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train_RFNet.py
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train_RFNet.py
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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_RAFNet import RFNet
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
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id
print('USE GPU {}'.format(opt.gpu_id))
cudnn.benchmark = True
#build the model
model = RFNet(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.val_rgb_root
val_gt_root = opt.val_gt_root
val_depth_root = opt.val_depth_root
save_path = './Checkpoint/RAFNet/'
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("RAFNet-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, gts)
# loss2 = structure_loss(pre_res[1], gts)
# loss3 = structure_loss(pre_res[2], gts)
# loss_seg = loss1 + loss2 + loss3
loss_seg = loss1
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}], Loss: {:.4f} '.
format(datetime.now(), epoch, opt.epoch, i, total_step, loss1.data))
logging.info('#TRAIN#:Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], Loss: {:.4f} '.
format( epoch, opt.epoch, i, total_step, loss1.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
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+'RAFNet_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)