diff options
Diffstat (limited to 'r_basicsr/models/srgan_model.py')
| -rw-r--r-- | r_basicsr/models/srgan_model.py | 149 |
1 files changed, 149 insertions, 0 deletions
diff --git a/r_basicsr/models/srgan_model.py b/r_basicsr/models/srgan_model.py new file mode 100644 index 0000000..a562a7d --- /dev/null +++ b/r_basicsr/models/srgan_model.py @@ -0,0 +1,149 @@ +import torch
+from collections import OrderedDict
+
+from r_basicsr.archs import build_network
+from r_basicsr.losses import build_loss
+from r_basicsr.utils import get_root_logger
+from r_basicsr.utils.registry import MODEL_REGISTRY
+from .sr_model import SRModel
+
+
+@MODEL_REGISTRY.register()
+class SRGANModel(SRModel):
+ """SRGAN model for single image super-resolution."""
+
+ def init_training_settings(self):
+ train_opt = self.opt['train']
+
+ self.ema_decay = train_opt.get('ema_decay', 0)
+ if self.ema_decay > 0:
+ logger = get_root_logger()
+ logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}')
+ # define network net_g with Exponential Moving Average (EMA)
+ # net_g_ema is used only for testing on one GPU and saving
+ # There is no need to wrap with DistributedDataParallel
+ self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
+ # load pretrained model
+ load_path = self.opt['path'].get('pretrain_network_g', None)
+ if load_path is not None:
+ self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
+ else:
+ self.model_ema(0) # copy net_g weight
+ self.net_g_ema.eval()
+
+ # define network net_d
+ self.net_d = build_network(self.opt['network_d'])
+ self.net_d = self.model_to_device(self.net_d)
+ self.print_network(self.net_d)
+
+ # load pretrained models
+ load_path = self.opt['path'].get('pretrain_network_d', None)
+ if load_path is not None:
+ param_key = self.opt['path'].get('param_key_d', 'params')
+ self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key)
+
+ self.net_g.train()
+ self.net_d.train()
+
+ # define losses
+ if train_opt.get('pixel_opt'):
+ self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device)
+ else:
+ self.cri_pix = None
+
+ if train_opt.get('ldl_opt'):
+ self.cri_ldl = build_loss(train_opt['ldl_opt']).to(self.device)
+ else:
+ self.cri_ldl = None
+
+ if train_opt.get('perceptual_opt'):
+ self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device)
+ else:
+ self.cri_perceptual = None
+
+ if train_opt.get('gan_opt'):
+ self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
+
+ self.net_d_iters = train_opt.get('net_d_iters', 1)
+ self.net_d_init_iters = train_opt.get('net_d_init_iters', 0)
+
+ # set up optimizers and schedulers
+ self.setup_optimizers()
+ self.setup_schedulers()
+
+ def setup_optimizers(self):
+ train_opt = self.opt['train']
+ # optimizer g
+ optim_type = train_opt['optim_g'].pop('type')
+ self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **train_opt['optim_g'])
+ self.optimizers.append(self.optimizer_g)
+ # optimizer d
+ optim_type = train_opt['optim_d'].pop('type')
+ self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d'])
+ self.optimizers.append(self.optimizer_d)
+
+ def optimize_parameters(self, current_iter):
+ # optimize net_g
+ for p in self.net_d.parameters():
+ p.requires_grad = False
+
+ self.optimizer_g.zero_grad()
+ self.output = self.net_g(self.lq)
+
+ l_g_total = 0
+ loss_dict = OrderedDict()
+ if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
+ # pixel loss
+ if self.cri_pix:
+ l_g_pix = self.cri_pix(self.output, self.gt)
+ l_g_total += l_g_pix
+ loss_dict['l_g_pix'] = l_g_pix
+ # perceptual loss
+ if self.cri_perceptual:
+ l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt)
+ if l_g_percep is not None:
+ l_g_total += l_g_percep
+ loss_dict['l_g_percep'] = l_g_percep
+ if l_g_style is not None:
+ l_g_total += l_g_style
+ loss_dict['l_g_style'] = l_g_style
+ # gan loss
+ fake_g_pred = self.net_d(self.output)
+ l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
+ l_g_total += l_g_gan
+ loss_dict['l_g_gan'] = l_g_gan
+
+ l_g_total.backward()
+ self.optimizer_g.step()
+
+ # optimize net_d
+ for p in self.net_d.parameters():
+ p.requires_grad = True
+
+ self.optimizer_d.zero_grad()
+ # real
+ real_d_pred = self.net_d(self.gt)
+ l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
+ loss_dict['l_d_real'] = l_d_real
+ loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
+ l_d_real.backward()
+ # fake
+ fake_d_pred = self.net_d(self.output.detach())
+ l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
+ loss_dict['l_d_fake'] = l_d_fake
+ loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
+ l_d_fake.backward()
+ self.optimizer_d.step()
+
+ self.log_dict = self.reduce_loss_dict(loss_dict)
+
+ if self.ema_decay > 0:
+ self.model_ema(decay=self.ema_decay)
+
+ def save(self, epoch, current_iter):
+ if hasattr(self, 'net_g_ema'):
+ self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
+ else:
+ self.save_network(self.net_g, 'net_g', current_iter)
+ self.save_network(self.net_d, 'net_d', current_iter)
+ self.save_training_state(epoch, current_iter)
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