import sys
import torch
from torch.backends import cudnn
from processor.processor import Processor
torch.backends.cudnn.deterministic = False
cudnn.benchmark = True # https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936
torch.cuda.empty_cache() # release cache
if __name__ == '__main__':
proc = Processor(sys.argv[1:])
proc.start()
parser.add_argument('--num_of_vertices',
type=int,
default=358,
help='The number of vertices')
parser.add_argument('--gen_config_args',
type=dict,
default=dict(),
help='The config of data generate')
return parser
if __name__ == '__main__':
parser = get_parser()
# load arg form config file
p = parser.parse_args()
if p.config is not None:
with open(p.config, 'r') as f:
default_arg = yaml.load(f)
key = vars(p).keys()
for k in default_arg.keys():
if k not in key:
print('WRONG ARG: {}'.format(k))
assert (k in key)
parser.set_defaults(**default_arg)
arg = parser.parse_args()
init_seed(0)
processor = Processor(arg)
processor.start()