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train_eval_syn.py
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train_eval_syn.py
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import torch.optim as optim
from torch.optim import lr_scheduler
import torch.nn as nn
from torch.utils.data import DataLoader
import argparse
import time
import shutil
from tensorboardX import SummaryWriter
from torchvision.transforms import transforms
# import setproctitle
from utils.training_util import MovingAverage, save_checkpoint, load_checkpoint
from utils.training_util import calculate_psnr, calculate_ssim
from utils.data_provider import *
from model.KPN import KPN
from utils.loss import LossFunc,WaveletLoss
from model.Att_KPN import Att_KPN
from model.Att_Weight_KPN import Att_Weight_KPN
def train(num_workers, cuda, restart_train, mGPU):
# torch.set_num_threads(num_threads)
color = True
batch_size = args.batch_size
lr = 2e-4
lr_decay = 0.89125093813
n_epoch = args.epoch
# num_workers = 8
save_freq = args.save_every
loss_freq = args.loss_every
lr_step_size = 100
burst_length = 8
# checkpoint path
checkpoint_dir = "checkpoints/" + args.checkpoint
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# logs path
logs_dir = "checkpoints/logs/" + args.checkpoint
if not os.path.exists(logs_dir):
os.makedirs(logs_dir)
shutil.rmtree(logs_dir)
log_writer = SummaryWriter(logs_dir)
# dataset and dataloader
data_set = MultiLoader(noise_dir=args.noise_dir,gt_dir=args.gt_dir,image_size=args.image_size)
data_loader = DataLoader(
data_set,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers
)
# model here
if args.model_type == "attKPN":
model = Att_KPN(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "attWKPN":
model = Att_Weight_KPN(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == 'KPN':
model = KPN(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=False,
spatial_att=False,
upMode="bilinear",
core_bias=False
)
else:
print(" Model type not valid")
return
if cuda:
model = model.cuda()
if mGPU:
model = nn.DataParallel(model)
model.train()
# loss function here
loss_func = LossFunc(
coeff_basic=1.0,
coeff_anneal=1.0,
gradient_L1=True,
alpha=0.9998,
beta=100.0
)
if args.wavelet_loss:
print("Use wavelet loss")
loss_func2 = WaveletLoss()
# Optimizer here
optimizer = optim.Adam(
model.parameters(),
lr=lr
)
optimizer.zero_grad()
# learning rate scheduler here
scheduler = lr_scheduler.StepLR(optimizer, step_size=lr_step_size, gamma=lr_decay)
average_loss = MovingAverage(save_freq)
if not restart_train:
try:
checkpoint = load_checkpoint(checkpoint_dir,cuda , 'latest')
start_epoch = checkpoint['epoch']
global_step = checkpoint['global_iter']
best_loss = checkpoint['best_loss']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['lr_scheduler'])
print('=> loaded checkpoint (epoch {}, global_step {})'.format(start_epoch, global_step))
except:
start_epoch = 0
global_step = 0
best_loss = np.inf
print('=> no checkpoint file to be loaded.')
else:
start_epoch = 0
global_step = 0
best_loss = np.inf
if os.path.exists(checkpoint_dir):
pass
# files = os.listdir(checkpoint_dir)
# for f in files:
# os.remove(os.path.join(checkpoint_dir, f))
else:
os.mkdir(checkpoint_dir)
print('=> training')
for epoch in range(start_epoch, n_epoch):
epoch_start_time = time.time()
# decay the learning rate
# print('='*20, 'lr={}'.format([param['lr'] for param in optimizer.param_groups]), '='*20)
t1 = time.time()
for step, (burst_noise, gt) in enumerate(data_loader):
# print(burst_noise.size())
# print(gt.size())
if cuda:
burst_noise = burst_noise.cuda()
gt = gt.cuda()
if color:
b, N, c, h, w = burst_noise.size()
feedData = burst_noise.view(b, -1, h, w)
else:
feedData = burst_noise
# print('white_level', white_level, white_level.size())
# print("feedData : ",feedData.size())
#
pred_i, pred = model(feedData, burst_noise[:, 0:burst_length, ...])
#
loss_basic, loss_anneal = loss_func(pred_i, pred, gt, global_step)
loss = loss_basic + loss_anneal
if args.wavelet_loss:
loss_wave = loss_func2(pred,gt)
# print(loss_wave)
loss = loss_basic + loss_anneal + loss_wave
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
# update the average loss
average_loss.update(loss)
# global_step
if not color:
pred = pred.unsqueeze(1)
gt = gt.unsqueeze(1)
if global_step %loss_freq ==0:
# calculate PSNR
print("burst_noise : ",burst_noise.size())
print("gt : ",gt.size())
print("feedData : ", feedData.size())
psnr = calculate_psnr(pred, gt)
ssim = calculate_ssim(pred, gt)
# add scalars to tensorboardX
log_writer.add_scalar('loss_basic', loss_basic, global_step)
log_writer.add_scalar('loss_anneal', loss_anneal, global_step)
log_writer.add_scalar('loss_total', loss, global_step)
log_writer.add_scalar('psnr', psnr, global_step)
log_writer.add_scalar('ssim', ssim, global_step)
# print
print('{:-4d}\t| epoch {:2d}\t| step {:4d}\t| loss_basic: {:.4f}\t| loss_anneal: {:.4f}\t|'
' loss: {:.4f}\t| PSNR: {:.2f}dB\t| SSIM: {:.4f}\t| time:{:.2f} seconds.'
.format(global_step, epoch, step, loss_basic, loss_anneal, loss, psnr, ssim, time.time()-t1))
t1 = time.time()
if global_step % save_freq == 0:
if average_loss.get_value() < best_loss:
is_best = True
best_loss = average_loss.get_value()
else:
is_best = False
save_dict = {
'epoch': epoch,
'global_iter': global_step,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
'lr_scheduler': scheduler.state_dict()
}
save_checkpoint(
save_dict, is_best, checkpoint_dir, global_step, max_keep=10
)
global_step += 1
print('Epoch {} is finished, time elapsed {:.2f} seconds.'.format(epoch, time.time()-epoch_start_time))
lr_cur = [param['lr'] for param in optimizer.param_groups]
if lr_cur[0] > 5e-6:
scheduler.step()
else:
for param in optimizer.param_groups:
param['lr'] = 5e-6
def eval(args):
color = True
print('Eval Process......')
burst_length = 8
checkpoint_dir = "checkpoints/" + args.checkpoint
if not os.path.exists(checkpoint_dir) or len(os.listdir(checkpoint_dir)) == 0:
print('There is no any checkpoint file in path:{}'.format(checkpoint_dir))
# the path for saving eval images
eval_dir = "eval_img"
if not os.path.exists(eval_dir):
os.mkdir(eval_dir)
files = os.listdir(eval_dir)
for f in files:
os.remove(os.path.join(eval_dir, f))
# dataset and dataloader
data_set = MultiLoader(noise_dir=args.noise_dir,gt_dir=args.gt_dir,image_size=args.image_size)
data_loader = DataLoader(
data_set,
batch_size=1,
shuffle=False,
num_workers=args.num_workers
)
# model here
if args.model_type == "attKPN":
model = Att_KPN(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "attWKPN":
model = Att_Weight_KPN(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "KPN":
model = KPN(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=False,
spatial_att=False,
upMode="bilinear",
core_bias=False
)
else:
print(" Model type not valid")
return
if args.cuda:
model = model.cuda()
if args.mGPU:
model = nn.DataParallel(model)
# load trained model
ckpt = load_checkpoint(checkpoint_dir,cuda=args.cuda,best_or_latest=args.load_type)
model.load_state_dict(ckpt['state_dict'])
print('The model has been loaded from epoch {}, n_iter {}.'.format(ckpt['epoch'], ckpt['global_iter']))
# switch the eval mode
model.eval()
# data_loader = iter(data_loader)
trans = transforms.ToPILImage()
with torch.no_grad():
psnr = 0.0
ssim = 0.0
for i, (burst_noise, gt) in enumerate(data_loader):
if i < 100:
# data = next(data_loader)
if args.cuda:
burst_noise = burst_noise.cuda()
gt = gt.cuda()
if color:
b, N, c, h, w = burst_noise.size()
feedData = burst_noise.view(b, -1, h, w)
else:
feedData = burst_noise
pred_i, pred = model(feedData, burst_noise[:, 0:burst_length, ...])
if not color:
psnr_t = calculate_psnr(pred.unsqueeze(1), gt.unsqueeze(1))
ssim_t = calculate_ssim(pred.unsqueeze(1), gt.unsqueeze(1))
psnr_noisy = calculate_psnr(burst_noise[:, 0, ...].unsqueeze(1), gt.unsqueeze(1))
else:
psnr_t = calculate_psnr(pred, gt)
ssim_t = calculate_ssim(pred, gt)
psnr_noisy = calculate_psnr(burst_noise[:, 0, ...], gt)
psnr += psnr_t
ssim += ssim_t
pred = torch.clamp(pred, 0.0, 1.0)
if args.cuda:
pred = pred.cpu()
gt = gt.cpu()
burst_noise = burst_noise.cpu()
trans(burst_noise[0, 0, ...].squeeze()).save(os.path.join(eval_dir, '{}_noisy_{:.2f}dB.png'.format(i, psnr_noisy)), quality=100)
trans(pred.squeeze()).save(os.path.join(eval_dir, '{}_pred_{:.2f}dB.png'.format(i, psnr_t)), quality=100)
trans(gt.squeeze()).save(os.path.join(eval_dir, '{}_gt.png'.format(i)), quality=100)
print('{}-th image is OK, with PSNR: {:.2f}dB, SSIM: {:.4f}'.format(i, psnr_t, ssim_t))
else:
break
# print('All images are OK, average PSNR: {:.2f}dB, SSIM: {:.4f}'.format(psnr/100, ssim/100))
if __name__ == '__main__':
# argparse
parser = argparse.ArgumentParser(description='parameters for training')
parser.add_argument('--noise_dir','-n', default='/home/dell/Downloads/noise', help='path to noise folder image')
parser.add_argument('--gt_dir', '-g' ,default='/home/dell/Downloads/gt', help='path to gt folder image')
parser.add_argument('--image_size', '-sz' , default=256, type=int, help='size of image')
parser.add_argument('--batch_size', '-bs' ,default=16, type=int, help='batch size')
parser.add_argument('--epoch', '-e' ,default=1000, type=int, help='batch size')
parser.add_argument('--save_every', '-se' ,default=200, type=int, help='save_every')
parser.add_argument('--loss_every', '-le' ,default=100, type=int, help='loss_every')
parser.add_argument('--restart', '-r' , action='store_true', help='Whether to remove all old files and restart the training process')
parser.add_argument('--num_workers', '-nw', default=4, type=int, help='number of workers in data loader')
parser.add_argument('--cuda', '-c', action='store_true', help='whether to train on the GPU')
parser.add_argument('--mGPU', '-mg', action='store_true', help='whether to train on multiple GPUs')
parser.add_argument('--eval', action='store_true', help='whether to work on the evaluation mode')
parser.add_argument('--checkpoint', '-ckpt', type=str, default='kpn',
help='the checkpoint to eval')
parser.add_argument('--color', '-cl' ,default=True, action='store_true')
parser.add_argument('--model_type', '-m' ,default="KPN", help='type of model : KPN, attKPN, attWKPN')
parser.add_argument('--load_type', "-l" ,default="best", type=str, help='Load type best_or_latest ')
parser.add_argument('--wavelet_loss','-wl' , default=False, action='store_true')
args = parser.parse_args()
#
if args.eval:
eval(args)
else:
train(args.num_workers,args.cuda, args.restart, args.mGPU)