def main(args):
# Init loggers
os.makedirs(args.log_dir, exist_ok=True)
if args.noise_type == 'gaussian':
if len(args.train_noiseL)>1:
noise_level = 'nL'+str(int(args.train_noiseL[0]))+','+str(int(args.train_noiseL[1]))
else:
noise_level = 'nL'+str(int(args.train_noiseL[0]))
elif args.noise_type == 'poisson_gaussian':
if len(args.train_noiseL)>2:
noise_level = 'sigmaS'+str(int(args.train_noiseL[1]))+'_sigmaC'+str(int(args.train_noiseL[2]))
else:
noise_level = 'sigmaS'+str(int(args.train_noiseL[0]))+'_sigmaC'+str(int(args.train_noiseL[1]))
else:
raise ValueError('Noise_type [%s] is not found.' % (args.noise_type))
checkpoint_dir = args.save_prefix + '_' + noise_level
ckpt_save_path = os.path.join(args.log_dir, checkpoint_dir)
os.makedirs(ckpt_save_path, exist_ok=True)
logger_fname = os.path.join(ckpt_save_path, checkpoint_dir+'_log.txt')
with open(logger_fname, "w") as log_file:
now = time.strftime("%c")
log_file.write('================ Log output (%s) ================\n' % now)
log_file.write('Parameters \n')
for key in opt.__dict__:
p = key+': '+str(opt.__dict__[key])
log_file.write('%s\n' % p)
# net architecture
dbsn_net = DBSN_Model(in_ch = args.input_channel,
out_ch = args.output_channel,
mid_ch = args.middle_channel,
blindspot_conv_type = args.blindspot_conv_type,
blindspot_conv_bias = args.blindspot_conv_bias,
br1_block_num = args.br1_block_num,
br1_blindspot_conv_ks =args.br1_blindspot_conv_ks,
br2_block_num = args.br2_block_num,
br2_blindspot_conv_ks = args.br2_blindspot_conv_ks,
activate_fun = args.activate_fun)
sigma_mu_net = Sigma_mu_Net(in_ch=args.middle_channel,
out_ch=args.sigma_mu_output_channel,
mid_ch=args.sigma_mu_middle_channel,
layers=args.sigma_mu_layers,
kernel_size=args.sigma_mu_kernel_size,
bias=args.sigma_mu_bias)
sigma_n_net = Sigma_n_Net(in_ch=args.sigma_n_input_channel,
out_ch=args.sigma_n_output_channel,
mid_ch=args.sigma_n_middle_channel,
layers=args.sigma_n_layers,
kernel_size=args.sigma_n_kernel_size,
bias=args.sigma_n_bias)
# loss function
criterion = MAPLoss().cuda()
# Move to GPU
dbsn_model = nn.DataParallel(dbsn_net, args.device_ids).cuda()
sigma_mu_model = nn.DataParallel(sigma_mu_net, args.device_ids).cuda()
sigma_n_model = nn.DataParallel(sigma_n_net, args.device_ids).cuda()
# new or continue
ckpt_save_prefix = args.save_prefix + '_ckpt_e'
initial_epoch = findLastCheckpoint(save_dir=ckpt_save_path, save_pre = ckpt_save_prefix)
if initial_epoch > 0:
print('*****resuming by loading epoch %03d' % initial_epoch)
args.resume = "continue"
args.last_ckpt = os.path.join(ckpt_save_path, ckpt_save_prefix + str(initial_epoch) + '.pth')
# Optimizer
training_params = None
optimizer_dbsn = None
optimizer_sigma_mu = None
optimizer_sigma_n = None
if args.resume == "continue":
tmp_ckpt=torch.load(args.last_ckpt,map_location=torch.device('cuda', args.device_ids[0]))
training_params = tmp_ckpt['training_params']
start_epoch = training_params['start_epoch']
# Initialize dbsn_model
pretrained_dict=tmp_ckpt['state_dict_dbsn']
model_dict=dbsn_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
dbsn_model.load_state_dict(model_dict)
optimizer_dbsn = optim.Adam(dbsn_model.parameters(), lr=args.lr_dbsn)
optimizer_dbsn.load_state_dict(tmp_ckpt['optimizer_state_dbsn'])
schedule_dbsn = torch.optim.lr_scheduler.MultiStepLR(optimizer_dbsn, milestones=args.steps, gamma=args.decay_rate)
# Initialize sigma_mu_model
pretrained_dict=tmp_ckpt['state_dict_sigma_mu']
model_dict=sigma_mu_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_mu_model.load_state_dict(model_dict)
optimizer_sigma_mu = optim.Adam(sigma_mu_model.parameters(), lr=args.lr_sigma_mu)
optimizer_sigma_mu.load_state_dict(tmp_ckpt['optimizer_state_sigma_mu'])
schedule_sigma_mu = torch.optim.lr_scheduler.MultiStepLR(optimizer_sigma_mu, milestones=args.steps, gamma=args.decay_rate)
# Initialize sigma_n_model
pretrained_dict=tmp_ckpt['state_dict_sigma_n']
model_dict=sigma_n_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_n_model.load_state_dict(model_dict)
optimizer_sigma_n = optim.Adam(sigma_n_model.parameters(), lr=args.lr_sigma_n)
optimizer_sigma_n.load_state_dict(tmp_ckpt['optimizer_state_sigma_n'])
schedule_sigma_n = torch.optim.lr_scheduler.MultiStepLR(optimizer_sigma_n, milestones=args.steps, gamma=args.decay_rate)
elif args.resume == "new":
training_params = {}
training_params['step'] = 0
start_epoch = 0
# Initialize dbsn
optimizer_dbsn = optim.Adam(dbsn_model.parameters(), lr=args.lr_dbsn)
schedule_dbsn = torch.optim.lr_scheduler.MultiStepLR(optimizer_dbsn, milestones=args.steps, gamma=args.decay_rate)
# Initialize net_sigma_mu
optimizer_sigma_mu = optim.Adam(sigma_mu_model.parameters(), lr=args.lr_sigma_mu)
schedule_sigma_mu = torch.optim.lr_scheduler.MultiStepLR(optimizer_sigma_mu, milestones=args.steps, gamma=args.decay_rate)
# Initialize optimizer for net_sigma_n
optimizer_sigma_n = optim.Adam(sigma_n_model.parameters(), lr=args.lr_sigma_n)
schedule_sigma_n = torch.optim.lr_scheduler.MultiStepLR(optimizer_sigma_n, milestones=args.steps, gamma=args.decay_rate)
# logging
dbsn_params = sum(param.numel() for param in dbsn_net.parameters() if param.requires_grad)/(1e6)
sigma_mu_params = sum(param.numel() for param in sigma_mu_net.parameters() if param.requires_grad)/(1e3)
sigma_n_params = sum(param.numel() for param in sigma_n_net.parameters() if param.requires_grad)/(1e3)
with open(logger_fname, "a") as log_file:
log_file.write('model created\n')
log_file.write('DBSN trainable parameters: {dbsn_params:.2f}M\n'.format(dbsn_params=dbsn_params))
log_file.write('SigmaMuNet trainable parameters: {sigma_mu_params:.2f}K\n'.format(sigma_mu_params=sigma_mu_params))
log_file.write('SigmaNNet trainable parameters: {sigma_n_params:.2f}K\n'.format(sigma_n_params=sigma_n_params))
# set training set
train_setname = args.trainset
dataset = create_dataset(train_setname, 'train', args).load_data()
dataset_num = len(dataset)
# set val set
val_setname = args.valset
dataset_val = create_dataset(val_setname, 'val', args).load_data()
# logging
with open(logger_fname, "a") as log_file:
log_file.write('training/val dataset created\n')
log_file.write('number of training examples {0} \n'.format(dataset_num))
# --------------------------------------------
# Checkpoint
# --------------------------------------------
if args.resume == 'continue':
# # evaluating the loaded model first ...
# print("Starting from epoch: %d "%(start_epoch))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('checkpoint evaluation on epoch {0} ... \n'.format(start_epoch))
dbsn_model.eval()
sigma_mu_model.eval()
sigma_n_model.eval()
with torch.no_grad():
psnr_val = 0
psnr_val_update = 0
for count, data in enumerate(dataset_val):
#
img_val = data['clean'].cuda()
img_noise_val = data['noisy'].cuda()
_,C,H,W = img_noise_val.shape
# forward
mu_out_val, mid_out_val = dbsn_model(img_noise_val)
sigma_mu_out_val = sigma_mu_model(mid_out_val)
#
sigma_mu_val = sigma_mu_out_val ** 2
if args.noise_type == 'gaussian':
sigma_n_out_val = sigma_n_model(img_noise_val)
sigma_n_out_val = sigma_n_out_val.mean(dim=(2,3), keepdim=True).repeat(1,1,H,W)
else:
sigma_n_out_val = sigma_n_model(mu_out_val)
noise_est_val = F.softplus(sigma_n_out_val - 4) + (1e-3)
sigma_n_val = noise_est_val ** 2
map_out_val = (img_noise_val * sigma_mu_val + mu_out_val * sigma_n_val) / (sigma_mu_val + sigma_n_val)
# compute PSNR
psnr = batch_psnr(mu_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
psnr_val+=psnr
psnr_update = batch_psnr(map_out_val.clamp(0., 1.), img_val, 1.)
psnr_val_update+=psnr_update
# print
print("Image: %d, psnr_mu: %.4f, psnr_mu_map: %.4f " % (count, psnr, psnr_update))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('Image {0} \t'
'psnr_mu:{psnr:.4f} \t'
'psnr_mu_map:{psnr_update:.4f} \n'.format(count, psnr=psnr,psnr_update=psnr_update))
psnr_val /= len(dataset_val)
psnr_val_update /= len(dataset_val)
# print
print("Checkpoint avg psnr_mu: %.4f, avg psnr_mu_map: %.4f \n" % (psnr_val, psnr_val_update))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('checkpoint avg psnr_mu:{psnr_val:.4f} \t'
'avg psnr_mu_map:{psnr_val:.4f} \n\n'.format(psnr_val=psnr_val, psnr_val_update=psnr_val_update))
val_psnr_pre = psnr_val
idx_epoch = start_epoch
val_psnr_curr = 0
# --------------------------------------------
# Training
# --------------------------------------------
# logging
with open(logger_fname, "a") as log_file:
log_file.write('started training\n')
for epoch in range(start_epoch, args.epoch):
epoch_start_time = time.time()
# print learning rate
print('lr: %f, %f, %f' % (optimizer_dbsn.state_dict()["param_groups"][0]["lr"],
optimizer_sigma_mu.state_dict()["param_groups"][0]["lr"],
optimizer_sigma_n.state_dict()["param_groups"][0]["lr"]))
# set training mode
dbsn_model.train()
sigma_mu_model.train()
sigma_n_model.train()
# begin to train
for i, data in enumerate(dataset):
# load training data
img_train = data['clean'].cuda()
img_noise = data['noisy'].cuda()
_,C,H,W = img_noise.shape
#
optimizer_dbsn.zero_grad()
optimizer_sigma_mu.zero_grad()
optimizer_sigma_n.zero_grad()
# forward
mu_out, mid_out = dbsn_model(img_noise)
sigma_mu_out = sigma_mu_model(mid_out)
# process sigma_mu & sigma_n
sigma_mu = sigma_mu_out ** 2
#
if args.noise_type == 'gaussian':
sigma_n_out = sigma_n_model(img_noise)
sigma_n_out = sigma_n_out.mean(dim=(2,3),keepdim=True).repeat(1,1,H,W)
else:
sigma_n_out = sigma_n_model(mu_out)
noise_est = F.softplus(sigma_n_out - 4) + (1e-3)
sigma_n = noise_est ** 2
#
sigma_y = sigma_mu + sigma_n
# compute loss
loss = criterion(img_noise, mu_out, sigma_mu, sigma_n, sigma_y)
loss = loss / (2*args.batch_size)
loss_value = loss.item()
#
loss.backward()
optimizer_dbsn.step()
optimizer_sigma_mu.step()
optimizer_sigma_n.step()
# Results
training_params['step'] += args.batch_size
# print results
if training_params['step'] % (args.batch_size*args.display_freq) == 0:
with torch.no_grad():
# compute map_out
map_out = (img_noise * sigma_mu + mu_out * sigma_n) / (sigma_mu + sigma_n)
# compute training psnr
train_psnr = batch_psnr(mu_out.clamp(0., 1.), img_train, 1.0)
train_psnr_update = batch_psnr(map_out.clamp(0., 1.), img_train, 1.)
# # print
# print(("[epoch %d/%d][%d/%d], loss:%.4f, psnr_mu: %.4f, psnr_dbsn: %.4f ") %
# (epoch, args.epoch, i*args.batch_size, len(dataset), loss_value, train_psnr, train_psnr_update))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('Epoch: [{0}/{1}][{2}/{3}] \t'
'loss {loss_value: .4f} \t'
'psnr_mu:{train_psnr:.4f} \t'
'psnr_dbsn:{train_psnr_update:.4f} \n'.format(epoch, args.epoch, i*args.batch_size, len(dataset),
loss_value=loss_value, train_psnr=train_psnr, train_psnr_update=train_psnr_update))
schedule_dbsn.step()
schedule_sigma_mu.step()
schedule_sigma_n.step()
# taking time for each epoch
tr_take_time = time.time() - epoch_start_time
# --------------------------------------------
# validation
# --------------------------------------------
# print
# print("Evaluating on "+str(val_setname[0]))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('Evaluation on %s \n' % (str(val_setname[0])) )
#
dbsn_model.eval()
sigma_mu_model.eval()
sigma_n_model.eval()
val_start_time = time.time()
with torch.no_grad():
psnr_val = 0
psnr_val_dbsn = 0
for count, data in enumerate(dataset_val):
# load input
img_val = data['clean'].cuda()
img_noise_val = data['noisy'].cuda()
_,C,H,W = img_noise_val.shape
# forward
mu_out_val, mid_out_val = dbsn_model(img_noise_val)
sigma_mu_out_val = sigma_mu_model(mid_out_val)
#
sigma_mu_val = sigma_mu_out_val ** 2
if args.noise_type == 'gaussian':
sigma_n_out_val = sigma_n_model(img_noise_val)
sigma_n_out_val = sigma_n_out_val.mean(dim=(2,3), keepdim=True).repeat(1,1,H,W)
else:
sigma_n_out_val = sigma_n_model(mu_out_val)
noise_est_val = F.softplus(sigma_n_out_val - 4) + (1e-3)
sigma_n_val = noise_est_val ** 2
map_out_val = (img_noise_val * sigma_mu_val + mu_out_val * sigma_n_val) / (sigma_mu_val + sigma_n_val)
# compute PSNR
psnr = batch_psnr(mu_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
psnr_val+=psnr
psnr_dbsn = batch_psnr(map_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
psnr_val_dbsn+=psnr_dbsn
# # print
# print("Image: %d, psnr_mu: %.4f, psnr_dbsn: %.4f " % (count, psnr, psnr_update))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('Image {0} \t'
'psnr_mu:{psnr:.4f} \t'
'psnr_dbsn:{psnr_dbsn:.4f} \n'.format(count, psnr=psnr,psnr_dbsn=psnr_dbsn))
torch.cuda.synchronize()
val_take_time = time.time() - val_start_time
psnr_val /= len(dataset_val)
psnr_val_dbsn /= len(dataset_val)
# Record the best PSNR
val_psnr_curr = psnr_val_dbsn
if epoch==0:
val_psnr_pre = val_psnr_curr
# print
print('Epoch [%d/%d] \t val psnr_mu: %.4f \t val psnr_dbsn: %.4f \t Train_time: %d sec \t Val_time: %d sec \t' %
(epoch, args.epoch, psnr_val, psnr_val_dbsn, tr_take_time, val_take_time))
# save model and checkpoint
if val_psnr_curr >= val_psnr_pre or (epoch+1) % args.save_model_freq == 0:
training_params['start_epoch'] = epoch
save_dict = {'state_dict_dbsn': dbsn_model.state_dict(),
'state_dict_sigma_mu': sigma_mu_model.state_dict(),
'state_dict_sigma_n': sigma_n_model.state_dict(),
'optimizer_state_dbsn': optimizer_dbsn.state_dict(),
'optimizer_state_sigma_mu': optimizer_sigma_mu.state_dict(),
'optimizer_state_sigma_n': optimizer_sigma_n.state_dict(),
'schedule_state_dbsn': schedule_dbsn.state_dict(),
'schedule_state_sigma_mu': schedule_sigma_mu.state_dict(),
'schedule_state_sigma_n': schedule_sigma_n.state_dict(),
'training_params': training_params,
'args': args}
torch.save(save_dict, os.path.join(ckpt_save_path, ckpt_save_prefix + '{}.pth'.format(epoch)))
if val_psnr_curr >= val_psnr_pre:
val_psnr_pre = val_psnr_curr
idx_epoch = epoch
torch.save(dbsn_model.state_dict(), os.path.join(ckpt_save_path, 'dbsn_net_best_e{}.pth'.format(epoch)))
torch.save(sigma_mu_model.state_dict(), os.path.join(ckpt_save_path, 'sigma_mu_net_best_e{}.pth'.format(epoch)))
torch.save(sigma_n_model.state_dict(), os.path.join(ckpt_save_path, 'sigma_n_net_best_e{}.pth'.format(epoch)))
del save_dict
# # print
# print('Best Val psnr_dbsn=%.4f with Epoch %d' % (val_psnr_pre, idx_epoch))
# logging
with open(logger_fname, "a") as log_file:
#[0/1/2]: 0-current epoch, 1-total epoch, 2-best epoch
log_file.write('Epoch: [{0}/{1}/{2}] \t'
'Train_time:{tr_take_time:.1f} sec \t'
'Val_time:{val_take_time:.1f} sec \t'
'VAL psnr_mu:{psnr_val:.4f} \t'
'VAL psnr_dbsn:{psnr_val_dbsn:.4f} \t'
'Best VAL psnr_dbsn:{val_psnr_pre:.4f} \n'.format(epoch, args.epoch, idx_epoch,
tr_take_time=tr_take_time, val_take_time=val_take_time,
psnr_val=psnr_val, psnr_val_dbsn=psnr_val_dbsn,val_psnr_pre=val_psnr_pre))
def main(args):
# Init loggers
os.makedirs(args.log_dir, exist_ok=True)
if args.noise_type == 'gaussian':
if len(args.train_noiseL) > 1:
noise_level = 'nL' + str(int(args.train_noiseL[0])) + ',' + str(
int(args.train_noiseL[1]))
else:
noise_level = 'nL' + str(int(args.train_noiseL[0]))
elif args.noise_type == 'poisson_gaussian':
if len(args.train_noiseL) > 2:
noise_level = 'sigmaS' + str(int(
args.train_noiseL[1])) + '_sigmaC' + str(
int(args.train_noiseL[2]))
else:
noise_level = 'sigmaS' + str(int(
args.train_noiseL[0])) + '_sigmaC' + str(
int(args.train_noiseL[1]))
else:
raise ValueError('Noise_type [%s] is not found.' % (args.noise_type))
checkpoint_dir = args.save_prefix + '_' + noise_level
ckpt_save_path = os.path.join(args.log_dir, checkpoint_dir)
os.makedirs(ckpt_save_path, exist_ok=True)
logger_fname = os.path.join(ckpt_save_path, checkpoint_dir + '_log.txt')
with open(logger_fname, "w") as log_file:
now = time.strftime("%c")
log_file.write('================ Log output (%s) ================\n' %
now)
log_file.write('Parameters \n')
for key in opt.__dict__:
p = key + ': ' + str(opt.__dict__[key])
log_file.write('%s\n' % p)
# set training set
train_setname = args.trainset
dataset = create_dataset(train_setname, 'train', args).load_data()
dataset_num = len(dataset)
# set val set
val_setname = args.valset
dataset_val = create_dataset(val_setname, 'val', args).load_data()
# logging
with open(logger_fname, "a") as log_file:
log_file.write('training/val dataset created\n')
log_file.write(
'number of training examples {0} \n'.format(dataset_num))
# net architecture
dbsn_net = DBSN_Model(in_ch=args.input_channel,
out_ch=args.output_channel,
mid_ch=args.middle_channel,
blindspot_conv_type=args.blindspot_conv_type,
blindspot_conv_bias=args.blindspot_conv_bias,
br1_block_num=args.br1_block_num,
br1_blindspot_conv_ks=args.br1_blindspot_conv_ks,
br2_block_num=args.br2_block_num,
br2_blindspot_conv_ks=args.br2_blindspot_conv_ks,
activate_fun=args.activate_fun)
sigma_mu_net = Sigma_mu_Net(in_ch=args.middle_channel,
out_ch=args.sigma_mu_output_channel,
mid_ch=args.sigma_mu_middle_channel,
layers=args.sigma_mu_layers,
kernel_size=args.sigma_mu_kernel_size,
bias=args.sigma_mu_bias)
sigma_n_net = Sigma_n_Net(in_ch=args.sigma_n_input_channel,
out_ch=args.sigma_n_output_channel,
mid_ch=args.sigma_n_middle_channel,
layers=args.sigma_n_layers,
kernel_size=args.sigma_n_kernel_size,
bias=args.sigma_n_bias)
cnn_denoiser_net = MWCNN()
# loss function
criterion = DistillationLoss().cuda()
# Move to GPU
dbsn_model = nn.DataParallel(dbsn_net, args.device_ids).cuda()
sigma_mu_model = nn.DataParallel(sigma_mu_net, args.device_ids).cuda()
sigma_n_model = nn.DataParallel(sigma_n_net, args.device_ids).cuda()
cnn_denoiser_model = cnn_denoiser_net.cuda()
# new or continue
ckpt_save_prefix = args.save_prefix + '_ckpt_e'
initial_epoch = findLastCheckpoint(save_dir=ckpt_save_path,
save_pre=ckpt_save_prefix)
if initial_epoch > 0:
print('*****resuming by loading epoch %03d' % initial_epoch)
args.resume = "continue"
args.last_ckpt = os.path.join(
ckpt_save_path, ckpt_save_prefix + str(initial_epoch) + '.pth')
# Optimizer
training_params = None
optimizer_cnn_denoiser = None
if args.resume == "continue":
tmp_ckpt = torch.load(args.last_ckpt,
map_location=torch.device(
'cuda', args.device_ids[0]))
training_params = tmp_ckpt['training_params']
start_epoch = training_params['start_epoch']
# Initialize dbsn_model
pretrained_dict = tmp_ckpt['state_dict_dbsn']
model_dict = dbsn_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
dbsn_model.load_state_dict(model_dict)
# Initialize sigma_mu_model
pretrained_dict = tmp_ckpt['state_dict_sigma_mu']
model_dict = sigma_mu_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_mu_model.load_state_dict(model_dict)
# Initialize sigma_n_model
pretrained_dict = tmp_ckpt['state_dict_sigma_n']
model_dict = sigma_n_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_n_model.load_state_dict(model_dict)
# Initialize cnn_denoiser_model
pretrained_dict = tmp_ckpt['state_dict_cnn_denoiser']
model_dict = cnn_denoiser_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
cnn_denoiser_model.load_state_dict(model_dict)
optimizer_cnn_denoiser = optim.Adam(cnn_denoiser_model.parameters(),
lr=args.lr_cnn_denoiser)
optimizer_cnn_denoiser.load_state_dict(
tmp_ckpt['optimizer_state_cnn_denoiser'])
schedule_cnn_denoiser = torch.optim.lr_scheduler.MultiStepLR(
optimizer_cnn_denoiser,
milestones=args.steps,
gamma=args.decay_rate)
elif args.resume == "new":
training_params = {}
training_params['step'] = 0
start_epoch = 0
tmp_ckpt = torch.load(args.dbsn_ckpt,
map_location=torch.device(
'cuda', args.device_ids[0]))
# Initialize dbsn_model
pretrained_dict = tmp_ckpt['state_dict_dbsn']
model_dict = dbsn_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
dbsn_model.load_state_dict(model_dict)
# Initialize sigma_mu_model
pretrained_dict = tmp_ckpt['state_dict_sigma_mu']
model_dict = sigma_mu_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_mu_model.load_state_dict(model_dict)
# Initialize sigma_n_model
pretrained_dict = tmp_ckpt['state_dict_sigma_n']
model_dict = sigma_n_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_n_model.load_state_dict(model_dict)
if args.isPretrain:
# Initialize dn_model
tmp_ckpt = torch.load(args.pretrained_cnn_denoiser_path)
pretrained_dict = tmp_ckpt['state_dict']
model_dict = cnn_denoiser_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
cnn_denoiser_model.load_state_dict(model_dict)
#
cnn_denoiser_model.eval()
with torch.no_grad():
psnr_val_cnn_denoiser = 0
for count, data in enumerate(dataset_val):
# input
img_val = data['clean'].cuda()
img_noise_val = data['noisy'].cuda()
# crop the input (8x MWCNN)
img_val = img_val[:, :, :-1, :-1]
img_noise_val = img_noise_val[:, :, :-1, :-1]
_, C, H, W = img_noise_val.shape
# forward
cnn_denoiser_out_val = cnn_denoiser_model(img_noise_val)
psnr_cnn_denoiser = batch_psnr(
cnn_denoiser_out_val.clamp(0., 1.),
img_val.clamp(0., 1.), 1.)
psnr_val_cnn_denoiser += psnr_cnn_denoiser
psnr_val_cnn_denoiser /= len(dataset_val)
print("Pre-trained CNN denoiser on BSD68, avg psnr: %.4f \n" %
(psnr_val_cnn_denoiser))
#
optimizer_cnn_denoiser = optim.Adam(cnn_denoiser_model.parameters(),
lr=args.lr_cnn_denoiser)
schedule_cnn_denoiser = torch.optim.lr_scheduler.MultiStepLR(
optimizer_cnn_denoiser,
milestones=args.steps,
gamma=args.decay_rate)
# logging
dbsn_params = sum(param.numel() for param in dbsn_net.parameters()
if param.requires_grad) / (1e6)
sigma_mu_params = sum(param.numel() for param in sigma_mu_net.parameters()
if param.requires_grad) / (1e3)
sigma_n_params = sum(param.numel() for param in sigma_n_net.parameters()
if param.requires_grad) / (1e3)
cnn_denoiser_params = sum(param.numel()
for param in cnn_denoiser_net.parameters()
if param.requires_grad) / (1e6)
with open(logger_fname, "a") as log_file:
log_file.write('model created\n')
log_file.write(
'DBSN trainable parameters: {dbsn_params:.2f}M\n'.format(
dbsn_params=dbsn_params))
log_file.write(
'SigmaMuNet trainable parameters: {sigma_mu_params:.2f}K\n'.format(
sigma_mu_params=sigma_mu_params))
log_file.write(
'SigmaNNet trainable parameters: {sigma_n_params:.2f}K\n'.format(
sigma_n_params=sigma_n_params))
log_file.write(
'CNNDenoiser trainable parameters: {cnn_denoiser_params:.2f}M\n'.
format(cnn_denoiser_params=cnn_denoiser_params))
# --------------------------------------------
# Checkpoint
# --------------------------------------------
if args.resume == 'continue':
# # evaluating the loaded model first ...
# print("Starting from epoch: %d "%(start_epoch))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('checkpoint evaluation on epoch {0} ... \n'.format(
start_epoch))
dbsn_model.eval()
sigma_mu_model.eval()
sigma_n_model.eval()
cnn_denoiser_model.eval()
with torch.no_grad():
psnr_val = 0
psnr_val_dbsn = 0
psnr_val_cnn_denoiser = 0
for count, data in enumerate(dataset_val):
# input
img_val = data['clean'].cuda()
img_noise_val = data['noisy'].cuda()
# crop the input (8x MWCNN)
img_val = img_val[:, :, :-1, :-1]
img_noise_val = img_noise_val[:, :, :-1, :-1]
_, C, H, W = img_noise_val.shape
# forward
cnn_denoiser_out_val = cnn_denoiser_model(img_noise_val)
mu_out_val, mid_out_val = dbsn_model(img_noise_val)
sigma_mu_out_val = sigma_mu_model(mid_out_val)
#
sigma_mu_val = sigma_mu_out_val**2
if args.noise_type == 'gaussian':
sigma_n_out_val = sigma_n_model(img_noise_val)
sigma_n_out_val = sigma_n_out_val.mean(
dim=(2, 3), keepdim=True).repeat(1, 1, H, W)
else:
sigma_n_out_val = sigma_n_model(mu_out_val)
noise_est_val = F.softplus(sigma_n_out_val - 4) + (1e-3)
sigma_n_val = noise_est_val**2
map_out_val = (img_noise_val * sigma_mu_val + mu_out_val *
sigma_n_val) / (sigma_mu_val + sigma_n_val)
# compute PSNR
psnr_mu = batch_psnr(mu_out_val.clamp(0., 1.),
img_val.clamp(0., 1.), 1.)
psnr_val += psnr_mu
psnr_dbsn = batch_psnr(map_out_val.clamp(0., 1.),
img_val.clamp(0., 1.), 1.)
psnr_val_dbsn += psnr_dbsn
psnr_cnn_denoiser = batch_psnr(
cnn_denoiser_out_val.clamp(0., 1.), img_val.clamp(0., 1.),
1.)
psnr_val_cnn_denoiser += psnr_cnn_denoiser
# print
print(
"Image[%d]: psnr_mu=%.4f, psnr_dbsn=%.4f, psnr_cnn_denoiser=%.4f "
% (count, psnr_mu, psnr_dbsn, psnr_cnn_denoiser))
# logging
with open(logger_fname, "a") as log_file:
log_file.write(
'Image {0} \t'
'psnr_mu:{psnr_mu:.4f} \t'
'psnr_dbsn:{psnr_dbsn:.4f} \n'
'psnr_cnn_denoiser:{psnr_cnn_denoiser:.4f} \n'.format(
count,
psnr_mu=psnr_mu,
psnr_dbsn=psnr_dbsn,
psnr_cnn_denoiser=psnr_cnn_denoiser))
psnr_val /= len(dataset_val)
psnr_val_dbsn /= len(dataset_val)
psnr_val_cnn_denoiser /= len(dataset_val)
# print
print(
"Checkpoint avg psnr_mu: %.4f, avg psnr_dbsn: %.4f, avg psnr_cnn_denoiser: %.4f \n"
% (psnr_val, psnr_val_dbsn, psnr_val_cnn_denoiser))
# logging
with open(logger_fname, "a") as log_file:
log_file.write(
'checkpoint avg psnr_mu:{psnr_val:.4f} \t'
'avg psnr_dbsn:{psnr_val_dbsn:.4f} \t'
'avg psnr_cnn_denoiser:{psnr_val_cnn_denoiser:.4f} \n\n'.
format(psnr_val=psnr_val,
psnr_val_dbsn=psnr_val_dbsn,
psnr_val_cnn_denoiser=psnr_val_cnn_denoiser))
val_psnr_pre = psnr_val_cnn_denoiser
idx_epoch = start_epoch
val_psnr_curr = 0
# --------------------------------------------
# Training
# --------------------------------------------
# logging
with open(logger_fname, "a") as log_file:
log_file.write('started training\n')
# training
for epoch in range(start_epoch, args.epoch):
epoch_start_time = time.time()
#
print('lr: %.10f' %
(optimizer_cnn_denoiser.state_dict()["param_groups"][0]["lr"]))
# set training mode
dbsn_model.eval()
sigma_mu_model.eval()
sigma_n_model.eval()
cnn_denoiser_model.train()
# train
for i, data in enumerate(dataset):
# input
img_clean = data['clean'].cuda()
img_noise = data['noisy'].cuda()
#
optimizer_cnn_denoiser.zero_grad()
#
batch_size, C, H, W = img_clean.size()
assert (batch_size % 8 == 0)
"""
# split the input data into two sub-sets: S1&S2
# S1: (x_i, y_i) using y_i only, to generate denoised image
# S2: (x_j, y_j) using x_j only, to generate noisy image
"""
x_i = img_clean[:batch_size // 8, :, :, :]
y_i = img_noise[:batch_size // 8, :, :, :]
x_j = img_clean[batch_size // 8:, :, :, :]
y_j = img_noise[batch_size // 8:, :, :, :]
num_noisy = batch_size // 8
num_clean = batch_size - num_noisy
# compute x_i_star
with torch.no_grad():
mu_out, mid_out = dbsn_model(y_i)
sigma_mu_out = sigma_mu_model(mid_out)
# process sigma_mu & sigma_n
sigma_mu = sigma_mu_out**2
#
if args.noise_type == 'gaussian':
sigma_n_out = sigma_n_model(y_i)
sigma_n_out = sigma_n_out.mean(dim=(2, 3),
keepdim=True).repeat(
1, 1, H, W)
else:
sigma_n_out = sigma_n_model(mu_out)
noise_est = F.softplus(sigma_n_out - 4) + (1e-3)
sigma_n = noise_est**2
x_i_star = (y_i * sigma_mu + mu_out * sigma_n) / (sigma_mu +
sigma_n)
# generate y_j
sigma_n_x_j = sigma_n_model(x_j)
if args.noise_type == 'gaussian':
sigma_n_x_j = sigma_n_x_j.mean(dim=(2, 3),
keepdim=True).repeat(
1, 1, H, W)
noise_est = F.softplus(sigma_n_x_j - 4) + (1e-3)
sigma_n = (noise_est**2)**0.5
disturb = torch.rand(1, device='cuda') * (1.1 - 0.9) + 0.9
sigma_tmp = torch.randn(x_j.shape,
device='cuda') * sigma_n * disturb
y_j = x_j + sigma_tmp
# train cnn_denoiser with {y_i=img_noise, y_j}
y_i_out = cnn_denoiser_model(y_i)
y_j_out = cnn_denoiser_model(y_j)
# compute loss
loss = criterion(y_i_out, x_i_star, y_j_out, x_j)
loss = loss / (2 * batch_size)
loss_value = loss.item()
# backward and update parameters
loss.backward()
optimizer_cnn_denoiser.step()
# Results
training_params['step'] += args.batch_size
# print results
if training_params['step'] % (args.batch_size *
args.display_freq) == 0:
#
cnn_out = torch.cat((y_i_out, y_j_out), 0)
train_psnr = batch_psnr(cnn_out.clamp(0., 1.),
img_clean.clamp(0., 1.), 1.0)
print(("[epoch %d][%d/%d], loss:%.4f, psnr: %.4f") %
(epoch, i * args.batch_size, len(dataset), loss_value,
train_psnr))
schedule_cnn_denoiser.step()
# taking time for each epoch
tr_take_time = time.time() - epoch_start_time
# --------------------------------------------
# validation
# --------------------------------------------
# print
# print("Evaluating on "+str(val_setname[0]))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('Evaluation on %s \n' % (str(val_setname[0])))
# set evaluation mode
dbsn_model.eval()
sigma_mu_model.eval()
sigma_n_model.eval()
cnn_denoiser_model.eval()
#
val_start_time = time.time()
with torch.no_grad():
psnr_val = 0
psnr_val_dbsn = 0
psnr_val_cnn_denoiser = 0
for count, data in enumerate(dataset_val):
# input
img_val = data['clean'].cuda()
img_noise_val = data['noisy'].cuda()
# crop the input (8x MWCNN)
img_val = img_val[:, :, :-1, :-1]
img_noise_val = img_noise_val[:, :, :-1, :-1]
_, C, H, W = img_noise_val.shape
# forward
cnn_denoiser_out_val = cnn_denoiser_model(img_noise_val)
mu_out_val, mid_out_val = dbsn_model(img_noise_val)
sigma_mu_out_val = sigma_mu_model(mid_out_val)
#
sigma_mu_val = sigma_mu_out_val**2
if args.noise_type == 'gaussian':
sigma_n_out_val = sigma_n_model(img_noise_val)
sigma_n_out_val = sigma_n_out_val.mean(
dim=(2, 3), keepdim=True).repeat(1, 1, H, W)
else:
sigma_n_out_val = sigma_n_model(mu_out_val)
noise_est_val = F.softplus(sigma_n_out_val - 4) + (1e-3)
sigma_n_val = noise_est_val**2
map_out_val = (img_noise_val * sigma_mu_val + mu_out_val *
sigma_n_val) / (sigma_mu_val + sigma_n_val)
# compute PSNR
psnr_mu = batch_psnr(mu_out_val.clamp(0., 1.),
img_val.clamp(0., 1.), 1.)
psnr_val += psnr_mu
psnr_dbsn = batch_psnr(map_out_val.clamp(0., 1.),
img_val.clamp(0., 1.), 1.)
psnr_val_dbsn += psnr_dbsn
psnr_cnn_denoiser = batch_psnr(
cnn_denoiser_out_val.clamp(0., 1.), img_val.clamp(0., 1.),
1.)
psnr_val_cnn_denoiser += psnr_cnn_denoiser
# # print
# print("Image[%d]: psnr_mu=%.4f, psnr_dbsn=%.4f, psnr_cnn_denoiser=%.4f" % (count, psnr_mu, psnr_dbsn, psnr_cnn_denoiser))
# logging
with open(logger_fname, "a") as log_file:
log_file.write(
'Image {0} \t'
'psnr_mu:{psnr_mu:.4f} \t'
'psnr_dbsn:{psnr_dbsn:.4f} \n'
'psnr_cnn_denoiser:{psnr_cnn_denoiser:.4f} \n'.format(
count,
psnr_mu=psnr_mu,
psnr_dbsn=psnr_dbsn,
psnr_cnn_denoiser=psnr_cnn_denoiser))
torch.cuda.synchronize()
val_take_time = time.time() - val_start_time
psnr_val /= len(dataset_val)
psnr_val_dbsn /= len(dataset_val)
psnr_val_cnn_denoiser /= len(dataset_val)
# Record the best PSNR
val_psnr_curr = psnr_val_cnn_denoiser
if epoch == 0:
val_psnr_pre = val_psnr_curr
# save model and checkpoint
if val_psnr_curr >= val_psnr_pre or (epoch +
1) % args.save_model_freq == 0:
# save model and checkpoint
savename = args.save_prefix
training_params['start_epoch'] = epoch
save_dict = {
'state_dict_dbsn': dbsn_model.state_dict(),
'state_dict_sigma_mu': sigma_mu_model.state_dict(),
'state_dict_sigma_n': sigma_n_model.state_dict(),
'state_dict_cnn_denoiser': cnn_denoiser_model.state_dict(),
'optimizer_state_cnn_denoiser':
optimizer_cnn_denoiser.state_dict(),
'schedule_state_cnn_denoiser':
schedule_cnn_denoiser.state_dict(),
'training_params': training_params,
'args': args
}
torch.save(
save_dict,
os.path.join(ckpt_save_path,
ckpt_save_prefix + '{}.pth'.format(epoch)))
if val_psnr_curr >= val_psnr_pre:
val_psnr_pre = val_psnr_curr
idx_epoch = epoch
torch.save(
dbsn_model.state_dict(),
os.path.join(ckpt_save_path,
'dbsn_net_best_e{}.pth'.format(epoch)))
torch.save(
sigma_mu_model.state_dict(),
os.path.join(ckpt_save_path,
'sigma_mu_net_best_e{}.pth'.format(epoch)))
torch.save(
sigma_n_model.state_dict(),
os.path.join(ckpt_save_path,
'sigma_n_net_best_e{}.pth'.format(epoch)))
torch.save(
cnn_denoiser_model.state_dict(),
os.path.join(
ckpt_save_path,
'cnn_denoiser_net_best_e{}.pth'.format(epoch)))
del save_dict
# print
print('Epoch [%d/%d/%d] \t Train_time: %d sec \t Val_time: %d sec \t' %
(epoch, args.epoch, idx_epoch, tr_take_time, val_take_time))
print(
'Epoch [%d/%d/%d] \t VAL psnr_dbsn: %.4f \t VAL psnr_cnn_denoiser: %.4f \t Best VAL psnr_cnn_denoiser: %.4f \t'
% (epoch, args.epoch, idx_epoch, psnr_val_dbsn,
psnr_val_cnn_denoiser, val_psnr_pre))
# logging
with open(logger_fname, "a") as log_file:
#[0/1/2]: 0-current epoch, 1-total epoch, 2-best epoch
log_file.write(
'Epoch: [{0}/{1}/{2}] \t'
'Train_time:{tr_take_time:.1f} sec \t'
'Val_time:{val_take_time:.1f} sec \t'
'VAL psnr_dbsnr:{psnr_val_dbsn:.4f} \t'
'VAL psnr_cnn_denoiser:{psnr_val_cnn_denoiser:.4f} \t'
'Best VAL psnr_cnn_denoiser:{val_psnr_pre:.4f} \n'.format(
epoch,
args.epoch,
idx_epoch,
tr_take_time=tr_take_time,
val_take_time=val_take_time,
psnr_val_dbsn=psnr_val_dbsn,
psnr_val_cnn_denoiser=psnr_val_cnn_denoiser,
val_psnr_pre=val_psnr_pre))
def main(args):
if args.noise_type == 'Poisson-Gaussian':
if args.data_type == 'RawRGB' and args.data_name == 'SIDD':
tr_data_dir = '../../../data/train_SIDD_25000_2.hdf5'
te_data_dir = '../../../data/test_SIDD.hdf5'
save_file_name = str(args.date)+ '_DBSN_' + str(args.data_type) +'_'+ str(args.data_name)
elif args.data_type == 'RawRGB' and args.data_name == 'DND':
tr_data_dir = '../../../data/train_DND_25000_2.hdf5'
te_data_dir = '../../../data/test_SIDD.hdf5'
save_file_name = str(args.date)+ '_DBSN_' + str(args.data_type) +'_'+ str(args.data_name)
elif args.data_type == 'Grayscale' and args.data_name == 'CF_FISH':
tr_data_dir = '../../../data/train_CF_FISH_25000x256x256_2.hdf5'
te_data_dir = '../../../data/test_CF_FISH_raw.hdf5'
save_file_name = str(args.date)+ '_DBSN_' + str(args.data_type) +'_'+ str(args.data_name)
elif args.data_type == 'Grayscale' and args.data_name == 'CF_MICE':
tr_data_dir = '../../../data/train_CF_MICE_25000x256x256_2.hdf5'
te_data_dir = '../../../data/test_CF_MICE_raw.hdf5'
save_file_name = str(args.date)+ '_DBSN_' + str(args.data_type) +'_'+ str(args.data_name)
elif args.data_type == 'Grayscale' and args.data_name == 'TP_MICE':
tr_data_dir = '../../../data/train_TP_MICE_25000x256x256_2.hdf5'
te_data_dir = '../../../data/test_TP_MICE_raw.hdf5'
save_file_name = str(args.date)+ '_DBSN_' + str(args.data_type) +'_'+ str(args.data_name)
elif args.data_type == 'RawRGB' and args.data_name == 'fivek' and args.alpha == 0 and args.beta == 0:
tr_data_dir = '../../../data/train_fivek_rawRGB_25000x256x256_cropped_random_noise.hdf5'
te_data_dir = '../../../data/test_fivek_rawRGB_random_noise.hdf5'
save_file_name = str(args.date)+ '_DBSN_' + str(args.data_type) +'_' + 'random_noise'
else:
tr_data_dir = '../../../data/train_fivek_rawRGB_25000x256x256_cropped_alpha_'+str(args.alpha)+'_beta_'+str(args.beta)+'.hdf5'
te_data_dir = '../../../data/test_fivek_rawRGB_alpha_'+str(args.alpha)+'_beta_'+str(args.beta)+'.hdf5'
save_file_name = str(args.date)+ '_DBSN_' + str(args.data_type) +'_'+ str(args.data_name)+ '_alpha_' + str(args.alpha) + '_beta_' + str(args.beta)
print ('tr data dir : ', tr_data_dir)
print ('te data dir : ', te_data_dir)
print ('save_file_name : ', save_file_name)
# # Init loggers
# os.makedirs(args.log_dir, exist_ok=True)
# if args.noise_type == 'gaussian':
# if len(args.train_noiseL)>1:
# noise_level = 'nL'+str(int(args.train_noiseL[0]))+','+str(int(args.train_noiseL[1]))
# else:
# noise_level = 'nL'+str(int(args.train_noiseL[0]))
# elif args.noise_type == 'poisson_gaussian':
# if len(args.train_noiseL)>2:
# noise_level = 'sigmaS'+str(int(args.train_noiseL[1]))+'_sigmaC'+str(int(args.train_noiseL[2]))
# else:
# noise_level = 'sigmaS'+str(int(args.train_noiseL[0]))+'_sigmaC'+str(int(args.train_noiseL[1]))
# else:
# raise ValueError('Noise_type [%s] is not found.' % (args.noise_type))
# checkpoint_dir = args.save_prefix + '_' + noise_level
# ckpt_save_path = os.path.join(args.log_dir, checkpoint_dir)
# os.makedirs(ckpt_save_path, exist_ok=True)
# logger_fname = os.path.join(ckpt_save_path, checkpoint_dir+'_log.txt')
# with open(logger_fname, "w") as log_file:
# now = time.strftime("%c")
# log_file.write('================ Log output (%s) ================\n' % now)
# log_file.write('Parameters \n')
# for key in opt.__dict__:
# p = key+': '+str(opt.__dict__[key])
# log_file.write('%s\n' % p)
# net architecture
dbsn_net = DBSN_Model(in_ch = args.input_channel,
out_ch = args.output_channel,
mid_ch = args.middle_channel,
blindspot_conv_type = args.blindspot_conv_type,
blindspot_conv_bias = args.blindspot_conv_bias,
br1_block_num = args.br1_block_num,
br1_blindspot_conv_ks =args.br1_blindspot_conv_ks,
br2_block_num = args.br2_block_num,
br2_blindspot_conv_ks = args.br2_blindspot_conv_ks,
activate_fun = args.activate_fun)
sigma_mu_net = Sigma_mu_Net(in_ch=args.middle_channel,
out_ch=args.sigma_mu_output_channel,
mid_ch=args.sigma_mu_middle_channel,
layers=args.sigma_mu_layers,
kernel_size=args.sigma_mu_kernel_size,
bias=args.sigma_mu_bias)
sigma_n_net = Sigma_n_Net(in_ch=args.sigma_n_input_channel,
out_ch=args.sigma_n_output_channel,
mid_ch=args.sigma_n_middle_channel,
layers=args.sigma_n_layers,
kernel_size=args.sigma_n_kernel_size,
bias=args.sigma_n_bias)
# loss function
criterion = MAPLoss().cuda()
# Move to GPU
dbsn_model = nn.DataParallel(dbsn_net, args.device_ids).cuda()
sigma_mu_model = nn.DataParallel(sigma_mu_net, args.device_ids).cuda()
sigma_n_model = nn.DataParallel(sigma_n_net, args.device_ids).cuda()
# new or continue
# ckpt_save_prefix = args.save_prefix + '_ckpt_e'
# initial_epoch = findLastCheckpoint(save_dir=ckpt_save_path, save_pre = ckpt_save_prefix)
# if initial_epoch > 0:
# print('*****resuming by loading epoch %03d' % initial_epoch)
# args.resume = "continue"
# args.last_ckpt = os.path.join(ckpt_save_path, ckpt_save_prefix + str(initial_epoch) + '.pth')
# Optimizer
training_params = None
optimizer_dbsn = None
optimizer_sigma_mu = None
optimizer_sigma_n = None
if args.resume == "continue":
tmp_ckpt=torch.load(args.last_ckpt,map_location=torch.device('cuda', args.device_ids[0]))
training_params = tmp_ckpt['training_params']
start_epoch = training_params['start_epoch']
# Initialize dbsn_model
pretrained_dict=tmp_ckpt['state_dict_dbsn']
model_dict=dbsn_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
dbsn_model.load_state_dict(model_dict)
optimizer_dbsn = optim.Adam(dbsn_model.parameters(), lr=args.lr_dbsn)
optimizer_dbsn.load_state_dict(tmp_ckpt['optimizer_state_dbsn'])
schedule_dbsn = torch.optim.lr_scheduler.MultiStepLR(optimizer_dbsn, milestones=args.steps, gamma=args.decay_rate)
# Initialize sigma_mu_model
pretrained_dict=tmp_ckpt['state_dict_sigma_mu']
model_dict=sigma_mu_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_mu_model.load_state_dict(model_dict)
optimizer_sigma_mu = optim.Adam(sigma_mu_model.parameters(), lr=args.lr_sigma_mu)
optimizer_sigma_mu.load_state_dict(tmp_ckpt['optimizer_state_sigma_mu'])
schedule_sigma_mu = torch.optim.lr_scheduler.MultiStepLR(optimizer_sigma_mu, milestones=args.steps, gamma=args.decay_rate)
# Initialize sigma_n_model
pretrained_dict=tmp_ckpt['state_dict_sigma_n']
model_dict=sigma_n_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_n_model.load_state_dict(model_dict)
optimizer_sigma_n = optim.Adam(sigma_n_model.parameters(), lr=args.lr_sigma_n)
optimizer_sigma_n.load_state_dict(tmp_ckpt['optimizer_state_sigma_n'])
schedule_sigma_n = torch.optim.lr_scheduler.MultiStepLR(optimizer_sigma_n, milestones=args.steps, gamma=args.decay_rate)
elif args.resume == "new":
training_params = {}
training_params['step'] = 0
start_epoch = 0
# Initialize dbsn
optimizer_dbsn = optim.Adam(dbsn_model.parameters(), lr=args.lr_dbsn)
schedule_dbsn = torch.optim.lr_scheduler.MultiStepLR(optimizer_dbsn, milestones=args.steps, gamma=args.decay_rate)
# Initialize net_sigma_mu
optimizer_sigma_mu = optim.Adam(sigma_mu_model.parameters(), lr=args.lr_sigma_mu)
schedule_sigma_mu = torch.optim.lr_scheduler.MultiStepLR(optimizer_sigma_mu, milestones=args.steps, gamma=args.decay_rate)
# Initialize optimizer for net_sigma_n
optimizer_sigma_n = optim.Adam(sigma_n_model.parameters(), lr=args.lr_sigma_n)
schedule_sigma_n = torch.optim.lr_scheduler.MultiStepLR(optimizer_sigma_n, milestones=args.steps, gamma=args.decay_rate)
# logging
dbsn_params = sum(param.numel() for param in dbsn_net.parameters() if param.requires_grad)/(1e6)
sigma_mu_params = sum(param.numel() for param in sigma_mu_net.parameters() if param.requires_grad)/(1e3)
sigma_n_params = sum(param.numel() for param in sigma_n_net.parameters() if param.requires_grad)/(1e3)
# with open(logger_fname, "a") as log_file:
# log_file.write('model created\n')
# log_file.write('DBSN trainable parameters: {dbsn_params:.2f}M\n'.format(dbsn_params=dbsn_params))
# log_file.write('SigmaMuNet trainable parameters: {sigma_mu_params:.2f}K\n'.format(sigma_mu_params=sigma_mu_params))
# log_file.write('SigmaNNet trainable parameters: {sigma_n_params:.2f}K\n'.format(sigma_n_params=sigma_n_params))
# set training set
# train_setname = args.trainset
# dataset = create_dataset(train_setname, 'train', args).load_data()
# dataset_num = len(dataset)
# set val set
# val_setname = args.valset
# dataset_val = create_dataset(val_setname, 'val', args).load_data()
tr_data_loader = TrdataLoader(tr_data_dir, args)
dataset = DataLoader(tr_data_loader, batch_size=args.batch_size, shuffle=True, num_workers=0, drop_last=True)
dataset_num = len(dataset)
te_data_loader = TedataLoader(te_data_dir, args)
dataset_val = DataLoader(te_data_loader, batch_size=4, shuffle=False, num_workers=0, drop_last=False)
# logging
# with open(logger_fname, "a") as log_file:
# log_file.write('training/val dataset created\n')
# log_file.write('number of training examples {0} \n'.format(dataset_num))
# # --------------------------------------------
# # Checkpoint
# # --------------------------------------------
# if args.resume == 'continue':
# # # evaluating the loaded model first ...
# # print("Starting from epoch: %d "%(start_epoch))
# # logging
# with open(logger_fname, "a") as log_file:
# log_file.write('checkpoint evaluation on epoch {0} ... \n'.format(start_epoch))
# dbsn_model.eval()
# sigma_mu_model.eval()
# sigma_n_model.eval()
# with torch.no_grad():
# psnr_val = 0
# psnr_val_update = 0
# for count, data in enumerate(dataset_val):
# #
# img_val = data['clean'].cuda()
# img_noise_val = data['noisy'].cuda()
# _,C,H,W = img_noise_val.shape
# # forward
# mu_out_val, mid_out_val = dbsn_model(img_noise_val)
# sigma_mu_out_val = sigma_mu_model(mid_out_val)
# #
# sigma_mu_val = sigma_mu_out_val ** 2
# if args.noise_type == 'gaussian':
# sigma_n_out_val = sigma_n_model(img_noise_val)
# sigma_n_out_val = sigma_n_out_val.mean(dim=(2,3), keepdim=True).repeat(1,1,H,W)
# else:
# sigma_n_out_val = sigma_n_model(mu_out_val)
# noise_est_val = F.softplus(sigma_n_out_val - 4) + (1e-3)
# sigma_n_val = noise_est_val ** 2
# map_out_val = (img_noise_val * sigma_mu_val + mu_out_val * sigma_n_val) / (sigma_mu_val + sigma_n_val)
# # compute PSNR
# psnr = batch_psnr(mu_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
# psnr_val+=psnr
# psnr_update = batch_psnr(map_out_val.clamp(0., 1.), img_val, 1.)
# psnr_val_update+=psnr_update
# # print
# print("Image: %d, psnr_mu: %.4f, psnr_mu_map: %.4f " % (count, psnr, psnr_update))
# # logging
# with open(logger_fname, "a") as log_file:
# log_file.write('Image {0} \t'
# 'psnr_mu:{psnr:.4f} \t'
# 'psnr_mu_map:{psnr_update:.4f} \n'.format(count, psnr=psnr,psnr_update=psnr_update))
# psnr_val /= len(dataset_val)
# psnr_val_update /= len(dataset_val)
# # print
# print("Checkpoint avg psnr_mu: %.4f, avg psnr_mu_map: %.4f \n" % (psnr_val, psnr_val_update))
# # logging
# with open(logger_fname, "a") as log_file:
# log_file.write('checkpoint avg psnr_mu:{psnr_val:.4f} \t'
# 'avg psnr_mu_map:{psnr_val:.4f} \n\n'.format(psnr_val=psnr_val, psnr_val_update=psnr_val_update))
# val_psnr_pre = psnr_val
# idx_epoch = start_epoch
result_psnr_arr = []
result_ssim_arr = []
result_mu_psnr_arr = []
result_mu_ssim_arr = []
result_time_arr = []
result_denoised_img_arr = []
result_te_loss_arr = []
result_tr_loss_arr = []
val_psnr_curr = 0
# --------------------------------------------
# Training
# --------------------------------------------
# logging
# with open(logger_fname, "a") as log_file:
# log_file.write('started training\n')
for epoch in range(start_epoch, args.epoch):
epoch_start_time = time.time()
# print learning rate
print('lr: %f, %f, %f' % (optimizer_dbsn.state_dict()["param_groups"][0]["lr"],
optimizer_sigma_mu.state_dict()["param_groups"][0]["lr"],
optimizer_sigma_n.state_dict()["param_groups"][0]["lr"]))
# set training mode
dbsn_model.train()
sigma_mu_model.train()
sigma_n_model.train()
# begin to train
# for i, data in enumerate(dataset):
# load training data
# img_train = data['clean'].cuda()
# img_noise = data['noisy'].cuda()
tr_loss = []
for batch_idx, (source, target) in enumerate(dataset):
img_train = target.cuda()
img_noise = source.cuda()
_,C,H,W = img_noise.shape
#
optimizer_dbsn.zero_grad()
optimizer_sigma_mu.zero_grad()
optimizer_sigma_n.zero_grad()
# forward
mu_out, mid_out = dbsn_model(img_noise)
sigma_mu_out = sigma_mu_model(mid_out)
# process sigma_mu & sigma_n
sigma_mu = sigma_mu_out ** 2
#
if args.noise_type == 'gaussian':
sigma_n_out = sigma_n_model(img_noise)
sigma_n_out = sigma_n_out.mean(dim=(2,3),keepdim=True).repeat(1,1,H,W)
else:
sigma_n_out = sigma_n_model(mu_out)
noise_est = F.softplus(sigma_n_out - 4) + (1e-3)
sigma_n = noise_est ** 2
#
sigma_y = sigma_mu + sigma_n
# compute loss
loss = criterion(img_noise, mu_out, sigma_mu, sigma_n, sigma_y)
loss = loss / (2*args.batch_size)
loss_value = loss.item()
#
loss.backward()
optimizer_dbsn.step()
optimizer_sigma_mu.step()
optimizer_sigma_n.step()
tr_loss.append(loss.detach().cpu().numpy())
# Results
training_params['step'] += args.batch_size
# print results
if training_params['step'] % (args.batch_size*args.display_freq) == 0:
with torch.no_grad():
# compute map_out
map_out = (img_noise * sigma_mu + mu_out * sigma_n) / (sigma_mu + sigma_n)
# compute training psnr
train_psnr = batch_psnr(mu_out.clamp(0., 1.), img_train, 1.0)
train_psnr_update = batch_psnr(map_out.clamp(0., 1.), img_train, 1.)
# # print
# print(("[epoch %d/%d][%d/%d], loss:%.4f, psnr_mu: %.4f, psnr_dbsn: %.4f ") %
# (epoch, args.epoch, i*args.batch_size, len(dataset), loss_value, train_psnr, train_psnr_update))
# logging
# with open(logger_fname, "a") as log_file:
# log_file.write('Epoch: [{0}/{1}][{2}/{3}] \t'
# 'loss {loss_value: .4f} \t'
# 'psnr_mu:{train_psnr:.4f} \t'
# 'psnr_dbsn:{train_psnr_update:.4f} \n'.format(epoch, args.epoch, i*args.batch_size, len(dataset),
# loss_value=loss_value, train_psnr=train_psnr, train_psnr_update=train_psnr_update))
mean_tr_loss = np.mean(tr_loss)
schedule_dbsn.step()
schedule_sigma_mu.step()
schedule_sigma_n.step()
# taking time for each epoch
tr_take_time = time.time() - epoch_start_time
# --------------------------------------------
# validation
# --------------------------------------------
# print
# print("Evaluating on "+str(val_setname[0]))
# logging
# with open(logger_fname, "a") as log_file:
# log_file.write('Evaluation on %s \n' % (str(val_setname[0])) )
#
dbsn_model.eval()
sigma_mu_model.eval()
sigma_n_model.eval()
ssim_arr = []
mu_ssim_arr = []
time_arr = []
with torch.no_grad():
psnr_val = 0
psnr_val_dbsn = 0
# for count, data in enumerate(dataset_val):
# # load input
# img_val = data['clean'].cuda()
# img_noise_val = data['noisy'].cuda()
for batch_idx, (source, target) in enumerate(dataset_val):
val_start_time = time.time()
img_noise_val = source.cuda()
img_val = target.cuda()
_,C,H,W = img_noise_val.shape
# forward
mu_out_val, mid_out_val = dbsn_model(img_noise_val)
sigma_mu_out_val = sigma_mu_model(mid_out_val)
#
sigma_mu_val = sigma_mu_out_val ** 2
if args.noise_type == 'gaussian':
sigma_n_out_val = sigma_n_model(img_noise_val)
sigma_n_out_val = sigma_n_out_val.mean(dim=(2,3), keepdim=True).repeat(1,1,H,W)
else:
sigma_n_out_val = sigma_n_model(mu_out_val)
noise_est_val = F.softplus(sigma_n_out_val - 4) + (1e-3)
sigma_n_val = noise_est_val ** 2
map_out_val = (img_noise_val * sigma_mu_val + mu_out_val * sigma_n_val) / (sigma_mu_val + sigma_n_val)
inference_time = (time.time() - val_start_time)
time_arr.append(inference_time)
# compute PSNR
psnr = batch_psnr(mu_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
psnr_val+=psnr
psnr_dbsn = batch_psnr(map_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
psnr_val_dbsn+=psnr_dbsn
map_out_val = map_out_val.cpu().numpy()
X_hat = np.clip(map_out_val, 0, 1)
X = target.cpu().numpy()
ssim_arr.append(get_SSIM(X[0], X_hat[0]))
mu_out_val = mu_out_val.cpu().numpy()
X_hat = np.clip(mu_out_val, 0, 1)
X = target.cpu().numpy()
mu_ssim_arr.append(get_SSIM(X[0], X_hat[0]))
# # print
# print("Image: %d, psnr_mu: %.4f, psnr_dbsn: %.4f " % (count, psnr, psnr_update))
# logging
# with open(logger_fname, "a") as log_file:
# log_file.write('Image {0} \t'
# 'psnr_mu:{psnr:.4f} \t'
# 'psnr_dbsn:{psnr_dbsn:.4f} \n'.format(count, psnr=psnr,psnr_dbsn=psnr_dbsn))
torch.cuda.synchronize()
psnr_val /= len(dataset_val)
psnr_val_dbsn /= len(dataset_val)
# Record the best PSNR
val_psnr_curr = psnr_val_dbsn
if epoch==0:
val_psnr_pre = val_psnr_curr
mean_ssim = np.mean(ssim_arr)
mean_mu_ssim = np.mean(mu_ssim_arr)
mean_time = np.mean(time_arr)
result_psnr_arr.append(psnr_val_dbsn)
result_ssim_arr.append(mean_ssim)
result_mu_psnr_arr.append(psnr_val)
result_mu_ssim_arr.append(mean_mu_ssim)
result_time_arr.append(mean_time)
result_te_loss_arr.append(0)
result_tr_loss_arr.append(mean_tr_loss)
result_denoised_img_arr.append(0)
sio.savemat('../../../result_data/'+save_file_name + '_result',{'tr_loss_arr':result_tr_loss_arr, 'te_loss_arr':result_te_loss_arr,'psnr_arr':result_psnr_arr, 'ssim_arr':result_ssim_arr,'mu_psnr_arr':result_mu_psnr_arr, 'mu_ssim_arr':result_mu_ssim_arr,'time_arr':result_time_arr, 'denoised_img':result_denoised_img_arr})
torch.save(dbsn_model.state_dict(), '../../../weights/'+save_file_name + '_dbsn_model.w')
orch.save(sigma_mu_model.state_dict(), '../../../weights/'+save_file_name + '_sigma_mu_model.w')
orch.save(sigma_n_model.state_dict(), '../../../weights/'+save_file_name + '_sigma_n_model.w')
# print
print('Epoch [%d/%d] \t tr loss: %.4f \t val psnr_mu: %.4f \t val SSIM_mu: %.4f \t val psnr_dbsn: %.4f \t SSIM_dbsn: %.4f \t Train_time: %f sec \t Val_time: %f sec \t' %
(epoch, args.epoch, mean_tr_loss, psnr_val, mean_mu_ssim, psnr_val_dbsn, mean_ssim, tr_take_time, mean_time))
# save model and checkpoint
if val_psnr_curr >= val_psnr_pre or (epoch+1) % args.save_model_freq == 0:
training_params['start_epoch'] = epoch
save_dict = {'state_dict_dbsn': dbsn_model.state_dict(),
'state_dict_sigma_mu': sigma_mu_model.state_dict(),
'state_dict_sigma_n': sigma_n_model.state_dict(),
'optimizer_state_dbsn': optimizer_dbsn.state_dict(),
'optimizer_state_sigma_mu': optimizer_sigma_mu.state_dict(),
'optimizer_state_sigma_n': optimizer_sigma_n.state_dict(),
'schedule_state_dbsn': schedule_dbsn.state_dict(),
'schedule_state_sigma_mu': schedule_sigma_mu.state_dict(),
'schedule_state_sigma_n': schedule_sigma_n.state_dict(),
'training_params': training_params,
'args': args}
# torch.save(save_dict, os.path.join(ckpt_save_path, ckpt_save_prefix + '{}.pth'.format(epoch)))
if val_psnr_curr >= val_psnr_pre:
val_psnr_pre = val_psnr_curr
idx_epoch = epoch
torch.save(dbsn_model.state_dict(), '../../../weights/'+save_file_name + '_dbsn_model_best.w')
torch.save(sigma_mu_model.state_dict(), '../../../weights/'+save_file_name + '_sigma_mu_model_best.w')
torch.save(sigma_n_model.state_dict(), '../../../weights/'+save_file_name + '_sigma_n_model_best.w')
del save_dict
def main(args):
# set val set
val_setname = args.valset
dataset_val = create_dataset(val_setname, 'val', args).load_data()
# net architecture
dbsn_net = DBSN_Model(in_ch = args.input_channel,
out_ch = args.output_channel,
mid_ch = args.middle_channel,
blindspot_conv_type = args.blindspot_conv_type,
blindspot_conv_bias = args.blindspot_conv_bias,
br1_block_num = args.br1_block_num,
br1_blindspot_conv_ks =args.br1_blindspot_conv_ks,
br2_block_num = args.br2_block_num,
br2_blindspot_conv_ks = args.br2_blindspot_conv_ks,
activate_fun = args.activate_fun)
sigma_mu_net = Sigma_mu_Net(in_ch=args.middle_channel,
out_ch=args.sigma_mu_output_channel,
mid_ch=args.sigma_mu_middle_channel,
layers=args.sigma_mu_layers,
kernel_size=args.sigma_mu_kernel_size,
bias=args.sigma_mu_bias)
sigma_n_net = Sigma_n_Net(in_ch=args.sigma_n_input_channel,
out_ch=args.sigma_n_output_channel,
mid_ch=args.sigma_n_middle_channel,
layers=args.sigma_n_layers,
kernel_size=args.sigma_n_kernel_size,
bias=args.sigma_n_bias)
cnn_denoiser_net = MWCNN()
# Move to GPU
dbsn_model = nn.DataParallel(dbsn_net, args.device_ids).cuda()
sigma_mu_model = nn.DataParallel(sigma_mu_net, args.device_ids).cuda()
sigma_n_model = nn.DataParallel(sigma_n_net, args.device_ids).cuda()
cnn_denoiser_model = cnn_denoiser_net.cuda()
tmp_ckpt=torch.load(args.last_ckpt,map_location=torch.device('cuda', args.device_ids[0]))
training_params = tmp_ckpt['training_params']
start_epoch = training_params['start_epoch']
# Initialize dbsn_model
pretrained_dict=tmp_ckpt['state_dict_dbsn']
model_dict=dbsn_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
dbsn_model.load_state_dict(model_dict)
# Initialize sigma_mu_model
pretrained_dict=tmp_ckpt['state_dict_sigma_mu']
model_dict=sigma_mu_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_mu_model.load_state_dict(model_dict)
# Initialize sigma_n_model
pretrained_dict=tmp_ckpt['state_dict_sigma_n']
model_dict=sigma_n_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_n_model.load_state_dict(model_dict)
# Initialize cnn_denoiser_model
pretrained_dict=tmp_ckpt['state_dict_cnn_denoiser']
model_dict=cnn_denoiser_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
cnn_denoiser_model.load_state_dict(model_dict)
# --------------------------------------------
# Evaluation
# --------------------------------------------
print("Evaluating on "+str(val_setname[0]))
dbsn_model.eval()
sigma_mu_model.eval()
sigma_n_model.eval()
cnn_denoiser_model.eval()
with torch.no_grad():
psnr_val = 0
psnr_val_dbsn = 0
psnr_val_cnn_denoiser = 0
for count, data in enumerate(dataset_val):
# input
img_val = data['clean'].cuda()
img_noise_val = data['noisy'].cuda()
# crop the input (8x MWCNN)
img_val = img_val[:,:,:-1,:-1]
img_noise_val = img_noise_val[:,:,:-1,:-1]
_,C,H,W = img_noise_val.shape
# forward
cnn_denoiser_out_val = cnn_denoiser_model(img_noise_val)
mu_out_val, mid_out_val = dbsn_model(img_noise_val)
sigma_mu_out_val = sigma_mu_model(mid_out_val)
#
sigma_mu_val = sigma_mu_out_val ** 2
if args.noise_type == 'gaussian':
sigma_n_out_val = sigma_n_model(img_noise_val)
sigma_n_out_val = sigma_n_out_val.mean(dim=(2,3), keepdim=True).repeat(1,1,H,W)
else:
sigma_n_out_val = sigma_n_model(mu_out_val)
noise_est_val = F.softplus(sigma_n_out_val - 4) + (1e-3)
sigma_n_val = noise_est_val ** 2
map_out_val = (img_noise_val * sigma_mu_val + mu_out_val * sigma_n_val) / (sigma_mu_val + sigma_n_val)
# compute PSNR
psnr_mu = batch_psnr(mu_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
psnr_val+=psnr_mu
psnr_dbsn = batch_psnr(map_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
psnr_val_dbsn+=psnr_dbsn
psnr_cnn_denoiser = batch_psnr(cnn_denoiser_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
psnr_val_cnn_denoiser+=psnr_cnn_denoiser
# print
print("Image[%d]: psnr_mu=%.4f, psnr_dbsn=%.4f, psnr_cnn_denoiser=%.4f " % (count, psnr_mu, psnr_dbsn, psnr_cnn_denoiser))
psnr_val /= len(dataset_val)
psnr_val_dbsn /= len(dataset_val)
psnr_val_cnn_denoiser /= len(dataset_val)
# print
print("VAL avg psnr_mu: %.4f, avg psnr_dbsn: %.4f, avg psnr_cnn_denoiser: %.4f \n" % (psnr_val, psnr_val_dbsn, psnr_val_cnn_denoiser))
def main(args):
# Init loggers
os.makedirs(args.log_dir, exist_ok=True)
if args.noise_type == 'gaussian':
if len(args.train_noiseL)>1:
noise_level = 'nL'+str(int(args.train_noiseL[0]))+','+str(int(args.train_noiseL[1]))
else:
noise_level = 'nL'+str(int(args.train_noiseL[0]))
elif args.noise_type == 'poisson_gaussian':
if len(args.train_noiseL)>2:
noise_level = 'sigmaS'+str(int(args.train_noiseL[1]))+'_sigmaC'+str(int(args.train_noiseL[2]))
else:
noise_level = 'sigmaS'+str(int(args.train_noiseL[0]))+'_sigmaC'+str(int(args.train_noiseL[1]))
else:
raise ValueError('Noise_type [%s] is not found.' % (args.noise_type))
checkpoint_dir = args.save_prefix + '_' + noise_level
ckpt_save_path = os.path.join(args.log_dir, checkpoint_dir)
os.makedirs(ckpt_save_path, exist_ok=True)
logger_fname = os.path.join(args.log_dir, checkpoint_dir+'_log.txt')
with open(logger_fname, "w") as log_file:
now = time.strftime("%c")
log_file.write('================ Log output (%s) ================\n' % now)
log_file.write('Parameters \n')
for key in opt.__dict__:
p = key+': '+str(opt.__dict__[key])
log_file.write('%s\n' % p)
# net architecture
dbsn_net = DBSN_Model(in_ch = args.input_channel,
out_ch = args.output_channel,
mid_ch = args.middle_channel,
blindspot_conv_type = args.blindspot_conv_type,
blindspot_conv_bias = args.blindspot_conv_bias,
br1_block_num = args.br1_block_num,
br1_blindspot_conv_ks =args.br1_blindspot_conv_ks,
br2_block_num = args.br2_block_num,
br2_blindspot_conv_ks = args.br2_blindspot_conv_ks,
activate_fun = args.activate_fun)
# loss function
criterion = L2Loss().cuda()
# Move to GPU
dbsn_model = nn.DataParallel(dbsn_net, args.device_ids).cuda()
# Optimizer
training_params = None
optimizer_dbsn = None
if args.resume == "continue":
tmp_ckpt=torch.load(args.last_ckpt,map_location=torch.device('cuda', args.device_ids[0]))
training_params = tmp_ckpt['training_params']
start_epoch = training_params['start_epoch']
# Initialize dbsn_model
pretrained_dict=tmp_ckpt['state_dict_dbsn']
model_dict=dbsn_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
dbsn_model.load_state_dict(model_dict)
optimizer_dbsn = optim.Adam(dbsn_model.parameters(), lr=args.lr_dbsn)
optimizer_dbsn.load_state_dict(tmp_ckpt['optimizer_state_dbsn'])
schedule_dbsn = torch.optim.lr_scheduler.MultiStepLR(optimizer_dbsn, milestones=args.steps, gamma=args.decay_rate)
elif args.resume == "new":
training_params = {}
training_params['step'] = 0
start_epoch = 0
# Initialize dbsn
optimizer_dbsn = optim.Adam(dbsn_model.parameters(), lr=args.lr_dbsn)
schedule_dbsn = torch.optim.lr_scheduler.MultiStepLR(optimizer_dbsn, milestones=args.steps, gamma=args.decay_rate)
# set training set
train_setname = args.trainset
dataset = create_dataset(train_setname, 'train', args).load_data()
dataset_num = len(dataset)
# set val set
val_setname = args.valset
dataset_val = create_dataset(val_setname, 'val', args).load_data()
# logging
with open(logger_fname, "a") as log_file:
log_file.write('training/val dataset created\n')
log_file.write('number of training examples {0} \n'.format(dataset_num))
# --------------------------------------------
# Checkpoint
# --------------------------------------------
if args.resume == 'continue':
# evaluating the loaded model first ...
print("Starting from epoch: %d "%(start_epoch))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('checkpoint evaluation on epoch {0} ... \n'.format(start_epoch))
dbsn_model.eval()
with torch.no_grad():
psnr_val = 0
for count, data in enumerate(dataset_val):
#
img_val = data['clean'].cuda()
img_noise_val = data['noisy'].cuda()
_,C,H,W = img_noise_val.shape
# forward
mu_out_val, _ = dbsn_model(img_noise_val)
# compute PSNR
psnr = batch_psnr(mu_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
psnr_val+=psnr
# print
print("Image: %d, psnr_mu: %.4f" % (count, psnr))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('Image {0} \t'
'psnr_mu:{psnr:.4f} \n'.format(count, psnr=psnr))
psnr_val /= len(dataset_val)
# print
print("Checkpoint avg psnr_mu: %.4f\n" % (psnr_val))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('checkpoint avg psnr_mu:{psnr_val:.4f} \n'.format(psnr_val=psnr_val))
val_psnr_pre = psnr_val
idx_epoch = start_epoch
# logging
with open(logger_fname, "a") as log_file:
log_file.write('started training\n')
val_psnr_curr = 0
for epoch in range(start_epoch, args.epoch):
epoch_start_time = time.time()
#
print('lr: %f' % (optimizer_dbsn.state_dict()["param_groups"][0]["lr"]))
#
dbsn_model.train()
# train
for i, data in enumerate(dataset):
# load training data
img_train = data['clean'].cuda()
img_noise = data['noisy'].cuda()
_,C,H,W = img_noise.shape
#
optimizer_dbsn.zero_grad()
# forward
mu_out, _ = dbsn_model(img_noise)
# compute loss
loss = criterion(img_noise, mu_out)
loss = loss / (2*args.batch_size)
loss_value = loss.item()
#
loss.backward()
optimizer_dbsn.step()
# Results
training_params['step'] += args.batch_size
# print results
if training_params['step'] % (args.batch_size*args.display_freq) == 0:
with torch.no_grad():
# compute training psnr
train_psnr = batch_psnr(mu_out.clamp(0., 1.), img_train, 1.0)
# print
print(("[epoch %d/%d][%d/%d], loss:%.4f, psnr_mu: %.4f") %
(epoch, args.epoch, i*args.batch_size, len(dataset), loss_value, train_psnr))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('Epoch: [{0}/{1}][{2}/{3}] \t'
'loss {loss_value: .4f} \t'
'psnr_mu:{train_psnr:.4f} \n'.format(epoch, args.epoch, i*args.batch_size, len(dataset),
loss_value=loss_value, train_psnr=train_psnr))
schedule_dbsn.step()
# taking time for each epoch
tr_take_time = time.time() - epoch_start_time
# --------------------------------------------
# validation
# --------------------------------------------
# print
# print("Evaluating on "+str(val_setname[0]))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('Evaluation on %s \n' % (str(val_setname[0])) )
#
dbsn_model.eval()
val_start_time = time.time()
with torch.no_grad():
psnr_val = 0
for count, data in enumerate(dataset_val):
# load input
img_val = data['clean'].cuda()
img_noise_val = data['noisy'].cuda()
_,C,H,W = img_noise_val.shape
# forward
mu_out_val, _ = dbsn_model(img_noise_val)
# compute PSNR
psnr = batch_psnr(mu_out_val.clamp(0., 1.), img_val.clamp(0., 1.), 1.)
psnr_val+=psnr
# print
print("Image: %d, psnr_mu: %.4f" % (count, psnr))
# logging
with open(logger_fname, "a") as log_file:
log_file.write('Image {0} \t'
'psnr_mu:{psnr:.4f} \n'.format(count, psnr=psnr))
torch.cuda.synchronize()
val_take_time = time.time() - val_start_time
psnr_val /= len(dataset_val)
val_psnr_curr = psnr_val
if epoch==0:
val_psnr_pre = val_psnr_curr
# print
print('Epoch [%d/%d] \t val psnr_mu: %.4f \t Train_time: %d sec \t Val_time: %d sec \t' %
(epoch, args.epoch, psnr_val, tr_take_time, val_take_time))
# save model and checkpoint
if val_psnr_curr >= val_psnr_pre or (epoch+1) % args.save_model_freq == 0:
training_params['start_epoch'] = epoch
save_dict = {'state_dict_dbsn': dbsn_model.state_dict(),
'optimizer_state_dbsn': optimizer_dbsn.state_dict(),
'schedule_state_dbsn': schedule_dbsn.state_dict(),
'training_params': training_params,
'args': args}
torch.save(save_dict, os.path.join(ckpt_save_path, args.save_prefix + '_ckpt_e{}.pth'.format(epoch)))
if val_psnr_curr >= val_psnr_pre:
val_psnr_pre = val_psnr_curr
idx_epoch = epoch
torch.save(dbsn_model.state_dict(), os.path.join(ckpt_save_path, 'dbsn_net_best_e{}.pth'.format(epoch)))
del save_dict
# print
print('Best Val psnr=%.4f with Epoch %d' % (val_psnr_pre, idx_epoch))
# logging
with open(logger_fname, "a") as log_file:
#[0/1/2]: 0-current epoch, 1-total epoch, 2-best epoch
log_file.write('Epoch: [{0}/{1}/{2}] \t'
'Train_time:{tr_take_time:.1f} sec \t'
'Val_time:{val_take_time:.1f} sec \t'
'Best VAL psnr_dbsn:{val_psnr_pre:.4f} \n'.format(epoch, args.epoch, idx_epoch,
tr_take_time=tr_take_time, val_take_time=val_take_time,
psnr_val=psnr_val, val_psnr_pre=val_psnr_pre))
def main(args):
# net architecture
dbsn_net = DBSN_Model(in_ch=args.input_channel,
out_ch=args.output_channel,
mid_ch=args.middle_channel,
blindspot_conv_type=args.blindspot_conv_type,
blindspot_conv_bias=args.blindspot_conv_bias,
br1_block_num=args.br1_block_num,
br1_blindspot_conv_ks=args.br1_blindspot_conv_ks,
br2_block_num=args.br2_block_num,
br2_blindspot_conv_ks=args.br2_blindspot_conv_ks,
activate_fun=args.activate_fun)
sigma_mu_net = Sigma_mu_Net(in_ch=args.middle_channel,
out_ch=args.sigma_mu_output_channel,
mid_ch=args.sigma_mu_middle_channel,
layers=args.sigma_mu_layers,
kernel_size=args.sigma_mu_kernel_size,
bias=args.sigma_mu_bias)
sigma_n_net = Sigma_n_Net(in_ch=args.sigma_n_input_channel,
out_ch=args.sigma_n_output_channel,
mid_ch=args.sigma_n_middle_channel,
layers=args.sigma_n_layers,
kernel_size=args.sigma_n_kernel_size,
bias=args.sigma_n_bias)
# Move to GPU
dbsn_model = nn.DataParallel(dbsn_net, args.device_ids).cuda()
sigma_mu_model = nn.DataParallel(sigma_mu_net, args.device_ids).cuda()
sigma_n_model = nn.DataParallel(sigma_n_net, args.device_ids).cuda()
tmp_ckpt = torch.load(args.last_ckpt,
map_location=torch.device('cuda',
args.device_ids[0]))
# Initialize dbsn_model
pretrained_dict = tmp_ckpt['state_dict_dbsn']
model_dict = dbsn_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
dbsn_model.load_state_dict(model_dict)
# Initialize sigma_mu_model
pretrained_dict = tmp_ckpt['state_dict_sigma_mu']
model_dict = sigma_mu_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_mu_model.load_state_dict(model_dict)
# Initialize sigma_n_model
pretrained_dict = tmp_ckpt['state_dict_sigma_n']
model_dict = sigma_n_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_n_model.load_state_dict(model_dict)
# set val set
val_setname = args.valset
dataset_val = create_dataset(val_setname, 'val', args).load_data()
# --------------------------------------------
# Evaluating
# --------------------------------------------
dbsn_model.eval()
sigma_mu_model.eval()
sigma_n_model.eval()
with torch.no_grad():
psnr_val = 0
for count, data in enumerate(dataset_val):
# prepare data
img_val = data['clean'].cuda()
img_noise_val = data['noisy'].cuda()
batch, C, H, W = img_noise_val.shape
# forward
mu_out_val, mid_out_val = dbsn_model(img_noise_val)
sigma_mu_out_val = sigma_mu_model(mid_out_val)
if args.noise_type == 'poisson_gaussian':
sigma_n_out_val = sigma_n_model(mu_out_val)
else:
sigma_n_out_val = sigma_n_model(img_noise_val)
#
if args.noise_type == 'gaussian':
#
sigma_mu_out_val = sigma_mu_out_val.repeat(1, 3, 1, 1)
index = torch.LongTensor([0, 4, 8]).cuda()
# sigma_mu
tmp1 = torch.zeros((batch, H, W, 9), device='cuda')
tmp1 = tmp1.index_copy_(3, index,
sigma_mu_out_val.permute(0, 2, 3, 1))
L_matrix = tmp1.view((batch, H, W, 3, 3))
sigma_mu = (L_matrix @ L_matrix.transpose(3, 4)) # bxmxnx3x3
# sigma_n
noise_est_val = sigma_n_out_val.mean(dim=(2, 3),
keepdim=True).repeat(
1, 3, H, W)
noise_est_val = F.softplus(noise_est_val - 4) + (1e-3)
tmp2 = torch.zeros((batch, H, W, 9), device='cuda')
tmp2 = tmp2.index_copy_(3, index,
noise_est_val.permute(0, 2, 3, 1))
P_matrix = tmp2.view((batch, H, W, 3, 3))
sigma_n = (P_matrix @ P_matrix.transpose(3, 4)) # bxmxnx3x3
elif args.noise_type == 'poisson_gaussian':
index = torch.LongTensor([0, 4, 8]).cuda()
# sigma_mu
tmp1 = torch.zeros((batch, H, W, 9), device='cuda')
tmp1 = tmp1.index_copy_(3, index,
sigma_mu_out_val.permute(0, 2, 3, 1))
L_matrix = tmp1.view((batch, H, W, 3, 3))
sigma_mu = (L_matrix @ L_matrix.transpose(3, 4)) # bxmxnx3x3
# sigma_n
noise_est = F.softplus(sigma_n_out_val - 4) + (1e-3)
tmp2 = torch.zeros((batch, H, W, 9), device='cuda')
tmp2 = tmp2.index_copy_(3, index,
noise_est.permute(0, 2, 3, 1))
P_matrix = tmp2.view((batch, H, W, 3, 3))
sigma_n = (P_matrix @ P_matrix.transpose(3, 4)) # bxmxnx3x3
elif args.noise_type == 'multivariate_gaussian':
index = torch.LongTensor([0, 1, 2, 4, 5, 8]).cuda()
# sigma_mu
tmp1 = torch.zeros((batch, H, W, 9), device='cuda')
tmp1 = tmp1.index_copy_(3, index,
sigma_mu_out_val.permute(0, 2, 3, 1))
L_matrix = tmp1.view((batch, H, W, 3, 3))
sigma_mu_matrix = (L_matrix @ L_matrix.transpose(3, 4)
) # bxmxnx3x3
sigma_mu = sigma_mu_matrix.mean(dim=(1, 2),
keepdim=True).repeat(
1, H, W, 1, 1)
# sigma_n
tmp2 = torch.zeros((batch, H, W, 9), device='cuda')
tmp2 = tmp2.index_copy_(3, index,
sigma_n_out_val.permute(0, 2, 3, 1))
P_matrix = tmp2.view((batch, H, W, 3, 3))
sigma_n_matrix = (P_matrix @ P_matrix.transpose(3, 4)
) # bxmxnx3x3
sigma_n = sigma_n_matrix.mean(dim=(1, 2), keepdim=True).repeat(
1, H, W, 1, 1)
else:
assert ('Unknown noise type!')
# compute map_out
mu_x = mu_out_val.permute(0, 2, 3, 1).unsqueeze(-1)
y = img_noise_val.permute(0, 2, 3, 1).unsqueeze(-1)
Ieps = (1e-6) * torch.eye(3, device='cuda').repeat(
batch, H, W, 1, 1)
sigma_mu_inv = (sigma_mu + Ieps).inverse()
sigma_n_inv = (sigma_n + Ieps).inverse()
term_c1 = (sigma_mu_inv + args.gamma * sigma_n_inv +
Ieps).inverse()
term_c2 = sigma_mu_inv @ mu_x + args.gamma * sigma_n_inv @ y
out = term_c1 @ term_c2
out = out.squeeze(-1).permute(0, 3, 1, 2)
# compute PSNR
psnr = batch_psnr(out.clamp(0., 1.), img_val, 1.)
psnr_val += psnr
# print
print("Image[%02d]: psnr_dbsn = %.4f " % (count, psnr))
psnr_val /= len(dataset_val)
# print
print("Avg psnr_dbsn: %.4f \n" % (psnr_val))
def main(args):
# net architecture
dbsn_net = DBSN_Model(in_ch=args.input_channel,
out_ch=args.output_channel,
mid_ch=args.middle_channel,
blindspot_conv_type=args.blindspot_conv_type,
blindspot_conv_bias=args.blindspot_conv_bias,
br1_block_num=args.br1_block_num,
br1_blindspot_conv_ks=args.br1_blindspot_conv_ks,
br2_block_num=args.br2_block_num,
br2_blindspot_conv_ks=args.br2_blindspot_conv_ks,
activate_fun=args.activate_fun)
sigma_mu_net = Sigma_mu_Net(in_ch=args.middle_channel,
out_ch=args.sigma_mu_output_channel,
mid_ch=args.sigma_mu_middle_channel,
layers=args.sigma_mu_layers,
kernel_size=args.sigma_mu_kernel_size,
bias=args.sigma_mu_bias)
sigma_n_net = Sigma_n_Net(in_ch=args.sigma_n_input_channel,
out_ch=args.sigma_n_output_channel,
mid_ch=args.sigma_n_middle_channel,
layers=args.sigma_n_layers,
kernel_size=args.sigma_n_kernel_size,
bias=args.sigma_n_bias)
# Move to GPU
dbsn_model = nn.DataParallel(dbsn_net, args.device_ids).cuda()
sigma_mu_model = nn.DataParallel(sigma_mu_net, args.device_ids).cuda()
sigma_n_model = nn.DataParallel(sigma_n_net, args.device_ids).cuda()
tmp_ckpt = torch.load(args.last_ckpt,
map_location=torch.device('cuda',
args.device_ids[0]))
# Initialize dbsn_model
pretrained_dict = tmp_ckpt['state_dict_dbsn']
model_dict = dbsn_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
dbsn_model.load_state_dict(model_dict)
# Initialize sigma_mu_model
pretrained_dict = tmp_ckpt['state_dict_sigma_mu']
model_dict = sigma_mu_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_mu_model.load_state_dict(model_dict)
# Initialize sigma_n_model
pretrained_dict = tmp_ckpt['state_dict_sigma_n']
model_dict = sigma_n_model.state_dict()
pretrained_dict_update = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
sigma_n_model.load_state_dict(model_dict)
# set val set
val_setname = args.valset
dataset_val = create_dataset(val_setname, 'val', args).load_data()
# --------------------------------------------
# Evaluation
# --------------------------------------------
print("Evaluation on : %s " % (val_setname))
dbsn_model.eval()
sigma_mu_model.eval()
sigma_n_model.eval()
with torch.no_grad():
psnr_val = 0
for count, data in enumerate(dataset_val):
# load input
img_val = data['clean'].cuda()
img_noise_val = data['noisy'].cuda()
_, C, H, W = img_noise_val.shape
# forward backbone
mu_out_val, mid_out_val = dbsn_model(img_noise_val)
# forward sigma_mu
sigma_mu_out_val = sigma_mu_model(mid_out_val)
sigma_mu_val = sigma_mu_out_val**2
# forward sigma_n
if args.noise_type == 'gaussian':
sigma_n_out_val = sigma_n_model(img_noise_val)
noise_est_val = sigma_n_out_val.mean(dim=(2, 3),
keepdim=True).repeat(
1, 1, H, W)
else:
sigma_n_out_val = sigma_n_model(mu_out_val)
noise_est_val = F.softplus(sigma_n_out_val - 4) + (1e-3)
sigma_n_val = noise_est_val**2
# MAP inference
map_out_val = (img_noise_val * sigma_mu_val +
args.gamma * mu_out_val * sigma_n_val) / (
sigma_mu_val + args.gamma * sigma_n_val)
# compute PSNR
psnr = batch_psnr(map_out_val.clamp(0., 1.), img_val.clamp(0., 1.),
1.)
psnr_val += psnr
# print
print("Image[%02d], val psnr_dbsn: %.4f" % (count, psnr))
psnr_val /= len(dataset_val)
# print
print("Avg psnr_dbsn: %.4f" % (psnr_val))