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train.py
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import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
sys.path.append('./models')
import numpy as np
from datetime import datetime
from torchvision.utils import make_grid
from net import SwinMCNet
from data import get_loader,test_dataset
from utils import clip_gradient
from tensorboardX import SummaryWriter
import logging
import torch.backends.cudnn as cudnn
from options import opt
from loss.ssim import SSIM
os.environ["CUDA_VISIBLE_DEVICES"] = "2,3"
cudnn.benchmark = True
#build the model
model = SwinMCNet()
if(opt.load is not None):
model.load_pre(opt.load)
print('load model from ',opt.load)
model = nn.DataParallel(model).cuda()
# model = model.cuda()
base, body = [], []
for name, param in model.named_parameters():
if 'swin_image' in name or 'swin_thermal' in name:
print(name)
base.append(param)
else:
print(name)
body.append(param)
optimizer = torch.optim.SGD([{'params': base}, {'params': body}], lr=opt.lr, momentum=opt.momentum,
weight_decay=opt.decay_rate, nesterov=True)
#set the path
train_root = opt.train_data_root
test_root = opt.val_data_root
save_path=opt.save_path
if not os.path.exists(save_path):
os.makedirs(save_path)
#load data
print('load data...')
num_gpus = torch.cuda.device_count()
print(f"========>num_gpus:{num_gpus}==========")
train_loader = get_loader(train_root, batchsize=opt.batchsize, trainsize=opt.trainsize)
test_loader = test_dataset(test_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("SwinMCNet-Train")
logging.info('epoch:{};lr:{};batchsize:{};trainsize:{};clip:{};decay_rate:{};load:{};save_path:{}'.format(opt.epoch,opt.lr,opt.batchsize,opt.trainsize,opt.clip,opt.decay_rate,opt.load,save_path))
# loss
def iou_loss(pred, mask):
pred = torch.sigmoid(pred)
inter = (pred * mask).sum(dim=(2, 3))
union = (pred + mask).sum(dim=(2, 3))
iou = 1 - (inter + 1) / (union - inter + 1)
return iou.mean()
ssim_loss = SSIM(window_size=11, size_average=True)
step=0
writer = SummaryWriter(save_path+'summary')
best_mae=1
best_epoch=1
#train function
def train(train_loader, model, optimizer, epoch,save_path):
global step
model.train()
loss_all=0
epoch_step=0
try:
for i, (images, ts, gts, bodys, details) in enumerate(train_loader, start=1):
optimizer.zero_grad()
image, t, gt, body, detail = images.cuda(), ts.cuda(), gts.cuda(), bodys.cuda(), details.cuda()
outi1, outt1, out1, outi2, outt2, out2 = model(image,t)
lossi1 = F.binary_cross_entropy_with_logits(outi1, body) + ssim_loss(outi1, body)
losst1 = F.binary_cross_entropy_with_logits(outt1, detail) + ssim_loss(outt1, detail)
loss1 = F.binary_cross_entropy_with_logits(out1, gt) + iou_loss(out1, gt) + ssim_loss(out1, gt)
lossi2 = F.binary_cross_entropy_with_logits(outi2, body) + ssim_loss(outi2, body)
losst2 = F.binary_cross_entropy_with_logits(outt2, detail) + ssim_loss(outt2, detail)
loss2 = F.binary_cross_entropy_with_logits(out2, gt) + iou_loss(out2, gt) + ssim_loss(out2, gt)
loss = (lossi1 + losst1 + loss1 + lossi2 + losst2 + loss2)/2
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('%s | epoch:%d/%d | step:%d/%d | lr=%.6f | lossi1=%.6f | losst1=%.6f | loss1=%.6f | lossi2=%.6f | losst2=%.6f | loss2=%.6f'
%(datetime.now(), epoch, opt.epoch, i, total_step, optimizer.param_groups[0]['lr'], lossi1.item(),
losst1.item(), loss1.item(), lossi2.item(), losst2.item(), loss2.item()))
logging.info('##TRAIN##:Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], lr_bk: {:.6f}, Loss1: {:.4f} Loss2: {:0.4f}'.
format( epoch, opt.epoch, i, total_step, optimizer.param_groups[0]['lr'], loss1.data, loss2.data))
writer.add_scalar('Loss', loss.data, global_step=step)
grid_image = make_grid(images[0].clone().cpu().data, 1, normalize=True)
writer.add_image('RGB', grid_image, step)
grid_image = make_grid(gts[0].clone().cpu().data, 1, normalize=True)
writer.add_image('Ground_truth', grid_image, step)
res=out1[0].clone()
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
writer.add_image('out1', torch.tensor(res), step,dataformats='HW')
res=out2[0].clone()
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
writer.add_image('out2', torch.tensor(res), step,dataformats='HW')
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) % 50 == 0:
torch.save(model.state_dict(), save_path+'SwinMCNet_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+'SwinMCNet_epoch_{}.pth'.format(epoch))
print('save checkpoints successfully!')
raise
#test function
def test(test_loader,model,epoch,save_path):
global best_mae,best_epoch
model.eval()
with torch.no_grad():
mae_sum=0
#for i in range(1000):
for i in range(test_loader.size):
image, t, gt, (H, W), name = test_loader.load_data()
gt = np.asarray(gt, np.float32)
gt /= (gt.max() + 1e-8)
image = image.cuda()
t = t.cuda()
#shape = (W,H)
outi1, outt1, out1, outi2, outt2, out2 = model(image,t)
res = out2
res = F.interpolate(res, size=gt.shape, mode='bilinear')
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('\n')
print('##TEST##:Epoch: {} MAE: {}'.format(epoch,mae))
if epoch==1:
best_mae=mae
else:
if mae<best_mae:
best_mae=mae
best_epoch=epoch
torch.save(model.state_dict(), save_path+'SwinMCNet_epoch_best.pth')
print('##SAVE##:bestEpoch: {} bestMAE: {}'.format(best_epoch,best_mae))
print('\n')
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 + 1):
optimizer.param_groups[0]['lr'] = (1 - abs((epoch) / (opt.epoch) * 2 - 1)) * opt.lr * 0.1
optimizer.param_groups[1]['lr'] = (1 - abs((epoch) / (opt.epoch) * 2 - 1)) * opt.lr
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], global_step=epoch)
train(train_loader, model, optimizer, epoch,save_path)
test(test_loader,model,epoch,save_path)