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on
T4
Running
on
T4
| import torch | |
| from collections import OrderedDict | |
| from basicsr.utils.registry import MODEL_REGISTRY | |
| from .srgan_model import SRGANModel | |
| class ESRGANModel(SRGANModel): | |
| """ESRGAN model for single image super-resolution.""" | |
| 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 (relativistic gan) | |
| real_d_pred = self.net_d(self.gt).detach() | |
| fake_g_pred = self.net_d(self.output) | |
| l_g_real = self.cri_gan(real_d_pred - torch.mean(fake_g_pred), False, is_disc=False) | |
| l_g_fake = self.cri_gan(fake_g_pred - torch.mean(real_d_pred), True, is_disc=False) | |
| l_g_gan = (l_g_real + l_g_fake) / 2 | |
| 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() | |
| # gan loss (relativistic gan) | |
| # In order to avoid the error in distributed training: | |
| # "Error detected in CudnnBatchNormBackward: RuntimeError: one of | |
| # the variables needed for gradient computation has been modified by | |
| # an inplace operation", | |
| # we separate the backwards for real and fake, and also detach the | |
| # tensor for calculating mean. | |
| # real | |
| fake_d_pred = self.net_d(self.output).detach() | |
| real_d_pred = self.net_d(self.gt) | |
| l_d_real = self.cri_gan(real_d_pred - torch.mean(fake_d_pred), True, is_disc=True) * 0.5 | |
| l_d_real.backward() | |
| # fake | |
| fake_d_pred = self.net_d(self.output.detach()) | |
| l_d_fake = self.cri_gan(fake_d_pred - torch.mean(real_d_pred.detach()), False, is_disc=True) * 0.5 | |
| l_d_fake.backward() | |
| self.optimizer_d.step() | |
| loss_dict['l_d_real'] = l_d_real | |
| loss_dict['l_d_fake'] = l_d_fake | |
| loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) | |
| loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) | |
| self.log_dict = self.reduce_loss_dict(loss_dict) | |
| if self.ema_decay > 0: | |
| self.model_ema(decay=self.ema_decay) | |