def __init__(self,
config_file,
im_size,
config_spec=None,
cropping="random",
cache_dir=None,
use_cache=False,
dataset_name="default_dataset_name"):
""" Dataset for generating degraded images on the fly.
Args:
pipeline_configs: dictionary of boolean flags controlling how
pipelines are created.
pipeline_param_ranges: dictionary of ranges of params.
patch_dir: directory to load linear patches.
config_file: path to data config file
im_size: tuple of (w, h)
config_spec: path to data config spec file
cropping: cropping mode ["random", "center"]
"""
super().__init__()
if config_spec is None:
config_spec = _configspec_path()
config = read_config(config_file, config_spec)
self.config_file = config_file
# directory to load linear patches
patch_dir = config['dataset_dir']
# dictionary of boolean flags controlling how pipelines are created
# (see data_configspec for detail).
pipeline_configs = config['pipeline_configs']
# dictionary of ranges of params (see data_configspec for detail).
pipeline_param_ranges = config['pipeline_param_ranges']
file_list = glob.glob(os.path.join(patch_dir, 'images/target/*.npy'))
file_list = [os.path.basename(f) for f in file_list]
file_list = [os.path.splitext(f)[0] for f in file_list]
self.file_list = sorted(file_list,
key=lambda x: zlib.adler32(x.encode('utf-8')))
self.pipeline_param_ranges = pipeline_param_ranges
self.pipeline_configs = pipeline_configs
print('Data Pipeline Configs: ', self.pipeline_configs)
print('Data Pipeline Param Ranges: ', self.pipeline_param_ranges)
self.data_root = patch_dir
self.im_size = im_size
self.cropping = cropping
self.use_cache = use_cache
self.cache_dir = cache_dir
sz = "{}x{}".format(self.im_size[0], self.im_size[1]) \
if self.im_size is not None else "None"
self.dataset_name = "_".join([dataset_name, sz])
def __init__(self,
config_file,
config_spec=None,
img_format='.bmp',
degamma=True,
color=True,
blind=False,
train=True):
super(TrainDataSet, self).__init__()
if config_spec is None:
config_spec = self._configspec_path()
config = read_config(config_file, config_spec)
self.dataset_config = config['dataset_configs']
self.dataset_dir = self.dataset_config['dataset_dir']
self.images = list(
filter(lambda x: True if img_format in x else False,
os.listdir(self.dataset_dir)))
self.burst_size = self.dataset_config['burst_length']
self.patch_size = self.dataset_config['patch_size']
self.upscale = self.dataset_config['down_sample']
self.big_jitter = self.dataset_config['big_jitter']
self.small_jitter = self.dataset_config['small_jitter']
# 对应下采样之前图像的最大偏移量
self.jitter_upscale = self.big_jitter * self.upscale
# 对应下采样之前的图像的patch尺寸
self.size_upscale = self.patch_size * self.upscale + 2 * self.jitter_upscale
# 产生大jitter和小jitter之间的delta 在下采样之前的尺度上
self.delta_upscale = (self.big_jitter -
self.small_jitter) * self.upscale
# 对应到原图的patch的尺寸
self.patch_size_upscale = self.patch_size * self.upscale
# 去伽马效应
self.degamma = degamma
# 是否用彩色图像进行处理
self.color = color
# 是否盲估计 盲估计即估计的噪声方差不会作为网络的输入
self.blind = blind
self.train = train
self.vertical_flip = Random_Vertical_Flip(p=0.5)
self.horizontal_flip = Random_Horizontal_Flip(p=0.5)
def eval(config, args):
train_config = config['training']
arch_config = config['architecture']
use_cache = train_config['use_cache']
print('Eval Process......')
checkpoint_dir = train_config['checkpoint_dir']
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 = train_config['eval_dir']
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 = TrainDataSet(train_config['dataset_configs'],
img_format='.bmp',
degamma=True,
color=False,
blind=arch_config['blind_est'],
train=False)
data_loader = DataLoader(data_set,
batch_size=1,
shuffle=False,
num_workers=args.num_workers)
dataset_config = read_config(train_config['dataset_configs'],
_configspec_path())['dataset_configs']
# model here
model = KPN(color=False,
burst_length=dataset_config['burst_length'],
blind_est=arch_config['blind_est'],
kernel_size=list(map(int, arch_config['kernel_size'].split())),
sep_conv=arch_config['sep_conv'],
channel_att=arch_config['channel_att'],
spatial_att=arch_config['spatial_att'],
upMode=arch_config['upMode'],
core_bias=arch_config['core_bias'])
if args.cuda:
model = model.cuda()
if args.mGPU:
model = nn.DataParallel(model)
# load trained model
ckpt = load_checkpoint(checkpoint_dir, args.checkpoint)
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)
burst_length = dataset_config['burst_length']
data_length = burst_length if arch_config['blind_est'] else burst_length + 1
patch_size = dataset_config['patch_size']
trans = transforms.ToPILImage()
with torch.no_grad():
psnr = 0.0
ssim = 0.0
for i, (burst_noise, gt, white_level) in enumerate(data_loader):
if i < 100:
# data = next(data_loader)
if args.cuda:
burst_noise = burst_noise.cuda()
gt = gt.cuda()
white_level = white_level.cuda()
pred_i, pred = model(burst_noise,
burst_noise[:, 0:burst_length,
...], white_level)
pred_i = sRGBGamma(pred_i)
pred = sRGBGamma(pred)
gt = sRGBGamma(gt)
burst_noise = sRGBGamma(burst_noise / white_level)
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))
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))
def train(config, num_workers, num_threads, cuda, restart_train, mGPU):
# torch.set_num_threads(num_threads)
train_config = config['training']
arch_config = config['architecture']
batch_size = train_config['batch_size']
lr = train_config['learning_rate']
weight_decay = train_config['weight_decay']
decay_step = train_config['decay_steps']
lr_decay = train_config['lr_decay']
n_epoch = train_config['num_epochs']
use_cache = train_config['use_cache']
print('Configs:', config)
# checkpoint path
checkpoint_dir = train_config['checkpoint_dir']
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# logs path
logs_dir = train_config['logs_dir']
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 = TrainDataSet(train_config['dataset_configs'],
img_format='.bmp',
degamma=True,
color=False,
blind=arch_config['blind_est'])
data_loader = DataLoader(data_set,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
dataset_config = read_config(train_config['dataset_configs'],
_configspec_path())['dataset_configs']
# model here
model = KPN(color=False,
burst_length=dataset_config['burst_length'],
blind_est=arch_config['blind_est'],
kernel_size=list(map(int, arch_config['kernel_size'].split())),
sep_conv=arch_config['sep_conv'],
channel_att=arch_config['channel_att'],
spatial_att=arch_config['spatial_att'],
upMode=arch_config['upMode'],
core_bias=arch_config['core_bias'])
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=arch_config['alpha'],
beta=arch_config['beta'])
# Optimizer here
if train_config['optimizer'] == 'adam':
optimizer = optim.Adam(model.parameters(), lr=lr)
elif train_config['optimizer'] == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=lr,
momentum=0.9,
weight_decay=weight_decay)
else:
raise ValueError(
"Optimizer must be 'sgd' or 'adam', but received {}.".format(
train_config['optimizer']))
optimizer.zero_grad()
# learning rate scheduler here
scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=lr_decay)
average_loss = MovingAverage(train_config['save_freq'])
if not restart_train:
try:
checkpoint = load_checkpoint(checkpoint_dir, 'best')
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')
burst_length = dataset_config['burst_length']
data_length = burst_length if arch_config['blind_est'] else burst_length + 1
patch_size = dataset_config['patch_size']
for epoch in range(start_epoch, n_epoch):
epoch_start_time = time.time()
# decay the learning rate
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
print(
'=' * 20,
'lr={}'.format([param['lr'] for param in optimizer.param_groups]),
'=' * 20)
t1 = time.time()
for step, (burst_noise, gt, white_level) in enumerate(data_loader):
if cuda:
burst_noise = burst_noise.cuda()
gt = gt.cuda()
# print('white_level', white_level, white_level.size())
#
pred_i, pred = model(burst_noise, burst_noise[:, 0:burst_length,
...], white_level)
#
loss_basic, loss_anneal = loss_func(sRGBGamma(pred_i),
sRGBGamma(pred), sRGBGamma(gt),
global_step)
loss = loss_basic + loss_anneal
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
# update the average loss
average_loss.update(loss)
# calculate PSNR
psnr = calculate_psnr(pred.unsqueeze(1), gt.unsqueeze(1))
ssim = calculate_ssim(pred.unsqueeze(1), gt.unsqueeze(1))
# 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()
# global_step
global_step += 1
if global_step % train_config['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=train_config['ckpt_to_keep'])
print('Epoch {} is finished, time elapsed {:.2f} seconds.'.format(
epoch,
time.time() - epoch_start_time))
parser.add_argument('--num_threads',
'-nt',
default=8,
type=int,
help='number of threads in data loader')
parser.add_argument('--cuda',
'-c',
action='store_true',
help='whether to train on the GPU')
parser.add_argument('--mGPU',
'-m',
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',
dest='checkpoint',
type=str,
default='best',
help='the checkpoint to eval')
args = parser.parse_args()
#
config = read_config(args.config_file, args.config_spec)
if args.eval:
eval(config, args)
else:
train(config, args.num_workers, args.num_threads, args.cuda,
args.restart, args.mGPU)
def __init__(self,
config_file,
config_spec=None,
blind=False,
cropping="random",
cache_dir=None,
use_cache=False,
dataset_name="synthetic"):
""" Dataset for generating degraded images on the fly.
Args:
pipeline_configs: dictionary of boolean flags controlling how
pipelines are created.
pipeline_param_ranges: dictionary of ranges of params.
patch_dir: directory to load linear patches.
config_file: path to data config file
im_size: tuple of (w, h)
config_spec: path to data config spec file
cropping: cropping mode ["random", "center"]
"""
super().__init__()
if config_spec is None:
config_spec = _configspec_path()
config = read_config(config_file, config_spec)
# self.config_file = config_file
# dictionary of dataset configs
self.dataset_configs = config['dataset_configs']
# directory to load linear patches
patch_dir = self.dataset_configs['dataset_dir']
# dictionary of boolean flags controlling how pipelines are created
# (see data_configspec for detail).
self.pipeline_configs = config['pipeline_configs']
# dictionary of ranges of params (see data_configspec for detail).
self.pipeline_param_ranges = config['pipeline_param_ranges']
file_list = glob.glob(os.path.join(patch_dir,
'*.pth'))
file_list = [os.path.basename(f) for f in file_list]
file_list = [os.path.splitext(f)[0] for f in file_list]
self.file_list = sorted(file_list, key=lambda x: zlib.adler32(x.encode('utf-8')))
# print(self.file_list)
# self.pipeline_param_ranges = pipeline_param_ranges
# self.pipeline_configs = pipeline_configs
# print('Data Pipeline Configs: ', self.pipeline_configs)
# print('Data Pipeline Param Ranges: ', self.pipeline_param_ranges)
# some variables about the setting of dataset
self.data_root = patch_dir
self.im_size = self.dataset_configs['patch_size'] # the size after down-sample
extra_for_bayer = 2 # extra size used for the random choice for bayer pattern
self.big_jitter = self.dataset_configs['big_jitter']
self.small_jitter = self.dataset_configs['small_jitter']
self.down_sample = self.dataset_configs['down_sample']
# image size corresponding to original image (include big jitter)
self.im_size_upscale = (self.im_size + 2 * self.big_jitter + extra_for_bayer) * self.down_sample
# from big jitter image to real image with extra pixels to random choose the bayer pattern
self.big_restore_upscale = self.big_jitter * self.down_sample
# the shift pixels of small jitter within upscale
self.small_restore_upscale = self.small_jitter * self.down_sample
# from big jitter images to small jitter images
self.big2small_upscale = (self.big_jitter - self.small_jitter) * self.down_sample
#
self.im_size_extra = (self.im_size + extra_for_bayer) * self.down_sample
# blind estimate?
self.blind = blind
# others
self.cropping = cropping
self.use_cache = use_cache
self.cache_dir = cache_dir
sz = "{}x{}".format(self.im_size, self.im_size) \
if self.im_size is not None else "None"
self.dataset_name = "_".join([dataset_name, sz])
# add the codes by Bin Zhang
self.burst_length = self.dataset_configs['burst_length']
def train():
log_writer = SummaryWriter('./logs')
parser = argparse.ArgumentParser()
parser.add_argument('--restart', '-r', action='store_true')
args = parser.parse_args()
config = read_config('kpn_specs/att_kpn_config.conf', 'kpn_specs/configspec.conf')
train_config = config['training']
data_set = TrainDataSet(
train_config['dataset_configs'],
img_format='.bmp',
degamma=True,
color=True,
blind=False
)
data_loader = DataLoader(
dataset=data_set,
batch_size=32,
shuffle=True,
num_workers=4
)
loss_fn = nn.L1Loss()
model = Network(True).cuda()
model.train()
optimizer = optim.Adam(model.parameters(), lr=5e-5)
if not args.restart:
model.load_state_dict(load_checkpoint('./noise_models', best_or_latest='best'))
global_iter = 0
min_loss = np.inf
loss_ave = MovingAverage(200)
import os
if not os.path.exists('./noise_models'):
os.mkdir('./noise_models')
for epoch in range(100):
for step, (data, A, B) in enumerate(data_loader):
feed = data[:, 0, ...].cuda()
gt = data[:, -1, ...].cuda()
# print(data.size())
pred = model(feed)
loss = loss_fn(pred, gt)
global_iter += 1
optimizer.zero_grad()
loss.backward()
optimizer.step()
log_writer.add_scalar('loss', loss, global_iter)
loss_ave.update(loss)
if global_iter % 200 == 0:
loss_t = loss_ave.get_value()
min_loss = min(min_loss, loss_t)
is_best = min_loss == loss_t
save_checkpoint(
model.state_dict(),
is_best=is_best,
checkpoint_dir='./noise_models',
n_iter=global_iter
)
print('{: 6d}, epoch {: 3d}, iter {: 4d}, loss {:.4f}'.format(global_iter, epoch, step, loss))
