-
Notifications
You must be signed in to change notification settings - Fork 2
/
main.py
75 lines (58 loc) · 2.74 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
74
75
import torch
import argparse
from model import A2NN
from dataset import Traffic_Light
from utils import get_train_val_names, check_folder
from trainer import Trainer
from validator import Validator
from logger import Logger
from torch.utils.data import DataLoader
def main():
parse = argparse.ArgumentParser()
parse.add_argument('--dataset_path', type=str, default='TL_Dataset/')
parse.add_argument('--remove_names', type=list, default=['README.txt',
'README.png',
'Testset'])
parse.add_argument('--img_resize_shape', type=tuple, default=(32, 32))
parse.add_argument('--batch_size', type=int, default=1024)
parse.add_argument('--lr', type=float, default=0.001)
parse.add_argument('--num_workers', type=int, default=4)
parse.add_argument('--epochs', type=int, default=200)
parse.add_argument('--val_size', type=float, default=0.3)
parse.add_argument('--save_model', type=bool, default=True)
parse.add_argument('--save_path', type=str, default='logs/')
args = vars(parse.parse_args())
check_folder(args['save_path'])
# pylint: disable=E1101
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# pylint: disable=E1101
model = A2NN().to(device)
names = get_train_val_names(args['dataset_path'], args['remove_names'])
train_dataset = Traffic_Light(names['train'], args['img_resize_shape'])
val_dataset = Traffic_Light(names['val'], args['img_resize_shape'])
train_dataload = DataLoader(train_dataset,
batch_size=args['batch_size'],
shuffle=True,
num_workers=args['num_workers'])
val_dataload = DataLoader(val_dataset,
batch_size=args['batch_size'],
shuffle=True,
num_workers=args['num_workers'])
loss_logger = Logger(args['save_path'])
logger_dict = {'train_losses': [],
'val_losses': []}
for epoch in range(args['epochs']):
print('<Main> epoch{}'.format(epoch))
trainer = Trainer(model, train_dataload, epoch, args['lr'], device)
train_loss = trainer.train()
if args['save_model']:
state = model.state_dict()
torch.save(state, 'logs/nn_state.t7')
validator = Validator(model, val_dataload, epoch,
device, args['batch_size'])
val_loss = validator.eval()
logger_dict['train_losses'].append(train_loss)
logger_dict['val_losses'].append(val_loss['val_loss'])
loss_logger.update(logger_dict)
if __name__ == '__main__':
main()