class Wrapper(t.nn.Module):
def __init__(self, model, device_ids: list):
super(Wrapper, self).__init__()
self.model = DataParallel(model.cuda(), device_ids)
def forward(self, *input):
return self.model.forward(*input)
def __getattr__(self, name):
try:
return super().__getattr__(name)
except AttributeError:
return getattr(self.model.module, name)
class MWGANModel(BaseModel):
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 feed_data(self, data, need_GT=True):
self.var_L = data['LQ'].to(self.device) # LQ
if need_GT:
self.var_H = data['GT'].to(self.device) # GT
# print(self.var_H.size())
self.var_H = self.var_H.squeeze(1)
# self.var_H = self.DWT(self.var_H)
input_ref = data['ref'] if 'ref' in data else data['GT']
self.var_ref = input_ref.to(self.device)
# print(self.var_ref.size())
self.var_ref = self.var_ref.squeeze(1)
# print(s)
# self.var_ref = self.DWT(self.var_ref)
def process_list(self, input1, input2):
result = []
for index in range(len(input1)):
result.append(input1[index] - torch.mean(input2[index]))
return result
def optimize_parameters(self, step):
# G
if not self.train_opt['only_G']:
for p in self.netD.parameters():
p.requires_grad = False
self.optimizer_G.zero_grad()
self.fake_H = self.netG(self.var_L)
# self.var_H = self.var_H.squeeze(1)
l_g_total = 0
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
if self.cri_pix: # pixel loss
l_g_pix = self.l_pix_w * self.cri_pix(self.fake_H, self.var_H)
l_g_total += l_g_pix
if self.cri_lpips: # pixel loss
l_g_lpips = torch.mean(
self.l_lpips_w *
self.cri_lpips.forward(self.fake_H, self.var_H))
l_g_total += l_g_lpips
if self.cri_fea: # feature loss
real_fea = self.netF(self.var_H).detach()
fake_fea = self.netF(self.fake_H)
real_fea_trans = self.fea_trans(real_fea)
fake_fea_trans = self.fea_trans(fake_fea)
l_g_fea_trans = self.l_fea_w * self.cri_fea(
fake_fea_trans, real_fea_trans) * 10
l_g_fea = self.l_fea_w * self.cri_fea(fake_fea, real_fea)
l_g_total += l_g_fea
l_g_total += l_g_fea_trans
if not self.train_opt['only_G']:
pred_g_fake = self.netD(self.fake_H)
if self.opt['train']['gan_type'] == 'gan':
l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True)
elif self.opt['train']['gan_type'] == 'ragan':
# self.var_ref = self.var_ref[:,1:,:,:]
pred_d_real = self.netD(self.var_ref)
pred_d_real = [ele.detach() for ele in pred_d_real]
l_g_gan = self.l_gan_w * (self.cri_gan(
self.process_list(pred_d_real, pred_g_fake), False
) + self.cri_gan(
self.process_list(pred_g_fake, pred_d_real), True)) / 2
elif self.opt['train']['gan_type'] == 'lsgan_ra':
# self.var_ref = self.var_ref[:,1:,:,:]
pred_d_real = self.netD(self.var_ref)
pred_d_real = [ele.detach() for ele in pred_d_real]
# l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True)
l_g_gan = self.l_gan_w * (self.cri_gan(
self.process_list(pred_d_real, pred_g_fake), False
) + self.cri_gan(
self.process_list(pred_g_fake, pred_d_real), True)) / 2
elif self.opt['train']['gan_type'] == 'lsgan':
# self.var_ref = self.var_ref[:,1:,:,:]
l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True)
l_g_total += l_g_gan
l_g_total.backward()
self.optimizer_G.step()
else:
self.var_ref = self.var_ref
if not self.train_opt['only_G']:
# D
for p in self.netD.parameters():
p.requires_grad = True
self.optimizer_D.zero_grad()
l_d_total = 0
pred_d_real = self.netD(self.var_ref)
pred_d_fake = self.netD(
self.fake_H.detach()) # detach to avoid BP to G
if self.opt['train']['gan_type'] == 'gan':
l_d_real = self.cri_gan(pred_d_real, True)
l_d_fake = self.cri_gan(pred_d_fake, False)
l_d_total += l_d_real + l_d_fake
elif self.opt['train']['gan_type'] == 'ragan':
l_d_real = self.cri_gan(
self.process_list(pred_d_real, pred_d_fake), True)
l_d_fake = self.cri_gan(
self.process_list(pred_d_fake, pred_d_real), False)
l_d_total += (l_d_real + l_d_fake) / 2
elif self.opt['train']['gan_type'] == 'lsgan':
l_d_real = self.cri_gan(pred_d_real, True)
l_d_fake = self.cri_gan(pred_d_fake, False)
l_d_total += (l_d_real + l_d_fake) / 2
l_d_total.backward()
self.optimizer_D.step()
# set log
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
if self.cri_pix:
self.log_dict['l_g_pix'] = l_g_pix.item() / self.l_pix_w
if self.cri_lpips:
self.log_dict['l_g_lpips'] = l_g_lpips.item() / self.l_lpips_w
if not self.train_opt['only_G']:
self.log_dict['l_g_gan'] = l_g_gan.item() / self.l_gan_w
if self.cri_fea:
self.log_dict['l_g_fea'] = l_g_fea.item() / self.l_fea_w
self.log_dict['l_g_fea_trans'] = l_g_fea_trans.item(
) / self.l_fea_w / 10
if not self.train_opt['only_G']:
self.log_dict['l_d_real'] = l_d_real.item()
self.log_dict['l_d_fake'] = l_d_fake.item()
self.log_dict['D_real'] = torch.mean(pred_d_real[0].detach())
self.log_dict['D_fake'] = torch.mean(pred_d_fake[0].detach())
def test(self, load_path=None, input_u=None, input_v=None):
if load_path is not None:
self.load_network(load_path, self.netG,
self.opt['path']['strict_load'])
print(
'***************************************************************'
)
print('Load model successfully')
print(
'***************************************************************'
)
self.netG.eval()
# self.var_H = self.var_H.squeeze(1)
# img_to_write = self.var_L.detach()[0].float().cpu()
# print(img_to_write.size())
# cv2.imwrite('./test.png',img_to_write.numpy().transpose(1,2,0)*255)
with torch.no_grad():
if self.var_L.size()[-1] > 1280:
width = self.var_L.size()[-1]
height = self.var_L.size()[-2]
fake_list = []
for height_start in [0, int(height / 2)]:
for width_start in [0, int(width / 2)]:
self.fake_slice = self.netG(
self.var_L[:, :, :, height_start:(height_start +
int(height / 2)),
width_start:(width_start +
int(width / 2))])
fake_list.append(self.fake_slice)
enhanced_frame_h1 = torch.cat([fake_list[0], fake_list[2]], 2)
enhanced_frame_h2 = torch.cat([fake_list[1], fake_list[3]], 2)
self.fake_H = torch.cat([enhanced_frame_h1, enhanced_frame_h2],
3)
else:
self.fake_H = self.netG(self.var_L)
if input_u is not None and input_v is not None:
self.var_L_u = input_u.to(self.device)
self.var_L_v = input_v.to(self.device)
self.fake_H_u_s = self.netG(self.var_L_u.float())
self.fake_H_v_s = self.netG(self.var_L_v.float())
# self.fake_H_u = torch.cat((self.fake_H_u_s[0], self.fake_H_u_s[1]), 1)
# self.fake_H_v = torch.cat((self.fake_H_v_s[0], self.fake_H_v_s[1]), 1)
self.fake_H_u = self.fake_H_u_s
self.fake_H_v = self.fake_H_v_s
# self.fake_H_u = self.IWT(self.fake_H_u)
# self.fake_H_v = self.IWT(self.fake_H_v)
else:
self.fake_H_u = None
self.fake_H_v = None
self.fake_H_all = self.fake_H
if self.opt['network_G']['out_nc'] == 4:
self.fake_H_all = self.IWT(self.fake_H_all)
if input_u is not None and input_v is not None:
self.fake_H_u = self.IWT(self.fake_H_u)
self.fake_H_v = self.IWT(self.fake_H_v)
# self.fake_H = self.var_L[:,2,:,:,:]
self.netG.train()
def get_current_log(self):
return self.log_dict
def get_current_visuals(self, need_GT=True):
out_dict = OrderedDict()
out_dict['LQ'] = self.var_L.detach()[0][2].float().cpu()
out_dict['SR'] = self.fake_H.detach()[0].float().cpu()
if self.fake_H_u is not None:
out_dict['SR_U'] = self.fake_H_u.detach()[0].float().cpu()
out_dict['SR_V'] = self.fake_H_v.detach()[0].float().cpu()
if need_GT:
out_dict['GT'] = self.var_H.detach()[0].float().cpu()
return out_dict
def print_network(self):
# Generator
s, n = self.get_network_description(self.netG)
if isinstance(self.netG, nn.DataParallel) or isinstance(
self.netG, DistributedDataParallel):
net_struc_str = '{} - {}'.format(
self.netG.__class__.__name__,
self.netG.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netG.__class__.__name__)
if self.rank <= 0:
logger.info(
'Network G structure: {}, with parameters: {:,d}'.format(
net_struc_str, n))
logger.info(s)
if self.is_train:
# Discriminator
s, n = self.get_network_description(self.netD)
if isinstance(self.netD, nn.DataParallel) or isinstance(
self.netD, DistributedDataParallel):
net_struc_str = '{} - {}'.format(
self.netD.__class__.__name__,
self.netD.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netD.__class__.__name__)
if self.rank <= 0:
logger.info(
'Network D structure: {}, with parameters: {:,d}'.format(
net_struc_str, n))
logger.info(s)
if self.cri_fea: # F, Perceptual Network
s, n = self.get_network_description(self.netF)
if isinstance(self.netF, nn.DataParallel) or isinstance(
self.netF, DistributedDataParallel):
net_struc_str = '{} - {}'.format(
self.netF.__class__.__name__,
self.netF.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netF.__class__.__name__)
if self.rank <= 0:
logger.info(
'Network F structure: {}, with parameters: {:,d}'.
format(net_struc_str, n))
logger.info(s)
def load(self):
load_path_G = self.opt['path']['pretrain_model_G']
if load_path_G is not None:
logger.info('Loading model for G [{:s}] ...'.format(load_path_G))
self.load_network(load_path_G, self.netG,
self.opt['path']['strict_load'])
print('G loaded')
load_path_D = self.opt['path']['pretrain_model_D']
if self.opt['is_train'] and load_path_D is not None:
logger.info('Loading model for D [{:s}] ...'.format(load_path_D))
self.load_network(load_path_D, self.netD,
self.opt['path']['strict_load'])
print('D loaded')
def save(self, iter_step):
if not self.train_opt['only_G']:
self.save_network(self.netG, 'G', iter_step)
self.save_network(self.netD, 'D', iter_step)
else:
self.save_network(self.netG,
self.opt['network_G']['which_model_G'],
iter_step, self.opt['path']['pretrain_model_G'])
