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train.py
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train.py
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import os
import torch
import random
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_pvt import XMSNet
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
import torch.nn as nn
# set the device for training
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id
print('USE GPU {}'.format(opt.gpu_id))
#print('USE GPU {}')
cudnn.benchmark = True
# build the model
model = MRNet()
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)
def set_random_seed(seed):
seed = int(seed) # """Set random seed for reproduce"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# set the path
set_random_seed(3407)
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/MRNet/'
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("MRNet-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()
loss_kl = nn.KLDivLoss(reduction='batchmean')
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()
##
S0,S1,S2,S3,S4,S5,S6= model(images,depths)
loss0 = 0.2*structure_loss(S0, gts)
loss1 = 0.4* structure_loss(S1, gts)
loss2 = 0.6* structure_loss(S2, gts)
loss3 = 0.8* structure_loss(S3, gts)
loss4 = 0.3*structure_loss(S4, gts)
loss5 = 0.6*structure_loss(S5, gts)
loss6 = 0.9*structure_loss(S6, gts)
losskl1 = loss_kl(F.log_softmax(S4, dim=1), F.softmax(S5, dim=1)) + loss_kl(F.log_softmax(S5, dim=1), F.softmax(S4, dim=1))
losskl2 =loss_kl(F.log_softmax(S5, dim=1), F.softmax(S6, dim=1)) + loss_kl(F.log_softmax(S6, dim=1), F.softmax(S5, dim=1))
loss_seg = loss0 + loss1 + loss2 + loss3 +loss4 + loss5 + loss6+ 0.5* losskl1 + 0.5 * losskl2
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()
S0,S1,S2,S3,S4,S5,S6 = model(image,depth)
res = S6
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 + 'XMSNet_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)