change names

mmdet3d/core/evaluation/__init__.py

 from .class_names import dataset_aliases, get_classes, kitti_classes
+from .indoor_utils import indoor_eval
 from .kitti_utils import kitti_eval, kitti_eval_coco_style
 
 __all__ = [
     'dataset_aliases', 'get_classes', 'kitti_classes', 'kitti_eval_coco_style',
-    'kitti_eval'
+    'kitti_eval', 'indoor_eval'
 ]

mmdet3d/core/evaluation/indoor_utils/__init__.py

+from .eval import indoor_eval
+
+__all__ = ['indoor_eval']

mmdet3d/core/evaluation/scannet_utils/eval.py → mmdet3d/core/evaluation/indoor_utils/eval.py

@@ -365,7 +365,7 @@ def boxes3d_depth_to_lidar(boxes3d, mid_to_bottom=True):
     return boxes3d_lidar
 
 
-def scannet_eval(gt_annos, dt_annos, metric, class2type):
+def indoor_eval(gt_annos, dt_annos, metric, class2type):
     """Scannet Evaluation.
 
     Evaluate the result of the detection.

mmdet3d/datasets/scannet_dataset.py

@@ -211,13 +211,13 @@ class ScannetDataset(torch_data.Dataset):
 
     def evaluate(self, results, metric=None, logger=None, pklfile_prefix=None):
         results = self._format_results(results)
-        from mmdet3d.core.evaluation.scannet_utils.eval import scannet_eval
+        from mmdet3d.core.evaluation import indoor_eval
         assert ('AP_IOU_THRESHHOLDS' in metric)
         gt_annos = [
             copy.deepcopy(info['annos']) for info in self.scannet_infos
         ]
-        ap_result_str, ap_dict = scannet_eval(gt_annos, results, metric,
-                                              self.class2type)
+        ap_result_str, ap_dict = indoor_eval(gt_annos, results, metric,
+                                             self.class2type)
         return ap_dict
 
     def __len__(self):