//  [python]
def build_whole_detection_network(self, input_img_batch, gtboxes_batch_h=None, gtboxes_batch_r=None, gpu_id=0):

        if self.is_training:
            gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
            gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)

        img_shape = tf.shape(input_img_batch)

        # 1. build backbone
        feature_pyramid = self.build_backbone(input_img_batch)

        # 2. build rpn
        fpn_box_pred, fpn_cls_score, fpn_cls_prob = self.rpn(feature_pyramid)

        # 3. generate anchors
        anchor_list = self.make_anchors(feature_pyramid)
        anchors = tf.concat(anchor_list, axis=0)

        # 4. postprocess rpn proposals. such as: decode, clip, NMS
        with tf.variable_scope('postprocess_FPN'):
            rois, roi_scores = self.postprocess_rpn_proposals(rpn_bbox_pred=fpn_box_pred,
                                                              rpn_cls_prob=fpn_cls_prob,
                                                              img_shape=img_shape,
                                                              anchors=anchors,
                                                              is_training=self.is_training,
                                                              gpu_id=gpu_id)

        # 5. sample minibatch
        if self.is_training:
            with tf.variable_scope('sample_anchors_minibatch'):

                overlaps = iou_rotate.iou_rotate_calculate(anchors, gtboxes_batch_r[:, :-1], use_gpu=True, gpu_id=gpu_id)

                fpn_labels, fpn_bbox_targets = \
                    tf.py_func(
                        self.anchor_sampler_rrpn.anchor_target_layer,
                        [gtboxes_batch_r, anchors, overlaps],
                        [tf.float32, tf.float32])
                fpn_bbox_targets = tf.reshape(fpn_bbox_targets, [-1, 5])
                fpn_labels = tf.to_int32(fpn_labels, name="to_int32")
                fpn_labels = tf.reshape(fpn_labels, [-1])
                self.add_anchor_img_smry(input_img_batch, anchors, fpn_labels, method=1)

            fpn_cls_category = tf.argmax(fpn_cls_prob, axis=1)
            kept_rpppn = tf.reshape(tf.where(tf.not_equal(fpn_labels, -1)), [-1])
            fpn_cls_category = tf.gather(fpn_cls_category, kept_rpppn)
            acc = tf.reduce_mean(tf.to_float(tf.equal(fpn_cls_category,
                                                      tf.to_int64(tf.gather(fpn_labels, kept_rpppn)))))
            tf.summary.scalar('ACC/fpn_accuracy', acc)

            with tf.control_dependencies([fpn_labels]):

                with tf.variable_scope('sample_RCNN_minibatch'):
                    overlaps = iou_rotate.iou_rotate_calculate(rois, gtboxes_batch_r[:, :-1], use_gpu=True,
                                                               gpu_id=gpu_id)

                    rois, labels, bbox_targets, _ = \
                        tf.py_func(self.proposal_sampler_rrpn.proposal_target_layer,
                                   [rois, gtboxes_batch_r, overlaps],
                                   [tf.float32, tf.float32, tf.float32, tf.float32])
                    rois = tf.reshape(rois, [-1, 5])
                    labels = tf.to_int32(labels)
                    labels = tf.reshape(labels, [-1])
                    bbox_targets = tf.reshape(bbox_targets, [-1, 5 * (self.cfgs.CLASS_NUM + 1)])
                    self.add_roi_batch_img_smry(input_img_batch, rois, labels, method=1)

        # 6. assign level
        if self.is_training:
            rois_list, labels, bbox_targets = self.assign_levels(all_rois=rois,
                                                                 labels=labels,
                                                                 bbox_targets=bbox_targets)
        else:
            rois_list = self.assign_levels(all_rois=rois)

        # 7. build Fast-RCNN, include roi align/pooling, box head
        bbox_pred, cls_score = self.box_head.fpn_fc_head(self.roi_extractor, rois_list, feature_pyramid,
                                                         img_shape, self.is_training, mode=1)
        rois = tf.concat(rois_list, axis=0, name='concat_rois')
        cls_prob = slim.softmax(cls_score, 'cls_prob')

        if self.is_training:
            cls_category = tf.argmax(cls_prob, axis=1)
            fast_acc = tf.reduce_mean(tf.to_float(tf.equal(cls_category, tf.to_int64(labels))))
            tf.summary.scalar('ACC/fast_acc', fast_acc)

        #  8. build loss
        if self.is_training:
            self.build_loss(rpn_box_pred=fpn_box_pred,
                            rpn_bbox_targets=fpn_bbox_targets,
                            rpn_cls_score=fpn_cls_score,
                            rpn_labels=fpn_labels,
                            bbox_pred=bbox_pred,
                            bbox_targets=bbox_targets,
                            cls_score=cls_score,
                            labels=labels)

        # 9. postprocess_fastrcnn
        final_bbox, final_scores, final_category = self.postprocess_fastrcnn(rois=rois,
                                                                             bbox_ppred=bbox_pred,
                                                                             scores=cls_prob,
                                                                             gpu_id=gpu_id)
        if self.is_training:
            return final_bbox, final_scores, final_category, self.losses_dict
        else:
            return final_bbox, final_scores, final_category