666

  pred_mean = pred.float().mean()
            label_img_mean = label_img.float().mean()
            pred_adjust = torch.clamp((pred * (label_img_mean / pred_mean)), 0, 1)

        per_psnr_1 = psnr(pred_adjust, label_img)
        per_ssim_1 = ssim(pred_adjust, label_img).item()

  if avg_psnr_1 >= max_psnr_2:
                max_psnr_2 = avg_psnr_1
                best_psnr_epoch_2 = epoch_idx
                max_ssim_2 = avg_ssim_1
                torch.save({
                    'epoch': epoch_idx,
                    'max_psnr_1': max_psnr_2,
                    'max_ssim_1': max_ssim_2,
                    'backbone_model': backbone_model.state_dict(),
                }, os.path.join(args.model_save_dir, 'Best_backbone_psnr.pkl'))
                print(f'\n===================Best psnr backbone_model saved at epoch:{epoch_idx}  max_psnr_2:{max_psnr_2:.4f}  max_ssim_2:{max_ssim_2:.4f}')
                trainLogger.write(f'\n===================Best psnr backbone_model saved at epoch:{epoch_idx}  max_psnr_2:{max_psnr_2:.4f}  max_ssim_1:{max_ssim_2:.4f}')

 

 print(f'\n max_psnr_1:{max_psnr_1:.4f}  max_ssim_1:{max_ssim_1:.4f}  best_psnr_epoch_1:{best_psnr_epoch_1}  best_ssim_epoch_1:{best_ssim_epoch_1}')
    trainLogger.write(f'\n max_psnr_2:{max_psnr_2:.4f}  max_ssim_2:{max_ssim_2:.4f}  best_psnr_epoch_2:{best_psnr_epoch_2} ')

 

RuntimeError: The size of tensor a (732) must match the size of tensor b (733) at non-singleton dimension 3