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digr_train.py
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digr_train.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 digr_res50 import DIGR
from data import get_loader, test_dataset
from utils import clip_gradient, adjust_lr, set_seed
from tensorboardX import SummaryWriter
import logging
import torch.backends.cudnn as cudnn
from options import opt
# from score_test import get_score
from decode_csv import decode_csv
# 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')
cudnn.benchmark = True
# set_seed(0)
# build the model
model = DIGR()
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
depth_root = opt.depth_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
if not os.path.exists(save_path):
os.makedirs(save_path)
# load data
print('load data...')
train_score=decode_csv('./score_folder/train_dut.csv')
val_score=decode_csv('./score_folder/val_dut.csv')
train_loader = get_loader(image_root, gt_root, depth_root,train_score, 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)
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("DIGRNet-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
# 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, scores, names) in enumerate(train_loader, start=1):
optimizer.zero_grad()
images = images.cuda()
gts = gts.cuda()
depths = depths.cuda()
scores = scores.cuda().unsqueeze(1).unsqueeze(2).unsqueeze(3).type_as(depths)
s1,s2,s3,s4,s5,s6,s7 = model(images, depths, scores)
loss1 = CE(s1, gts)
loss2 = CE(s2, gts)
loss3 = CE(s3, gts)
loss4 = CE(s4, gts)
loss5 = CE(s5, gts)
loss6 = CE(s6, gts)
loss7 = CE(s7, gts)
loss = loss1 + loss2 + loss3 + loss4 + loss5 + loss6 + loss7
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}], Loss1: {:.4f}, Loss2: {:.4f}, Loss3: {:.4f}, Loss4: {:.4f}, '
'Loss5: {:.4f}, Loss6: {:.4f} , Loss7: {:.4f}'.
format(datetime.now(), epoch, opt.epoch, i, total_step, loss1.data, loss2.data, loss3.data, loss4.data, loss5.data, loss6.data, loss7.data))
logging.info('#TRAIN#:Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], Loss1: {:.4f}, Loss2: {:.4f}, Loss3: {:.4f}, Loss4: {:.4f},Loss5: {:.4f}, Loss6: {:.4f}, Loss7: {:.4f} '.
format(epoch, opt.epoch, i, total_step, loss1.data, loss2.data, loss3.data, loss4.data, loss5.data, loss6.data, loss7.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 = s1[0].clone()
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
writer.add_image('s1', 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 + 'DIGRNet_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 + 'DIGRNet_epoch_{}.pth'.format(epoch + 1))
print('save checkpoints successfully!')
raise
# test function
def Jtest(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()
# print(name)
gt = np.asarray(gt, np.float32)
gt /= (gt.max() + 1e-8)
image = image.cuda()
depth = depth.cuda()
score = val_score[i]
score = torch.from_numpy(np.array([score]))
score = score.cuda().unsqueeze(1).unsqueeze(2).unsqueeze(3).type_as(depth)
_,_,_,_,_,_,res = model(image, depth, score)
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 + 'DIGRNet_epoch_best.pth')
print('best epoch:{}'.format(epoch))
logging.info('#TEST#:Epoch:{} MAE:{} bestEpoch:{} bestMAE:{}'.format(epoch, mae, best_epoch, best_mae))
import csv
def generate_csv(output_folder,scores):
if not os.path.exists(output_folder):
os.makedirs(output_folder)
path_format = os.path.join(output_folder, "{}.csv") # 生成文件名
out_csv1 = path_format.format('train-shuffle')
for i in range(len(scores)):
with open(out_csv1, "a", encoding="utf-8",newline='') as f:
writer = csv.writer(f)
writer.writerow([scores[i]])
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)
Jtest(test_loader, model, epoch, save_path)