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train_stage2_pretext2.py
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import datetime
import os
import torch
from torch import optim
from torch.autograd import Variable
from torchvision import transforms
from torch.utils.data import DataLoader
import utils_ssl.joint_transforms
from utils_ssl.datasets_stage2 import ImageFolder
from utils_ssl.misc import AvgMeter, check_mkdir
from model.model_stage1 import Crossmodal_Autoendoer
from model.model_stage2 import Contour_Estimation
from torch.backends import cudnn
from utils_downstream.ssim_loss import SSIM
import torch.nn as nn
import torch.nn.functional as F
cudnn.benchmark = True
torch.manual_seed(2018)
torch.cuda.set_device(0)
##########################hyperparameters###############################
ckpt_path = './saved_model'
exp_name = 'pretext_task2_stage2'
args = {
'iter_num': 79900, #50epoch
'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 = utils_ssl.joint_transforms.Compose([
utils_ssl.joint_transforms.RandomCrop(256, 256), # change to resize
utils_ssl.joint_transforms.RandomHorizontallyFlip(),
utils_ssl.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()
##########################################################################
image_root = ''
depth_root = ''
gt_root = ''
train_set = ImageFolder(image_root, gt_root,depth_root, joint_transform, img_transform, target_transform)
train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=0, shuffle=True)
criterion = nn.BCEWithLogitsLoss().cuda()
criterion_BCE = nn.BCELoss().cuda()
criterion_mse = nn.MSELoss().cuda()
criterion_mae = nn.L1Loss().cuda()
criterion_ssim = SSIM(window_size=11,size_average=True)
def ssimmae(pre,gt):
maeloss = criterion_mae(pre,gt)
ssimloss = 1-criterion_ssim(pre,gt)
loss = ssimloss+maeloss
return loss
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_pretext1 = Crossmodal_Autoendoer()
net_pretext1 = model_pretext1.cuda()
net_pretext1.load_state_dict(torch.load(os.path.join('./saved_model/pretext_task1.pth')))
net_pretext1.eval()
model_pretext2 = Contour_Estimation()
net = model_pretext2.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_pretext1,net, optimizer)
#########################################################################
def train(net_pretext1,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 = 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']
# data\binarizing\Variable
images, depths, gts = data
gts[gts > 0.5] = 1
gts[gts != 1] = 0
batch_size = images.size(0)
inputs = Variable(images).cuda()
labels = Variable(gts).cuda()
depths = Variable(depths).cuda()
b, c, h, w = labels.size()
optimizer.zero_grad()
target_1 = F.upsample(labels, size=h // 2, mode='nearest')
target_2 = F.upsample(labels, size=h // 4, mode='nearest')
target_3 = F.upsample(labels, size=h // 8, mode='nearest')
target_4 = F.upsample(labels, size=h // 16, mode='nearest')
##########loss#############
depth_3 = torch.cat((depths, depths, depths), 1)
e5_rgb,e4_rgb,e3_rgb,e2_rgb,e1_rgb, e5_depth,e4_depth,e3_depth,e2_depth,e1_depth = net_pretext1(
inputs, depth_3) # hed
sideout5, sideout4, sideout3, sideout2, output1 = net(e5_rgb, e4_rgb, e3_rgb, e2_rgb, e1_rgb, e5_depth, e4_depth, e3_depth, e2_depth, e1_depth)
loss1 = criterion_mae(F.sigmoid(sideout5), target_4)
loss2 = criterion_mae(F.sigmoid(sideout4), target_3)
loss3 = criterion_mae(F.sigmoid(sideout3), target_2)
loss4 = criterion_mae(F.sigmoid(sideout2), target_1)
loss5 = criterion_mae(F.sigmoid(output1), labels)
total_loss = loss1 + loss2 + loss3 + loss4 + loss5
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)
#############log###############
curr_iter += 1
log = '[iter %d], [total loss %.5f],[loss4 %.5f],[loss5 %.5f],[lr %.13f] ' % \
(curr_iter, total_loss_record.avg, loss4_record.avg, loss5_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()