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Running
on
Zero
Running
on
Zero
| import torch | |
| from collections import OrderedDict | |
| from basicsr.archs import build_network | |
| from basicsr.losses import build_loss | |
| from basicsr.utils import get_root_logger | |
| from basicsr.utils.registry import MODEL_REGISTRY | |
| from .sr_model import SRModel | |
| 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) | |