def simple_test_bboxes(self, x, img_metas, proposals, rcnn_test_cfg, rescale=False): """Test only det bboxes without augmentation.""" rois = bbox2roi(proposals) bbox_results = self._bbox_forward(x, rois, img_metas) cls_score = bbox_results['cls_score'] img_shape = img_metas[0]['img_shape'] crop_quadruple = np.array([0, 0, 1, 1]) flip = False if 'crop_quadruple' in img_metas[0]: crop_quadruple = img_metas[0]['crop_quadruple'] if 'flip' in img_metas[0]: flip = img_metas[0]['flip'] det_bboxes, det_labels = self.bbox_head.get_det_bboxes( rois, cls_score, img_shape, flip=flip, crop_quadruple=crop_quadruple, cfg=rcnn_test_cfg) return det_bboxes, det_labels
def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, img_metas): """Run forward function and calculate loss for box head in training.""" rois = bbox2roi([res.bboxes for res in sampling_results]) bbox_results = self._bbox_forward(x, rois, img_metas) bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes, gt_labels, self.train_cfg) loss_bbox = self.bbox_head.loss(bbox_results['cls_score'], bbox_results['bbox_pred'], rois, *bbox_targets) bbox_results.update(loss_bbox=loss_bbox) return bbox_results