refactor

mmdet3d/apis/inferencers/__init__.py

@@ -1,11 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .base_det3d_inferencer import BaseDet3DInferencer
-from .base_seg3d_inferencer import BaseSeg3DInferencer
 from .lidar_det3d_inferencer import LidarDet3DInferencer
 from .lidar_seg3d_inferencer import LidarSeg3DInferencer
 from .mono_det3d_inferencer import MonoDet3DInferencer
 
 __all__ = [
     'BaseDet3DInferencer', 'MonoDet3DInferencer', 'LidarDet3DInferencer',
-    'BaseSeg3DInferencer', 'LidarSeg3DInferencer'
+    'LidarSeg3DInferencer'
 ]

mmdet3d/apis/inferencers/base_det3d_inferencer.py

@@ -295,11 +295,18 @@ def pred2dict(self, data_sample: InstanceData) -> Dict:
         It's better to contain only basic data elements such as strings and
         numbers in order to guarantee it's json-serializable.
         """
-        pred_instances = data_sample.pred_instances_3d.numpy()
-        result = {
-            'bboxes_3d': pred_instances.bboxes_3d.tensor.cpu().tolist(),
-            'labels_3d': pred_instances.labels_3d.tolist(),
-            'scores_3d': pred_instances.scores_3d.tolist()
-        }
+        result = {}
+        if 'pred_instances_3d' in data_sample:
+            pred_instances_3d = data_sample.pred_instances_3d.numpy()
+            result = {
+                'bboxes_3d': pred_instances_3d.bboxes_3d.tensor.cpu().tolist(),
+                'labels_3d': pred_instances_3d.labels_3d.tolist(),
+                'scores_3d': pred_instances_3d.scores_3d.tolist()
+            }
+
+        if 'pred_pts_seg' in data_sample:
+            pred_pts_seg = data_sample.pred_pts_seg.numpy()
+            result['pts_semantic_mask'] = \
+                pred_pts_seg.pts_semantic_mask.tolist()
 
         return result

mmdet3d/apis/inferencers/lidar_seg3d_inferencer.py

@@ -10,7 +10,7 @@
 
 from mmdet3d.registry import INFERENCERS
 from mmdet3d.utils import ConfigType
-from .base_seg3d_inferencer import BaseSeg3DInferencer
+from .base_det3d_inferencer import BaseDet3DInferencer
 
 InstanceList = List[InstanceData]
 InputType = Union[str, np.ndarray]
@@ -22,7 +22,7 @@
 
 @INFERENCERS.register_module(name='seg3d-lidar')
 @INFERENCERS.register_module()
-class LidarSeg3DInferencer(BaseSeg3DInferencer):
+class LidarSeg3DInferencer(BaseDet3DInferencer):
     """The inferencer of LiDAR-based segmentation.
 
     Args:
@@ -46,7 +46,8 @@ class LidarSeg3DInferencer(BaseSeg3DInferencer):
     preprocess_kwargs: set = set()
     forward_kwargs: set = set()
     visualize_kwargs: set = {
-        'return_vis', 'show', 'wait_time', 'draw_pred', 'img_out_dir'
+        'return_vis', 'show', 'wait_time', 'draw_pred', 'pred_score_thr',
+        'img_out_dir'
     }
     postprocess_kwargs: set = {
         'print_result', 'pred_out_file', 'return_datasample'