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train.py
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train.py
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import os
import torch
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 models.E2Net import E2Net
from data import get_loader, test_dataset
from utils import clip_gradient, adjust_lr
from tensorboardX import SummaryWriter
import logging
import torch.backends.cudnn as cudnn
from options import opt
cudnn.benchmark = True
############################# build the model #############################
model = E2Net()
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 #############################
image_root = opt.rgb_root
gt_root = opt.gt_root
t_root=opt.T_root
test_image_root=opt.test_rgb_root
test_gt_root=opt.test_gt_root
test_t_root=opt.test_T_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(image_root, gt_root, t_root, batchsize=opt.batchsize, trainsize=opt.trainsize)
test_loader = test_dataset(test_image_root, test_gt_root, test_t_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("E2Net-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 #############################
def structure_loss(pred, mask):
"""
loss function (ref: F3Net-AAAI-2020)
"""
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()
step=0
writer = SummaryWriter(save_path+'summary')
best_mae=1
best_epoch=0
############################## 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, gts, ts) in enumerate(train_loader, start=1):
optimizer.zero_grad()
images = images.cuda()
gts = gts.cuda()
ts = ts.cuda()
s = model(images, ts)
loss = structure_loss(s, gts)
loss.backward()
clip_gradient(optimizer, opt.clip)
optimizer.step()
step+=1
epoch_step+=1
loss_all+=loss.data
if i % 100 == 0 or i == total_step or i==1:
print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], Loss: {:.4f}'.
format(datetime.now(), epoch, opt.epoch, i, total_step, loss.data))
logging.info('#TRAIN#:Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], Loss: {:0.4f}'.
format( epoch, opt.epoch, i, total_step, loss.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= s[0].clone()
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
writer.add_image('s', 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) % 5 == 0:
torch.save(model.state_dict(), save_path+'E2Net_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+'E2Net_epoch_{}.pth'.format(epoch+1))
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(test_loader.size):
image, gt, t, name,img_for_post = test_loader.load_data()
gt = np.asarray(gt, np.float32)
gt /= (gt.max() + 1e-8)
image = image.cuda()
t = t.cuda()
res = model(image, t)
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+'E2NetNet_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_loader, model, optimizer, epoch,save_path)
test(test_loader,model,epoch,save_path)