-
Notifications
You must be signed in to change notification settings - Fork 8
/
train.py
184 lines (142 loc) · 6.33 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
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
import os
import torch
import torch.nn.functional as F
import sys
import numpy as np
from datetime import datetime
from torchvision.utils import make_grid
from Code.lib.model import SPNet
from Code.utils.data import get_loader,test_dataset
from Code.utils.utils import clip_gradient, adjust_lr
from tensorboardX import SummaryWriter
import logging
import torch.backends.cudnn as cudnn
from Code.utils.options import opt
#set the device for training
if opt.gpu_id=='0':
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
print('USE GPU 0')
cudnn.benchmark = True
#build the model
model = SPNet(32,50)
if(opt.load is not None):
model.load_state_dict(torch.load(opt.load))
print('load model from ',opt.load)
model.cuda()
params = model.parameters()
optimizer = torch.optim.Adam(params, opt.lr)
#set the path
train_image_root = opt.rgb_label_root
train_gt_root = opt.gt_label_root
train_depth_root = opt.depth_label_root
val_image_root = opt.test_rgb_root
val_gt_root = opt.test_gt_root
val_depth_root = opt.test_depth_root
save_path = opt.save_path
if not os.path.exists(save_path):
os.makedirs(save_path)
#load data
print('load data...')
train_loader = get_loader(train_image_root, train_gt_root,train_depth_root, batchsize=opt.batchsize, trainsize=opt.trainsize)
test_loader = test_dataset(val_image_root, val_gt_root,val_depth_root, opt.trainsize)
total_step = len(train_loader)
logging.basicConfig(filename=save_path+'log.log',format='[%(asctime)s-%(filename)s-%(levelname)s:%(message)s]', level = logging.INFO,filemode='a',datefmt='%Y-%m-%d %I:%M:%S %p')
logging.info("BBSNet_unif-Train")
logging.info("Config")
logging.info('epoch:{};lr:{};batchsize:{};trainsize:{};clip:{};decay_rate:{};load:{};save_path:{};decay_epoch:{}'.format(opt.epoch,opt.lr,opt.batchsize,opt.trainsize,opt.clip,opt.decay_rate,opt.load,save_path,opt.decay_epoch))
#set loss function
CE = torch.nn.BCEWithLogitsLoss()
step = 0
writer = SummaryWriter(save_path+'summary')
best_mae = 1
best_epoch = 0
print(len(train_loader))
def structure_loss(pred, mask):
weit = 1+5*torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15)-mask)
wbce = F.binary_cross_entropy_with_logits(pred, mask, reduce='none')
wbce = (weit*wbce).sum(dim=(2,3))/weit.sum(dim=(2,3))
pred = torch.sigmoid(pred)
inter = ((pred*mask)*weit).sum(dim=(2,3))
union = ((pred+mask)*weit).sum(dim=(2,3))
wiou = 1-(inter+1)/(union-inter+1)
return (wbce+wiou).mean()
def train(train_loader, model, optimizer, epoch,save_path):
global step
model.train()
loss_all=0
epoch_step=0
try:
for i, (images, gts, depths) in enumerate(train_loader, start=1):
optimizer.zero_grad()
images = images.cuda()
gts = gts.cuda()
depths = depths.cuda()
##
pre_res = model(images,depths)
loss1 = structure_loss(pre_res[0], gts)
loss2 = structure_loss(pre_res[1], gts)
loss3 = structure_loss(pre_res[2], gts)
loss_seg = loss1 + loss2 + loss3
loss = loss_seg
loss.backward()
clip_gradient(optimizer, opt.clip)
optimizer.step()
step+=1
epoch_step+=1
loss_all+=loss.data
if i % 50 == 0 or i == total_step or i==1:
print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], Loss1: {:.4f} Loss2: {:0.4f} Loss3: {:0.4f}'.
format(datetime.now(), epoch, opt.epoch, i, total_step, loss1.data, loss2.data, loss3.data))
logging.info('#TRAIN#:Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], Loss1: {:.4f} Loss2: {:0.4f} Loss3: {:0.4f}'.
format( epoch, opt.epoch, i, total_step, loss1.data, loss2.data, loss3.data))
loss_all/=epoch_step
logging.info('#TRAIN#:Epoch [{:03d}/{:03d}], Loss_AVG: {:.4f}'.format( epoch, opt.epoch, loss_all))
writer.add_scalar('Loss-epoch', loss_all, global_step=epoch)
if (epoch) % 5 == 0:
torch.save(model.state_dict(), save_path+'HyperNet_epoch_{}.pth'.format(epoch))
except KeyboardInterrupt:
print('Keyboard Interrupt: save model and exit.')
if not os.path.exists(save_path):
os.makedirs(save_path)
torch.save(model.state_dict(), save_path+'HyperNet_epoch_{}.pth'.format(epoch+1))
print('save checkpoints successfully!')
raise
#test function
def val(test_loader,model,epoch,save_path):
global best_mae,best_epoch
model.eval()
with torch.no_grad():
mae_sum=0
for i in range(test_loader.size):
image, gt,depth, name,img_for_post = test_loader.load_data()
gt = np.asarray(gt, np.float32)
gt /= (gt.max() + 1e-8)
image = image.cuda()
depth = depth.cuda()
pre_res = model(image,depth)
res = pre_res[2]
res = F.upsample(res, size=gt.shape, mode='bilinear', align_corners=False)
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
mae_sum += np.sum(np.abs(res-gt))*1.0/(gt.shape[0]*gt.shape[1])
mae = mae_sum/test_loader.size
writer.add_scalar('MAE', torch.tensor(mae), global_step=epoch)
print('Epoch: {} MAE: {} #### bestMAE: {} bestEpoch: {}'.format(epoch,mae,best_mae,best_epoch))
if epoch==1:
best_mae = mae
else:
if mae<best_mae:
best_mae = mae
best_epoch = epoch
torch.save(model.state_dict(), save_path+'SPNet_epoch_best.pth')
print('best epoch:{}'.format(epoch))
logging.info('#TEST#:Epoch:{} MAE:{} bestEpoch:{} bestMAE:{}'.format(epoch,mae,best_epoch,best_mae))
if __name__ == '__main__':
print("Start train...")
for epoch in range(1, opt.epoch):
cur_lr = adjust_lr(optimizer, opt.lr, epoch, opt.decay_rate, opt.decay_epoch)
writer.add_scalar('learning_rate', cur_lr, global_step=epoch)
# train
train(train_loader, model, optimizer, epoch,save_path)
#test
val(test_loader,model,epoch,save_path)