233

# 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, label_img)
            per_ssim_1 = ssim(pred, label_img).item()
            per_lpips_value = loss_fn(pred, label_img).item()