Spaces:
Runtime error
Runtime error
File size: 2,727 Bytes
2bd24e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
import argparse
import os
import torch
from train_helper import Trainer
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
def parse_args():
parser = argparse.ArgumentParser(description='Train')
parser.add_argument('--data-dir', default='data/UCF-Train-Val-Test', help='data path')
parser.add_argument('--dataset', default='qnrf', help='dataset name: qnrf, nwpu, sha, shb')
parser.add_argument('--lr', type=float, default=1e-5,
help='the initial learning rate')
parser.add_argument('--weight-decay', type=float, default=1e-4,
help='the weight decay')
parser.add_argument('--resume', default='', type=str,
help='the path of resume training model')
parser.add_argument('--max-epoch', type=int, default=1000,
help='max training epoch')
parser.add_argument('--val-epoch', type=int, default=5,
help='the num of steps to log training information')
parser.add_argument('--val-start', type=int, default=50,
help='the epoch start to val')
parser.add_argument('--batch-size', type=int, default=10,
help='train batch size')
parser.add_argument('--device', default='0', help='assign device')
parser.add_argument('--num-workers', type=int, default=3,
help='the num of training process')
parser.add_argument('--crop-size', type=int, default=512,
help='the crop size of the train image')
parser.add_argument('--wot', type=float, default=0.1, help='weight on OT loss')
parser.add_argument('--wtv', type=float, default=0.01, help='weight on TV loss')
parser.add_argument('--reg', type=float, default=10.0,
help='entropy regularization in sinkhorn')
parser.add_argument('--num-of-iter-in-ot', type=int, default=100,
help='sinkhorn iterations')
parser.add_argument('--norm-cood', type=int, default=0, help='whether to norm cood when computing distance')
args = parser.parse_args()
if args.dataset.lower() == 'qnrf':
args.crop_size = 512
elif args.dataset.lower() == 'nwpu':
args.crop_size = 384
args.val_epoch = 50
elif args.dataset.lower() == 'sha':
args.crop_size = 256
elif args.dataset.lower() == 'shb':
args.crop_size = 512
else:
raise NotImplementedError
return args
if __name__ == '__main__':
args = parse_args()
torch.backends.cudnn.benchmark = True
os.environ['CUDA_VISIBLE_DEVICES'] = args.device.strip() # set vis gpu
trainer = Trainer(args)
trainer.setup()
trainer.train()
|