import tensorflow as tf

                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)

        self._summary_op = tf.summary.merge_all()   #  tf.summary.merge_all()函数来整理所有的日志生成操作
        if not testing:
            self._summary_op_val = tf.summary.merge(val_summaries)

        return layers_to_output

    def get_variables_to_restore(self, variables, var_keep_dic):
        raise NotImplementedError

    def fix_variables(self, sess, pretrained_model):
        raise NotImplementedError

    # Extract the head feature maps, for example for vgg16 it is conv5_3
    # only useful during testing mode
    def extract_head(self, sess, image):
        feed_dict = {self._image: image}
