def simple_test(self, feats_dict, img_metas, points):
bbox_preds = self._bbox_forward(feats_dict)
feats_dict.update(bbox_preds)
bbox_list = self.bbox_head.get_bboxes(points, bbox_preds, img_metas)
bbox_results = [
bbox3d2result(bboxes, scores, labels)
for bboxes, scores, labels in bbox_list
]
return bbox_results
def simple_test(self, feats_dict, voxels_dict, img_metas, proposal_list,
**kwargs):
"""Simple testing forward function of PartAggregationROIHead.
Note:
This function assumes that the batch size is 1
Args:
feats_dict (dict): Contains features from the first stage.
voxels_dict (dict): Contains information of voxels.
img_metas (list[dict]): Meta info of each image.
proposal_list (list[dict]): Proposal information from rpn.
Returns:
dict: Bbox results of one frame.
"""
assert self.with_bbox, 'Bbox head must be implemented.'
assert self.with_semantic
semantic_results = self.semantic_head(feats_dict['seg_features'])
rois = bbox3d2roi([res['boxes_3d'].tensor for res in proposal_list])
labels_3d = [res['labels_3d'] for res in proposal_list]
cls_preds = [res['cls_preds'] for res in proposal_list]
bbox_results = self._bbox_forward(feats_dict['seg_features'],
semantic_results['part_feats'],
voxels_dict, rois)
bbox_list = self.bbox_head.get_bboxes(rois,
bbox_results['cls_score'],
bbox_results['bbox_pred'],
labels_3d,
cls_preds,
img_metas,
cfg=self.test_cfg)
bbox_results = [
bbox3d2result(bboxes, scores, labels)
for bboxes, scores, labels in bbox_list
]
return bbox_results[0]
def simple_test(self, feats_dict, img_metas, points, rescale=False):
"""Simple testing forward function of PartAggregationROIHead.
Note:
This function assumes that the batch size is 1
Args:
feats_dict (dict): Contains features from the first stage.
img_metas (list[dict]): Contain pcd and img's meta info.
points (torch.Tensor): Input points.
rescale (bool): Whether to rescale results.
Returns:
dict: Bbox results of one frame.
"""
sample_mod = self.test_cfg.sample_mod
assert sample_mod in ['vote', 'seed', 'random']
result_z = self.primitive_z(feats_dict, sample_mod)
feats_dict.update(result_z)
result_xy = self.primitive_xy(feats_dict, sample_mod)
feats_dict.update(result_xy)
result_line = self.primitive_line(feats_dict, sample_mod)
feats_dict.update(result_line)
bbox_preds = self.bbox_head(feats_dict, sample_mod)
feats_dict.update(bbox_preds)
bbox_list = self.bbox_head.get_bboxes(
points,
feats_dict,
img_metas,
rescale=rescale,
suffix='_optimized')
bbox_results = [
bbox3d2result(bboxes, scores, labels)
for bboxes, scores, labels in bbox_list
]
return bbox_results