-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain.py
170 lines (109 loc) · 5.75 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
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
import os
import config
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join([str(i) for i in config.args.gpu])
import time
import torch
import torch
import logging
from tqdm import tqdm
from os.path import join
from rich.progress import track
from collections import OrderedDict
import torch.optim as optim
import utils.logger as logger
import utils.metrics as metrics
from utils.build_model import *
from utils.Earlystopping import *
from utils.helper import get_device
from utils.helper import test_single_volume
from utils.torch_poly_lr_decay import PolynomialLRDecay
from datasets.data_loader import *
device = get_device()
if 'logout' not in os.listdir():
os.mkdir('logout')
logging.basicConfig(filename = join('logout', args.model + '_' + args.model_remark +'.log'),
level=logging.DEBUG,
format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s')
log = logging.getLogger(name="logger")
def train(args, model, train_loader, optimizer, criterion, epoch):
# print("=======Epoch:{}=======lr:{}".format(epoch, optimizer.state_dict()['param_groups'][0]['lr']))
model.train()
start_time = time.time()
train_loss = metrics.LossAverage()
train_dice = metrics.DiceAverage(n_classes=args.n_classes)
for idx, sample in track(enumerate(train_loader), total=len(train_loader), description='training'):
image, label = sample['image'], sample['label']
image, label = image.to(device), label.to(device)
output = model(image)
loss = 0
if args.loss_func == 'pdc':
loss = criterion(output, [label, sample['edge'].to(device)], epoch)
elif args.loss_func == 'DiceConsis' or args.loss_func == 'sat':
loss = criterion(output, label, epoch)
else:
loss = criterion(output, label)
optimizer.zero_grad()
# mixed precision training
# with amp.scale_loss(loss, optimizer) as scaled_loss:
# scaled_loss.backward()
loss.backward()
optimizer.step()
train_loss.update(loss.item(), image.size(0))
predict = output[0] if isinstance(output, list) else output
output = torch.round(predict)
train_dice.update(predict, label)
train_log = OrderedDict({'Train_Loss': train_loss.avg, 'Train_dice_vessel': train_dice.avg[0]})
time_elapsed = time.time() - start_time
log.info('Train time: {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Train time: {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
return train_log
def val(args, model, val_loader, criterion, epoch):
start_time = time.time()
val_loss = metrics.LossAverage()
val_dice = metrics.DiceAverage(n_classes=args.n_classes)
for idx, sample in track(enumerate(val_loader),total=len(val_loader), description='validation'):
image, label = sample['image'], sample['label']
PR = test_single_volume(image[0], model, args.batch_size * len(args.gpu), device, dsv=args.dsv, multi_loss=(args.model=='ATM_V9')).to(torch.float32)
GT = label[0].to(torch.float32)
assert len(PR.shape) == len(GT.shape) and PR.shape == GT.shape
loss = criterion(PR, GT)
val_loss.update(loss.item())
PR = torch.round(PR)
val_dice.update(PR, GT)
log.info('val dice per case: {}'.format(val_dice.get_dices(PR, GT)))
print('val dice per case: {}'.format(val_dice.get_dices(PR, GT)))
val_log = OrderedDict({'Val_Loss': val_loss.avg, 'Val_dice_vessel': val_dice.avg[0]})
time_elapsed = time.time() - start_time
log.info("Val time: {:.0f}m {:.0f}s".format(time_elapsed // 60, time_elapsed % 60))
print('Val time: {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
return val_log
if __name__ == '__main__':
args = config.args
train_loader, val_loader = get_data_loader(args)
model = get_model(args, mode='train', device=device, device_ids=[i for i in range(len(args.gpu))])
# optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-5)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=0.0001)
# 加入混合精度训练
# model, optimizer = amp.initialize(model, optimizer, opt_level="O1")
scheduler = PolynomialLRDecay(optimizer, max_decay_steps=args.epoch, end_learning_rate=0.0, power=0.9)
# scheduler = StepLR(optimizer, step_size=50, gamma=0.5)
criterion = get_criterion(args, device=device)
early_stopping = EarlyStopping(args=args, patience=args.patience, verbose=True, model_name=args.model)
train_logger = logger.Train_Logger(args, "_train_log")
for epoch in tqdm(range(args.begin_epoch, args.epoch+1)):
train_log = train(args, model, train_loader, optimizer, criterion, epoch)
val_log = val(args, model, val_loader, criterion, epoch)
train_logger.update(epoch, train_log, val_log)
if scheduler is not None:
scheduler.step()
if args.sample_slices >3 and epoch == 50:
for param in model.parameters():
param.requires_grad =True
# print("Epoch: [{}]\nTrain: {} \nValid: {}".format(epoch, train_log, val_log))
log.info("\nEpoch: [{}]\nTrain: {} \nValid: {} \nlearning rate:{} ".format\
(epoch, train_log, val_log, optimizer.state_dict()['param_groups'][0]['lr']))
print("\nEpoch: [{}]\nTrain: {} \nValid: {} \nlearning rate:{} ".format\
(epoch, train_log, val_log, optimizer.state_dict()['param_groups'][0]['lr']))
early_stopping(val_log['Val_Loss'], val_log['Val_dice_vessel'], model, epoch, log)
log.info('\n' * 3)
print('\n' * 5)