[Fix]Fix det3ddataset for new input version

mmdet3d/datasets/det3d_dataset.py

@@ -253,7 +253,8 @@ class Det3DDataset(BaseDataset):
         if not self.test_mode and self.filter_empty_gt:
             # after pipeline drop the example with empty annotations
             # return None to random another in `__getitem__`
-            if example is None or len(example['gt_labels_3d']) == 0:
+            if example is None or len(
+                    example['data_sample'].gt_instances_3d.labels_3d) == 0:
                 return None
         return example
 

mmdet3d/datasets/kitti_dataset.py

@@ -27,7 +27,6 @@ class KittiDataset(Det3DDataset):
     Args:
         data_root (str): Path of dataset root.
         ann_file (str): Path of annotation file.
-        split (str): Split of input data.
         pipeline (list[dict], optional): Pipeline used for data processing.
             Defaults to None.
         modality (dict, optional): Modality to specify the sensor data used

tests/test_data/test_datasets/test_kitti_dataset.py

@@ -3,9 +3,11 @@
 import numpy as np
 import torch
 from mmcv.transforms.base import BaseTransform
+from mmengine.data import InstanceData
 from mmengine.registry import TRANSFORMS
 
 from mmdet3d.core import LiDARInstance3DBoxes
+from mmdet3d.core.data_structures import Det3DDataSample
 from mmdet3d.datasets import KittiDataset
 
 
@@ -23,6 +25,11 @@ def _generate_kitti_dataset_config():
             def transform(self, info):
                 if 'ann_info' in info:
                     info['gt_labels_3d'] = info['ann_info']['gt_labels_3d']
+                data_sample = Det3DDataSample()
+                gt_instances_3d = InstanceData()
+                gt_instances_3d.labels_3d = info['gt_labels_3d']
+                data_sample.gt_instances_3d = gt_instances_3d
+                info['data_sample'] = data_sample
                 return info
 
     pipeline = [