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train.py
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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 DANet import RGBD_sal
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 = './model'
exp_name = 'model_vgg16_DANet'
args = {
'iter_num':20500,
'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_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()
log_path = os.path.join(ckpt_path, exp_name, str(datetime.datetime.now()) + '.txt')
def main():
model = RGBD_sal()
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']
inputs, depth, labels= data
labels[labels>0.5] = 1
labels[labels!=1] = 0
batch_size = inputs.size(0)
inputs = Variable(inputs).cuda()
depth = Variable(depth).cuda()
labels = Variable(labels).cuda()
outputs,outputs_fg,outputs_bg,attention1,attention2,attention3,attention4,attention5 = net(inputs,depth) #hed
##########loss#############
optimizer.zero_grad()
labels1 = functional.interpolate(labels, size=24, mode='bilinear')
labels2 = functional.interpolate(labels, size=48, mode='bilinear')
labels3 = functional.interpolate(labels, size=96, mode='bilinear')
labels4 = functional.interpolate(labels, size=192, mode='bilinear')
loss1 = criterion_BCE(attention1, labels1)
loss2 = criterion_BCE(attention2, labels2)
loss3 = criterion_BCE(attention3, labels3)
loss4 = criterion_BCE(attention4, labels4)
loss5 = criterion_BCE(attention5, labels)
loss6 = criterion(outputs_fg, labels)
loss7 = criterion(outputs_bg, (1-labels))
loss8 = criterion(outputs, labels)
total_loss = loss1+loss2+loss3+loss4+loss5+loss6+loss7+loss8
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)
curr_iter += 1
#############log###############
if curr_iter %2050==0:
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))
log = '[iter %d], [total loss %.5f],[loss1 %.5f],,[loss2 %.5f],[loss3 %.5f],[loss4 %.5f],[loss5 %.5f],[loss6 %.5f],[loss7 %.5f],[loss8 %.5f],[lr %.13f] ' % \
(curr_iter, total_loss_record.avg, loss1_record.avg,loss2_record.avg,loss3_record.avg,loss4_record.avg,loss5_record.avg,loss6_record.avg,loss7_record.avg,loss8_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()