def main():
parser = argparse.ArgumentParser(
description="Discrimination-aware channel pruning")
parser.add_argument('conf_path',
type=str,
metavar='conf_path',
help='configuration path')
parser.add_argument('--model_path',
type=str,
metavar='model_path',
help='model path of the pruned model')
parser.add_argument('--softmax-weight',
type=float,
metavar='softmax_weight',
help='weight of softmax loss in equation (6)')
parser.add_argument('--mse_weight',
type=float,
metavar='softmax_weight',
help='weight of softmax loss in equation (6)')
args = parser.parse_args()
option = Option(args.conf_path)
if args.model_path:
option.pretrained = args.model_path
if args.softmax_weight:
option.softmax_weight = args.softmax_weight
if args.mse_weight:
option.mse_weight = args.mse_weight
experiment = Experiment(option)
experiment.channel_selection_for_network()
def main():
parser = argparse.ArgumentParser(description="Experiments")
parser.add_argument('conf_path', type=str, metavar='conf_path',
help='configuration path')
parser.add_argument('--model_path', type=str, metavar='model_path',
help='model path of the pruned model')
args = parser.parse_args()
option = Option(args.conf_path)
if args.model_path:
option.pretrained = args.model_path
if args.softmax_weight:
option.softmax_weight = args.softmax_weight
if args.mse_weight:
option.mse_weight = args.mse_weight
experiment = Experiment(option)
# your job
job()
