[Fix] Fix nus metric and cbgs (#1753)

* fix nus metric and CBGS

* add nus namemap

* fix comments

* fix comments

mmdet3d/datasets/convert_utils.py

@@ -19,7 +19,7 @@ nus_attributes = ('cycle.with_rider', 'cycle.without_rider',
                   'pedestrian.moving', 'pedestrian.standing',
                   'pedestrian.sitting_lying_down', 'vehicle.moving',
                   'vehicle.parked', 'vehicle.stopped', 'None')
-NameMapping = {
+NuScenesNameMapping = {
     'movable_object.barrier': 'barrier',
     'vehicle.bicycle': 'bicycle',
     'vehicle.bus.bendy': 'bus',
@@ -35,6 +35,17 @@ NameMapping = {
     'vehicle.trailer': 'trailer',
     'vehicle.truck': 'truck'
 }
+LyftNameMapping = {
+    'bicycle': 'bicycle',
+    'bus': 'bus',
+    'car': 'car',
+    'emergency_vehicle': 'emergency_vehicle',
+    'motorcycle': 'motorcycle',
+    'other_vehicle': 'other_vehicle',
+    'pedestrian': 'pedestrian',
+    'truck': 'truck',
+    'animal': 'animal'
+}
 
 
 def get_2d_boxes(nusc, sample_data_token: str, visibilities: List[str]):
@@ -390,9 +401,9 @@ def generate_record(ann_rec: dict, x1: float, y1: float, x2: float, y2: float,
     repro_rec['bbox_corners'] = [x1, y1, x2, y2]
     repro_rec['filename'] = filename
 
-    if repro_rec['category_name'] not in NameMapping:
+    if repro_rec['category_name'] not in NuScenesNameMapping:
         return None
-    cat_name = NameMapping[repro_rec['category_name']]
+    cat_name = NuScenesNameMapping[repro_rec['category_name']]
     coco_rec['bbox_label'] = nus_categories.index(cat_name)
     coco_rec['bbox_label_3d'] = nus_categories.index(cat_name)
     coco_rec['bbox'] = [x1, y1, x2, y2]

mmdet3d/datasets/dataset_wrappers.py

@@ -18,7 +18,8 @@ class CBGSDataset(object):
 
     def __init__(self, dataset):
         self.dataset = DATASETS.build(dataset)
-        self.CLASSES = self.dataset.metainfo['CLASSES']
+        self.metainfo = self.dataset.metainfo
+        self.CLASSES = self.metainfo['CLASSES']
         self.cat2id = {name: i for i, name in enumerate(self.CLASSES)}
         self.sample_indices = self._get_sample_indices()
         # self.dataset.data_infos = self.data_infos

mmdet3d/evaluation/metrics/nuscenes_metric.py

@@ -490,7 +490,7 @@ class NuScenesMetric(BaseMetric):
         print('Start to convert detection format...')
         for i, det in enumerate(mmengine.track_iter_progress(results)):
             annos = []
-            boxes = output_to_nusc_box(det)
+            boxes, attrs = output_to_nusc_box(det)
             sample_id = sample_id_list[i]
             sample_token = self.data_infos[sample_id]['token']
             boxes = lidar_nusc_box_to_global(self.data_infos[sample_id], boxes,

tools/dataset_converters/lyft_converter.py

@@ -9,7 +9,7 @@ import numpy as np
 from lyft_dataset_sdk.lyftdataset import LyftDataset as Lyft
 from pyquaternion import Quaternion
 
-from mmdet3d.datasets import LyftDataset
+from mmdet3d.datasets.convert_utils import LyftNameMapping
 from .nuscenes_converter import (get_2d_boxes, get_available_scenes,
                                  obtain_sensor2top)
 
@@ -190,8 +190,8 @@ def _fill_trainval_infos(lyft,
 
             names = [b.name for b in boxes]
             for i in range(len(names)):
-                if names[i] in LyftDataset.NameMapping:
-                    names[i] = LyftDataset.NameMapping[names[i]]
+                if names[i] in LyftNameMapping:
+                    names[i] = LyftNameMapping[names[i]]
             names = np.array(names)
 
             # we need to convert box size to

tools/dataset_converters/nuscenes_converter.py

@@ -12,7 +12,7 @@ from nuscenes.utils.geometry_utils import view_points
 from pyquaternion import Quaternion
 from shapely.geometry import MultiPoint, box
 
-from mmdet3d.datasets import NuScenesDataset
+from mmdet3d.datasets.convert_utils import NuScenesNameMapping
 from mmdet3d.structures import points_cam2img
 
 nus_categories = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
@@ -248,8 +248,8 @@ def _fill_trainval_infos(nusc,
 
             names = [b.name for b in boxes]
             for i in range(len(names)):
-                if names[i] in NuScenesDataset.NameMapping:
-                    names[i] = NuScenesDataset.NameMapping[names[i]]
+                if names[i] in NuScenesNameMapping:
+                    names[i] = NuScenesNameMapping[names[i]]
             names = np.array(names)
             # we need to convert box size to
             # the format of our lidar coordinate system
@@ -618,9 +618,9 @@ def generate_record(ann_rec: dict, x1: float, y1: float, x2: float, y2: float,
     coco_rec['image_id'] = sample_data_token
     coco_rec['area'] = (y2 - y1) * (x2 - x1)
 
-    if repro_rec['category_name'] not in NuScenesDataset.NameMapping:
+    if repro_rec['category_name'] not in NuScenesNameMapping:
         return None
-    cat_name = NuScenesDataset.NameMapping[repro_rec['category_name']]
+    cat_name = NuScenesNameMapping[repro_rec['category_name']]
     coco_rec['category_name'] = cat_name
     coco_rec['category_id'] = nus_categories.index(cat_name)
     coco_rec['bbox'] = [x1, y1, x2 - x1, y2 - y1]