def main():
torch.cuda.manual_seed_all(Args.seed)
train_transform = transforms.Compose([
transforms.Resize(Args.resized),
transforms.Grayscale(Args.num_channel),
transforms.ToTensor()
])
coco_train = COCO(Args.train_path, transform=train_transform)
trainloader = DataLoader(coco_train,
batch_size=Args.batch_size,
shuffle=True,
num_workers=min(4, Args.batch_size),
pin_memory=True)
loaders = {'train': trainloader}
model = DenseFuse(num_channel=Args.num_channel)
model.to(Args.device)
optimizer = optim.Adam(model.parameters(), lr=Args.lr)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='min', patience=1, factor=Args.lr_decay_factor)
ms_ssim = MS_SSIM(data_range=1.0, size_average=True, channel=1)
criterions = {'ms_ssim': ms_ssim}
ckpt_name = time.ctime().replace(' ', '-').replace(':', '-')
ckptPath = Args.ckptPath.joinpath(ckpt_name)
train(loaders,
model,
criterions,
optimizer,
Args.num_epochs,
ckptPath,
scheduler=scheduler)
def ssim_loss(X, Y):
# # X: (N,3,H,W) a batch of non-negative RGB images (0~255)
# # Y: (N,3,H,W)
#
# # calculate ssim & ms-ssim for each image
# ssim_val = ssim(X, Y, data_range=255, size_average=False) # return (N,)
# ms_ssim_val = ms_ssim(X, Y, data_range=255, size_average=False) # (N,)
#
# # set 'size_average=True' to get a scalar value as loss.
# ssim_loss = 1 - ssim(X, Y, data_range=255, size_average=True) # return a scalar
# ms_ssim_loss = 1 - ms_ssim(X, Y, data_range=255, size_average=True)
# reuse the gaussian kernel with SSIM & MS_SSIM.
ssim_module = SSIM(data_range=255,
size_average=True,
channel=1,
nonnegative_ssim=False)
ms_ssim_module = MS_SSIM(data_range=255,
size_average=True,
channel=1,
nonnegative_ssim=False)
ssim_loss = 1 - ssim_module(X, Y)
ms_ssim_loss = 1 - ms_ssim_module(X, Y)
return ms_ssim_loss
def __init__(self, name,
dip_n_iter=8000, net='skip',
lr=0.001, reg_std=1./100,
w_proj_loss=1.0, w_perceptual_loss=0.0,
w_ssim_loss=0.0, w_tv_loss=0.0, randomize_projs=None,
channels=[16, 32, 64, 128, 256]):
super(DgrReconstructor, self).__init__(name)
self.n_iter = dip_n_iter
assert net in ['skip', 'skipV2', 'skipV3', 'unet', 'dncnn']
self.net = net
self.channels = channels
self.lr = lr
self.reg_std = reg_std
# loss weights
self.w_proj_loss = w_proj_loss
self.w_perceptual_loss = w_perceptual_loss
self.w_tv_loss = w_tv_loss
self.w_ssim_loss = w_ssim_loss
self.randomize_projs = randomize_projs
# loss functions
self.mse = torch.nn.MSELoss().to(self.DEVICE)
self.ssim = MS_SSIM(data_range=1.0, size_average=True, channel=self.IMAGE_DEPTH).to(self.DEVICE)
self.perceptual = VGGPerceptualLoss(resize=True).to(self.DEVICE)
self.gt = None
self.noisy = None
self.FOCUS = None
self.log_dir = None
def forward(self, input, target):
loss = self.a * (1 - MS_SSIM(win_size=self.win_size,
data_range=self.data_range,
channel=self.channel)(input, target)
) + (1 - self.a) * torch.nn.L1Loss()(input, target)
return loss
def criterions(name):
if name == "mse":
return nn.MSELoss()
elif name == "l1":
return nn.L1Loss()
elif name == "lpips":
return lpips.LPIPS(net="vgg").cuda()
elif name == "ms_ssim":
return MS_SSIM(data_range=1.0)
def __init__(self, eps=1e-6, lambda_=1):
super(CharbonnierLossPlusMSSSIM, self).__init__()
self.eps = eps
self.lambda_ = lambda_
self.ms_ssim_module = MS_SSIM(win_size=11,
win_sigma=1.5,
data_range=1.0,
size_average=True,
channel=3)
def __init__(self, kernel_w=3, sigma=1.5, channels=1, weights=None):
super().__init__()
self.kernel_w = kernel_w
self.sigma = sigma
#number of weights determines the depth of the pyramid
#standard are 5, too deep for MNist resolution
if weights is None:
self.weights = [0.0516, 0.32949, 0.34622, 0.27261]
else:
self.weights = weights
self.ssim_d = MS_SSIM(win_size=kernel_w, win_sigma=sigma, data_range=1.0,
channel=channels, weights=self.weights, size_average=False)
self.config = {'Distance' : 'SSIM', 'win_size': kernel_w, 'win_sigma': sigma}
def set_criterion(self, level=None):
assert level in [0, 1, 2, 3, 4, 5], 'unknown level'
criterions = [nn.MSELoss()]
coefficients = [1.0]
if level<4:
perceptual_loss = VGG19Loss(['relu5_4'])
perceptual_loss.to(self.device)
criterions.append(perceptual_loss)
coefficients.append(0.01)
if level==0:
criterions.append(MS_SSIM(data_range=1.0, size_average=True, channel=3))
if self.perceptual:
coefficients.append(-0.1)
else:
coefficients.append(-0.01)
self.criterions = criterions
self.coefficients = torch.FloatTensor(coefficients).to(self.device)
def __init__(self, args, ckp):
super(Loss, self).__init__()
print('Preparing loss function:')
self.n_GPUs = args.n_GPUs
self.loss = []
self.loss_module = nn.ModuleList()
for loss in args.loss.split('+'):
weight, loss_type = loss.split('*')
if loss_type == 'MSE': # L2 loss
loss_function = nn.MSELoss()
elif loss_type == 'L1':
loss_function = nn.L1Loss()
elif loss_type.find('VGG') >= 0:
module = import_module('loss.vgg')
loss_function = getattr(module,
'VGG')(loss_type[3:],
rgb_range=args.rgb_range)
elif loss_type.find('TextureL') >= 0:
module = import_module('loss.vgg')
loss_function = getattr(module,
'VGG')(loss_type[3:],
rgb_range=args.rgb_range,
texture_loss=True)
elif loss_type.find('GAN') >= 0:
module = import_module('loss.adversarial')
loss_function = getattr(module, 'Adversarial')(args, loss_type)
elif loss_type.find('TVLoss') >= 0:
module = import_module('loss.tvloss')
loss_function = getattr(module, 'TVLoss')()
elif loss_type.find('SSIM') >= 0:
from pytorch_msssim import SSIM
loss_function = SSIM(win_size=7,
win_sigma=1,
data_range=args.rgb_range,
size_average=True,
channel=3)
elif loss_type.find('MS-SSIM') >= 0:
from pytorch_msssim import MS_SSIM
loss_function = MS_SSIM(win_sigma=1,
data_range=args.rgb_range,
size_average=True,
channel=3)
self.loss.append({
'type': loss_type,
'weight': float(weight),
'function': loss_function
})
if loss_type.find('GAN') >= 0:
self.loss.append({
'type': 'DIS',
'weight': 1,
'function': None
})
if len(self.loss) > 1:
self.loss.append({'type': 'Total', 'weight': 0, 'function': None})
for l in self.loss:
if l['function'] is not None:
print('{:.6f} * {}'.format(l['weight'], l['type']))
self.loss_module.append(l['function'])
self.log = torch.Tensor()
device = torch.device('cpu' if args.cpu else 'cuda')
self.loss_module.to(device)
if args.precision == 'half': self.loss_module.half()
if not args.cpu and args.n_GPUs > 1:
self.loss_module = nn.DataParallel(self.loss_module,
range(args.n_GPUs))
if args.load != '': self.load(ckp.dir, cpu=args.cpu)
def __init__(self, alpha=0.84):
super(ReconstructionLoss, self).__init__()
self.alpha = alpha
self.l1 = nn.L1Loss()
self.ms_ssim = MS_SSIM(data_range=1, size_average=True)
0.02,
gpu_id=device)
# VGG for perceptual loss
if opt.lamb_content > 0:
vgg = Vgg16()
init_vgg16(root_path)
vgg.load_state_dict(torch.load(os.path.join(root_path, "vgg16.weight")))
vgg.to(device)
# define loss
criterionL1 = nn.L1Loss().to(device)
criterionL2 = nn.MSELoss().to(device)
criterionMSE = nn.MSELoss().to(device)
criterionSSIM = SSIM(data_range=255, size_average=True, channel=3)
criterionMSSSIM1 = MS_SSIM(data_range=255, size_average=True, channel=1)
criterionMSSSIM3 = MS_SSIM(data_range=255, size_average=True, channel=3)
# setup optimizer
optimizer_i = optim.Adam(net_i.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
optimizer_r = optim.Adam(net_r.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
net_i_scheduler = get_scheduler(optimizer_i, opt)
net_r_scheduler = get_scheduler(optimizer_r, opt)
loss_i_list = []
loss_r_list = []
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
channel).to(img1.device).type(img1.dtype)
self.window = window
self.channel = channel
return ssim(img1,
img2,
window=window,
window_size=self.window_size,
size_average=self.size_average)
class MSSSIM(torch.nn.Module):
def __init__(self, window_size=11, size_average=True, channel=3):
super(MSSSIM, self).__init__()
self.window_size = window_size
self.size_average = size_average
self.channel = channel
def forward(self, img1, img2):
# TODO: store window between calls if possible
return msssim(img1,
img2,
window_size=self.window_size,
size_average=self.size_average)
vgg_model = VGG19().cuda()
vgg_model = vgg_model.eval()
GAN_loss_calculator = GANLoss()
mssim_calculator = MS_SSIM(data_range=1.0, size_average=True, channel=3)
def tensor_ssim_module():
# reuse the gaussian kernel with SSIM & MS_SSIM.
ssim_module = SSIM(data_range=255, size_average=True, channel=3)
ms_ssim_module = MS_SSIM(data_range=255, size_average=True, channel=3)
def do_learn(opt, run_dir="./runs"):
print('Starting ', opt.run_path)
path_data = os.path.join(run_dir, opt.run_path)
# ----------
# Tensorboard
# ----------
if do_tensorboard:
# stats are stored in "runs", within subfolder opt.run_path.
writer = SummaryWriter(log_dir=path_data)
# Create a time tag
import datetime
try:
tag = datetime.datetime.now().isoformat(sep='_', timespec='seconds')
except TypeError:
# Python 3.5 and below
# 'timespec' is an invalid keyword argument for this function
tag = datetime.datetime.now().replace(microsecond=0).isoformat(sep='_')
tag = tag.replace(':', '-')
# Configure data loader
dataloader = load_data(opt.datapath,
opt.img_size,
opt.batch_size,
rand_hflip=opt.rand_hflip,
rand_affine=opt.rand_affine)
if opt.do_SSIM:
# from pytorch_msssim import NMSSSIM
# E_loss = NMSSSIM(window_size=opt.window_size, val_range=1., size_average=True, channel=3, normalize=True)
# from pytorch_msssim import NSSIM #as neg_SSIM
# E_loss = NSSIM(window_size=opt.window_size, val_range=1., size_average=True)
# NEW: we use https://github.com/VainF/pytorch-msssim instead of https://github.com/SpikeAI/pytorch-msssim
#from pytorch_msssim import msssim, ssim
from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM
E_loss = MS_SSIM(win_size=opt.window_size,
data_range=1,
size_average=True,
channel=3)
else:
E_loss = torch.nn.MSELoss(reduction='sum')
sigmoid = torch.nn.Sigmoid()
# Initialize generator and discriminator
generator = Generator(opt)
discriminator = Discriminator(opt)
encoder = Encoder(opt)
if opt.verbose:
print_network(generator)
print_network(discriminator)
print_network(encoder)
eye = 1 - torch.eye(opt.batch_size)
use_cuda = True if torch.cuda.is_available() else False
if use_cuda:
#print("Nombre de GPU : ",torch.cuda.device_count())
print("Running on GPU : ", torch.cuda.get_device_name())
# if torch.cuda.device_count() > opt.GPU:
# torch.cuda.set_device(opt.GPU)
generator.cuda()
discriminator.cuda()
# adversarial_loss.cuda()
encoder.cuda()
# MSE_loss.cuda()
E_loss.cuda()
eye = eye.cuda()
Tensor = torch.cuda.FloatTensor
else:
print("Running on CPU ")
Tensor = torch.FloatTensor
# Initialize weights
if opt.init_weight:
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
encoder.apply(weights_init_normal)
# Optimizers
if opt.optimizer == 'rmsprop':
# https://pytorch.org/docs/stable/optim.html#torch.optim.RMSprop
opts = dict(momentum=1 - opt.beta1, alpha=opt.beta2)
optimizer = torch.optim.RMSprop
elif opt.optimizer == 'adam':
# https://pytorch.org/docs/stable/optim.html#torch.optim.Adam
opts = dict(betas=(opt.beta1, opt.beta2))
optimizer = torch.optim.Adam
elif opt.optimizer == 'sgd':
opts = dict(momentum=1 - opt.beta1,
nesterov=True,
weight_decay=1 - opt.beta2)
optimizer = torch.optim.SGD
else:
raise ('wrong optimizer')
optimizer_G = optimizer(generator.parameters(), lr=opt.lrG, **opts)
optimizer_D = optimizer(discriminator.parameters(), lr=opt.lrD, **opts)
if opt.do_joint:
import itertools
optimizer_E = optimizer(itertools.chain(encoder.parameters(),
generator.parameters()),
lr=opt.lrE,
**opts)
else:
optimizer_E = optimizer(encoder.parameters(), lr=opt.lrE, **opts)
# TODO parameterize scheduler !
# gamma = .1 ** (1 / opt.n_epochs)
# schedulers = []
# for optimizer in [optimizer_G, optimizer_D, optimizer_E]:
# schedulers.append(torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=gamma))
# ----------
# Training
# ----------
nb_batch = len(dataloader)
stat_record = init_hist(opt.n_epochs, nb_batch)
# https://github.com/soumith/dcgan.torch/issues/14 dribnet commented on 21 Mar 2016
# https://arxiv.org/abs/1609.04468
def slerp(val, low, high):
corr = np.diag(
(low / np.linalg.norm(low)) @ (high / np.linalg.norm(high)).T)
omega = np.arccos(np.clip(corr, -1, 1))[:, None]
so = np.sin(omega)
out = np.sin(
(1.0 - val) * omega) / so * low + np.sin(val * omega) / so * high
# L'Hopital's rule/LERP
out[so[:, 0] == 0, :] = (
1.0 - val) * low[so[:, 0] == 0, :] + val * high[so[:, 0] == 0, :]
return out
def norm2(z):
"""
L2-norm of a tensor.
outputs a scalar
"""
# return torch.mean(z.pow(2)).pow(.5)
return (z**2).sum().sqrt()
def gen_z(imgs=None, rho=.25, do_slerp=opt.do_slerp):
"""
Generate noise in the feature space.
outputs a vector
"""
if not imgs is None:
z_imgs = encoder(imgs).cpu().numpy()
if do_slerp:
z_shuffle = z_imgs.copy()
z_shuffle = z_shuffle[torch.randperm(opt.batch_size), :]
z = slerp(rho, z_imgs, z_shuffle)
else:
z /= norm2(z)
z_imgs /= norm2(z_imgs)
z = (1 - rho) * z_imgs + rho * z
z /= norm2(z)
else:
z = np.random.normal(0, 1, (opt.batch_size, opt.latent_dim))
# convert to tensor
return Variable(Tensor(z), requires_grad=False)
def gen_noise(imgs):
"""
Generate noise in the image space
outputs an image
"""
v_noise = np.random.normal(0, 1, imgs.shape) # one random image
# one contrast value per image
v_noise *= np.abs(
np.random.normal(0, 1, (imgs.shape[0], opt.channels, 1, 1)))
# convert to tensor
v_noise = Variable(Tensor(v_noise), requires_grad=False)
return v_noise
# Vecteur z fixe pour faire les samples
fixed_noise = gen_z()
real_imgs_samples = None
# z_zeros = Variable(Tensor(opt.batch_size, opt.latent_dim).fill_(0), requires_grad=False)
# z_ones = Variable(Tensor(opt.batch_size, opt.latent_dim).fill_(1), requires_grad=False)
# Adversarial ground truths
# valid = Variable(Tensor(opt.batch_size, 1).fill_(1), requires_grad=False)
# fake = Variable(Tensor(opt.batch_size, 1).fill_(0), requires_grad=False)
t_total = time.time()
for i_epoch, epoch in enumerate(range(1, opt.n_epochs + 1)):
t_epoch = time.time()
for iteration, (imgs, _) in enumerate(dataloader):
t_batch = time.time()
# ---------------------
# Train Encoder
# ---------------------
for p in generator.parameters():
p.requires_grad = opt.do_joint
for p in encoder.parameters():
p.requires_grad = True
# the following is not necessary as we do not use D here and only optimize ||G(E(x)) - x ||^2
for p in discriminator.parameters():
p.requires_grad = False # to avoid learning D when learning E
real_imgs = Variable(imgs.type(Tensor), requires_grad=False)
# init samples used to visualize performance of the AE
if real_imgs_samples is None:
real_imgs_samples = real_imgs[:opt.N_samples]
# add noise here to real_imgs
real_imgs_ = real_imgs * 1.
if opt.E_noise > 0:
real_imgs_ += opt.E_noise * gen_noise(real_imgs)
z_imgs = encoder(real_imgs_)
decoded_imgs = generator(z_imgs)
# Loss measures Encoder's ability to generate vectors suitable with the generator
e_loss = 1. - E_loss(real_imgs, decoded_imgs)
# energy = 1. # E_loss(real_imgs, zero_target) # normalize on the energy of imgs
# if opt.do_joint:
# e_loss = E_loss(real_imgs, decoded_imgs) / energy
# else:
# e_loss = E_loss(real_imgs, decoded_imgs.detach()) / energy
if opt.lambdaE > 0:
# We wish to make sure the intermediate vector z_imgs get closer to a iid normal (centered gausian of variance 1)
e_loss += opt.lambdaE * (torch.sum(z_imgs) / opt.batch_size /
opt.latent_dim).pow(2)
e_loss += opt.lambdaE * (torch.sum(z_imgs.pow(2)) /
opt.batch_size / opt.latent_dim -
1).pow(2).pow(.5)
# Backward
optimizer_E.zero_grad()
e_loss.backward()
optimizer_E.step()
valid_smooth = np.random.uniform(opt.valid_smooth,
1.0 - (1 - opt.valid_smooth) / 2,
(opt.batch_size, 1))
valid_smooth = Variable(Tensor(valid_smooth), requires_grad=False)
fake_smooth = np.random.uniform((1 - opt.valid_smooth) / 2,
1 - opt.valid_smooth,
(opt.batch_size, 1))
fake_smooth = Variable(Tensor(fake_smooth), requires_grad=False)
# ---------------------
# Train Discriminator
# ---------------------
# Discriminator Requires grad, Encoder + Generator requires_grad = False
for p in discriminator.parameters():
p.requires_grad = True
for p in generator.parameters():
p.requires_grad = False # to avoid computation
for p in encoder.parameters():
p.requires_grad = False # to avoid computation
# Configure input
real_imgs = Variable(imgs.type(Tensor), requires_grad=False)
real_imgs_ = real_imgs * 1.
if opt.D_noise > 0:
real_imgs_ += opt.D_noise * gen_noise(real_imgs)
if opt.do_insight:
# the discriminator can not access the images directly but only
# what is visible through the auto-encoder
real_imgs_ = generator(encoder(real_imgs_))
# Discriminator decision (in logit units)
# TODO : group images by sub-batches and train to discriminate from all together
# should allow to avoid mode collapse
logit_d_x = discriminator(real_imgs_)
# ---------------------
# Train Discriminator
# ---------------------
if opt.GAN_loss == 'wasserstein':
# weight clipping
for p in discriminator.parameters():
p.data.clamp_(-0.01, 0.01)
# Measure discriminator's ability to classify real from generated samples
if opt.GAN_loss == 'ian':
# eq. 14 in https://arxiv.org/pdf/1701.00160.pdf
real_loss = -torch.sum(1 / (1. - 1 / sigmoid(logit_d_x)))
elif opt.GAN_loss == 'hinge':
# TODO check if we use p or log p
real_loss = nn.ReLU()(valid_smooth - sigmoid(logit_d_x)).mean()
elif opt.GAN_loss == 'wasserstein':
real_loss = torch.mean(
torch.abs(valid_smooth - sigmoid(logit_d_x)))
elif opt.GAN_loss == 'alternative':
# https://www.inference.vc/an-alternative-update-rule-for-generative-adversarial-networks/
real_loss = -torch.sum(torch.log(sigmoid(logit_d_x)))
elif opt.GAN_loss == 'alternativ2':
# https://www.inference.vc/an-alternative-update-rule-for-generative-adversarial-networks/
real_loss = -torch.sum(
torch.log(sigmoid(logit_d_x) / (1. - sigmoid(logit_d_x))))
elif opt.GAN_loss == 'alternativ3':
# to maximize D(x), we minimize - sum(logit_d_x)
real_loss = -torch.sum(logit_d_x)
elif opt.GAN_loss == 'original':
real_loss = F.binary_cross_entropy(sigmoid(logit_d_x),
valid_smooth)
else:
print('GAN_loss not defined', opt.GAN_loss)
# Generate a batch of fake images and learn the discriminator to treat them as such
z = gen_z(imgs=real_imgs_)
gen_imgs = generator(z)
if opt.D_noise > 0: gen_imgs += opt.D_noise * gen_noise(real_imgs)
# Discriminator decision for fake data
logit_d_fake = discriminator(gen_imgs.detach())
# Measure discriminator's ability to classify real from generated samples
if opt.GAN_loss == 'wasserstein':
fake_loss = torch.mean(sigmoid(logit_d_fake))
elif opt.GAN_loss == 'hinge':
# TODO check if we use p or log p
real_loss = nn.ReLU()(1.0 + sigmoid(logit_d_fake)).mean()
elif opt.GAN_loss == 'alternative':
# https://www.inference.vc/an-alternative-update-rule-for-generative-adversarial-networks/
fake_loss = -torch.sum(torch.log(1 - sigmoid(logit_d_fake)))
elif opt.GAN_loss == 'alternativ2':
# https://www.inference.vc/an-alternative-update-rule-for-generative-adversarial-networks/
fake_loss = torch.sum(
torch.log(
sigmoid(logit_d_fake) / (1. - sigmoid(logit_d_fake))))
elif opt.GAN_loss == 'alternativ3':
# to minimize D(G(z)), we minimize sum(logit_d_fake)
fake_loss = torch.sum(logit_d_fake)
elif opt.GAN_loss in ['original', 'ian']:
fake_loss = F.binary_cross_entropy(sigmoid(logit_d_fake),
fake_smooth)
else:
print('GAN_loss not defined', opt.GAN_loss)
# Backward
optimizer_D.zero_grad()
real_loss.backward()
fake_loss.backward()
# apply the gradients
optimizer_D.step()
# -----------------
# Train Generator
# -----------------
for p in generator.parameters():
p.requires_grad = True
for p in discriminator.parameters():
p.requires_grad = False # to avoid computation
for p in encoder.parameters():
p.requires_grad = False # to avoid computation
# Generate a batch of fake images
z = gen_z(imgs=real_imgs_)
gen_imgs = generator(z)
if opt.G_noise > 0: gen_imgs += opt.G_noise * gen_noise(real_imgs)
# New discriminator decision (since we just updated D)
logit_d_g_z = discriminator(gen_imgs)
# Loss functions
# Loss measures generator's ability to fool the discriminator
if opt.GAN_loss == 'ian':
# eq. 14 in https://arxiv.org/pdf/1701.00160.pdf
# https://en.wikipedia.org/wiki/Logit
g_loss = -torch.sum(
sigmoid(logit_d_g_z) / (1 - sigmoid(logit_d_g_z)))
elif opt.GAN_loss == 'wasserstein' or opt.GAN_loss == 'hinge':
g_loss = torch.mean(
torch.abs(valid_smooth - sigmoid(logit_d_g_z)))
elif opt.GAN_loss == 'alternative':
# https://www.inference.vc/an-alternative-update-rule-for-generative-adversarial-networks/
g_loss = -torch.sum(torch.log(sigmoid(logit_d_g_z)))
elif opt.GAN_loss == 'alternativ2':
# https://www.inference.vc/an-alternative-update-rule-for-generative-adversarial-networks/
g_loss = -torch.sum(
torch.log(
sigmoid(logit_d_g_z) / (1. - sigmoid(logit_d_g_z))))
# g_loss = torch.sum(torch.log(1./sigmoid(logit_d_g_z) - 1.))
elif opt.GAN_loss == 'alternativ3':
# to maximize D(G(z)), we minimize - sum(logit_d_g_z)
g_loss = -torch.sum(logit_d_g_z)
elif opt.GAN_loss == 'original':
# https://pytorch.org/docs/stable/nn.html?highlight=bcewithlogitsloss#torch.nn.BCEWithLogitsLoss
#adversarial_loss = torch.nn.BCEWithLogitsLoss() # eq. 8 in https://arxiv.org/pdf/1701.00160.pdf
#
# https://medium.com/swlh/gan-to-generate-images-of-cars-5f706ca88da
# adversarial_loss = torch.nn.BCE() # eq. 8 in https://arxiv.org/pdf/1701.00160.pdf
g_loss = F.binary_cross_entropy(sigmoid(logit_d_g_z),
valid_smooth)
else:
print('GAN_loss not defined', opt.GAN_loss)
# penalize low variability in a batch, that is, mode collapse
# TODO maximize sum of the distances to the nearest neighbors
if opt.lambdaG > 0:
e_g_z = encoder(gen_imgs) # get normal vectors
Xcorr = torch.tensordot(e_g_z, torch.transpose(e_g_z, 0, 1),
1) / opt.latent_dim
Xcorr *= eye # set the diagonal elements to zero
g_loss += opt.lambdaG * torch.sum(Xcorr.pow(2)).pow(.5)
# Backward
optimizer_G.zero_grad()
g_loss.backward()
# apply the gradients
optimizer_G.step()
# -----------------
# Recording stats
# -----------------
d_loss = real_loss + fake_loss
# Compensation pour le BCElogits
d_fake = sigmoid(logit_d_fake)
d_x = sigmoid(logit_d_x)
d_g_z = sigmoid(logit_d_g_z)
print(
"%s [Epoch %d/%d] [Batch %d/%d] [E loss: %f] [D loss: %f] [G loss: %f] [D(x) %f] [D(G(z)) %f] [D(G(z')) %f] [Time: %fs]"
% (opt.run_path, epoch, opt.n_epochs, iteration + 1,
len(dataloader), e_loss.item(), d_loss.item(),
g_loss.item(), torch.mean(d_x), torch.mean(d_fake),
torch.mean(d_g_z), time.time() - t_batch))
# Save Losses and scores for Tensorboard
save_hist_batch(stat_record, iteration, i_epoch, g_loss, d_loss,
e_loss, d_x, d_g_z)
if do_tensorboard:
# Tensorboard save
writer.add_scalar('loss/E', e_loss.item(), global_step=epoch)
# writer.add_histogram('coeffs/z', z, global_step=epoch)
try:
writer.add_histogram('coeffs/E_x', z_imgs, global_step=epoch)
except:
pass
# writer.add_histogram('image/x', real_imgs, global_step=epoch)
# try:
# writer.add_histogram('image/E_G_x', decoded_imgs, global_step=epoch)
# except:
# pass
# try:
# writer.add_histogram('image/G_z', gen_imgs, global_step=epoch)
# except:
# pass
writer.add_scalar('loss/G', g_loss.item(), global_step=epoch)
# writer.add_scalar('score/D_fake', hist["d_fake_mean"][i], global_step=epoch)
# print(stat_record["d_g_z_mean"])
writer.add_scalar('score/D_g_z',
np.mean(stat_record["d_g_z_mean"]),
global_step=epoch)
writer.add_scalar('loss/D', d_loss.item(), global_step=epoch)
writer.add_scalar('score/D_x',
np.mean(stat_record["d_x_mean"]),
global_step=epoch)
# Save samples
if epoch % opt.sample_interval == 0:
"""
Use generator model and noise vector to generate images.
Save them to tensorboard
"""
generator.eval()
gen_imgs = generator(fixed_noise)
from torchvision.utils import make_grid
grid = make_grid(gen_imgs,
normalize=True,
nrow=16,
range=(0, 1))
writer.add_image('Generated images', grid, epoch)
generator.train()
"""
Use auto-encoder model and original images to generate images.
Save them to tensorboard
"""
# grid_imgs = make_grid(real_imgs_samples, normalize=True, nrow=8, range=(0, 1))
# writer.add_image('Images/original', grid_imgs, epoch)
generator.eval()
encoder.eval()
enc_imgs = encoder(real_imgs_samples)
dec_imgs = generator(enc_imgs)
grid_dec = make_grid(dec_imgs,
normalize=True,
nrow=16,
range=(0, 1))
# writer.add_image('Images/auto-encoded', grid_dec, epoch)
writer.add_image('Auto-encoded', grid_dec, epoch)
generator.train()
encoder.train()
# writer.add_graph(encoder, real_imgs_samples)
# writer.add_graph(generator, enc_imgs)
# writer.add_graph(discriminator, real_imgs_samples)
#
# if epoch % opt.sample_interval == 0 :
# sampling(fixed_noise, generator, path_data, epoch, tag)
# # do_plot(hist, start_epoch, epoch)
print("[Epoch Time: ", time.time() - t_epoch, "s]")
sampling(fixed_noise, generator, path_data, epoch, tag, nrow=16)
# for scheduler in schedulers: scheduler.step()
t_final = time.gmtime(time.time() - t_total)
print("[Total Time: ",
t_final.tm_mday - 1,
"j:",
time.strftime("%Hh:%Mm:%Ss", t_final),
"]",
sep='')
if do_tensorboard:
writer.close()
def __init__(self, **kwargs):
super().__init__()
self.save_hyperparameters()
device = torch.device(
"cuda:0" if torch.cuda.is_available() and self.hparams.cuda else "cpu")
if self.hparams.modelID == 0:
self.net = ResNet(in_channels=self.hparams.in_channels, out_channels=self.hparams.out_channels, res_blocks=self.hparams.model_res_blocks,
starting_nfeatures=self.hparams.model_starting_nfeatures, updown_blocks=self.hparams.model_updown_blocks,
is_relu_leaky=self.hparams.model_relu_leaky, do_batchnorm=self.hparams.model_do_batchnorm,
res_drop_prob=self.hparams.model_drop_prob, is_replicatepad=self.hparams.model_is_replicatepad, out_act=self.hparams.model_out_act, forwardV=self.hparams.model_forwardV,
upinterp_algo=self.hparams.model_upinterp_algo, post_interp_convtrans=self.hparams.model_post_interp_convtrans, is3D=self.hparams.is3D) # TODO think of 2D
# self.net = nn.Conv3d(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1)
elif self.hparams.modelID == 2:
self.net = DualSpaceResNet(in_channels=self.hparams.in_channels, out_channels=self.hparams.out_channels, res_blocks=self.hparams.model_res_blocks,
starting_nfeatures=self.hparams.model_starting_nfeatures, updown_blocks=self.hparams.model_updown_blocks,
is_relu_leaky=self.hparams.model_relu_leaky, do_batchnorm=self.hparams.model_do_batchnorm,
res_drop_prob=self.hparams.model_drop_prob, is_replicatepad=self.hparams.model_is_replicatepad, out_act=self.hparams.model_out_act, forwardV=self.hparams.model_forwardV,
upinterp_algo=self.hparams.model_upinterp_algo, post_interp_convtrans=self.hparams.model_post_interp_convtrans, is3D=self.hparams.is3D,
connect_mode=self.hparams.model_dspace_connect_mode, inner_norm_ksp=self.hparams.model_inner_norm_ksp)
elif self.hparams.modelID == 3: #Primal-Dual Network, complex Primal
self.net = PrimalDualNetwork(n_primary=5, n_dual=5, n_iterations=10,
use_original_block = True,
use_original_init = True,
use_complex_primal = True,
g_normtype = "magmax",
transform = "Fourier",
return_abs = True)
elif self.hparams.modelID == 4: #Primal-Dual Network, absolute Primal
self.net = PrimalDualNetwork(n_primary=5, n_dual=5, n_iterations=10,
use_original_block = True,
use_original_init = True,
use_complex_primal = False,
g_normtype = "magmax",
transform = "Fourier")
elif self.hparams.modelID == 5: #Primal-Dual UNet Network, absolute Primal
self.net = PrimalDualNetwork(n_primary=4, n_dual=5, n_iterations=2,
use_original_block = False,
use_original_init = False,
use_complex_primal = False,
g_normtype = "magmax",
transform = "Fourier")
elif self.hparams.modelID == 6: #Primal-Dual Network v2 (no residual), complex Primal
self.net = PrimalDualNetworkNoResidue(n_primary=5, n_dual=5, n_iterations=10,
use_original_block = True,
use_original_init = True,
use_complex_primal = True,
residuals=False,
g_normtype = "magmax",
transform = "Fourier",
return_abs = True)
else:
# TODO: other models
sys.exit("Only ReconResNet and DualSpaceResNet have been implemented so far in ReconEngine")
if bool(self.hparams.preweights_path):
print("Pre-weights found, loding...")
chk = torch.load(self.hparams.preweights_path, map_location='cpu')
self.net.load_state_dict(chk['state_dict'])
if self.hparams.lossID == 0:
if self.hparams.in_channels != 1 or self.hparams.out_channels != 1:
sys.exit(
"Perceptual Loss used here only works for 1 channel input and output")
self.loss = PerceptualLoss(device=device, loss_model="unet3Dds", resize=None,
loss_type=self.hparams.ploss_type, n_level=self.hparams.ploss_level) # TODO thinkof 2D
elif self.hparams.lossID == 1:
self.loss = nn.L1Loss(reduction='mean')
elif self.hparams.lossID == 2:
self.loss = MS_SSIM(channel=self.hparams.out_channels, data_range=1, spatial_dims=3 if self.hparams.is3D else 2, nonnegative_ssim=False).to(device)
elif self.hparams.lossID == 3:
self.loss = SSIM(channel=self.hparams.out_channels, data_range=1, spatial_dims=3 if self.hparams.is3D else 2, nonnegative_ssim=False).to(device)
else:
sys.exit("Invalid Loss ID")
self.dataspace = DataSpaceHandler(**self.hparams)
if self.hparams.ds_mode == 0:
trans = tioTransforms
augs = tioAugmentations
elif self.hparams.ds_mode == 1:
trans = pytTransforms
augs = pytAugmentations
# TODO parameterised everything
self.init_transforms = []
self.aug_transforms = []
self.transforms = []
if self.hparams.ds_mode == 0 and self.hparams.cannonicalResample: # Only applicable for TorchIO
self.init_transforms += [tio.ToCanonical(), tio.Resample('gt')]
if self.hparams.ds_mode == 0 and self.hparams.forceNormAffine: # Only applicable for TorchIO
self.init_transforms += [trans.ForceAffine()]
if self.hparams.croppad and self.hparams.ds_mode == 1:
self.init_transforms += [
trans.CropOrPad(size=self.hparams.input_shape)]
self.init_transforms += [trans.IntensityNorm(type=self.hparams.norm_type, return_meta=self.hparams.motion_return_meta)]
# dataspace_transforms = self.dataspace.getTransforms() #TODO: dataspace transforms are not in use
# self.init_transforms += dataspace_transforms
if bool(self.hparams.random_crop) and self.hparams.ds_mode == 1:
self.aug_transforms += [augs.RandomCrop(
size=self.hparams.random_crop, p=self.hparams.p_random_crop)]
if self.hparams.p_contrast_augment > 0:
self.aug_transforms += [augs.getContrastAugs(
p=self.hparams.p_contrast_augment)]
# if the task if MoCo and pre-corrupted vols are not supplied
if self.hparams.taskID == 1 and not bool(self.hparams.train_path_inp):
if self.hparams.motion_mode == 0 and self.hparams.ds_mode == 0:
motion_params = {k.split('motionmg_')[
1]: v for k, v in self.hparams.items() if k.startswith('motionmg')}
self.transforms += [tioMotion.RandomMotionGhostingFast(
**motion_params), trans.IntensityNorm()]
elif self.hparams.motion_mode == 1 and self.hparams.ds_mode == 1 and not self.hparams.is3D:
self.transforms += [pytMotion.Motion2Dv0(
sigma_range=self.hparams.motion_sigma_range, n_threads=self.hparams.motion_n_threads, p=self.hparams.motion_p, return_meta=self.hparams.motion_return_meta)]
elif self.hparams.motion_mode == 2 and self.hparams.ds_mode == 1 and not self.hparams.is3D:
self.transforms += [pytMotion.Motion2Dv1(sigma_range=self.hparams.motion_sigma_range, n_threads=self.hparams.motion_n_threads,
restore_original=self.hparams.motion_restore_original, p=self.hparams.motion_p, return_meta=self.hparams.motion_return_meta)]
else:
sys.exit(
"Error: invalid motion_mode, ds_mode, is3D combo. Please double check!")
self.static_metamat = sio.loadmat(self.hparams.static_metamat_file) if bool(
self.hparams.static_metamat_file) else None
if self.hparams.taskID == 0 and self.hparams.use_datacon:
self.datacon = DataConsistency(
isRadial=self.hparams.is_radial, metadict=self.static_metamat)
else:
self.datacon = None
input_shape = self.hparams.input_shape if self.hparams.is3D else self.hparams.input_shape[
:-1]
self.example_input_array = torch.empty(
self.hparams.batch_size, self.hparams.in_channels, *input_shape).float()
self.saver = ResSaver(
self.hparams.res_path, save_inp=self.hparams.save_inp, do_norm=self.hparams.do_savenorm)
def loss_new_msssim(x, y):
msssim_loss = MS_SSIM(data_range=10, channel=2)
loss = 1 - msssim_loss(x, y)
return loss
import torch
# X: (N,3,H,W) a batch of RGB images (0~255)
# Y: (N,3,H,W)
X = torch.rand(4, 3, 512, 512)
Y = torch.rand(4, 3, 512, 512)
#Y = X
# ssim_val = ssim( X, Y, data_range=1.0, size_average=False) # return (N,)
# ms_ssim_val = ms_ssim( X, Y, data_range=1.0, size_average=False ) #(N,)
# # or set 'size_average=True' to get a scalar value as loss.
# ssim_loss = ssim( X, Y, data_range=1.0, size_average=True) # return a scalar
# ms_ssim_loss = ms_ssim( X, Y, data_range=1.0, size_average=True )
# or reuse windows with SSIM & MS_SSIM.
ssim_module = SSIM(win_size=11,
win_sigma=1.5,
data_range=1.0,
size_average=True,
channel=3)
ms_ssim_module = MS_SSIM(win_size=11,
win_sigma=1.5,
data_range=1.0,
size_average=True,
channel=3)
ssim_loss = 1 - ssim_module(X, Y)
ms_ssim_loss = 1 - ms_ssim_module(X, Y)
X = torch.rand(4, 3, 512, 512)
Y = torch.rand(4, 3, 512, 512)
def MyDNN(opt):
# ----------------------------------------
# Network training parameters
# ----------------------------------------
# cudnn benchmark
cudnn.benchmark = opt.cudnn_benchmark
# configurations
save_folder = os.path.join(opt.save_path, opt.task)
sample_folder = os.path.join(opt.sample_path, opt.task)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
if not os.path.exists(sample_folder):
os.makedirs(sample_folder)
# Loss functions
criterion_L1 = torch.nn.L1Loss().cuda()
criterion_L2 = torch.nn.MSELoss().cuda()
mse_loss = nn.MSELoss().cuda()
ms_ssim_module = MS_SSIM(data_range=2,
size_average=True,
channel=3,
nonnegative_ssim=True)
# Pretrained VGG
# vgg = MINCFeatureExtractor(opt).cuda()
# Initialize Generator
generator = utils.create_MyDNN(opt)
use_checkpoint = False
if use_checkpoint:
checkpoint_path = './MyDNN1_denoise_epoch175_bs1'
# Load a pre-trained network
pretrained_net = torch.load(checkpoint_path + '.pth')
load_dict(generator, pretrained_net)
print('Generator is loaded!')
# To device
if opt.multi_gpu:
generator = nn.DataParallel(generator)
generator = generator.cuda()
else:
generator = generator.cuda()
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(),
lr=opt.lr_g,
betas=(opt.b1, opt.b2),
weight_decay=opt.weight_decay)
# Learning rate decrease
def adjust_learning_rate(opt, epoch, iteration, optimizer):
#Set the learning rate to the initial LR decayed by "lr_decrease_factor" every "lr_decrease_epoch" epochs
if opt.lr_decrease_mode == 'epoch':
lr = opt.lr_g * (opt.lr_decrease_factor
**(epoch // opt.lr_decrease_epoch))
if epoch < 200:
lr = 0.0001
if epoch >= 200:
lr = 0.00005
if epoch >= 300:
lr = 0.00001
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if opt.lr_decrease_mode == 'iter':
lr = opt.lr_g * (opt.lr_decrease_factor
**(iteration // opt.lr_decrease_iter))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
# Save the model if pre_train == True
def save_model(opt, epoch, iteration, len_dataset, generator, val_PSNR,
best_PSNR):
"""Save the model at "checkpoint_interval" and its multiple"""
if opt.save_best_model and best_PSNR == val_PSNR:
torch.save(generator,
'final_%s_epoch%d_best.pth' % (opt.task, epoch))
print('The best model is successfully saved at epoch %d' % (epoch))
if opt.multi_gpu == True:
if opt.save_mode == 'epoch':
if (epoch % opt.save_by_epoch
== 0) and (iteration % len_dataset == 0):
if opt.save_name_mode:
torch.save(
generator.module, 'MyDNN1_%s_epoch%d_bs%d.pth' %
(opt.task, epoch, opt.batch_size))
print(
'The trained model is successfully saved at epoch %d'
% (epoch))
if opt.save_mode == 'iter':
if iteration % opt.save_by_iter == 0:
if opt.save_name_mode:
torch.save(
generator.module, 'MyDNN1_%s_iter%d_bs%d.pth' %
(opt.task, iteration, opt.batch_size))
print(
'The trained model is successfully saved at iteration %d'
% (iteration))
else:
if opt.save_mode == 'epoch':
if (epoch % opt.save_by_epoch
== 0) and (iteration % len_dataset == 0):
if opt.save_name_mode:
torch.save(
generator, 'final_%s_epoch%d_bs%d.pth' %
(opt.task, epoch, opt.batch_size))
print(
'The trained model is successfully saved at epoch %d'
% (epoch))
if opt.save_mode == 'iter':
if iteration % opt.save_by_iter == 0:
if opt.save_name_mode:
torch.save(
generator, 'final_%s_iter%d_bs%d.pth' %
(opt.task, iteration, opt.batch_size))
print(
'The trained model is successfully saved at iteration %d'
% (iteration))
# ----------------------------------------
# Network dataset
# ----------------------------------------
# Define the dataloader
# trainset = dataset.TestDataset(opt)
trainset = dataset.Noise2CleanDataset(opt)
print('The overall number of training images:', len(trainset))
testset = dataset.TestDataset(opt)
valset = dataset.ValDataset(opt)
print('The overall number of val images:', len(valset))
# Define the dataloader
dataloader = DataLoader(trainset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True)
val_loader = DataLoader(valset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True)
test_loader = DataLoader(testset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True)
# ----------------------------------------
# Training
# ----------------------------------------
# Count start time
prev_time = time.time()
best_PSNR = 0
# For loop training
for epoch in range(opt.epochs):
total_loss = 0
total_ploss = 0
total_sobel = 0
total_Lap = 0
for i, (true_input, simulated_input, true_target,
noise_level_map) in enumerate(dataloader):
# To device
true_input = true_input.cuda()
true_target = true_target.cuda()
simulated_input = simulated_input.cuda()
noise_level_map = noise_level_map.cuda()
# Train Generator
optimizer_G.zero_grad()
pre_clean = generator(true_input)
# Parse through VGGMINC layers
# features_y = vgg(pre_clean)
# features_x = vgg(true_input)
# content_loss = criterion_L2(features_y, features_x).
pre = pre_clean[0, :, :, :].data.permute(1, 2, 0).cpu().numpy()
pre = rgb2gray(pre)
true = true_input[0, :, :, :].data.permute(1, 2, 0).cpu().numpy()
true = rgb2gray(true)
laplacian_pre = cv2.Laplacian(pre, cv2.CV_32F) #CV_64F为图像深度
laplacian_gt = cv2.Laplacian(true, cv2.CV_32F) #CV_64F为图像深度
sobel_pre = 0.5 * (cv2.Sobel(pre, cv2.CV_32F, 1, 0, ksize=5) +
cv2.Sobel(pre, cv2.CV_32F, 0, 1, ksize=5)
) #1，0参数表示在x方向求一阶导数
sobel_gt = 0.5 * (cv2.Sobel(true, cv2.CV_32F, 1, 0, ksize=5) +
cv2.Sobel(true, cv2.CV_32F, 0, 1, ksize=5)
) #0,1参数表示在y方向求一阶导数
sobel_loss = mean_squared_error(sobel_pre, sobel_gt)
laplacian_loss = mean_squared_error(laplacian_pre, laplacian_gt)
# L1 Loss
Pixellevel_L1_Loss = criterion_L1(pre_clean, true_target)
# MS-SSIM loss
ms_ssim_loss = 1 - ms_ssim_module(pre_clean + 1, true_target + 1)
# Overall Loss and optimize
loss = Pixellevel_L1_Loss + 0.5 * laplacian_loss
# loss = Pixellevel_L1_Loss
loss.backward()
optimizer_G.step()
# Determine approximate time left
iters_done = epoch * len(dataloader) + i
iters_left = opt.epochs * len(dataloader) - iters_done
time_left = datetime.timedelta(seconds=iters_left *
(time.time() - prev_time))
prev_time = time.time()
total_loss = Pixellevel_L1_Loss.item() + total_loss
# total_ploss = content_loss.item() + total_ploss
total_sobel = sobel_loss + total_sobel
total_Lap = laplacian_loss + total_Lap
# # Print log
print(
"\r[Epoch %d/%d] [Batch %d/%d] [Pixellevel L1 Loss: %.4f] [laplacian_loss Loss: %.4f] [sobel_loss Loss: %.4f] Time_left: %s"
% ((epoch + 1), opt.epochs, i, len(dataloader),
Pixellevel_L1_Loss.item(), laplacian_loss.item(),
sobel_loss.item(), time_left))
img_list = [pre_clean, true_target, true_input]
name_list = ['pred', 'gt', 'noise']
utils.save_sample_png(sample_folder=sample_folder,
sample_name='MyDNN_MS_epoch%d' % (epoch + 1),
img_list=img_list,
name_list=name_list,
pixel_max_cnt=255)
# Learning rate decrease at certain epochs
lr = adjust_learning_rate(opt, (epoch + 1), (iters_done + 1),
optimizer_G)
print(
"\r[Epoch %d/%d] [Batch %d/%d] [Pixellevel L1 Loss: %.4f] [laplacian_loss Loss: %.4f] [sobel_loss Loss: %.4f] Time_left: %s"
% ((epoch + 1), opt.epochs, i, len(dataloader), total_loss / 320,
total_Lap / 320, total_sobel / 320, time_left))
### Validation
val_PSNR = 0
be_PSNR = 0
num_of_val_image = 0
for j, (true_input, simulated_input, true_target,
noise_level_map) in enumerate(val_loader):
# To device
# A is for input image, B is for target image
true_input = true_input.cuda()
true_target = true_target.cuda()
# Forward propagation
with torch.no_grad():
pre_clean = generator(true_input)
# Accumulate num of image and val_PSNR
num_of_val_image += true_input.shape[0]
val_PSNR += utils.psnr(pre_clean, true_target,
255) * true_input.shape[0]
be_PSNR += utils.psnr(true_input, true_target,
255) * true_input.shape[0]
val_PSNR = val_PSNR / num_of_val_image
be_PSNR = be_PSNR / num_of_val_image
# Record average PSNR
print('PSNR at epoch %d: %.4f' % ((epoch + 1), val_PSNR))
print('PSNR before denoising %d: %.4f' % ((epoch + 1), be_PSNR))
best_PSNR = max(val_PSNR, best_PSNR)
# Save model at certain epochs or iterations
save_model(opt, (epoch + 1), (iters_done + 1), len(dataloader),
generator, val_PSNR, best_PSNR)
