def get_rescore_bboxes(self, guided_anchors, cls_scores, anchor_labels,
img_metas, cfg):
det_bboxes = list()
det_scores = list()
det_labels = list()
for i in range(len(img_metas)):
bbox_pred = guided_anchors[i]
scores = cls_scores[i]
labels = anchor_labels[i]
if scores.numel() == 0:
det_bboxes.append(None)
det_scores.append(None)
det_labels.append(None)
continue
bbox_pred = bbox_pred.view(-1, 7)
scores = torch.sigmoid(scores).view(-1)
select = scores > cfg.score_thr
bbox_pred = bbox_pred[select, :]
scores = scores[select]
labels = labels[select]
if scores.numel() == 0:
det_bboxes.append(None)
det_scores.append(None)
det_labels.append(None)
continue
boxes_for_nms = boxes3d_to_bev_torch(bbox_pred)
keep = rotate_nms_torch(boxes_for_nms,
scores,
iou_threshold=cfg.nms.iou_thr)
bbox_pred = bbox_pred[keep, :]
scores = scores[keep]
labels = labels[keep]
det_bboxes.append(bbox_pred.detach().cpu().numpy())
det_scores.append(scores.detach().cpu().numpy())
det_labels.append(labels.detach().cpu().numpy())
return det_bboxes, det_scores, det_labels
def get_det_bboxes(self, rois, cls_scores, bbox_preds, img_metas, cfg):
det_bboxes = list()
det_scores = list()
for i in range(len(img_metas)):
inds = torch.nonzero(rois[:, 0] == i).squeeze()
roi = rois[inds, 1:]
scores = cls_scores[inds, :]
if bbox_preds is not None:
bbox_pred = bbox_preds[inds, :]
else:
bbox_pred = roi
scores = torch.sigmoid(scores).view(-1)
select = scores > cfg.score_thr
bbox_pred = bbox_pred[select, :]
roi = roi[select, :]
scores = scores[select]
if scores.numel() == 0:
det_bboxes.append(bbox_pred)
det_scores.append(scores)
continue
if bbox_preds is not None:
bbox_pred = delta2rbbox3d(roi, bbox_pred, self.target_means,
self.target_stds)
boxes_for_nms = boxes3d_to_bev_torch(bbox_pred)
keep = rotate_nms_torch(boxes_for_nms,
scores,
iou_threshold=cfg.nms.iou_thr)
bbox_pred = bbox_pred[keep, :]
scores = scores[keep]
det_bboxes.append(bbox_pred.detach().cpu().numpy())
det_scores.append(scores.detach().cpu().numpy())
return det_bboxes, det_scores