser.create_model()
# 3. training
learning_rate, save_dir, weight_name, n_epoches, checkpoint_path = config_parser.get_train_params(
)
train_fn(model,
train_generator,
valid_generator,
learning_rate=learning_rate,
save_dir=save_dir,
weight_name=weight_name,
num_epoches=n_epoches,
configs=config_parser)
