-
Notifications
You must be signed in to change notification settings - Fork 15
/
Copy pathtrain.py
153 lines (135 loc) · 6.41 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
import datetime
import os
import torch
from torch import nn
from torch import optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
import joint_transforms
from config import train_data
from datasets import ImageFolder
from misc import AvgMeter, check_mkdir
from model_GateNet_ResNet import GateNet,Bottleneck
from torch.backends import cudnn
from torch.utils import model_zoo
import torch.nn.functional as functional
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
# vis = visdom.Visdom(env='train')
cudnn.benchmark = True
torch.manual_seed(2018)
torch.cuda.set_device(0)
##########################hyperparameters###############################
ckpt_path = './model'
exp_name = 'model_gatenet'
args = {
'iter_num': 100000,
'train_batch_size': 4,
'last_iter': 0,
'lr': 1e-3,
'lr_decay': 0.9,
'weight_decay': 0.0005,
'momentum': 0.9,
'snapshot': ''
}
##########################data augmentation###############################
joint_transform = joint_transforms.Compose([
joint_transforms.RandomCrop(384, 384), # change to resize
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomRotate(10)
])
img_transform = transforms.Compose([
transforms.ColorJitter(0.1, 0.1, 0.1),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
target_transform = transforms.ToTensor()
##########################################################################
train_set = ImageFolder(train_data, joint_transform, img_transform, target_transform)
train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=0, shuffle=True)
###multi-scale-train
# train_set = ImageFolder_multi_scale(train_data, joint_transform, img_transform, target_transform)
# train_loader = DataLoader(train_set, collate_fn=train_set.collate, batch_size=args['train_batch_size'], num_workers=12, shuffle=True, drop_last=True)
criterion = nn.BCEWithLogitsLoss().cuda()
criterion_BCE = nn.BCELoss().cuda()
criterion_mse = nn.MSELoss().cuda()
criterion_mae = nn.L1Loss().cuda()
log_path = os.path.join(ckpt_path, exp_name, str(datetime.datetime.now()) + '.txt')
def main():
#############################ResNet pretrained###########################
#res18[2,2,2,2],res34[3,4,6,3],res50[3,4,6,3],res101[3,4,23,3],res152[3,8,36,3]
model = GateNet(Bottleneck, [3,4,6,3])
pretrained_dict = model_zoo.load_url(model_urls['resnet50'])
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
##############################Optim setting###############################
# model = GateNet()
net = model.cuda().train()
optimizer = optim.SGD([
{'params': [param for name, param in net.named_parameters() if name[-4:] == 'bias'],
'lr': 2 * args['lr']},
{'params': [param for name, param in net.named_parameters() if name[-4:] != 'bias'],
'lr': args['lr'], 'weight_decay': args['weight_decay']}
], momentum=args['momentum'])
if len(args['snapshot']) > 0:
print('training resumes from ' + args['snapshot'])
net.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, args['snapshot'] + '.pth')))
optimizer.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, args['snapshot'] + '_optim.pth')))
optimizer.param_groups[0]['lr'] = 2 * args['lr']
optimizer.param_groups[1]['lr'] = args['lr']
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
open(log_path, 'w').write(str(args) + '\n\n')
train(net, optimizer)
#########################################################################
def train(net, optimizer):
curr_iter = args['last_iter']
while True:
total_loss_record, loss1_record, loss2_record, loss3_record, loss4_record, loss5_record, loss6_record, loss7_record, loss8_record = AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter()
for i, data in enumerate(train_loader):
optimizer.param_groups[0]['lr'] = 2 * args['lr'] * (1 - float(curr_iter) / args['iter_num']
) ** args['lr_decay']
optimizer.param_groups[1]['lr'] = args['lr'] * (1 - float(curr_iter) / args['iter_num']
) ** args['lr_decay']
# data\binarizing\Variable
inputs, labels = data
labels[labels > 0.5] = 1
labels[labels != 1] = 0
batch_size = inputs.size(0)
inputs = Variable(inputs).cuda()
labels = Variable(labels).cuda()
optimizer.zero_grad()
output_fpn, output_final = net(inputs)
##########loss#############
loss1 = criterion(output_fpn, labels)
loss2 = criterion(output_final, labels)
total_loss = loss1+loss2
total_loss.backward()
optimizer.step()
total_loss_record.update(total_loss.item(), batch_size)
loss1_record.update(loss1.item(), batch_size)
loss2_record.update(loss2.item(), batch_size)
#############log###############
curr_iter += 1
log = '[iter %d], [total loss %.5f],[loss1 %.5f],[loss1 %.5f],[lr %.13f] ' % \
(curr_iter, total_loss_record.avg, loss1_record.avg, loss2_record.avg, optimizer.param_groups[1]['lr'])
print(log)
open(log_path, 'a').write(log + '\n')
if curr_iter == args['iter_num']:
torch.save(net.state_dict(), os.path.join(ckpt_path, exp_name, '%d.pth' % curr_iter))
torch.save(optimizer.state_dict(),
os.path.join(ckpt_path, exp_name, '%d_optim.pth' % curr_iter))
return
## #############end###############
if __name__ == '__main__':
main()