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train_denoising.py
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train_denoising.py
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import argparse
import torch.optim as optim
import torchvision.utils as utils
from torch.autograd import Variable
from torch.utils.data import DataLoader
import models
from data.denoising_dataset import prepare_data, Dataset
from tools import saver, mutils
from util import *
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
parser = argparse.ArgumentParser(description="DnCNN")
parser.add_argument("--preprocess", type=bool, default=False, help='run prepare_data or not')
parser.add_argument("--batchSize", type=int, default=128, help="Training batch size")
parser.add_argument("--num_of_layers", type=int, default=17, help="Number of total layers")
parser.add_argument("--epochs", type=int, default=50, help="Number of training epochs")
parser.add_argument("--milestone", type=int, default=30, help="When to decay learning rate; should be less than epochs")
parser.add_argument("--lr", type=float, default=1e-3, help="Initial learning rate")
parser.add_argument("--outf", type=str, default="logs", help='path of log files')
parser.add_argument("--mode", type=str, default="S", help='with known noise level (S) or blind training (B)')
parser.add_argument("--noiseL", type=float, default=25, help='noise level; ignored when mode=B')
parser.add_argument("--val_noiseL", type=float, default=25, help='noise level used on validation set')
parser.add_argument("--inet", type=str, default="DnCNN", help='Model name')
parser.add_argument('--icnn_path', type=str, default=None, help='icnn checkpoint to use.')
parser.add_argument('--name', type=str, default='default', help='name of the experiment')
parser.add_argument('--log_path', type=str, default='logs/')
parser.add_argument('--saved_path', type=str, default='logs/')
parser.add_argument('--criterion2', type=str, default='MSELoss')
parser.add_argument('--ratio2', type=float, default=1.0)
opt = parser.parse_args()
def save_checkpoint(model, name):
if isinstance(model, nn.DataParallel):
torch.save(model.module.state_dict(), os.path.join(opt.saved_path, name))
else:
torch.save(model.state_dict(), os.path.join(opt.saved_path, name))
def main():
base_path = os.path.join(opt.log_path, opt.name, mutils.get_formatted_time())
opt.saved_path = os.path.join(base_path, 'weights')
opt.log_path = os.path.join(base_path, 'tensorboard')
saver.base_url = os.path.join(base_path, 'samples')
os.makedirs(opt.log_path, exist_ok=True)
os.makedirs(opt.saved_path, exist_ok=True)
os.makedirs(saver.base_url, exist_ok=True)
with open(os.path.join(base_path, 'args.txt'), mode='w') as fp:
fp.write(' '.join(sys.argv))
# Load dataset
print('Loading dataset ...\n')
dataset_train = Dataset(train=True, name=opt.name)
dataset_val = Dataset(train=False, name=opt.name)
loader_train = DataLoader(dataset=dataset_train, num_workers=4, batch_size=opt.batchSize, shuffle=True)
print("# of training samples: %d\n" % int(len(dataset_train)))
# Build model
net = getattr(models, opt.inet)
print(net)
net = net(in_channels=1, out_channels=1, num_of_layers=opt.num_of_layers, act=False)
print(net)
# net.apply(weights_init_kaiming)
criterion = nn.MSELoss(size_average=False)
# Move to GPU
device_ids = [0]
model = nn.DataParallel(net, device_ids=device_ids).cuda()
criterion.cuda()
# Optimizer
optimizer = optim.Adam(model.parameters(), lr=opt.lr)
# training
writer = SummaryWriter(opt.log_path)
step = 0
noiseL_B = [0, 75] # ingnored when opt.mode=='S'
for epoch in range(opt.epochs):
if epoch < opt.milestone:
current_lr = opt.lr
else:
current_lr = opt.lr / 10.
# set learning rate
for param_group in optimizer.param_groups:
param_group["lr"] = current_lr
print('learning rate %f' % current_lr)
# train
for i, data in enumerate(loader_train, 0):
# training step
model.train()
model.zero_grad()
optimizer.zero_grad()
img_train = data
if opt.mode == 'S':
noise = torch.FloatTensor(img_train.size()).normal_(mean=0, std=opt.noiseL / 255.)
if opt.mode == 'B':
noise = torch.zeros(img_train.size())
stdN = np.random.uniform(noiseL_B[0], noiseL_B[1], size=noise.size()[0])
for n in range(noise.size()[0]):
sizeN = noise[0, :, :, :].size()
noise[n, :, :, :] = torch.FloatTensor(sizeN).normal_(mean=0, std=stdN[n] / 255.)
imgn_train = img_train + noise
img_train, imgn_train = Variable(img_train.cuda()), Variable(imgn_train.cuda())
noise = Variable(noise.cuda())
if isinstance(model.module, models.DnCNN):
out_train = model(imgn_train)
loss = criterion(out_train, noise) / (imgn_train.size()[0] * 2)
else:
out_train, out_train_b = model(imgn_train)
loss1 = criterion(out_train, img_train) / (imgn_train.size()[0] * 2)
loss2 = criterion(out_train_b, noise) / (imgn_train.size()[0] * 2)
loss3 = criterion(out_train + out_train_b, imgn_train) / (imgn_train.size()[0] * 2)
loss = loss1 + loss2 * 2 + loss3 * 3
loss.backward()
optimizer.step()
# results
model.eval()
if isinstance(model.module, models.DnCNN):
out_train = torch.clamp(imgn_train - out_train, 0., 1.)
else:
out_train = torch.clamp(out_train, 0., 1.)
psnr_train = batch_PSNR(out_train, img_train, 1.)
print("[epoch %d][%d/%d] loss: %.4f PSNR_train: %.4f" %
(epoch + 1, i + 1, len(loader_train), loss.item(), psnr_train))
# if you are using older version of PyTorch, you may need to change loss.item() to loss.data[0]
if step % 10 == 0:
# Log the scalar values
writer.add_scalar('loss', loss.item(), step)
writer.add_scalar('PSNR on training data', psnr_train, step)
writer.add_scalar('learning_rate', current_lr, step)
step += 1
model.eval()
# validate
psnr_val = 0
with torch.no_grad():
for k in range(len(dataset_val)):
img_val = torch.unsqueeze(dataset_val[k], 0)
noise = torch.FloatTensor(img_val.size()).normal_(mean=0, std=opt.val_noiseL / 255.)
imgn_val = img_val + noise
img_val, imgn_val = Variable(img_val.cuda(), volatile=True), Variable(imgn_val.cuda(), volatile=True)
if isinstance(model.module, models.DnCNN):
out_val = torch.clamp(imgn_val - model(imgn_val), 0., 1.)
else:
Irecon, Imgn = model(imgn_val)
out_val = torch.clamp(Irecon, 0., 1.)
psnr_val += batch_PSNR(out_val, img_val, 1.)
if isinstance(model.module, models.DnCNN):
imgn_val = imgn_val.cpu().detach()
Nrecon = model(imgn_val).cpu().detach()
Irecon = torch.clamp(imgn_val - Nrecon, 0., 1.).cpu().detach()
Nrecon = torch.clamp(Nrecon, 0., 1.).cpu().detach()
Img = utils.make_grid(img_val, nrow=8, normalize=True, scale_each=True)
Imgn = utils.make_grid(imgn_val, nrow=8, normalize=True, scale_each=True)
Irecon = utils.make_grid(Irecon, nrow=8, normalize=True, scale_each=True)
Nrecon = utils.make_grid(Nrecon, nrow=8, normalize=True, scale_each=True)
writer.add_image('clean image', Img, epoch)
writer.add_image('noisy image', Imgn, epoch)
writer.add_image('reconstructed image', Irecon, epoch)
writer.add_image('reconstructed noise', Nrecon, epoch)
else:
imgn_val = imgn_val.cpu().detach()
Irecon, Nrecon = model(imgn_val)
Irecon, Nrecon = Irecon.cpu().detach(), Nrecon.cpu().detach()
Irecon = torch.clamp(Irecon, 0., 1.).cpu().detach()
Nrecon = torch.clamp(Nrecon, 0., 1.).cpu().detach()
epc = '%03d' % epoch
idx = '%03d' % k
saver.save_image(img_val, f'ISource', split_dir=f'{epc}/{idx}')
saver.save_image(noise, f'noise', split_dir=f'{epc}/{idx}')
saver.save_image(imgn_val, f'INoisy', split_dir=f'{epc}/{idx}')
saver.save_image(Irecon, f'PImg', split_dir=f'{epc}/{idx}')
saver.save_image(Nrecon, f'PNoisy', split_dir=f'{epc}/{idx}')
psnr_val /= len(dataset_val)
print("\n[epoch %d] PSNR_val: %.4f" % (epoch + 1, psnr_val))
writer.add_scalar('PSNR on validation data', psnr_val, epoch)
# save model
save_checkpoint(model, f'{opt.model}_{epoch}_{step}.pth')
if __name__ == "__main__":
if opt.preprocess:
if opt.mode == 'S':
prepare_data(data_path='data', patch_size=40, stride=10, aug_times=1,
name=opt.name)
if opt.mode == 'B':
prepare_data(data_path='data', patch_size=50, stride=10, aug_times=2,
name=opt.name)
main()