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train.py
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import os
import numpy as np
import torch
import time
from datetime import datetime, timedelta
from torchvision.utils import make_grid
from model import Net
from data import get_loader, test_dataset
from utils import clip_gradient, adjust_lr, setup_seed
from loss import build_loss
import torch.nn.functional as F
from tensorboardX import SummaryWriter
import logging
from options import opt
# set the device for training
torch.autograd.set_detect_anomaly(True)
setup_seed()
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id
print('USE GPU', opt.gpu_id)
# build the model
model = Net(opt)
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.img_root
gt_root = opt.gt_root
edge_root = opt.edge_root
test_image_root = opt.test_img_root
test_gt_root = opt.test_gt_root
save_path = opt.save_path
if os.path.exists(os.path.join(save_path, 'models')):
raise Exception("directory exists! Please change save path")
if not os.path.exists(os.path.join(save_path, 'models')):
os.makedirs(os.path.join(save_path, 'models'))
with open('%s/args.txt' % (opt.save_path), 'w') as f:
for arg in vars(opt):
print('%s: %s' % (arg, getattr(opt, arg)), file=f)
# load data
print('load data...')
train_loader = get_loader(image_root, gt_root, edge_root, batchsize=opt.batchsize, trainsize=opt.trainsize)
test_loader = test_dataset(test_image_root, test_gt_root, opt.trainsize)
total_step = len(train_loader)
logging.basicConfig(filename=os.path.join(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("CFNet-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
criterior_mask, criterior_edge = build_loss(opt)
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:
start_time = time.time()
for i, (images, gts, gt_edges) in enumerate(train_loader, start=1):
optimizer.zero_grad()
images = images.cuda()
gts = gts.cuda()
gt_edges = gt_edges.cuda()
pred0, edge0, [pred2, pred3, pred4], [edge2, edge3, edge4] = model(images)
mask_loss0 = criterior_mask(pred0, gts)
mask_loss2 = criterior_mask(pred2, gts)
mask_loss3 = criterior_mask(pred3, gts)
mask_loss4 = criterior_mask(pred4, gts)
edge_loss0 = criterior_edge(edge0, gt_edges)
edge_loss2 = criterior_edge(edge2, gt_edges)
edge_loss3 = criterior_edge(edge3, gt_edges)
edge_loss4 = criterior_edge(edge4, gt_edges)
mask_loss = (mask_loss0 + mask_loss2) + mask_loss3 / 2 + mask_loss4 / 4
edge_loss = (edge_loss0 + edge_loss2) + edge_loss3 / 2 + edge_loss4 / 4
total_loss = mask_loss + opt.ratio * edge_loss
total_loss.backward()
# clip_gradient(optimizer, opt.clip)
optimizer.step()
step += 1
epoch_step += 1
loss_all += total_loss.data
if i % 100 == 0:
end_time = time.time()
duration_time = end_time - start_time
time_second_avg = duration_time / (opt.batchsize * 100)
eta_sec = time_second_avg * (
(opt.epoch - epoch) * len(train_loader) * opt.batchsize + (
len(train_loader) - i - 1) * opt.batchsize
)
eta_str = str(timedelta(seconds=int(eta_sec)))
print(
'{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], eta: {},'
' MaskLoss0: {:0.4f}, MaskLoss2: {:0.4f}, MaskLoss3: {:0.4f}, MaskLoss4: {:0.4f}'.
format(datetime.now(), epoch, opt.epoch, i, total_step, eta_str,
mask_loss0.data, mask_loss2.data, mask_loss3.data, mask_loss4.data))
logging.info(
'#TRAIN Mask#: {} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], eta: {}, MaskLoss0: {:0.4f}, '
'MaskLoss2: {:0.4f}, MaskLoss3: {:0.4f}, MaskLoss4: {:0.4f}'.
format(datetime.now(), epoch, opt.epoch, i, total_step, eta_str,
mask_loss0.data, mask_loss2.data, mask_loss3.data, mask_loss4.data))
logging.info(
'#TRAIN Edge#: {} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], eta: {}, EdgeLoss0: {:0.4f}, '
'EdgeLoss2: {:0.4f}, EdgeLoss3: {:0.4f}, EdgeLoss4: {:0.4f}'.
format(datetime.now(), epoch, opt.epoch, i, total_step, eta_str, edge_loss0.data,
edge_loss2.data, edge_loss3.data, edge_loss4.data))
writer.add_scalar('Loss', total_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 = pred0[0][0].clone()
res = res.data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
writer.add_image('Init_Pred', torch.tensor(res), step, dataformats='HW')
res = edge0[0][0].clone()
res = res.data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
writer.add_image('Init_Edge', torch.tensor(res), step, dataformats='HW')
res = pred2[0][0].clone()
res = res.data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
writer.add_image('Final_Pred', torch.tensor(res), step, dataformats='HW')
res = edge2[0][0].clone()
res = res.data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
writer.add_image('Final_Edge', torch.tensor(res), step, dataformats='HW')
start_time = time.time()
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) % 10 == 0:
torch.save(model.state_dict(), os.path.join(save_path, 'models', 'BgNet_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(), os.path.join(save_path, 'models', 'BgNet_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, name, img_for_post = test_loader.load_data()
gt = np.asarray(gt, np.float32)
gt /= (gt.max() + 1e-8)
image = image.cuda()
_, _, res, _ = model(image)
res = F.upsample(res[0], size=gt.shape, mode='bilinear', align_corners=False)
res = res.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(), os.path.join(save_path, 'models', 'BgNet_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...")
cur_lr = opt.lr
for epoch in range(1, opt.epoch + 1):
if epoch % opt.decay_epoch == 0:
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)