def define_G(opt):
opt_net = opt['netG']
net_type = opt_net['net_type']
# ----------------------------------------
# denoising task
# ----------------------------------------
# ----------------------------------------
# DnCNN
# ----------------------------------------
if net_type == 'dncnn':
from models.network_dncnn import DnCNN as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'], # total number of conv layers
act_mode=opt_net['act_mode'])
# ----------------------------------------
# Flexible DnCNN
# ----------------------------------------
elif net_type == 'fdncnn':
from models.network_dncnn import FDnCNN as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'], # total number of conv layers
act_mode=opt_net['act_mode'])
# ----------------------------------------
# FFDNet
# ----------------------------------------
elif net_type == 'ffdnet':
from models.network_ffdnet import FFDNet as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
act_mode=opt_net['act_mode'])
# ----------------------------------------
# others
# ----------------------------------------
# ----------------------------------------
# super-resolution task
# ----------------------------------------
# ----------------------------------------
# SRMD
# ----------------------------------------
elif net_type == 'srmd':
from models.network_srmd import SRMD as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# super-resolver prior of DPSR
# ----------------------------------------
elif net_type == 'dpsr':
from models.network_dpsr import MSRResNet_prior as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# modified SRResNet v0.0
# ----------------------------------------
elif net_type == 'msrresnet0':
from models.network_msrresnet import MSRResNet0 as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# modified SRResNet v0.1
# ----------------------------------------
elif net_type == 'msrresnet1':
from models.network_msrresnet import MSRResNet1 as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# RRDB
# ----------------------------------------
elif net_type == 'rrdb': # RRDB
from models.network_rrdb import RRDB as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
gc=opt_net['gc'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# IMDB
# ----------------------------------------
elif net_type == 'imdn': # IMDB
from models.network_imdn import IMDN as net
netG = net(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
upscale=opt_net['scale'],
act_mode=opt_net['act_mode'],
upsample_mode=opt_net['upsample_mode'])
# ----------------------------------------
# USRNet
# ----------------------------------------
elif net_type == 'usrnet': # USRNet
from models.network_usrnet import USRNet as net
netG = net(n_iter=opt_net['n_iter'],
h_nc=opt_net['h_nc'],
in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nc=opt_net['nc'],
nb=opt_net['nb'],
act_mode=opt_net['act_mode'],
downsample_mode=opt_net['downsample_mode'],
upsample_mode=opt_net['upsample_mode']
)
# ----------------------------------------
# others
# ----------------------------------------
# TODO
else:
raise NotImplementedError('netG [{:s}] is not found.'.format(net_type))
# ----------------------------------------
# initialize weights
# ----------------------------------------
if opt['is_train']:
init_weights(netG,
init_type=opt_net['init_type'],
init_bn_type=opt_net['init_bn_type'],
gain=opt_net['init_gain'])
return netG
def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
noise_level_img = 15 # noise level for noisy image
noise_level_model = noise_level_img # noise level for model
model_name = 'fdncnn_gray' # 'fdncnn_gray' | 'fdncnn_color' | 'fdncnn_color_clip' | 'fdncnn_gray_clip'
testset_name = 'bsd68' # test set, 'bsd68' | 'cbsd68' | 'set12'
need_degradation = True # default: True
x8 = False # default: False, x8 to boost performance
show_img = False # default: Falsedefault: False
task_current = 'dn' # 'dn' for denoising | 'sr' for super-resolution
sf = 1 # unused for denoising
if 'color' in model_name:
n_channels = 3 # 3 for color image
else:
n_channels = 1 # 1 for grayscale image
if 'clip' in model_name:
use_clip = True # clip the intensities into range of [0, 1]
else:
use_clip = False
model_pool = 'model_zoo' # fixed
testsets = 'testsets' # fixed
results = 'results' # fixed
result_name = testset_name + '_' + model_name
border = sf if task_current == 'sr' else 0 # shave boader to calculate PSNR and SSIM
model_path = os.path.join(model_pool, model_name + '.pth')
# ----------------------------------------
# L_path, E_path, H_path
# ----------------------------------------
L_path = os.path.join(testsets,
testset_name) # L_path, for Low-quality images
H_path = L_path # H_path, for High-quality images
E_path = os.path.join(results, result_name) # E_path, for Estimated images
util.mkdir(E_path)
if H_path == L_path:
need_degradation = True
logger_name = result_name
utils_logger.logger_info(logger_name,
log_path=os.path.join(E_path,
logger_name + '.log'))
logger = logging.getLogger(logger_name)
need_H = True if H_path is not None else False
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ----------------------------------------
# load model
# ----------------------------------------
from models.network_dncnn import FDnCNN as net
model = net(in_nc=n_channels + 1,
out_nc=n_channels,
nc=64,
nb=20,
act_mode='R')
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
logger.info('Model path: {:s}'.format(model_path))
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('Params number: {}'.format(number_parameters))
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
logger.info('model_name:{}, model sigma:{}, image sigma:{}'.format(
model_name, noise_level_img, noise_level_model))
logger.info(L_path)
L_paths = util.get_image_paths(L_path)
H_paths = util.get_image_paths(H_path) if need_H else None
for idx, img in enumerate(L_paths):
# ------------------------------------
# (1) img_L
# ------------------------------------
img_name, ext = os.path.splitext(os.path.basename(img))
# logger.info('{:->4d}--> {:>10s}'.format(idx+1, img_name+ext))
img_L = util.imread_uint(img, n_channels=n_channels)
img_L = util.uint2single(img_L)
if need_degradation: # degradation process
np.random.seed(seed=0) # for reproducibility
img_L += np.random.normal(0, noise_level_img / 255., img_L.shape)
if use_clip:
img_L = util.uint2single(util.single2uint(img_L))
util.imshow(util.single2uint(img_L),
title='Noisy image with noise level {}'.format(
noise_level_img)) if show_img else None
img_L = util.single2tensor4(img_L)
noise_level_map = torch.ones(
(1, 1, img_L.size(2), img_L.size(3)),
dtype=torch.float).mul_(noise_level_model / 255.)
img_L = torch.cat((img_L, noise_level_map), dim=1)
img_L = img_L.to(device)
# ------------------------------------
# (2) img_E
# ------------------------------------
if not x8:
img_E = model(img_L)
else:
img_E = utils_model.test_mode(model, img_L, mode=3)
img_E = util.tensor2uint(img_E)
if need_H:
# --------------------------------
# (3) img_H
# --------------------------------
img_H = util.imread_uint(H_paths[idx], n_channels=n_channels)
img_H = img_H.squeeze()
# --------------------------------
# PSNR and SSIM
# --------------------------------
psnr = util.calculate_psnr(img_E, img_H, border=border)
ssim = util.calculate_ssim(img_E, img_H, border=border)
test_results['psnr'].append(psnr)
test_results['ssim'].append(ssim)
logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.4f}.'.format(
img_name + ext, psnr, ssim))
util.imshow(np.concatenate([img_E, img_H], axis=1),
title='Recovered / Ground-truth') if show_img else None
# ------------------------------------
# save results
# ------------------------------------
util.imsave(img_E, os.path.join(E_path, img_name + ext))
if need_H:
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
logger.info(
'Average PSNR/SSIM(RGB) - {} - PSNR: {:.2f} dB; SSIM: {:.4f}'.
format(result_name, ave_psnr, ave_ssim))
def denoising(noise_im,
clean_im,
LR=1e-2,
sigma=5,
rho=1,
eta=0.5,
total_step=20,
prob1_iter=500,
result_root=None,
f=None):
input_depth = 3
latent_dim = 3
en_net = Encoder(input_depth,
latent_dim,
down_sample_norm='batchnorm',
up_sample_norm='batchnorm').cuda()
de_net = Decoder(latent_dim,
input_depth,
down_sample_norm='batchnorm',
up_sample_norm='batchnorm').cuda()
model = net(3, 3, nc=64, nb=20, act_mode='R')
model_path = '/home/dihan/KAIR/model_zoo/dncnn_color_blind.pth'
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.cuda()
noise_im_torch = np_to_torch(noise_im)
noise_im_torch = noise_im_torch.cuda()
with torch.no_grad():
r_dncnn_np = torch_to_np(model(noise_im_torch))
psnr_dncnn = compare_psnr(clean_im.transpose(1, 2, 0),
r_dncnn_np.transpose(1, 2, 0), 1)
ssim_dncnn = compare_ssim(r_dncnn_np.transpose(1, 2, 0),
clean_im.transpose(1, 2, 0),
multichannel=True,
data_range=1)
print('PSNR_DNCNN: {}, SSIM_DNCNN: {}'.format(psnr_dncnn, ssim_dncnn),
file=f,
flush=True)
parameters = [p for p in en_net.parameters()
] + [p for p in de_net.parameters()]
optimizer = torch.optim.Adam(parameters, lr=LR)
l2_loss = torch.nn.MSELoss(reduction='sum').cuda()
i0 = np_to_torch(noise_im).cuda()
Y = torch.zeros_like(noise_im_torch).cuda()
i0_til_torch = np_to_torch(noise_im).cuda()
diff_original_np = noise_im.astype(np.float32) - clean_im.astype(
np.float32)
diff_original_name = 'Original_dis.png'
save_hist(diff_original_np, result_root + diff_original_name)
best_psnr = 0
best_ssim = 0
for i in range(total_step):
############################### sub-problem 1 #################################
for i_1 in range(prob1_iter):
optimizer.zero_grad()
mean, log_var = en_net(noise_im_torch)
z = sample_z(mean, log_var)
out = de_net(z)
total_loss = 0.5 * l2_loss(out, noise_im_torch)
total_loss += kl_loss(mean, log_var, i0, sigma)
total_loss += (rho / 2) * l2_loss(i0 + Y, i0_til_torch)
total_loss.backward()
optimizer.step()
with torch.no_grad():
i0 = ((1 / sigma**2) * mean + rho *
(i0_til_torch - Y)) / ((1 / sigma**2) + rho)
with torch.no_grad():
############################### sub-problem 2 #################################
i0_til_torch = model(i0 + Y)
############################### sub-problem 3 #################################
Y = Y + eta * (i0 - i0_til_torch)
###############################################################################
i0_np = torch_to_np(i0)
Y_np = torch_to_np(Y)
denoise_obj_pil = np_to_pil((i0_np + Y_np).clip(0, 1))
Y_norm_np = np.sqrt((Y_np * Y_np).sum(0))
i0_pil = np_to_pil(i0_np)
mean_np = torch_to_np(mean)
mean_pil = np_to_pil(mean_np)
out_np = torch_to_np(out)
out_pil = np_to_pil(out_np)
diff_np = mean_np - clean_im
denoise_obj_name = 'denoise_obj_{:04d}'.format(i) + '.png'
Y_name = 'Y_{:04d}'.format(i) + '.png'
i0_name = 'i0_num_epoch_{:04d}'.format(i) + '.png'
mean_i_name = 'Latent_im_num_epoch_{:04d}'.format(i) + '.png'
out_name = 'res_of_dec_num_epoch_{:04d}'.format(i) + '.png'
diff_name = 'Latent_dis_num_epoch_{:04d}'.format(i) + '.png'
denoise_obj_pil.save(result_root + denoise_obj_name)
save_heatmap(Y_norm_np, result_root + Y_name)
i0_pil.save(result_root + i0_name)
mean_pil.save(result_root + mean_i_name)
out_pil.save(result_root + out_name)
save_hist(diff_np, result_root + diff_name)
i0_til_np = torch_to_np(i0_til_torch).clip(0, 1)
psnr = compare_psnr(clean_im.transpose(1, 2, 0),
i0_til_np.transpose(1, 2, 0), 1)
ssim = compare_ssim(clean_im.transpose(1, 2, 0),
i0_til_np.transpose(1, 2, 0),
multichannel=True,
data_range=1)
i0_til_pil = np_to_pil(i0_til_np)
i0_til_pil.save(os.path.join(result_root, '{}'.format(i) + '.png'))
print('Iteration: %02d, VAE Loss: %f, PSNR: %f, SSIM: %f' %
(i, total_loss.item(), psnr, ssim),
file=f,
flush=True)
if best_psnr < psnr:
best_psnr = psnr
best_ssim = ssim
else:
break
return i0_til_np, best_psnr, best_ssim
def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
model_name = 'dncnn3' # 'dncnn3'- can be used for blind Gaussian denoising, JPEG deblocking (quality factor 5-100) and super-resolution (x234)
# important!
testset_name = 'bsd68' # test set, low-quality grayscale/color JPEG images
n_channels = 1 # set 1 for grayscale image, set 3 for color image
x8 = False # default: False, x8 to boost performance
testsets = 'testsets' # fixed
results = 'results' # fixed
result_name = testset_name + '_' + model_name # fixed
L_path = os.path.join(
testsets, testset_name
) # L_path, for Low-quality grayscale/Y-channel JPEG images
E_path = os.path.join(results, result_name) # E_path, for Estimated images
util.mkdir(E_path)
model_pool = 'model_zoo' # fixed
model_path = os.path.join(model_pool, model_name + '.pth')
logger_name = result_name
utils_logger.logger_info(logger_name,
log_path=os.path.join(E_path,
logger_name + '.log'))
logger = logging.getLogger(logger_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ----------------------------------------
# load model
# ----------------------------------------
from models.network_dncnn import DnCNN as net
model = net(in_nc=1, out_nc=1, nc=64, nb=20, act_mode='R')
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
logger.info('Model path: {:s}'.format(model_path))
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('Params number: {}'.format(number_parameters))
logger.info(L_path)
L_paths = util.get_image_paths(L_path)
for idx, img in enumerate(L_paths):
# ------------------------------------
# (1) img_L
# ------------------------------------
img_name, ext = os.path.splitext(os.path.basename(img))
logger.info('{:->4d}--> {:>10s}'.format(idx + 1, img_name + ext))
img_L = util.imread_uint(img, n_channels=n_channels)
img_L = util.uint2single(img_L)
if n_channels == 3:
ycbcr = util.rgb2ycbcr(img_L, False)
img_L = ycbcr[..., 0:1]
img_L = util.single2tensor4(img_L)
img_L = img_L.to(device)
# ------------------------------------
# (2) img_E
# ------------------------------------
if not x8:
img_E = model(img_L)
else:
img_E = utils_model.test_mode(model, img_L, mode=3)
img_E = util.tensor2single(img_E)
if n_channels == 3:
ycbcr[..., 0] = img_E
img_E = util.ycbcr2rgb(ycbcr)
img_E = util.single2uint(img_E)
# ------------------------------------
# save results
# ------------------------------------
util.imsave(img_E, os.path.join(E_path, img_name + '.png'))
def main(args):
# Model name
if args['model'] is None:
model_name = 'dncnn_50' # 'dncnn_25' | 'dncnn_50' | 'dncnn_gray_blind' | 'dncnn_color_blind' | 'dncnn3'
else:
model_name = args['model']
# RGB or Gray Scale mode
if args['color'] == 'rgb':
model_name = 'dncnn_color_blind'
# Error Handling for unavailable model
try:
model_path = os.path.join(model_pool, model_name + '.pth')
print("Using model %s" % (model_name))
print("---------------------------------")
except:
print('Model not found')
exit()
# Disabled now - Found to reduce quality of output
x8 = False # default: False, x8 to boost performance
if args['type'] == None:
task_current = 'dn' # 'dn' for denoising | 'sr' for super-resolution
else:
task_current = args['type']
sf = 1 # unused for denoising
# Identify if model used is color
if 'color' in model_name:
n_channels = 3 # fixed, 1 for grayscale image, 3 for color image
else:
n_channels = 1 # fixed for grayscale image
if model_name in ['dncnn_gray_blind', 'dncnn_color_blind', 'dncnn3']:
nb = 20 # fixed
else:
nb = 17 # fixed
border = sf if task_current == 'sr' else 0 # shave boader to calculate PSNR and SSIM
need_H = False
# Load Model
model = net(in_nc=n_channels,
out_nc=n_channels,
nc=64,
nb=nb,
act_mode='R')
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
# Load Image
if (args['input'] != None) and (args['batch'] == None): # Single image
img = args['input']
predict(img, n_channels, model, x8)
elif (args['input'] == None) and (args['batch'] != None): # Batch Mode
# Load each image from directory and predict
mypath = args['batch']
# Check if path exist and load list of files
if os.path.exists(mypath):
files = [
f for f in listdir(mypath)
if os.path.isfile(os.path.join(mypath, f))
]
print("Found %d files" % (len(files)))
# Predict for each file and save results
for item in files:
try:
filepath = os.path.join(mypath, item)
predict(filepath, n_channels, model, x8)
except:
print("Error with %s" % (item))
else:
print("Path does not exist")
exit()
