import tensorflow as tf

                                          [tf.float32, tf.float32])
            rois.set_shape([None, 5])
            rpn_scores.set_shape([None, 1])

        return rois, rpn_scores

    def _crop_pool_layer(self, bottom, rois, name):
        with tf.variable_scope(name):
            # tf.squeeze()返回一个张量，这个张量是将原始input中所有维度中为1的那些维都删掉的结果
            batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1])
            # Get the normalized coordinates of bboxes
            bottom_shape = tf.shape(bottom)
            height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0])
            width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0])
