[Fix]Fix about kitti pp bug

configs/_base_/models/hv_pointpillars_secfpn_kitti.py

@@ -2,6 +2,7 @@ voxel_size = [0.16, 0.16, 4]
 
 model = dict(
     type='VoxelNet',
+    data_preprocessor=dict(type='Det3DDataPreprocessor'),
     voxel_layer=dict(
         max_num_points=32,  # max_points_per_voxel
         point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1],

configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py

@@ -26,7 +26,6 @@ model = dict(
         allowed_border=0,
         pos_weight=-1,
         debug=False))
-
 # dataset settings
 dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
@@ -52,7 +51,6 @@ train_pipeline = [
     dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
     dict(
         type='Pack3DDetInputs',
         keys=['points', 'gt_labels_3d', 'gt_bboxes_3d'])
@@ -82,4 +80,5 @@ train_dataloader = dict(
         type='RepeatDataset',
         times=2,
         dataset=dict(pipeline=train_pipeline, metainfo=metainfo)))
-test_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
+test_dataloader = dict(dataset=dict(metainfo=metainfo))
+val_dataloader = dict(dataset=dict(metainfo=metainfo))

mmdet3d/core/evaluation/kitti_utils/eval.py

@@ -352,8 +352,8 @@ def calculate_iou_partly(dt_annos, gt_annos, metric, num_parts=50):
         num_parts (int): A parameter for fast calculate algorithm.
     """
     assert len(dt_annos) == len(gt_annos)
-    total_dt_num = np.stack([len(a['name']) for a in gt_annos], 0)
-    total_gt_num = np.stack([len(a['name']) for a in dt_annos], 0)
+    total_gt_num = np.stack([len(a['name']) for a in gt_annos], 0)
+    total_dt_num = np.stack([len(a['name']) for a in dt_annos], 0)
     num_examples = len(dt_annos)
     split_parts = get_split_parts(num_examples, num_parts)
     parted_overlaps = []
@@ -363,39 +363,39 @@ def calculate_iou_partly(dt_annos, gt_annos, metric, num_parts=50):
         dt_annos_part = dt_annos[example_idx:example_idx + num_part]
         gt_annos_part = gt_annos[example_idx:example_idx + num_part]
         if metric == 0:
-            gt_boxes = np.concatenate([a['bbox'] for a in dt_annos_part], 0)
-            dt_boxes = np.concatenate([a['bbox'] for a in gt_annos_part], 0)
-            overlap_part = image_box_overlap(gt_boxes, dt_boxes)
+            dt_boxes = np.concatenate([a['bbox'] for a in dt_annos_part], 0)
+            gt_boxes = np.concatenate([a['bbox'] for a in gt_annos_part], 0)
+            overlap_part = image_box_overlap(dt_boxes, gt_boxes)
         elif metric == 1:
             loc = np.concatenate(
                 [a['location'][:, [0, 2]] for a in dt_annos_part], 0)
             dims = np.concatenate(
                 [a['dimensions'][:, [0, 2]] for a in dt_annos_part], 0)
             rots = np.concatenate([a['rotation_y'] for a in dt_annos_part], 0)
-            gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
+            dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
                                       axis=1)
             loc = np.concatenate(
                 [a['location'][:, [0, 2]] for a in gt_annos_part], 0)
             dims = np.concatenate(
                 [a['dimensions'][:, [0, 2]] for a in gt_annos_part], 0)
             rots = np.concatenate([a['rotation_y'] for a in gt_annos_part], 0)
-            dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
+            gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
                                       axis=1)
-            overlap_part = bev_box_overlap(gt_boxes,
-                                           dt_boxes).astype(np.float64)
+            overlap_part = bev_box_overlap(dt_boxes,
+                                           gt_boxes).astype(np.float64)
         elif metric == 2:
             loc = np.concatenate([a['location'] for a in dt_annos_part], 0)
             dims = np.concatenate([a['dimensions'] for a in dt_annos_part], 0)
             rots = np.concatenate([a['rotation_y'] for a in dt_annos_part], 0)
-            gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
+            dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
                                       axis=1)
             loc = np.concatenate([a['location'] for a in gt_annos_part], 0)
             dims = np.concatenate([a['dimensions'] for a in gt_annos_part], 0)
             rots = np.concatenate([a['rotation_y'] for a in gt_annos_part], 0)
-            dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
+            gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
                                       axis=1)
-            overlap_part = d3_box_overlap(gt_boxes,
-                                          dt_boxes).astype(np.float64)
+            overlap_part = d3_box_overlap(dt_boxes,
+                                          gt_boxes).astype(np.float64)
         else:
             raise ValueError('unknown metric')
         parted_overlaps.append(overlap_part)
@@ -403,20 +403,18 @@ def calculate_iou_partly(dt_annos, gt_annos, metric, num_parts=50):
     overlaps = []
     example_idx = 0
     for j, num_part in enumerate(split_parts):
-        dt_annos_part = dt_annos[example_idx:example_idx + num_part]
-        gt_annos_part = gt_annos[example_idx:example_idx + num_part]
         gt_num_idx, dt_num_idx = 0, 0
         for i in range(num_part):
             gt_box_num = total_gt_num[example_idx + i]
             dt_box_num = total_dt_num[example_idx + i]
             overlaps.append(
-                parted_overlaps[j][gt_num_idx:gt_num_idx + gt_box_num,
-                                   dt_num_idx:dt_num_idx + dt_box_num])
+                parted_overlaps[j][dt_num_idx:dt_num_idx + dt_box_num,
+                                   gt_num_idx:gt_num_idx + gt_box_num])
             gt_num_idx += gt_box_num
             dt_num_idx += dt_box_num
         example_idx += num_part
 
-    return overlaps, parted_overlaps, total_gt_num, total_dt_num
+    return overlaps, parted_overlaps, total_dt_num, total_gt_num
 
 
 def _prepare_data(gt_annos, dt_annos, current_class, difficulty):

mmdet3d/datasets/kitti_dataset.py

@@ -39,7 +39,10 @@ class KittiDataset(Det3DDataset):
             Default: [0, -40, -3, 70.4, 40, 0.0].
     """
     # TODO: use full classes of kitti
-    METAINFO = {'CLASSES': ('Pedestrian', 'Cyclist', 'Car')}
+    METAINFO = {
+        'CLASSES': ('Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck',
+                    'Person_sitting', 'Tram', 'Misc')
+    }
 
     def __init__(self,
                  data_root: str,

mmdet3d/metrics/kitti_metric.py

@@ -64,13 +64,21 @@ class KittiMetric(BaseMetric):
                 raise KeyError("metric should be one of 'bbox', 'img_bbox', "
                                'but got {metric}.')
 
-    def convert_annos_to_kitti_annos(self, data_annos: list,
-                                     classes: list) -> list:
+    def convert_annos_to_kitti_annos(
+        self,
+        data_annos: list,
+        classes: list = [
+            'Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck', 'Person_sitting',
+            'Tram', 'Misc'
+        ]
+    ) -> list:
         """Convert loading annotations to Kitti annotations.
 
         Args:
             data_annos (list[dict]): Annotations loaded from ann_file.
-            classes (list[str]): Classes used in the dataset.
+            classes (list[str]): Classes used in the dataset. Default used
+                ['Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck',
+                'Person_sitting', 'Tram', 'Misc'].
 
         Returns:
             List[dict]: List of Kitti annotations.
@@ -102,7 +110,10 @@ class KittiMetric(BaseMetric):
                     'score': []
                 }
                 for instance in annos['instances']:
-                    kitti_annos['name'].append(classes[instance['bbox_label']])
+                    labels = instance['bbox_label']
+                    if labels == -1:
+                        continue
+                    kitti_annos['name'].append(classes[labels])
                     kitti_annos['truncated'].append(instance['truncated'])
                     kitti_annos['occluded'].append(instance['occluded'])
                     kitti_annos['alpha'].append(instance['alpha'])
@@ -148,7 +159,7 @@ class KittiMetric(BaseMetric):
             for pred_result in pred:
                 for attr_name in pred[pred_result]:
                     pred[pred_result][attr_name] = pred[pred_result][
-                        attr_name].to(self.collect_device)
+                        attr_name].to('cpu')
                 result[pred_result] = pred[pred_result]
                 sample_idx = data['data_sample']['sample_idx']
                 result['sample_idx'] = sample_idx
@@ -165,14 +176,11 @@ class KittiMetric(BaseMetric):
             the metrics, and the values are corresponding results.
         """
         logger: MMLogger = MMLogger.get_current_instance()
-
         self.classes = self.dataset_meta['CLASSES']
 
         # load annotations
         pkl_annos = self.load_annotations(self.ann_file)['data_list']
-        self.data_infos = self.convert_annos_to_kitti_annos(
-            pkl_annos, self.classes)
-
+        self.data_infos = self.convert_annos_to_kitti_annos(pkl_annos)
         result_dict, tmp_dir = self.format_results(
             results,
             pklfile_prefix=self.pklfile_prefix,
@@ -200,7 +208,7 @@ class KittiMetric(BaseMetric):
         return metric_dict
 
     def kitti_evaluate(self,
-                       result_dict: List[dict],
+                       results_dict: List[dict],
                        gt_annos: List[dict],
                        metric: str = None,
                        classes: List[str] = None,
@@ -221,13 +229,13 @@ class KittiMetric(BaseMetric):
             dict[str, float]: Results of each evaluation metric.
         """
         ap_dict = dict()
-        for name in result_dict:
+        for name in results_dict:
             if name == 'pred_instances' or metric == 'img_bbox':
                 eval_types = ['bbox']
             else:
                 eval_types = ['bbox', 'bev', '3d']
             ap_result_str, ap_dict_ = kitti_eval(
-                gt_annos, result_dict[name], classes, eval_types=eval_types)
+                gt_annos, results_dict[name], classes, eval_types=eval_types)
             for ap_type, ap in ap_dict_.items():
                 ap_dict[f'{name}/{ap_type}'] = float('{:.4f}'.format(ap))
 

tools/data_converter/update_infos_to_v2.py

@@ -212,7 +212,8 @@ def update_kitti_infos(pkl_path, out_dir):
     # TODO update to full label
     # TODO discuss how to process 'Van', 'DontCare'
     METAINFO = {
-        'CLASSES': ('Pedestrian', 'Cyclist', 'Car'),
+        'CLASSES': ('Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck',
+                    'Person_sitting', 'Tram', 'Misc'),
     }
     print(f'Reading from input file: {pkl_path}.')
     data_list = mmcv.load(pkl_path)