-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathtrain_stage1.py
166 lines (144 loc) · 7.42 KB
/
train_stage1.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
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
from utils import joint_transforms
from utils.datasets import ImageFolder
from utils.misc import AvgMeter, check_mkdir
from utils.ssim_loss import SSIM
from models.stage1 import stage1
from torch.backends import cudnn
import torch.nn.functional as functional
cudnn.benchmark = True
torch.manual_seed(2018)
torch.cuda.set_device(0)
##########################hyperparameters###############################
ckpt_path = './saved_model'
exp_name = 'stage1'
args = {
'iter_num':76425,
'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),
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_data = os.path.join('/RGBDSOD/train_data')
train_set = ImageFolder(train_data, joint_transform, img_transform, target_transform)
train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=12, shuffle=True)
criterion = nn.BCEWithLogitsLoss().cuda()
criterion_BCE = nn.BCELoss().cuda()
criterion_MAE = nn.L1Loss().cuda()
criterion_MSE = nn.MSELoss().cuda()
criterion_smoothl1 = nn.SmoothL1Loss().cuda()
criterion_ssim = SSIM(window_size=11,size_average=True)
criterion_triplet_loss = nn.TripletMarginLoss(margin=1.5, p=2)
criterion_triplet_loss_e2 = nn.TripletMarginLoss(margin=1, p=2)
log_path = os.path.join(ckpt_path, exp_name, str(datetime.datetime.now()) + '.txt')
def ssimmae(pre,gt):
maeloss = criterion_MAE(pre,gt)
ssimloss = 1-criterion_ssim(pre,gt)
loss = ssimloss+maeloss
return loss
def main():
model = stage1()
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,loss9_record ,loss10_record,loss11_record ,loss12_record ,loss13_record ,loss14_record ,loss15_record ,loss16_record,loss17_record ,loss18_record,loss19_record, loss20_record,loss21_record,loss22_record,loss23_record,loss24_record,loss25_record,loss26_record,loss27_record ,loss28_record,loss29_record ,loss30_record = AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter(),AvgMeter(),AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter(),AvgMeter(),AvgMeter(),AvgMeter(),AvgMeter(),AvgMeter(),AvgMeter(),AvgMeter(),AvgMeter(),AvgMeter(),AvgMeter(),AvgMeter(), AvgMeter(), AvgMeter(), 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']
inputs, depth, labels = data
labels[labels > 0.5] = 1
labels[labels != 1] = 0
batch_size = inputs.size(0)
h = inputs.size(2)
inputs = Variable(inputs).cuda()
depth = Variable(depth).cuda()
labels = Variable(labels).cuda()
P5_D,P4_D,P3_D,P2_D,P1_D,P5_sal,P4_sal,P3_sal,P2_sal,P1_sal, = net(inputs)
depth6 = functional.interpolate(depth, size=h//32, mode='bilinear')
depth5 = functional.interpolate(depth, size=h//16, mode='bilinear')
depth4 = functional.interpolate(depth, size=h//8, mode='bilinear')
depth3 = functional.interpolate(depth, size=h//4, mode='bilinear')
depth2 = functional.interpolate(depth, size=h//2, mode='bilinear')
labels6 = functional.interpolate(labels, size=h//32, mode='bilinear')
labels5 = functional.interpolate(labels, size=h//16, mode='bilinear')
labels4 = functional.interpolate(labels, size=h//8, mode='bilinear')
labels3 = functional.interpolate(labels, size=h//4, mode='bilinear')
labels2 = functional.interpolate(labels, size=h//2, mode='bilinear')
optimizer.zero_grad()
loss1 = ssimmae(P1_D,depth)
loss2 = ssimmae(P2_D,depth3)
loss3 = ssimmae(P3_D,depth4)
loss4 = ssimmae(P4_D,depth5)
loss5 = ssimmae(P5_D,depth6)
loss6 = criterion_BCE(P1_sal,labels)
loss7 = criterion_BCE(P2_sal,labels3)
loss8 = criterion_BCE(P3_sal,labels4)
loss9 = criterion_BCE(P4_sal,labels5)
loss10 = criterion_BCE(P5_sal,labels6)
total_loss = loss1+loss2+loss3+loss4+loss5+loss6+loss7+loss8+loss9+loss10
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)
loss3_record.update(loss3.item(), batch_size)
loss4_record.update(loss4.item(), batch_size)
loss5_record.update(loss5.item(), batch_size)
loss6_record.update(loss6.item(), batch_size)
loss7_record.update(loss7.item(), batch_size)
loss8_record.update(loss8.item(), batch_size)
loss9_record.update(loss9.item(), batch_size)
loss10_record.update(loss10.item(), batch_size)
curr_iter += 1
log = '[iter %d], [total loss %.5f],[loss1 %.5f],[loss6 %.5f],[lr %.13f] ' % \
(curr_iter, total_loss_record.avg, loss1_record.avg, loss6_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))
return
#############end###############
if __name__ == '__main__':
main()