DCRNN/dcrnn_train_pytorch.py

31 lines
972 B
Python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
def main(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config['data'].get('graph_pkl_filename')
sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(graph_pkl_filename)
supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
supervisor.train()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config_filename', default=None, type=str,
help='Configuration filename for restoring the model.')
parser.add_argument('--use_cpu_only', default=False, type=bool, help='Set to true to only use cpu.')
args = parser.parse_args()
main(args)