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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 net import DFMNet
from data import get_loader,test_dataset
from utils import clip_gradient, LR_Scheduler
from torch.utils.tensorboard import SummaryWriter
import logging
import torch.backends.cudnn as cudnn
from options import opt
import torch.nn as nn
import torch.nn.functional as F
def upsample(x, size):
return F.interpolate(x, size, mode='bilinear', align_corners=True)
#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, depths) in enumerate(train_loader, start=1):
optimizer.zero_grad()
images = images.cuda()
gts = gts.cuda()
depths=depths.cuda()
cur_lr = lr_scheduler(optimizer, i, epoch)
writer.add_scalar('learning_rate', cur_lr, global_step=(epoch-1)*total_step + i)
out,feature_r,feature_d = model(images,depths)
loss_f = F.binary_cross_entropy_with_logits(out[0], gts)
loss_d = F.binary_cross_entropy_with_logits(out[1], gts)
loss = loss_f + loss_d
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}, loss_final: {:.4f}, loss_d: {:.4f}'.
format(datetime.now(), epoch, opt.epoch, i, total_step, loss,loss_f.data,loss_d.data ))
logging.info('#TRAIN#:Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], Loss1: {:.4f} '.
format( epoch, opt.epoch, i, total_step, loss.data))
writer.add_scalar('Loss', loss.data, global_step=step)
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 == 300:
torch.save(model.state_dict(), save_path+'/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+'/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,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()
res,_,_ = model(image,depth)
res = F.upsample(res[0], 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
torch.save(model.state_dict(), save_path + '/epoch_best.pth')
else:
if mae<best_mae:
best_mae=mae
best_epoch=epoch
torch.save(model.state_dict(), save_path+'/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__':
# set the device for training
if opt.gpu_id == '0':
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
print('USE GPU 0')
elif opt.gpu_id == '1':
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
print('USE GPU 1')
elif opt.gpu_id == 'all':
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
print('USE GPU 0,1')
cudnn.benchmark = True
# build the model
model = DFMNet()
model.cuda()
params = model.parameters()
num_params = 0
for p in model.parameters():
num_params += p.numel()
print(num_params)
optimizer = torch.optim.Adam(filter(lambda p:p.requires_grad,model.parameters()),opt.lr)
# set the path
image_root = opt.rgb_root
gt_root = opt.gt_root
depth_root = opt.depth_root
edge_root = opt.edge_root
test_image_root = opt.test_rgb_root
test_gt_root = opt.test_gt_root
test_depth_root = opt.test_depth_root
save_path = opt.save_path
os.mkdir(save_path)
# load data
print('load data...')
train_loader = get_loader(image_root, gt_root, depth_root,edge_root, batchsize=opt.batchsize, trainsize=opt.trainsize)
test_loader = test_dataset(test_image_root, test_gt_root, test_depth_root, opt.trainsize)
total_step = len(train_loader)
lr_scheduler = LR_Scheduler('poly', opt.lr, opt.epoch, total_step)
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("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))
step = 0
writer = SummaryWriter(save_path + '/summary')
best_mae = 1
best_epoch = 0
print("Start train...")
for epoch in range(1, opt.epoch):
train(train_loader, model, optimizer, epoch,save_path)