def __init__(self, opt):
super(SRModel, self).__init__(opt)
train_opt = opt['train']
# define network and load pretrained models
self.netG = networks.define_G(opt).to(self.device)
self.load()
if self.is_train:
self.netG.train()
# loss
loss_type = train_opt['pixel_criterion']
if loss_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif loss_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] is not recognized.'.format(loss_type))
self.l_pix_w = train_opt['pixel_weight']
# G feature loss
if 'feature_weight' in train_opt and train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False).to(self.device)
# optimizers
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
logger.warning('Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(
optim_params, lr=train_opt['lr_G'], weight_decay=wd_G)
self.optimizers.append(self.optimizer_G)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
# print network
self.print_network()
def __init__(self, opt):
super(HyperRIMModel, self).__init__(opt)
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device)
if self.is_train:
self.netG.train()
self.load()
# store the number of levels and code channel
self.num_levels = int(math.log(opt['scale'], 2))
self.code_nc = opt['network_G']['code_nc']
self.map_nc = opt['network_G']['map_nc']
# define losses, optimizer and scheduler
self.netF = networks.define_F(opt).to(self.device)
self.projections = None
if self.is_train:
# G
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
map_network_params = []
core_network_params = []
# can freeze weights for any of the levels
freeze_level = train_opt['freeze_level']
for k, v in self.netG.named_parameters():
if v.requires_grad:
if freeze_level:
if "level_%d" % freeze_level not in k:
if 'map' in k:
map_network_params.append(v)
else:
core_network_params.append(v)
else:
if 'map' in k:
map_network_params.append(v)
else:
core_network_params.append(v)
else:
print('WARNING: params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam([{'params': core_network_params},
{'params': map_network_params, 'lr': 1e-2 * train_opt['lr_G']}],
lr=train_opt['lr_G'], weight_decay=wd_G,
betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# for resume training - load the previous optimizer stats
self.load_optimizer()
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, train_opt['lr_steps'],
train_opt['lr_gamma']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
print('---------- Model initialized ------------------')
self.print_network()
print('-----------------------------------------------')
def initialize_networks(self, opt):
netG = networks.define_G(opt)
netD = networks.define_D(opt) if opt.isTrain else None
netE = networks.define_E(opt) if opt.use_vae else None
netF = networks.define_F(opt) if opt.use_F else None
if not opt.isTrain or opt.continue_train:
netG = util.load_network(netG, 'G', opt.which_epoch, opt)
if opt.isTrain:
netD = util.load_network(netD, 'D', opt.which_epoch, opt)
if opt.use_vae:
netE = util.load_network(netE, 'E', opt.which_epoch, opt)
if opt.use_F:
netF = util.load_network(netF, 'F', opt.which_epoch, opt)
return netG, netD, netE, netF
def get_network(self, opt, mode='G'):
assert(mode in ['G', 'D', 'F'])
if mode == 'G':
net = networks.define_G(opt).to(self.device)
elif mode == 'D':
net = networks.define_D(opt).to(self.device)
elif mode == 'F':
net = networks.define_F(opt).to(self.device)
if opt['dist']:
net = DistributedDataParallel(net,
device_ids=[torch.cuda.current_device()],
find_unused_parameters=True,
broadcast_buffers=False)
else:
net = DataParallel(net)
return net
def __init__(self, opt):
super(SRGANModel, self).__init__(opt)
if opt['dist']:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
train_opt = opt['train']
self.train_opt = train_opt
self.opt = opt
self.segmentor = None
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device)
if opt['dist']:
self.netG = DistributedDataParallel(
self.netG, device_ids=[torch.cuda.current_device()])
else:
self.netG = DataParallel(self.netG)
if self.is_train:
self.netD = networks.define_D(opt).to(self.device)
if train_opt.get("gan_video_weight", 0) > 0:
self.net_video_D = networks.define_video_D(opt).to(self.device)
if opt['dist']:
self.netD = DistributedDataParallel(
self.netD, device_ids=[torch.cuda.current_device()])
if train_opt.get("gan_video_weight", 0) > 0:
self.net_video_D = DistributedDataParallel(
self.net_video_D,
device_ids=[torch.cuda.current_device()])
else:
self.netD = DataParallel(self.netD)
if train_opt.get("gan_video_weight", 0) > 0:
self.net_video_D = DataParallel(self.net_video_D)
self.netG.train()
self.netD.train()
if train_opt.get("gan_video_weight", 0) > 0:
self.net_video_D.train()
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# Pixel mask loss
if train_opt.get("pixel_mask_weight", 0) > 0:
l_pix_type = train_opt['pixel_mask_criterion']
self.cri_pix_mask = LMaskLoss(
l_pix_type=l_pix_type,
segm_mask=train_opt['segm_mask']).to(self.device)
self.l_pix_mask_w = train_opt['pixel_mask_weight']
else:
logger.info('Remove pixel mask loss.')
self.cri_pix_mask = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
if opt['dist']:
self.netF = DistributedDataParallel(
self.netF, device_ids=[torch.cuda.current_device()])
else:
self.netF = DataParallel(self.netF)
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# Video gan weight
if train_opt.get("gan_video_weight", 0) > 0:
self.cri_video_gan = GANLoss(train_opt['gan_video_type'], 1.0,
0.0).to(self.device)
self.l_gan_video_w = train_opt['gan_video_weight']
# can't use optical flow with i and i+1 because we need i+2 lr to calculate i+1 oflow
if 'train' in self.opt['datasets'].keys():
key = "train"
else:
key = 'test_1'
assert self.opt['datasets'][key][
'optical_flow_with_ref'] == True, f"Current value = {self.opt['datasets'][key]['optical_flow_with_ref']}"
# D_update_ratio and D_init_iters
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt[
'D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt[
'D_init_iters'] else 0
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params,
lr=train_opt['lr_G'],
weight_decay=wd_G,
betas=(train_opt['beta1_G'],
train_opt['beta2_G']))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt['weight_decay_D'] if train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
lr=train_opt['lr_D'],
weight_decay=wd_D,
betas=(train_opt['beta1_D'],
train_opt['beta2_D']))
self.optimizers.append(self.optimizer_D)
# Video D
if train_opt.get("gan_video_weight", 0) > 0:
self.optimizer_video_D = torch.optim.Adam(
self.net_video_D.parameters(),
lr=train_opt['lr_D'],
weight_decay=wd_D,
betas=(train_opt['beta1_D'], train_opt['beta2_D']))
self.optimizers.append(self.optimizer_video_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(
optimizer,
train_opt['lr_steps'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights'],
gamma=train_opt['lr_gamma'],
clear_state=train_opt['clear_state']))
elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer,
train_opt['T_period'],
eta_min=train_opt['eta_min'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
self.print_network() # print network
self.load() # load G and D if needed
def __init__(self, opt):
super(DualGAN, self).__init__(opt)
train_opt = opt['train']
# define networks and load pretrained models
self.netG1 = networks.define_G1(opt).to(self.device) # G1
if self.is_train:
self.netG2 = networks.define_G2(opt).to(self.device) # G2
self.netD1 = networks.define_D(opt).to(self.device) # D
self.netD2 = networks.define_D(opt).to(self.device) # D
self.netQ = networks.define_Q(opt).to(self.device)
self.netG1.train()
self.netG2.train()
self.netD1.train()
self.netD2.train()
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False,Rlu=True).to(self.device) #Rlu=True if feature taken before relu, else false
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters are for WGAN
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt['D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt['D_init_iters'] else 0
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
self.optimizer_G = torch.optim.Adam(itertools.chain(self.netG1.parameters(), self.netG2.parameters()),lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD1.parameters(), self.netD2.parameters()),lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
# print network
self.print_network()
def __init__(self, opt):
super(ICPR_model, self).__init__(opt)
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G1(opt).to(self.device) # G1
if self.is_train:
self.netV = networks.define_D(opt).to(self.device) # G1
self.netD = networks.define_D2(opt).to(self.device)
#self.netQ = networks.define_Q(opt).to(self.device)
self.netG.train()
self.netV.train()
self.netD.train()
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
self.weight_kl = 1e-2
self.weight_D = 1e-4
self.l_gan_w = 1e-3
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False, Rlu=True).to(
self.device
) #Rlu=True if feature taken before relu, else false
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0,
0.0).to(self.device)
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
#D
wd_D = train_opt['weight_decay_D'] if train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D)
self.optimizer_V = torch.optim.Adam(self.netV.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_V)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
# print network
self.print_network()
def __init__(self, opt):
super(SFTGAN_ACD_Model, self).__init__(opt)
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device) # G
if self.is_train:
self.netD = networks.define_D(opt).to(self.device) # D
self.netG.train()
self.netD.train()
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
print('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
print('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters are for WGAN
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt[
'D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt[
'D_init_iters'] else 0
if train_opt['gan_type'] == 'wgan-gp':
self.random_pt = torch.Tensor(1, 1, 1, 1).to(self.device)
# gradient penalty loss
self.cri_gp = GradientPenaltyLoss(device=self.device).to(
self.device)
self.l_gp_w = train_opt['gp_weigth']
# D cls loss
self.cri_ce = nn.CrossEntropyLoss(ignore_index=0).to(self.device)
# ignore background, since bg images may conflict with other classes
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params_SFT = []
optim_params_other = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if 'SFT' in k or 'Cond' in k:
optim_params_SFT.append(v)
else:
optim_params_other.append(v)
self.optimizer_G_SFT = torch.optim.Adam(optim_params_SFT, lr=train_opt['lr_G']*5, \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizer_G_other = torch.optim.Adam(optim_params_other, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G_SFT)
self.optimizers.append(self.optimizer_G_other)
# D
wd_D = train_opt['weight_decay_D'] if train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
print('---------- Model initialized ------------------')
self.print_network()
print('-----------------------------------------------')
def __init__(self, opt):
super(SRDRLModel, self).__init__(opt)
train_opt = opt['train']
self.netG = networks.define_G(opt).to(self.device) # Generator
if self.is_train:
self.print_freq = opt['logger']['print_freq']
self.netG.train()
self.l_gan_w = train_opt['gan_weight'] # gan loss weight
if self.l_gan_w: # use gan loss
self.netD = networks.define_D(opt).to(self.device)
self.netD.train()
self.l_deg_w = train_opt['degradation_weight'] # degradation reconstruction loss weight
if self.l_deg_w: # use degradation reconstruction loss
self.netR = networks.define_R(opt).to(self.device)
self.l_fea_w = train_opt['feature_weight'] # perceptual loss weight
if self.l_fea_w: # use VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False).to(self.device)
self.load() # load G, D and R if needed
# define losses, optimizer and scheduler
if self.is_train:
# pixel loss for G
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logging.info('Remove pixel loss.')
self.cri_pix = None
# feature loss for G
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
else:
logging.info('Remove feature loss.')
self.cri_fea = None
# gan loss for G,D
if train_opt['gan_weight'] > 0:
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
else:
logging.info('Remove gan loss.')
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt['D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt['D_init_iters'] else 0
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(): # can optimize for a part of the model
optim_params.append(v)
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# D
if self.l_gan_w:
wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
# print network
self.print_network()
def __init__(self, opt):
super(PPONModel, self).__init__(opt)
train_opt = opt['train']
if self.is_train:
if opt['datasets']['train']['znorm']:
z_norm = opt['datasets']['train']['znorm']
else:
z_norm = False
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device) # G
if self.is_train:
self.netG.train()
if train_opt['gan_weight']:
self.netD = networks.define_D(opt).to(self.device) # D
self.netD.train()
#PPON
"""
self.phase1_s = train_opt['phase1_s']
if self.phase1_s is None:
self.phase1_s = 138000
self.phase2_s = train_opt['phase2_s']
if self.phase2_s is None:
self.phase2_s = 138000+34500
self.phase3_s = train_opt['phase3_s']
if self.phase3_s is None:
self.phase3_s = 138000+34500+34500
"""
self.phase1_s = train_opt['phase1_s'] if train_opt[
'phase1_s'] else 138000
self.phase2_s = train_opt['phase2_s'] if train_opt[
'phase2_s'] else (138000 + 34500)
self.phase3_s = train_opt['phase3_s'] if train_opt[
'phase3_s'] else (138000 + 34500 + 34500)
self.train_phase = train_opt['train_phase'] - 1 if train_opt[
'train_phase'] else 0 #change to start from 0 (Phase 1: from 0 to 1, Phase 1: from 1 to 2, etc)
self.restarts = train_opt['restarts'] if train_opt[
'restarts'] else [0]
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# Define if the generator will have a final capping mechanism in the output
self.outm = None
if train_opt['finalcap']:
self.outm = train_opt['finalcap']
# G pixel loss
#"""
if train_opt['pixel_weight']:
if train_opt['pixel_criterion']:
l_pix_type = train_opt['pixel_criterion']
else: #default to cb
l_fea_type = 'cb'
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
elif l_pix_type == 'cb':
self.cri_pix = CharbonnierLoss().to(self.device)
elif l_pix_type == 'elastic':
self.cri_pix = ElasticLoss().to(self.device)
elif l_pix_type == 'relativel1':
self.cri_pix = RelativeL1().to(self.device)
elif l_pix_type == 'l1cosinesim':
self.cri_pix = L1CosineSim().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
#"""
# G feature loss
#"""
if train_opt['feature_weight']:
if train_opt['feature_criterion']:
l_fea_type = train_opt['feature_criterion']
else: #default to l1
l_fea_type = 'l1'
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
elif l_fea_type == 'cb':
self.cri_fea = CharbonnierLoss().to(self.device)
elif l_fea_type == 'elastic':
self.cri_fea = ElasticLoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
#"""
#HFEN loss
#"""
if train_opt['hfen_weight']:
l_hfen_type = train_opt['hfen_criterion']
if train_opt['hfen_presmooth']:
pre_smooth = train_opt['hfen_presmooth']
else:
pre_smooth = False #train_opt['hfen_presmooth']
if l_hfen_type:
if l_hfen_type == 'rel_l1' or l_hfen_type == 'rel_l2':
relative = True
else:
relative = False #True #train_opt['hfen_relative']
if l_hfen_type:
self.cri_hfen = HFENLoss(loss_f=l_hfen_type,
device=self.device,
pre_smooth=pre_smooth,
relative=relative).to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_hfen_type))
self.l_hfen_w = train_opt['hfen_weight']
else:
logger.info('Remove HFEN loss.')
self.cri_hfen = None
#"""
#TV loss
#"""
if train_opt['tv_weight']:
self.l_tv_w = train_opt['tv_weight']
l_tv_type = train_opt['tv_type']
if train_opt['tv_norm']:
tv_norm = train_opt['tv_norm']
else:
tv_norm = 1
if l_tv_type == 'normal':
self.cri_tv = TVLoss(self.l_tv_w,
p=tv_norm).to(self.device)
elif l_tv_type == '4D':
self.cri_tv = TVLoss4D(self.l_tv_w).to(
self.device
) #Total Variation regularization in 4 directions
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_tv_type))
else:
logger.info('Remove TV loss.')
self.cri_tv = None
#"""
#SSIM loss
#"""
if train_opt['ssim_weight']:
self.l_ssim_w = train_opt['ssim_weight']
if train_opt['ssim_type']:
l_ssim_type = train_opt['ssim_type']
else: #default to ms-ssim
l_ssim_type = 'ms-ssim'
if l_ssim_type == 'ssim':
self.cri_ssim = SSIM(win_size=11,
win_sigma=1.5,
size_average=True,
data_range=1.,
channel=3).to(self.device)
elif l_ssim_type == 'ms-ssim':
self.cri_ssim = MS_SSIM(win_size=11,
win_sigma=1.5,
size_average=True,
data_range=1.,
channel=3).to(self.device)
else:
logger.info('Remove SSIM loss.')
self.cri_ssim = None
#"""
#LPIPS loss
"""
lpips_spatial = False
if train_opt['lpips_spatial']:
#lpips_spatial = True if train_opt['lpips_spatial'] == True else False
lpips_spatial = True if train_opt['lpips_spatial'] else False
lpips_GPU = False
if train_opt['lpips_GPU']:
#lpips_GPU = True if train_opt['lpips_GPU'] == True else False
lpips_GPU = True if train_opt['lpips_GPU'] else False
#"""
#"""
lpips_spatial = True #False # Return a spatial map of perceptual distance. Meeds to use .mean() for the backprop if True, the mean distance is approximately the same as the non-spatial distance
lpips_GPU = True # Whether to use GPU for LPIPS calculations
if train_opt['lpips_weight']:
if z_norm == True: # if images are in [-1,1] range
self.lpips_norm = False # images are already in the [-1,1] range
else:
self.lpips_norm = True # normalize images from [0,1] range to [-1,1]
self.l_lpips_w = train_opt['lpips_weight']
# Can use original off-the-shelf uncalibrated networks 'net' or Linearly calibrated models (LPIPS) 'net-lin'
if train_opt['lpips_type']:
lpips_type = train_opt['lpips_type']
else: # Default use linearly calibrated models, better results
lpips_type = 'net-lin'
# Can set net = 'alex', 'squeeze' or 'vgg' or Low-level metrics 'L2' or 'ssim'
if train_opt['lpips_net']:
lpips_net = train_opt['lpips_net']
else: # Default use VGG for feature extraction
lpips_net = 'vgg'
self.cri_lpips = models.PerceptualLoss(
model=lpips_type,
net=lpips_net,
use_gpu=lpips_GPU,
model_path=None,
spatial=lpips_spatial) #.to(self.device)
# Linearly calibrated models (LPIPS)
# self.cri_lpips = models.PerceptualLoss(model='net-lin', net='alex', use_gpu=lpips_GPU, model_path=None, spatial=lpips_spatial) #.to(self.device)
# self.cri_lpips = models.PerceptualLoss(model='net-lin', net='vgg', use_gpu=lpips_GPU, model_path=None, spatial=lpips_spatial) #.to(self.device)
# Off-the-shelf uncalibrated networks
# Can set net = 'alex', 'squeeze' or 'vgg'
# self.cri_lpips = models.PerceptualLoss(model='net', net='alex', use_gpu=lpips_GPU, model_path=None, spatial=lpips_spatial)
# Low-level metrics
# self.cri_lpips = models.PerceptualLoss(model='L2', colorspace='Lab', use_gpu=lpips_GPU)
# self.cri_lpips = models.PerceptualLoss(model='ssim', colorspace='RGB', use_gpu=lpips_GPU)
else:
logger.info('Remove LPIPS loss.')
self.cri_lpips = None
#"""
#SPL loss
#"""
if train_opt['spl_weight']:
self.l_spl_w = train_opt['spl_weight']
l_spl_type = train_opt['spl_type']
# SPL Normalization (from [-1,1] images to [0,1] range, if needed)
if z_norm == True: # if images are in [-1,1] range
self.spl_norm = True # normalize images to [0, 1]
else:
self.spl_norm = False # images are already in [0, 1] range
# YUV Normalization (from [-1,1] images to [0,1] range, if needed, but mandatory)
if z_norm == True: # if images are in [-1,1] range
self.yuv_norm = True # normalize images to [0, 1] for yuv calculations
else:
self.yuv_norm = False # images are already in [0, 1] range
if l_spl_type == 'spl': # Both GPL and CPL
# Gradient Profile Loss
self.cri_gpl = spl.GPLoss(spl_norm=self.spl_norm)
# Color Profile Loss
# You can define the desired color spaces in the initialization
# default is True for all
self.cri_cpl = spl.CPLoss(rgb=True,
yuv=True,
yuvgrad=True,
spl_norm=self.spl_norm,
yuv_norm=self.yuv_norm)
elif l_spl_type == 'gpl': # Only GPL
# Gradient Profile Loss
self.cri_gpl = spl.GPLoss(spl_norm=self.spl_norm)
self.cri_cpl = None
elif l_spl_type == 'cpl': # Only CPL
# Color Profile Loss
# You can define the desired color spaces in the initialization
# default is True for all
self.cri_cpl = spl.CPLoss(rgb=True,
yuv=True,
yuvgrad=True,
spl_norm=self.spl_norm,
yuv_norm=self.yuv_norm)
self.cri_gpl = None
else:
logger.info('Remove SPL loss.')
self.cri_gpl = None
self.cri_cpl = None
#"""
# GD gan loss
#"""
if train_opt['gan_weight']:
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters are for WGAN
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt[
'D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt[
'D_init_iters'] else 0
if train_opt['gan_type'] == 'wgan-gp':
self.random_pt = torch.Tensor(1, 1, 1, 1).to(self.device)
# gradient penalty loss
self.cri_gp = GradientPenaltyLoss(device=self.device).to(
self.device)
self.l_gp_w = train_opt['gp_weigth']
else:
logger.info('Remove GAN loss.')
self.cri_gan = None
#"""
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# D
if self.cri_gan:
wd_D = train_opt['weight_decay_D'] if train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
elif train_opt['lr_scheme'] == 'MultiStepLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_schedulerR.MultiStepLR_Restart(
optimizer,
train_opt['lr_steps'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights'],
gamma=train_opt['lr_gamma'],
clear_state=train_opt['clear_state']))
elif train_opt['lr_scheme'] == 'StepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.StepLR(optimizer, \
train_opt['lr_step_size'], train_opt['lr_gamma']))
elif train_opt['lr_scheme'] == 'StepLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_schedulerR.StepLR_Restart(
optimizer,
step_sizes=train_opt['lr_step_sizes'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights'],
gamma=train_opt['lr_gamma'],
clear_state=train_opt['clear_state']))
elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_schedulerR.CosineAnnealingLR_Restart(
optimizer,
train_opt['T_period'],
eta_min=train_opt['eta_min'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights']))
elif train_opt['lr_scheme'] == 'ReduceLROnPlateau':
for optimizer in self.optimizers:
self.schedulers.append(
#lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
lr_scheduler.ReduceLROnPlateau(
optimizer,
mode=train_opt['plateau_mode'],
factor=train_opt['plateau_factor'],
threshold=train_opt['plateau_threshold'],
patience=train_opt['plateau_patience']))
else:
raise NotImplementedError(
'Learning rate scheme ("lr_scheme") not defined or not recognized.'
)
self.log_dict = OrderedDict()
# print network
self.print_network()
def __init__(self, opt):
super(MWGANModel, self).__init__(opt)
if opt['dist']:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
self.train_opt = opt['train']
self.DWT = common.DWT()
self.IWT = common.IWT()
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device)
# pretrained_dict = torch.load(opt['path']['pretrain_model_others'])
# netG_dict = self.netG.state_dict()
# pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in netG_dict}
# netG_dict.update(pretrained_dict)
# self.netG.load_state_dict(netG_dict)
if opt['dist']:
self.netG = DistributedDataParallel(
self.netG, device_ids=[torch.cuda.current_device()])
else:
self.netG = DataParallel(self.netG)
if self.is_train:
if not self.train_opt['only_G']:
self.netD = networks.define_D(opt).to(self.device)
# init_weights(self.netD)
if opt['dist']:
self.netD = DistributedDataParallel(
self.netD, device_ids=[torch.cuda.current_device()])
else:
self.netD = DataParallel(self.netD)
self.netG.train()
self.netD.train()
else:
self.netG.train()
else:
self.netG.train()
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if self.train_opt['pixel_weight'] > 0:
l_pix_type = self.train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
elif l_pix_type == 'cb':
self.cri_pix = CharbonnierLoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = self.train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
if self.train_opt['lpips_weight'] > 0:
l_lpips_type = self.train_opt['lpips_criterion']
if l_lpips_type == 'lpips':
self.cri_lpips = lpips.LPIPS(net='vgg').to(self.device)
if opt['dist']:
self.cri_lpips = DistributedDataParallel(
self.cri_lpips,
device_ids=[torch.cuda.current_device()])
else:
self.cri_lpips = DataParallel(self.cri_lpips)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(
l_lpips_type))
self.l_lpips_w = self.train_opt['lpips_weight']
else:
logger.info('Remove lpips loss.')
self.cri_lpips = None
# G feature loss
if self.train_opt['feature_weight'] > 0:
self.fea_trans = GramMatrix().to(self.device)
l_fea_type = self.train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
elif l_fea_type == 'cb':
self.cri_fea = CharbonnierLoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = self.train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
if opt['dist']:
self.netF = DistributedDataParallel(
self.netF, device_ids=[torch.cuda.current_device()])
else:
self.netF = DataParallel(self.netF)
# GD gan loss
self.cri_gan = GANLoss(self.train_opt['gan_type'], 1.0,
0.0).to(self.device)
self.l_gan_w = self.train_opt['gan_weight']
# D_update_ratio and D_init_iters
self.D_update_ratio = self.train_opt[
'D_update_ratio'] if self.train_opt['D_update_ratio'] else 1
self.D_init_iters = self.train_opt[
'D_init_iters'] if self.train_opt['D_init_iters'] else 0
# optimizers
# G
wd_G = self.train_opt['weight_decay_G'] if self.train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(
optim_params,
lr=self.train_opt['lr_G'],
weight_decay=wd_G,
betas=(self.train_opt['beta1_G'], self.train_opt['beta2_G']))
self.optimizers.append(self.optimizer_G)
if not self.train_opt['only_G']:
# D
wd_D = self.train_opt['weight_decay_D'] if self.train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(
self.netD.parameters(),
lr=self.train_opt['lr_D'],
weight_decay=wd_D,
betas=(self.train_opt['beta1_D'],
self.train_opt['beta2_D']))
self.optimizers.append(self.optimizer_D)
# schedulers
if self.train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(
optimizer,
self.train_opt['lr_steps'],
restarts=self.train_opt['restarts'],
weights=self.train_opt['restart_weights'],
gamma=self.train_opt['lr_gamma'],
clear_state=self.train_opt['clear_state']))
elif self.train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer,
self.train_opt['T_period'],
eta_min=self.train_opt['eta_min'],
restarts=self.train_opt['restarts'],
weights=self.train_opt['restart_weights']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
if self.is_train:
if not self.train_opt['only_G']:
self.print_network() # print network
else:
self.print_network() # print network
try:
self.load() # load G and D if needed
print('Pretrained model loaded')
except Exception as e:
print('No pretrained model found')
def __init__(self, opt):
super(DASR_Adaptive_Model, self).__init__(opt)
train_opt = opt['train']
self.chop = opt['chop']
self.scale = opt['scale']
self.val_lpips = opt['val_lpips']
self.use_domain_distance_map = opt['use_domain_distance_map']
if self.is_train:
self.use_patchD_opt = opt['network_patchD']['use_patchD_opt']
# GD gan loss
self.ragan = train_opt['ragan']
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
self.l_gan_H_target_w = train_opt['gan_H_target']
self.l_gan_H_source_w = train_opt['gan_H_source']
# patchD gan loss
self.cri_patchD_gan = discriminator_loss
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device) # G
self.net_patchD = networks.define_patchD(opt).to(self.device)
if self.is_train:
if self.l_gan_H_target_w > 0:
self.netD_target = networks.define_D(opt).to(self.device) # D
self.netD_target.train()
if self.l_gan_H_source_w > 0:
self.netD_source = networks.define_pairD(opt).to(self.device) # D
self.netD_source.train()
self.netG.train()
self.load() # load G and D if needed
# Frequency Separation
self.norm = opt['FS_norm']
if opt['FS']['fs'] == 'wavelet':
# Wavelet
self.DWT2 = DWTForward(J=1, mode='reflect', wave='haar').to(self.device)
self.fs = self.wavelet_s
self.filter_high = FilterHigh(kernel_size=opt['FS']['fs_kernel_size'], gaussian=True).to(self.device)
elif opt['FS']['fs'] == 'gau':
# Gaussian
self.filter_low, self.filter_high = FilterLow(kernel_size=opt['FS']['fs_kernel_size'], gaussian=True).to(self.device), \
FilterHigh(kernel_size=opt['FS']['fs_kernel_size'], gaussian=True).to(self.device)
self.fs = self.filter_func
elif opt['FS']['fs'] == 'avgpool':
# avgpool
self.filter_low, self.filter_high = FilterLow(kernel_size=opt['FS']['fs_kernel_size']).to(self.device), \
FilterHigh(kernel_size=opt['FS']['fs_kernel_size']).to(self.device)
self.fs = self.filter_func
else:
raise NotImplementedError('FS type [{:s}] not recognized.'.format(opt['FS']['fs']))
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
self.l_pix_LL_w = train_opt['pixel_LL_weight']
self.sup_LL = train_opt['sup_LL']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
self.l_fea_type = train_opt['feature_criterion']
# G feature loss
if train_opt['feature_weight'] > 0:
if self.l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif self.l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
elif self.l_fea_type == 'LPIPS':
self.cri_fea = PerceptualLoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(self.l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea and self.l_fea_type in ['l1', 'l2']: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False).to(self.device)
# D_update_ratio and D_init_iters are for WGAN
self.G_update_inter = train_opt['G_update_inter']
self.D_update_inter = train_opt['D_update_inter']
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt['D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt['D_init_iters'] else 0
if train_opt['gan_type'] == 'wgan-gp':
self.random_pt = torch.Tensor(1, 1, 1, 1).to(self.device)
# gradient penalty loss
self.cri_gp = GradientPenaltyLoss(device=self.device).to(self.device)
self.l_gp_w = train_opt['gp_weigth']
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
logger.warning('Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# D
if self.l_gan_H_target_w > 0:
wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
self.optimizer_D_target = torch.optim.Adam(self.netD_target.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D_target)
if self.l_gan_H_source_w > 0:
wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
self.optimizer_D_source = torch.optim.Adam(self.netD_source.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D_source)
# Patch Discriminator
if self.use_patchD_opt:
self.optimizer_patchD = torch.optim.Adam(self.net_patchD.parameters(),
lr=opt['network_patchD']['lr'],
betas=[opt['network_patchD']['beta1_G'], 0.999])
self.optimizers.append(self.optimizer_patchD)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
# print network
self.print_network()
self.fake_H = None
# # Debug
if self.val_lpips:
self.cri_fea_lpips = val_lpips(model='net-lin', net='alex').to(self.device)
def __init__(self, opt=None, device = 'cpu', allow_featnets=True):
super(GeneratorLoss, self).__init__()
train_opt = opt['train']
#TODO: these checks can be moved to options.py when everything is stable
# parsing the losses options
pixel_weight = train_opt.get('pixel_weight', 0)
pixel_criterion = train_opt.get('pixel_criterion', None) # 'skip'
if allow_featnets:
feature_weight = train_opt.get('feature_weight', 0)
feature_network = train_opt.get('feature_network', 'vgg19') # TODO
feature_criterion = check_loss_names(feature_criterion=train_opt['feature_criterion'], feature_network=feature_network)
else:
feature_weight = 0
hfen_weight = train_opt.get('hfen_weight', 0)
hfen_criterion = check_loss_names(hfen_criterion=train_opt['hfen_criterion'])
grad_weight = train_opt.get('grad_weight', 0)
grad_type = train_opt.get('grad_type', None)
tv_weight = train_opt.get('tv_weight', 0)
tv_type = check_loss_names(tv_type=train_opt['tv_type'], tv_norm=train_opt['tv_norm'])
ssim_weight = train_opt.get('ssim_weight', 0)
ssim_type = train_opt.get('ssim_type', None)
if allow_featnets:
lpips_weight = train_opt.get('lpips_weight', 0)
lpips_network = train_opt.get('lpips_net', 'vgg')
lpips_type = train_opt.get('lpips_type', 'net-lin')
lpips_criterion = check_loss_names(lpips_criterion=train_opt['lpips_type'], lpips_network=lpips_network)
else:
lpips_weight = 0
color_weight = train_opt.get('color_weight', 0)
color_criterion = train_opt.get('color_criterion', None)
avg_weight = train_opt.get('avg_weight', 0)
avg_criterion = train_opt.get('avg_criterion', None)
ms_weight = train_opt.get('ms_weight', 0)
ms_criterion = train_opt.get('ms_criterion', None)
spl_weight = train_opt.get('spl_weight', 0)
spl_type = train_opt.get('spl_type', None)
gpl_type = None
gpl_weight = -1
cpl_type = None
cpl_weight = -1
if spl_type == 'spl':
cpl_type = 'cpl'
cpl_weight = spl_weight
gpl_type = 'gpl'
gpl_weight = spl_weight
elif spl_type == 'cpl':
cpl_type = 'cpl'
cpl_weight = spl_weight
elif spl_type == 'gpl':
gpl_type = 'gpl'
gpl_weight = spl_weight
if allow_featnets:
cx_weight = train_opt.get('cx_weight', 0)
cx_type = train_opt.get('cx_type', None)
else:
cx_weight = 0
fft_weight = train_opt.get('fft_weight', 0)
fft_type = train_opt.get('fft_type', None)
of_weight = train_opt.get('of_weight', 0)
of_type = train_opt.get('of_type', None)
# building the loss
self.loss_list = []
if pixel_weight > 0 and pixel_criterion:
cri_pix = get_loss_fn(pixel_criterion, pixel_weight)
self.loss_list.append(cri_pix)
if hfen_weight > 0 and hfen_criterion:
cri_hfen = get_loss_fn(hfen_criterion, hfen_weight)
self.loss_list.append(cri_hfen)
if grad_weight > 0 and grad_type:
cri_grad = get_loss_fn(grad_type, grad_weight, device = device)
self.loss_list.append(cri_grad)
if ssim_weight > 0 and ssim_type:
cri_ssim = get_loss_fn(ssim_type, ssim_weight, opt = train_opt, allow_featnets = allow_featnets)
self.loss_list.append(cri_ssim)
if tv_weight > 0 and tv_type:
cri_tv = get_loss_fn(tv_type, tv_weight)
self.loss_list.append(cri_tv)
if cx_weight > 0 and cx_type:
cri_cx = get_loss_fn(cx_type, cx_weight, device = device, opt = train_opt)
self.loss_list.append(cri_cx)
if feature_weight > 0 and feature_criterion:
#TODO: can move the self.netF to the loss class instead, like lpips, change where the network is printed from
self.netF = networks.define_F(opt, use_bn=False).to(device)
cri_fea = get_loss_fn(feature_criterion, feature_weight, network=self.netF)
self.loss_list.append(cri_fea)
self.cri_fea = True
else:
self.cri_fea = None
if lpips_weight > 0 and lpips_criterion:
lpips_spatial = True #False # Return a spatial map of perceptual distance. Needs to use .mean() for the backprop if True, the mean distance is approximately the same as the non-spatial distance
#self.netF = networks.define_F(opt, use_bn=False).to(device)
# TODO: fix use_gpu
lpips_network = ps.PerceptualLoss(model=lpips_type, net=lpips_network, use_gpu=torch.cuda.is_available(), model_path=None, spatial=lpips_spatial) #.to(self.device)
cri_lpips = get_loss_fn(lpips_criterion, lpips_weight, network=lpips_network, opt = opt)
self.loss_list.append(cri_lpips)
if cpl_weight > 0 and cpl_type:
cri_cpl = get_loss_fn(cpl_type, cpl_weight)
self.loss_list.append(cri_cpl)
if gpl_weight > 0 and gpl_type:
cri_gpl = get_loss_fn(gpl_type, gpl_weight)
self.loss_list.append(cri_gpl)
if fft_weight > 0 and fft_type:
cri_fft = get_loss_fn(fft_type, fft_weight, device = device)
self.loss_list.append(cri_fft)
if of_weight > 0 and of_type:
cri_of = get_loss_fn(of_type, of_weight, device = device)
self.loss_list.append(cri_of)
if color_weight > 0 and color_criterion:
cri_color = get_loss_fn(color_criterion, color_weight, opt = opt)
self.loss_list.append(cri_color)
if avg_weight > 0 and avg_criterion:
cri_avg = get_loss_fn(avg_criterion, avg_weight, opt = opt)
self.loss_list.append(cri_avg)
if ms_weight > 0 and ms_criterion:
cri_avg = get_loss_fn(ms_criterion, ms_weight, opt = opt)
self.loss_list.append(cri_avg)
def __init__(self, opt):
super(DePatch_wavelet_GANModel, self).__init__(opt)
train_opt = opt['train']
self.chop = opt['chop']
self.scale = opt['scale']
self.is_test = opt['is_test']
self.val_lpips = opt['val_lpips']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device) # G
if self.is_train:
self.netD = networks.define_D(opt).to(self.device) # D
self.netG.train()
self.netD.train()
if self.is_test:
self.netD = networks.define_D(opt).to(self.device)
self.netD.train()
self.load() # load G and D if needed
# Wavelet
# self.DWT2 = DWTForward(J=1, mode='symmetric', wave='haar').to(self.device)
self.DWT2 = DWT().to(self.device)
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
self.l_fea_type = train_opt['feature_criterion']
if self.l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif self.l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
elif self.l_fea_type == 'LPIPS':
self.cri_fea = PerceptualLoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
self.l_fea_type = None
if self.cri_fea and self.l_fea_type in ['l1', 'l2']: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False).to(self.device)
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
self.ragan = train_opt['ragan']
self.cri_gan_G = generator_loss
self.cri_gan_D = discriminator_loss
# D_update_ratio and D_init_iters are for WGAN
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt['D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt['D_init_iters'] else 0
if train_opt['gan_type'] == 'wgan-gp':
self.random_pt = torch.Tensor(1, 1, 1, 1).to(self.device)
# gradient penalty loss
self.cri_gp = GradientPenaltyLoss(device=self.device).to(self.device)
self.l_gp_w = train_opt['gp_weigth']
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
logger.warning('Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
# print network
self.print_network()
self.cri_fea_lpips = val_lpips(model='net-lin', net='alex').to(self.device)
def __init__(self, opt):
super(SRVarModel, self).__init__(opt)
if opt['dist']:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
train_opt = opt['train']
self.use_gpu = opt['network_G']['use_gpu']
self.use_gpu = True
# define network and load pretrained models
if self.use_gpu:
self.netG = networks.define_G(opt).to(self.device)
if opt['dist']:
self.netG = DistributedDataParallel(
self.netG, device_ids=[torch.cuda.current_device()])
else:
self.netG = DataParallel(self.netG)
else:
self.netG = networks.define_G(opt)
# print network
self.print_network()
self.load()
if self.is_train:
self.netG.train()
# pixel loss
loss_type = train_opt['pixel_criterion']
if loss_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif loss_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
elif loss_type == 'cb':
self.cri_pix = CharbonnierLoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] is not recognized.'.format(loss_type))
self.l_pix_w = train_opt['pixel_weight']
# CX loss
if train_opt['CX_weight']:
l_CX_type = train_opt['CX_criterion']
if l_CX_type == 'contextual_loss':
self.cri_CX = ContextualLoss()
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_CX_type))
self.l_CX_w = train_opt['CX_weight']
else:
logger.info('Remove CX loss.')
self.cri_CX = None
# ssim loss
if train_opt['ssim_weight']:
self.cri_ssim = train_opt['ssim_criterion']
self.l_ssim_w = train_opt['ssim_weight']
self.ssim_window = train_opt['ssim_window']
else:
logger.info('Remove ssim loss.')
self.cri_ssim = None
# load VGG perceptual loss if use CX loss
if train_opt['CX_weight']:
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
if opt['dist']:
pass # do not need to use DistributedDataParallel for netF
else:
self.netF = DataParallel(self.netF)
# optimizers
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params,
lr=train_opt['lr_G'],
weight_decay=wd_G,
betas=(train_opt['beta1'],
train_opt['beta2']))
self.optimizers.append(self.optimizer_G)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(
optimizer,
train_opt['lr_steps'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights'],
gamma=train_opt['lr_gamma'],
clear_state=train_opt['clear_state']))
elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer,
train_opt['T_period'],
eta_min=train_opt['eta_min'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
def __init__(self, opt):
super(InpaintingModel, self).__init__(opt)
train_opt = opt['train']
# define networks and load pretrained model
self.netG = networks.define_G(opt).to(self.device)
if self.is_train:
self.netD = networks.define_D(opt).to(self.device)
self.netG.train()
self.netD.train()
self.load() # load G and D
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
elif l_pix_type == 'ml1':
self.cri_pix = MultiscaleL1Loss().to(self.device)
else:
raise NotImplementedError('Unsupported loss type: {}'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Unsupported loss type: {}'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
self.guided_cri_fea = MaskedL1Loss().to(self.device)
else:
self.cri_fea = None
if self.cri_fea: # load VGG model
# self.vgg = Vgg19()
# self.vgg.load_state_dict(torch.load(vgg_model))
# for param in self.vgg.parameters():
# param.requires_grad = False
self.vgg = networks.define_F(opt)
self.vgg.to(self.device)
self.vgg_layers = ['r11', 'r21', 'r31', 'r41', 'r51']
self.vgg_weights = [1e3 / n ** 2 for n in [64, 128, 256, 512, 512]]
self.vgg_fns = [self.cri_fea] * len(self.vgg_layers)
## discriminator features
if train_opt['dis_feature_weight'] > 0:
l_dis_fea_type = train_opt['dis_feature_criterion']
if l_dis_fea_type == 'l1':
self.cri_dis_fea = nn.L1Loss().to(self.device)
elif l_dis_fea_type == 'l2':
self.cri_dis_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Unsupported loss type: {}'.format(l_dis_fea_type))
self.l_dis_fea_w = train_opt['dis_feature_weight']
else:
self.cri_dis_fea = None
if self.cri_dis_fea:
self.dis_weights = [1e3 / n ** 2 for n in [64, 128, 256, 512, 512]]
self.dis_fns = [self.cri_dis_fea] * len(self.dis_weights)
## center loss weight
if train_opt['center_weight'] > 0:
self.l_center_w = train_opt['center_weight']
else:
self.l_center_w = 0
# G & D gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# optimizers
optim_params = []
for k, v in self.netG.named_parameters():
if v.requires_grad:
optim_params.append(v)
else:
print('Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], betas=(0.5, 0.999))
self.optimizers.append(self.optimizer_G)
# D
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'], betas=(0.5, 0.999))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_policy'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer,
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError('Unsupported learning scheme: {}'.format(train_opt['lr_policy']))
self.log_dict = OrderedDict()
# print network
self.print_network()
def initialize(self, opt):
super(SRGANModel, self).initialize(opt)
assert opt['is_train']
self.input_L = self.Tensor()
self.input_H = self.Tensor()
print('Pytorch version:', torch.__version__)
# For generator (G)
# Spatial
if opt["train"].get("lambda_spatial") is not None:
self.use_spatial_G = True
else:
self.use_spatial_G = None
self.lambda_spatial = opt["train"].get(
"lambda_spatial") if self.use_spatial_G else 0.0
if self.use_spatial_G:
self.criterion_spatial_G = opt['train'].get('criterion_spatial_G')
self.loss_spatial_G = Loss(self.criterion_spatial_G)()
if opt['gpu_ids']:
self.loss_spatial_G.cuda(opt['gpu_ids'][0])
# VGG
self.use_vgg_G = opt['train'].get('lambda_vgg_G') is not None
self.lambda_vgg_G = opt['train'].get(
'lambda_vgg_G') if self.use_vgg_G else 0.0
if self.use_vgg_G:
self.netF = networks.define_F(opt)
self.loss_vgg_G = Loss(opt['train'].get('criterion_vgg_G'))()
if opt['gpu_ids']:
self.loss_vgg_G.cuda(opt['gpu_ids'][0])
# For discriminator (D)
# Adversarial
self.use_adversarial_D = opt['train'].get(
'lambda_adversarial_G') is not None and opt['train'].get(
'lambda_adversarial_D') is not None
self.lambda_adversarial_G = opt['train'].get(
'lambda_adversarial_G') if self.use_adversarial_D else 0.0
self.lambda_adversarial_D = opt['train'].get(
'lambda_adversarial_D') if self.use_adversarial_D else 0.0
if self.use_adversarial_D:
self.netD = networks.define_D(
opt) # Should use model "single_label_96"
self.update_steps_D = 1 # Number of updates of D per each training iteration
self.loss_adversarial_D = Loss(
opt['train'].get('criterion_adversarial_D'))(
opt['train'].get('criterion_adversarial_D'))
if opt['gpu_ids']:
self.loss_adversarial_D.cuda(opt['gpu_ids'][0])
# Always define netG
self.netG = networks.define_G(opt) # Should use model "sr_resnet"
# Load pretrained_models (F always pretrained)
self.load_path_G = opt['path'].get('pretrain_model_G')
self.load_path_D = opt['path'].get('pretrain_model_D')
self.load_path_F = opt['path'].get('pretrain_model_F')
self.load()
if opt['train'].get('lr_scheme') == 'multi_steps':
self.lr_steps = self.opt['train'].get('lr_steps')
self.lr_gamma = self.opt['train'].get('lr_gamma')
self.optimizers = []
self.lr_G = opt['train'].get('lr_G')
self.weight_decay_G = opt['train'].get(
'weight_decay_G') if opt['train'].get('weight_decay_G') else 0.0
self.optimizer_G = torch.optim.Adam(self.netG.parameters(),
lr=self.lr_G,
weight_decay=self.weight_decay_G)
self.optimizers.append(self.optimizer_G)
self.lr_D = opt['train'].get('lr_D')
self.weight_decay_D = opt['train'].get(
'weight_decay_D') if opt['train'].get('weight_decay_D') else 0.0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
lr=self.lr_D,
weight_decay=self.weight_decay_D)
self.optimizers.append(self.optimizer_D)
print('---------- Model initialized -------------')
self.write_description()
print('------------------------------------------')
def get_loss_fn(loss_type=None,
weight=0,
recurrent=False,
reduction='mean',
network=None,
device='cuda',
opt=None,
allow_featnets=True):
if loss_type == 'skip':
loss_function = None
# pixel / content losses
if loss_type in ('MSE', 'l2'):
loss_function = nn.MSELoss(reduction=reduction)
loss_type = 'pix-{}'.format(loss_type)
elif loss_type in ('L1', 'l1'):
loss_function = nn.L1Loss(reduction=reduction)
loss_type = 'pix-{}'.format(loss_type)
elif loss_type == 'cb':
loss_function = CharbonnierLoss()
loss_type = 'pix-{}'.format(loss_type)
elif loss_type == 'elastic':
loss_function = ElasticLoss(reduction=reduction)
loss_type = 'pix-{}'.format(loss_type)
elif loss_type == 'relativel1':
loss_function = RelativeL1(reduction=reduction)
loss_type = 'pix-{}'.format(loss_type)
# TODO
# elif loss_type == 'relativel2':
# loss_function = RelativeL2(reduction=reduction)
# loss_type = 'pix-{}'.format(loss_type)
elif loss_type in ('l1cosinesim', 'L1CosineSim'):
loss_function = L1CosineSim(reduction=reduction)
loss_type = 'pix-{}'.format(loss_type)
elif loss_type == 'clipl1':
loss_function = ClipL1()
loss_type = 'pix-{}'.format(loss_type)
elif loss_type.find('multiscale') >= 0:
# multiscale content/pixel loss
ms_loss_f = get_loss_fn(loss_type.split('-')[1],
recurrent=True,
device=device)
loss_function = MultiscalePixelLoss(loss_f=ms_loss_f)
loss_type = 'pix-{}'.format(loss_type)
elif loss_type == 'fro':
# Frobenius norm
#TODO: pass arguments
loss_function = FrobeniusNormLoss()
loss_type = 'pix-{}'.format(loss_type)
elif loss_type in ('ssim', 'SSIM'): # l_ssim_type
# SSIM loss
# TODO: pass SSIM options from opt_train
if not allow_featnets:
image_channels = 1
else:
image_channels = opt['image_channels'] if opt[
'image_channels'] else 3
loss_function = SSIM(window_size=11,
window_sigma=1.5,
size_average=True,
data_range=1.,
channels=image_channels)
elif loss_type in ('ms-ssim', 'MSSSIM'): # l_ssim_type
# MS-SSIM losses
# TODO: pass MS-SSIM options from opt_train
if not allow_featnets:
image_channels = 1
else:
image_channels = opt['image_channels'] if opt[
'image_channels'] else 3
loss_function = MS_SSIM(window_size=11,
window_sigma=1.5,
size_average=True,
data_range=1.,
channels=image_channels,
normalize='relu')
elif loss_type.find('hfen') >= 0:
# HFEN loss
hfen_loss_f = get_loss_fn(loss_type.split('-')[1],
recurrent=True,
reduction='sum',
device=device)
# print(hfen_loss_f)
# TODO: can pass function options from opt_train
loss_function = HFENLoss(loss_f=hfen_loss_f)
elif loss_type.find('grad') >= 0:
# gradient loss
gradientdir = loss_type.split('-')[1]
grad_loss_f = get_loss_fn(loss_type.split('-')[2],
recurrent=True,
device=device)
# TODO: can pass function options from opt_train
loss_function = GradientLoss(loss_f=grad_loss_f,
gradientdir=gradientdir)
elif loss_type == 'gpl':
# SPL losses: Gradient Profile Loss
z_norm = opt['datasets']['train'].get('znorm', False)
loss_function = GPLoss(spl_denorm=z_norm)
elif loss_type == 'cpl':
# SPL losses: Color Profile Loss
# TODO: pass function options from opt_train
z_norm = opt['datasets']['train'].get('znorm', False)
loss_function = CPLoss(rgb=True,
yuv=True,
yuvgrad=True,
spl_denorm=z_norm,
yuv_denorm=z_norm)
elif loss_type.find('tv') >= 0:
# TV regularization
tv_type = loss_type.split('-')[0]
tv_norm = loss_type.split('-')[1]
if 'tv' in tv_type:
loss_function = TVLoss(tv_type=tv_type, p=tv_norm)
elif loss_type.find('fea') >= 0:
# feature loss
# fea-vgg19-l1, fea-vgg16-l2, fea-lpips-... ("vgg" | "alex" | "squeeze" / net-lin | net )
if loss_type.split('-')[1] == 'lpips':
# TODO: make lpips behave more like regular feature networks
loss_function = PerceptualLoss(criterion='lpips',
network=network,
opt=opt)
else:
# if loss_type.split('-')[1][:3] == 'vgg': #if vgg16, vgg19, resnet, etc
fea_loss_f = get_loss_fn(loss_type.split('-')[2],
recurrent=True,
reduction='mean',
device=device)
network = networks.define_F(opt).to(device)
loss_function = PerceptualLoss(criterion=fea_loss_f,
network=network,
opt=opt)
elif loss_type == 'contextual':
# contextual loss
layers = opt['train'].get('cx_vgg_layers', {
"conv3_2": 1.0,
"conv4_2": 1.0
})
z_norm = opt['datasets']['train'].get('znorm', False)
loss_function = Contextual_Loss(layers,
max_1d_size=64,
distance_type='cosine',
calc_type='regular',
z_norm=z_norm)
# loss_function = Contextual_Loss(layers, max_1d_size=32,
# distance_type=0, crop_quarter=True) # for L1, L2
elif loss_type == 'fft':
loss_function = FFTloss()
elif loss_type == 'overflow':
loss_function = OFLoss()
elif loss_type == 'range':
# range limiting loss
legit_range = [-1, 1] if opt['datasets']['train'].get(
'znorm', False) else [0, 1]
loss_function = RangeLoss(legit_range=legit_range)
elif loss_type.find('color') >= 0:
color_loss_f = get_loss_fn(loss_type.split('-')[1],
recurrent=True,
device=device)
ds_f = torch.nn.AvgPool2d(kernel_size=opt['scale'])
loss_function = ColorLoss(loss_f=color_loss_f, ds_f=ds_f)
elif loss_type.find('avg') >= 0:
avg_loss_f = get_loss_fn(loss_type.split('-')[1],
recurrent=True,
device=device)
ds_f = torch.nn.AvgPool2d(kernel_size=opt['scale'])
loss_function = AverageLoss(loss_f=avg_loss_f, ds_f=ds_f)
elif loss_type == 'fdpl':
diff_means = opt.get('diff_means',
"./models/modules/FDPL/diff_means.pt")
loss_function = FDPLLoss(dataset_diff_means_file=diff_means,
device=device)
else:
loss_function = None
# raise NotImplementedError('Loss type [{:s}] not recognized.'.format(loss_type))
if loss_function:
if recurrent:
return loss_function.to(device)
else:
loss = {
'name': loss_type,
'weight': float(weight), # TODO: check if float is needed
'function': loss_function.to(device)
}
return loss
def __init__(self, opt):
super(SRIMModel, self).__init__(opt)
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device) # G
if self.is_train:
self.netG.train()
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
print('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
print('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
# add extra pixel loss
if 'pixel_weight_1' in train_opt:
l_pix_type = train_opt['pixel_criterion_1']
if l_pix_type == 'l1':
self.cri_pix_1 = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix_1 = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w_1 = train_opt['pixel_weight_1']
else:
# print('Remove pixel loss.')
self.cri_pix_1 = None
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
print(
'WARNING: params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
print('---------- Model initialized ------------------')
self.print_network()
print('-----------------------------------------------')
def __init__(self, opt):
super(SRGANModel, self).__init__(opt)
if opt["dist"]:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
train_opt = opt["train"]
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device)
if opt["dist"]:
self.netG = DistributedDataParallel(
self.netG, device_ids=[torch.cuda.current_device()])
else:
self.netG = DataParallel(self.netG)
if self.is_train:
self.netD = networks.define_D(opt).to(self.device)
if opt["dist"]:
self.netD = DistributedDataParallel(
self.netD, device_ids=[torch.cuda.current_device()])
else:
self.netD = DataParallel(self.netD)
self.netG.train()
self.netD.train()
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt["pixel_weight"] > 0:
l_pix_type = train_opt["pixel_criterion"]
if l_pix_type == "l1":
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == "l2":
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
"Loss type [{:s}] not recognized.".format(l_pix_type))
self.l_pix_w = train_opt["pixel_weight"]
else:
logger.info("Remove pixel loss.")
self.cri_pix = None
# G feature loss
if train_opt["feature_weight"] > 0:
l_fea_type = train_opt["feature_criterion"]
if l_fea_type == "l1":
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == "l2":
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
"Loss type [{:s}] not recognized.".format(l_fea_type))
self.l_fea_w = train_opt["feature_weight"]
else:
logger.info("Remove feature loss.")
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
if opt["dist"]:
self.netF = DistributedDataParallel(
self.netF, device_ids=[torch.cuda.current_device()])
else:
self.netF = DataParallel(self.netF)
# GD gan loss
self.cri_gan = GANLoss(train_opt["gan_type"], 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt["gan_weight"]
# D_update_ratio and D_init_iters
self.D_update_ratio = (train_opt["D_update_ratio"]
if train_opt["D_update_ratio"] else 1)
self.D_init_iters = (train_opt["D_init_iters"]
if train_opt["D_init_iters"] else 0)
# optimizers
# G
wd_G = train_opt["weight_decay_G"] if train_opt[
"weight_decay_G"] else 0
optim_params = []
for (
k,
v,
) in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning(
"Params [{:s}] will not optimize.".format(k))
self.optimizer_G = torch.optim.Adam(
optim_params,
lr=train_opt["lr_G"],
weight_decay=wd_G,
betas=(train_opt["beta1_G"], train_opt["beta2_G"]),
)
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt["weight_decay_D"] if train_opt[
"weight_decay_D"] else 0
self.optimizer_D = torch.optim.Adam(
self.netD.parameters(),
lr=train_opt["lr_D"],
weight_decay=wd_D,
betas=(train_opt["beta1_D"], train_opt["beta2_D"]),
)
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt["lr_scheme"] == "MultiStepLR":
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(
optimizer,
train_opt["lr_steps"],
restarts=train_opt["restarts"],
weights=train_opt["restart_weights"],
gamma=train_opt["lr_gamma"],
clear_state=train_opt["clear_state"],
))
elif train_opt["lr_scheme"] == "CosineAnnealingLR_Restart":
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer,
train_opt["T_period"],
eta_min=train_opt["eta_min"],
restarts=train_opt["restarts"],
weights=train_opt["restart_weights"],
))
else:
raise NotImplementedError(
"MultiStepLR learning rate scheme is enough.")
self.log_dict = OrderedDict()
self.print_network() # print network
self.load() # load G and D if needed
def __init__(self, opt):
super(SRGANModel, self).__init__(opt)
if opt['dist']:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device)
if opt['dist']:
self.netG = DistributedDataParallel(
self.netG, device_ids=[torch.cuda.current_device()])
else:
self.netG = DataParallel(self.netG)
if self.is_train:
self.netD = networks.define_D(opt).to(self.device)
if opt['dist']:
self.netD = DistributedDataParallel(
self.netD, device_ids=[torch.cuda.current_device()])
else:
self.netD = DataParallel(self.netD)
self.netG.train()
self.netD.train()
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
if opt['dist']:
self.netF = DistributedDataParallel(
self.netF, device_ids=[torch.cuda.current_device()])
else:
self.netF = DataParallel(self.netF)
# G Rank-content loss
if train_opt['R_weight'] > 0:
self.l_R_w = train_opt['R_weight'] # load rank-content loss
self.R_bias = train_opt['R_bias']
self.netR = networks.define_R(opt).to(self.device)
if opt['dist']:
self.netR = DistributedDataParallel(
self.netR, device_ids=[torch.cuda.current_device()])
else:
self.netR = DataParallel(self.netR)
else:
logger.info('Remove rank-content loss.')
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt[
'D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt[
'D_init_iters'] else 0
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params,
lr=train_opt['lr_G'],
weight_decay=wd_G,
betas=(train_opt['beta1_G'],
train_opt['beta2_G']))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt['weight_decay_D'] if train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
lr=train_opt['lr_D'],
weight_decay=wd_D,
betas=(train_opt['beta1_D'],
train_opt['beta2_D']))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(
optimizer,
train_opt['lr_steps'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights'],
gamma=train_opt['lr_gamma'],
clear_state=train_opt['clear_state']))
elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer,
train_opt['T_period'],
eta_min=train_opt['eta_min'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
self.print_network() # print network
self.load() # load G and D if needed
def __init__(self, opt):
super(SRRaGANModel, self).__init__(opt)
train_opt = opt["train"]
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device) # G
if self.is_train:
self.netD = networks.define_D(opt).to(self.device) # D
self.netG.train()
self.netD.train()
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt["pixel_weight"] > 0:
l_pix_type = train_opt["pixel_criterion"]
if l_pix_type == "l1":
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == "l2":
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
"Loss type [{:s}] not recognized.".format(l_pix_type))
self.l_pix_w = train_opt["pixel_weight"]
else:
logger.info("Remove pixel loss.")
self.cri_pix = None
# G feature loss
if train_opt["feature_weight"] > 0:
l_fea_type = train_opt["feature_criterion"]
if l_fea_type == "l1":
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == "l2":
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
"Loss type [{:s}] not recognized.".format(l_fea_type))
self.l_fea_w = train_opt["feature_weight"]
else:
logger.info("Remove feature loss.")
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
# GD gan loss
self.cri_gan = GANLoss(train_opt["gan_type"], 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt["gan_weight"]
# D_update_ratio and D_init_iters are for WGAN
self.D_update_ratio = (train_opt["D_update_ratio"]
if train_opt["D_update_ratio"] else 1)
self.D_init_iters = (train_opt["D_init_iters"]
if train_opt["D_init_iters"] else 0)
if train_opt["gan_type"] == "wgan-gp":
self.random_pt = torch.Tensor(1, 1, 1, 1).to(self.device)
# gradient penalty loss
self.cri_gp = GradientPenaltyLoss(device=self.device).to(
self.device)
self.l_gp_w = train_opt["gp_weigth"]
# optimizers
# G
wd_G = train_opt["weight_decay_G"] if train_opt[
"weight_decay_G"] else 0
optim_params = []
for (
k,
v,
) in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
logger.warning(
"Params [{:s}] will not optimize.".format(k))
self.optimizer_G = torch.optim.Adam(
optim_params,
lr=train_opt["lr_G"],
weight_decay=wd_G,
betas=(train_opt["beta1_G"], 0.999),
)
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt["weight_decay_D"] if train_opt[
"weight_decay_D"] else 0
self.optimizer_D = torch.optim.Adam(
self.netD.parameters(),
lr=train_opt["lr_D"],
weight_decay=wd_D,
betas=(train_opt["beta1_D"], 0.999),
)
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt["lr_scheme"] == "MultiStepLR":
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR(optimizer,
train_opt["lr_steps"],
train_opt["lr_gamma"]))
else:
raise NotImplementedError(
"MultiStepLR learning rate scheme is enough.")
self.log_dict = OrderedDict()
# print network
self.print_network()
def __init__(self, opt):
super(IRNpModel, self).__init__(opt)
if opt['dist']:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
train_opt = opt['train']
test_opt = opt['test']
self.train_opt = train_opt
self.test_opt = test_opt
self.netG = networks.define_G(opt).to(self.device)
if opt['dist']:
self.netG = DistributedDataParallel(
self.netG, device_ids=[torch.cuda.current_device()])
else:
self.netG = DataParallel(self.netG)
# print network
self.print_network()
self.load()
self.Quantization = Quantization()
if self.is_train:
self.netD = networks.define_D(opt).to(self.device)
if opt['dist']:
self.netD = DistributedDataParallel(
self.netD, device_ids=[torch.cuda.current_device()])
else:
self.netD = DataParallel(self.netD)
self.netG.train()
self.netD.train()
# loss
self.Reconstruction_forw = ReconstructionLoss(
losstype=self.train_opt['pixel_criterion_forw'])
self.Reconstruction_back = ReconstructionLoss(
losstype=self.train_opt['pixel_criterion_back'])
# feature loss
if train_opt['feature_weight'] > 0:
self.Reconstructionf = ReconstructionLoss(
losstype=self.train_opt['feature_criterion'])
self.l_fea_w = train_opt['feature_weight']
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
if opt['dist']:
self.netF = DistributedDataParallel(
self.netF, device_ids=[torch.cuda.current_device()])
else:
self.netF = DataParallel(self.netF)
else:
self.l_fea_w = 0
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt[
'D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt[
'D_init_iters'] else 0
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters():
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params,
lr=train_opt['lr_G'],
weight_decay=wd_G,
betas=(train_opt['beta1'],
train_opt['beta2']))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt['weight_decay_D'] if train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
lr=train_opt['lr_D'],
weight_decay=wd_D,
betas=(train_opt['beta1_D'],
train_opt['beta2_D']))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(
optimizer,
train_opt['lr_steps'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights'],
gamma=train_opt['lr_gamma'],
clear_state=train_opt['clear_state']))
elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer,
train_opt['T_period'],
eta_min=train_opt['eta_min'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
def __init__(self, opt):
super(SRGANModel, self).__init__(opt)
train_opt = opt['train']
self.input_L = self.Tensor()
self.input_H = self.Tensor()
self.input_ref = self.Tensor() # for Discriminator reference
# define networks and load pretrained models
self.netG = networks.define_G(opt) # G
if self.is_train:
self.netD = networks.define_D(opt) # D
self.netG.train()
self.netD.train()
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss()
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss()
else:
raise NotImplementedError(
'Loss type [%s] is not recognized.' % l_pix_type)
self.l_pix_w = train_opt['pixel_weight']
else:
print('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss()
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss()
else:
raise NotImplementedError(
'Loss type [%s] is not recognized.' % l_fea_type)
self.l_fea_w = train_opt['feature_weight']
else:
print('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False)
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0,
self.Tensor)
self.l_gan_w = train_opt['gan_weight']
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt[
'D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt[
'D_init_iters'] else 0
if train_opt['gan_type'] == 'wgan-gp':
self.random_pt = Variable(self.Tensor(1, 1, 1, 1))
# gradient penalty loss
self.cri_gp = GradientPenaltyLoss(tensor=self.Tensor)
self.l_gp_w = train_opt['gp_weigth']
if self.use_gpu:
if self.cri_pix:
self.cri_pix.cuda()
if self.cri_fea:
self.cri_fea.cuda()
self.cri_gan.cuda()
if train_opt['gan_type'] == 'wgan-gp':
self.cri_gp.cuda()
# optimizers
self.optimizers = [] # G and D
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
print('WARNING: params [%s] will not optimize.' % k)
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt['weight_decay_D'] if train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D)
# schedulers
self.schedulers = []
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
print('---------- Model initialized ------------------')
self.print_network()
print('-----------------------------------------------')
def __init__(self, opt):
super(SRA_GANModel, self).__init__(opt)
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device) # G
if self.is_train:
self.netD = networks.define_D(opt).to(self.device) # D
self.netG.train()
self.netD.train()
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
# network A
if train_opt['aesthetic_criterion'] == "include":
self.cri_aes = True
self.netA = networks.define_A(opt).to(self.device)
self.l_aes_w = train_opt['aesthetic_weight']
else:
self.cri_aes = None
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters are for WGAN
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt[
'D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt[
'D_init_iters'] else 0
if train_opt['gan_type'] == 'wgan-gp':
self.random_pt = torch.Tensor(1, 1, 1, 1).to(self.device)
# gradient penalty loss
self.cri_gp = GradientPenaltyLoss(device=self.device).to(
self.device)
self.l_gp_w = train_opt['gp_weigth']
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt['weight_decay_D'] if train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
# print network
self.print_network()
def __init__(self, opt, is_train):
super(SRGANModel, self).__init__(opt, is_train)
train_opt = opt
self.rank = 0
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device)
self.netG = DataParallel(self.netG)
if self.is_train:
self.netD = networks.define_D(opt).to(self.device)
self.netD = DataParallel(self.netD)
self.netG.train()
self.netD.train()
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt.pixel_weight > 0:
l_pix_type = train_opt.pixel_criterion
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt.pixel_weight
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt.feature_weight > 0:
l_fea_type = train_opt.feature_criterion
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt.feature_weight
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
self.netF = DataParallel(self.netF)
# GD gan loss
self.cri_gan = GANLoss(train_opt.gan_type, 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt.gan_weight
# D_update_ratio and D_init_iters
self.D_update_ratio = train_opt.D_update_ratio if train_opt.D_update_ratio else 1
self.D_init_iters = train_opt.D_init_iters if train_opt.D_init_iters else 0
# optimizers
# G
wd_G = train_opt.weight_decay_G if train_opt.weight_decay_G else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params,
lr=train_opt.lr_G,
weight_decay=wd_G,
betas=(train_opt.beta1_G,
train_opt.beta2_G))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt.weight_decay_D if train_opt.weight_decay_D else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
lr=train_opt.lr_D,
weight_decay=wd_D,
betas=(train_opt.beta1_D,
train_opt.beta2_D))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt.lr_scheme == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(
optimizer,
train_opt.lr_steps,
restarts=None,
weights=None,
gamma=train_opt.lr_gamma,
clear_state=False))
elif train_opt.lr_scheme == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer,
train_opt.T_period,
eta_min=train_opt.eta_min,
restarts=train_opt.restarts,
weights=train_opt.restart_weights))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
self.print_network() # print network
self.load() # load G and D if needed
def __init__(self, args):
super(PPONModel, self).__init__(args)
# define networks and load pre-trained models
self.netG = networks.define_G(args).cuda()
if self.is_train:
if args.which_model == 'perceptual':
self.netD = networks.define_D().cuda()
self.netD.train()
self.netG.train()
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if args.pixel_weight > 0:
l_pix_type = args.pixel_criterion
if l_pix_type == 'l1': # loss pixel type
self.cri_pix = nn.L1Loss().cuda()
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().cuda()
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = args.pixel_weight
else:
print('Remove pixel loss.')
self.cri_pix = None # critic pixel
# G structure loss
if args.structure_weight > 0:
self.cri_msssim = pytorch_msssim.MS_SSIM(data_range=args.rgb_range).cuda()
self.cri_ml1 = MultiscaleL1Loss().cuda()
else:
print('Remove structure loss.')
self.cri_msssim = None
self.cri_ml1 = None
# G feature loss
if args.feature_weight > 0:
l_fea_type = args.feature_criterion
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().cuda()
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().cuda()
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = args.feature_weight
else:
print('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.vgg = networks.define_F().cuda()
if args.gan_weight > 0:
# gan loss
self.cri_gan = GANLoss(args.gan_type, 1.0, 0.0).cuda()
self.l_gan_w = args.gan_weight
else:
self.cri_gan = None
# optimizers
# G
if args.which_model == 'structure':
for param in self.netG.CFEM.parameters():
param.requires_grad = False
for param in self.netG.CRM.parameters():
param.requires_grad = False
if args.which_model == 'perceptual':
for param in self.netG.CFEM.parameters():
param.requires_grad = False
for param in self.netG.CRM.parameters():
param.requires_grad = False
for param in self.netG.SFEM.parameters():
param.requires_grad = False
for param in self.netG.SRM.parameters():
param.requires_grad = False
optim_params = []
for k, v in self.netG.named_parameters():
if v.requires_grad:
optim_params.append(v)
else:
print('Warning: params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=args.lr_G)
self.optimizers.append(self.optimizer_G)
# D
if args.which_model == 'perceptual':
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=args.lr_D)
self.optimizers.append(self.optimizer_D)
# schedulers
if args.lr_scheme == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer,
args.lr_steps, args.lr_gamma))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
print('------------- Model initialized -------------')
self.print_network()
print('---------------------------------------------')
type=str,
default=
"/GPUFS/nsccgz_yfdu_16/ouyry/SISRC/FaceSR-ESRGAN/dataset/CelebA/SR",
help='Path to val SR.')
parser.add_argument('--Norm', type=int, default=1, help='Use Input Norm.')
args = parser.parse_args()
opt['dataset']['dataroot_SR'] = args.SR_Root
opt['dataset']['dataroot_HR'] = args.HR_Root
opt['network_F']['norm'] = args.Norm
test_set = create_dataset(opt['dataset'])
test_loader = create_dataloader(test_set, opt['dataset'])
device = torch.device('cuda' if opt['gpu_ids'] is not None else 'cpu')
sphere = networks.define_F(opt).to(device)
IS = 0
idx = 0
cos = torch.nn.CosineSimilarity()
for data in test_loader:
SR = data['SR'].to(device)
HR = data['HR'].to(device)
SR_vec = sphere(SR)
HR_vec = sphere(HR)
now_IS = cos(SR_vec, HR_vec)
IS += now_IS
idx += 1
def __init__(self, opt):
super(PPONModel, self).__init__(opt)
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device) # G
if self.is_train:
self.netG.train()
if train_opt['gan_weight'] > 0:
self.netD = networks.define_D(opt).to(self.device) # D
self.netD.train()
#PPON
self.start_p1 = train_opt['start_p1'] if train_opt[
'start_p1'] else 0
self.phase1_s = train_opt['phase1_s'] if train_opt[
'phase1_s'] else 138000
self.phase2_s = train_opt['phase2_s'] if train_opt[
'phase2_s'] else 138000 + 34500
self.phase3_s = train_opt['phase3_s'] if train_opt[
'phase3_s'] else 138000 + 34500 + 34500
self.phase = 0
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
elif l_pix_type == 'cb':
self.cri_pix = CharbonnierLoss().to(self.device)
elif l_pix_type == 'elastic':
self.cri_pix = ElasticLoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
elif l_fea_type == 'cb':
self.cri_fea = CharbonnierLoss().to(self.device)
elif l_fea_type == 'elastic':
self.cri_fea = ElasticLoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
#HFEN loss
if train_opt['hfen_weight'] > 0:
l_hfen_type = train_opt['hfen_criterion']
if l_hfen_type == 'l1':
self.cri_hfen = HFENL1Loss().to(
self.device) #RelativeHFENL1Loss().to(self.device)
elif l_hfen_type == 'l2':
self.cri_hfen = HFENL2Loss().to(self.device)
elif l_hfen_type == 'rel_l1':
self.cri_hfen = RelativeHFENL1Loss().to(self.device)
elif l_hfen_type == 'rel_l2':
self.cri_hfen = RelativeHFENL2Loss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_hfen_type))
self.l_hfen_w = train_opt['hfen_weight']
else:
logger.info('Remove HFEN loss.')
self.cri_hfen = None
#TV loss
if train_opt['tv_weight'] > 0:
self.l_tv_w = train_opt['tv_weight']
l_tv_type = train_opt['tv_type']
if l_tv_type == 'normal':
self.cri_tv = TVLoss(self.l_tv_w).to(self.device)
elif l_tv_type == '4D':
self.cri_tv = TVLoss4D(self.l_tv_w).to(
self.device
) #Total Variation regularization in 4 directions
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_tv_type))
else:
logger.info('Remove TV loss.')
self.cri_tv = None
#SSIM loss
if train_opt['ssim_weight'] > 0:
self.l_ssim_w = train_opt['ssim_weight']
l_ssim_type = train_opt['ssim_type']
if l_ssim_type == 'ssim':
self.cri_ssim = SSIM(win_size=11,
win_sigma=1.5,
size_average=True,
data_range=1.,
channel=3).to(self.device)
elif l_ssim_type == 'ms-ssim':
self.cri_ssim = MS_SSIM(win_size=7,
win_sigma=1.5,
size_average=True,
data_range=1.,
channel=3).to(self.device)
#Note: win_size should be 11 by default, but it produces a convolution error when the images are smaller than the kernel (8x8), so leaving at 7
else:
logger.info('Remove SSIM loss.')
self.cri_ssim = None
# GD gan loss
if train_opt['gan_weight'] > 0:
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters are for WGAN
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt[
'D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt[
'D_init_iters'] else 0
if train_opt['gan_type'] == 'wgan-gp':
self.random_pt = torch.Tensor(1, 1, 1, 1).to(self.device)
# gradient penalty loss
self.cri_gp = GradientPenaltyLoss(device=self.device).to(
self.device)
self.l_gp_w = train_opt['gp_weigth']
else:
logger.info('Remove GAN loss.')
self.cri_gan = None
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# D
if self.cri_gan:
wd_D = train_opt['weight_decay_D'] if train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
self.print_network()
def initialize(self, opt):
BaseModel.initialize(self, opt)
self.l_fea_w = opt.l_fea_w
self.cri_fea = opt.cri_fea
self.device = torch.device('cuda:%s' %(opt.gpu_ids[0]))
nb = opt.batchSize
size = opt.fineSize
self.target_weight = []
self.input_A = self.Tensor(nb, opt.input_nc, size, size)
self.input_B = self.Tensor(nb, opt.output_nc, size, size)
self.input_C = self.Tensor(nb, opt.output_nc, size, size)
self.input_C_sr = self.Tensor(nb, opt.output_nc, size, size)
self.input_B_hd = self.Tensor(nb, opt.output_nc, size, size)
if opt.aux:
self.A_aux = self.Tensor(nb, opt.input_nc, size, size)
self.B_aux = self.Tensor(nb, opt.output_nc, size, size)
self.C_aux = self.Tensor(nb, opt.output_nc, size, size)
self.netE_A = networks.define_G(opt.input_nc, opt.output_nc,
opt.ngf,'ResnetEncoder_my', opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids, opt=opt, n_downsampling=2)
mult = self.netE_A.get_mult()
self.netE_C = networks.define_G(opt.input_nc, opt.output_nc,
64 ,'ResnetEncoder_my', opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids, opt=opt, n_downsampling=3)
self.net_D = networks.define_G(opt.input_nc, opt.output_nc,
opt.ngf,'ResnetDecoder_my', opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids, opt=opt, mult = mult)
mult = self.net_D.get_mult()
self.net_Dc = networks.define_G(opt.input_nc, opt.output_nc,
opt.ngf, 'ResnetDecoder_my', opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids, opt=opt, mult=mult, n_upsampling=1)
self.netG_A = networks.define_G(opt.input_nc, opt.output_nc,
opt.ngf, 'GeneratorLL', opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids, opt=opt, mult=mult)
mult = self.net_Dc.get_mult()
self.netG_C = networks.define_G(opt.input_nc, opt.output_nc,
opt.ngf, 'GeneratorLL', opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids, opt=opt, mult=mult)
# self.netG_A_running = networks.define_G(opt.input_nc, opt.output_nc,
# opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids, opt=opt)
# set_eval(self.netG_A_running)
# accumulate(self.netG_A_running, self.netG_A, 0)
self.netG_B = networks.define_G(opt.output_nc, opt.input_nc,
opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids, opt=opt)
# self.netG_B_running = networks.define_G(opt.output_nc, opt.input_nc,
# opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids, opt=opt)
# set_eval(self.netG_B_running)
# accumulate(self.netG_B_running, self.netG_B, 0)
if self.isTrain:
use_sigmoid = opt.no_lsgan
self.netD_A = networks.define_D(opt.output_nc, opt.ndf,
opt.which_model_netD,
opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids, opt=opt)
self.netD_B = networks.define_D(opt.input_nc, opt.ndf,
opt.which_model_netD,
opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids, opt=opt)
self.netD_C = networks.define_D(256, opt.ndf,
opt.which_model_netD,
opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids, opt=opt)
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False).to(self.device)
print('---------- Networks initialized -------------')
networks.print_network(self.netG_B, opt, (opt.input_nc, opt.fineSize, opt.fineSize))
networks.print_network(self.netE_C, opt, (opt.input_nc, opt.fineSize, opt.fineSize))
networks.print_network(self.net_D, opt, (opt.ngf*4, opt.fineSize/4, opt.fineSize/4))
networks.print_network(self.net_Dc, opt, (opt.ngf, opt.CfineSize/2, opt.CfineSize/2))
# networks.print_network(self.netG_B, opt)
if self.isTrain:
networks.print_network(self.netD_A, opt)
# networks.print_network(self.netD_r, opt)
print('-----------------------------------------------')
if not self.isTrain or opt.continue_train:
print('Loaded model')
which_epoch = opt.which_epoch
self.load_network(self.netG_A, 'G_A', which_epoch)
self.load_network(self.netG_B, 'G_B', which_epoch)
if self.isTrain:
self.load_network(self.netG_A_running, 'G_A', which_epoch)
self.load_network(self.netG_B_running, 'G_B', which_epoch)
self.load_network(self.netD_A, 'D_A', which_epoch)
self.load_network(self.netD_r, 'D_r', which_epoch)
if self.isTrain and opt.load_path != '':
print('Loaded model from load_path')
which_epoch = opt.which_epoch
load_network_with_path(self.netG_A, 'G_A', opt.load_path, epoch_label=which_epoch)
load_network_with_path(self.netG_B, 'G_B', opt.load_path, epoch_label=which_epoch)
load_network_with_path(self.netD_A, 'D_A', opt.load_path, epoch_label=which_epoch)
load_network_with_path(self.netD_r, 'D_r', opt.load_path, epoch_label=which_epoch)
if self.isTrain:
self.old_lr = opt.lr
self.fake_A_pool = ImagePool(opt.pool_size)
self.fake_B_pool = ImagePool(opt.pool_size)
self.fake_C_pool = ImagePool(opt.pool_size)
# define loss functions
if len(self.target_weight) == opt.num_D:
print(self.target_weight)
self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor, target_weight=self.target_weight, gan=opt.gan)
else:
self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor, gan=opt.gan)
self.criterionCycle = torch.nn.L1Loss()
self.criterionIdt = torch.nn.L1Loss()
self.criterionColor = networks.ColorLoss()
# initialize optimizers
self.optimizer_G = torch.optim.Adam(itertools.chain(self.netE_A.parameters(),self.net_D.parameters(),self.netG_A.parameters(), self.netG_B.parameters(),self.net_Dc.parameters(),self.netG_C.parameters()),
lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_AE = torch.optim.Adam(itertools.chain(self.netE_C.parameters(),self.net_D.parameters(),self.net_Dc.parameters(),self.netG_C.parameters()),lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_G_A_hd = torch.optim.Adam(itertools.chain(self.netE_A.parameters(),self.net_D.parameters(),self.net_Dc.parameters(),self.netG_C.parameters()),
lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_AE_sr = torch.optim.Adam(itertools.chain(self.netE_C.parameters(),self.net_D.parameters(),self.netG_A.parameters()),lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_D_A = torch.optim.Adam(self.netD_A.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_D_B = torch.optim.Adam(self.netD_B.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_D_C = torch.optim.Adam(self.netD_C.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizers = []
self.schedulers = []
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_AE)
self.optimizers.append(self.optimizer_G_A_hd)
self.optimizers.append(self.optimizer_AE_sr)
self.optimizers.append(self.optimizer_D_A)
self.optimizers.append(self.optimizer_D_B)
self.optimizers.append(self.optimizer_D_C)
for optimizer in self.optimizers:
self.schedulers.append(networks.get_scheduler(optimizer, opt))
