import tensorflow as tf

                step=global_step)
            tf.contrib.summary.scalar(
                'rpn_score_loss', tf.reduce_mean(rpn_score_loss),
                step=global_step)
            tf.contrib.summary.scalar(
                'rpn_box_loss', tf.reduce_mean(rpn_box_loss), step=global_step)
            tf.contrib.summary.scalar(
                'total_fast_rcnn_loss', tf.reduce_mean(total_fast_rcnn_loss),
                step=global_step)
            tf.contrib.summary.scalar(
                'fast_rcnn_class_loss', tf.reduce_mean(fast_rcnn_class_loss),
                step=global_step)
            tf.contrib.summary.scalar(
                'fast_rcnn_box_loss', tf.reduce_mean(fast_rcnn_box_loss),
                step=global_step)
            if params['include_mask']:
              tf.contrib.summary.scalar(
                  'mask_loss', tf.reduce_mean(mask_loss), step=global_step)
            tf.contrib.summary.scalar(
                'learning_rate', tf.reduce_mean(learning_rate),
