def main():
args = parse_args()
args.seed = init_rand(seed=args.seed)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
logger.set_logger_dir(args.save_dir)
batch_size = prepare_tf_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
classes = 1000
net, inputs_desc = prepare_model(
model_name=args.model,
classes=classes,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip())
train_dataflow = get_data(is_train=True,
batch_size=batch_size,
data_dir_path=args.data_dir)
val_dataflow = get_data(is_train=False,
batch_size=batch_size,
data_dir_path=args.data_dir)
train_net(net=net,
session_init=inputs_desc,
batch_size=batch_size,
num_epochs=args.num_epochs,
train_dataflow=train_dataflow,
val_dataflow=val_dataflow)
def main():
args = parse_args()
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
batch_size = prepare_tf_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
classes = 1000
net, inputs_desc = prepare_model(
model_name=args.model,
classes=classes,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip())
val_dataflow = get_data(is_train=False,
batch_size=batch_size,
data_dir_path=args.data_dir)
assert (args.use_pretrained or args.resume.strip())
test(net=net,
session_init=inputs_desc,
val_dataflow=val_dataflow,
do_calc_flops=args.calc_flops,
extended_log=True)
