import tensorflow as tf

        for var in tf.trainable_variables():
            self._train_summaries.append(var)

        if mode == 'TEST':
            # FLAGS2["bbox_normalize_means"] = (0.0, 0.0, 0.0, 0.0)
            # FLAGS2["bbox_normalize_stds"] = (0.1, 0.1, 0.1, 0.1)
            stds = np.tile(np.array(cfg.FLAGS2["bbox_normalize_stds"]), (self._num_classes))
            means = np.tile(np.array(cfg.FLAGS2["bbox_normalize_means"]), (self._num_classes))
            self._predictions["bbox_pred"] *= stds
            self._predictions["bbox_pred"] += means
        else:
            self._add_losses()
            layers_to_output.update(self._losses)

        val_summaries = []   # 保存添加tf.summary.image和添加self._losses的操作
        with tf.device("/cpu:0"):
            val_summaries.append(self._add_image_summary(self._image, self._gt_boxes))
            for key, var in self._event_summaries.items():    #添加self._losses
                val_summaries.append(tf.summary.scalar(key, var))
            for key, var in self._score_summaries.items():          #self._score_summaries.update(self._anchor_targets)   self._score_summaries.update(self._proposal_targets) 
                self._add_score_summary(key, var)    
            for var in self._act_summaries:    # 添加head网络和rpn层
                self._add_act_summary(var)
            '''
            for var in tf.trainable_variables():
            self._train_summaries.append(var)
            '''
            for var in self._train_summaries:      #添加tf.trainable_variables()，显示张量分布监控数据随着迭代轮数的变化趋势
                self._add_train_summary(var)

