-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
73 lines (57 loc) · 2.6 KB
/
main.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
import argparse
import datetime
import logging
import re
import os
import torch
import torch.nn as nn
from model import pretrained
from train import train
from eval import test
def parse():
parser = argparse.ArgumentParser()
parser.add_argument('--batch-size', type=int, default=2)
parser.add_argument('--block-size', type=int, default=128)
parser.add_argument('--pretrained-model', type=str, default='bert')
parser.add_argument('--pretrained-weights', type=str, default=None)
parser.add_argument('--pretrained-hidden-size', type=int, default=768)
parser.add_argument('--pretrained-heads', type=int, default=12)
parser.add_argument('--dont-freeze', action='store_true')
parser.add_argument('--epochs', type=int, default=1000)
parser.add_argument('--no-gpu', action='store_true')
parser.add_argument('--model-parallel', action='store_true')
parser.add_argument('--liar-dataset-dir', type=str, default='')
parser.add_argument('--save-file', type=str, default='model.pt')
parser.add_argument('--dont-save', action='store_true')
parser.add_argument('--logger-name', default=None)
parser.add_argument('--dont-log', action='store_true')
parser.add_argument('--dont-load-model-from-file', action='store_true')
parser.add_argument('--test', action='store_true')
return parser.parse_args()
def main():
args = parse()
if args.logger_name is not None:
logger_name = args.logger_name
else:
logger_name = datetime.datetime.now().isoformat()
if not args.dont_log:
logging.getLogger(logger_name)
logger_filename = re.subn('\\D+', '', logger_name)[0] + '.log'
logging.basicConfig(filename=logger_filename, level=logging.DEBUG)
save_file = None if args.dont_save else args.save_file
root = args.liar_dataset_dir
tokenizer, model = pretrained(model=args.pretrained_model, weights=args.pretrained_weights,
freeze=not args.dont_freeze)
if not args.dont_load_model_from_file and os.path.exists(args.save_file):
model.load_state_dict(torch.load(args.save_file))
device = torch.device("cuda:0" if torch.cuda.is_available() and not args.no_gpu else "cpu")
print('using', device)
model = model.to(device)
if torch.cuda.device_count() > 1 and args.model_parallel:
model = nn.DataParallel(model)
if args.test:
test(root, model, tokenizer, batch_size=args.batch_size, device=device)
else:
train(root, model, tokenizer, epochs=args.epochs, batch_size=args.batch_size, save_file=save_file, device=device)
if __name__ == '__main__':
main()