forked from rockeyben/DCCF
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
75 lines (51 loc) · 2.83 KB
/
train.py
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
66
67
68
69
70
71
72
73
74
75
import argparse
import importlib.util
import torch
from iharm.utils.exp import init_experiment
import warnings
warnings.filterwarnings("ignore")
def main():
args = parse_args()
model_script = load_module(args.model_path)
cfg = init_experiment(args)
torch.backends.cudnn.benchmark = True
#torch.multiprocessing.set_start_method('spawn', force=True)
#torch.multiprocessing.set_sharing_strategy('file_system')
model_script.main(cfg)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('model_path', type=str,
help='Path to the model script.')
parser.add_argument('--exp-name', type=str, default='',
help='Here you can specify the name of the experiment. '
'It will be added as a suffix to the experiment folder.')
parser.add_argument('--workers', type=int, default=4,
metavar='N', help='Dataloader threads.')
parser.add_argument('--local_rank', type=int, default=0,
metavar='N', help='Dataloader threads.')
parser.add_argument('--batch-size', type=int, default=-1,
help='You can override model batch size by specify positive number.')
parser.add_argument('--ngpus', type=int, default=1,
help='Number of GPUs. '
'If you only specify "--gpus" argument, the ngpus value will be calculated automatically. '
'You should use either this argument or "--gpus".')
parser.add_argument('--gpus', type=str, default='', required=False,
help='Ids of used GPUs. You should use either this argument or "--ngpus".')
parser.add_argument('--resume-exp', type=str, default=None,
help='The prefix of the name of the experiment to be continued. '
'If you use this field, you must specify the "--resume-prefix" argument.')
parser.add_argument('--resume-prefix', type=str, default='latest',
help='The prefix of the name of the checkpoint to be loaded.')
parser.add_argument('--start-epoch', type=int, default=0,
help='The number of the starting epoch from which training will continue. '
'(it is important for correct logging and learning rate)')
parser.add_argument('--weights', type=str, default=None,
help='Model weights will be loaded from the specified path if you use this argument.')
return parser.parse_args()
def load_module(script_path):
spec = importlib.util.spec_from_file_location("model_script", script_path)
model_script = importlib.util.module_from_spec(spec)
spec.loader.exec_module(model_script)
return model_script
if __name__ == '__main__':
main()