def METHOD_NAME(self):
    psnr_list, ssim_list, lpips_list = [], [], []
    with torch.no_grad():
        for (pred, label) in zip(self.preds, self.labels):
            # norm to 0-1
            height, width = label.size(2), label.size(3)
            pred = pred[:, :, 0:height, 0:width]
            psnr_list.append(calculate_psnr(label, pred))
            ssim_list.append(calculate_ssim(label, pred))
            lpips_list.append(
                calculate_lpips(label, pred, self.loss_fn_alex))
    return {
        MetricKeys.PSNR: np.mean(psnr_list),
        MetricKeys.SSIM: np.mean(ssim_list),
        MetricKeys.LPIPS: np.mean(lpips_list)
    }