def METHOD_NAME(self, average=False):
    total_time = self.preprocess_time_s.value(
    ) * self.img_num + self.inference_time_s.value(
    ) + self.postprocess_time_s.value() * self.img_num
    total_time = round(total_time, 4)
    logging.METHOD_NAME("------------------ Inference Time Info ----------------------")
    logging.METHOD_NAME("total_time(ms): {}, img_num: {}, batch_size: {}".format(
        total_time * 1000, self.img_num, self.img_num))
    preprocess_time = round(
        self.preprocess_time_s.value() / self.repeats,
        4) if average else self.preprocess_time_s.value()
    postprocess_time = round(
        self.postprocess_time_s.value() / self.repeats,
        4) if average else self.postprocess_time_s.value()
    inference_time = round(self.inference_time_s.value() / self.repeats,
                           4) if average else self.inference_time_s.value()
    average_latency = total_time / self.repeats
    logging.METHOD_NAME("average latency time(ms): {:.2f}, QPS: {:2f}".format(
        average_latency * 1000, 1 / average_latency))
    logging.METHOD_NAME("preprocess_time_per_im(ms): {:.2f}, "
          "inference_time_per_batch(ms): {:.2f}, "
          "postprocess_time_per_im(ms): {:.2f}".format(
              preprocess_time * 1000, inference_time * 1000,
              postprocess_time * 1000))