[Refactor]Refactor nus dataset

configs/_base_/datasets/nus-3d.py

@@ -6,17 +6,14 @@ class_names = [
     'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
     'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
 ]
+metainfo = dict(CLASSES=class_names)
 dataset_type = 'NuScenesDataset'
 data_root = 'data/nuscenes/'
 # Input modality for nuScenes dataset, this is consistent with the submission
 # format which requires the information in input_modality.
-input_modality = dict(
-    use_lidar=True,
-    use_camera=False,
-    use_radar=False,
-    use_map=False,
-    use_external=False)
+input_modality = dict(use_lidar=True, use_camera=False)
 file_client_args = dict(backend='disk')
+data_prefix = dict(pts='samples/LIDAR_TOP', img='')
 # Uncomment the following if use ceph or other file clients.
 # See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
 # for more details.
@@ -48,8 +45,9 @@ train_pipeline = [
     dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='ObjectNameFilter', classes=class_names),
     dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
 ]
 test_pipeline = [
     dict(
@@ -61,6 +59,7 @@ test_pipeline = [
     dict(
         type='LoadPointsFromMultiSweeps',
         sweeps_num=10,
+        test_mode=True,
         file_client_args=file_client_args),
     dict(
         type='MultiScaleFlipAug3D',
@@ -75,13 +74,9 @@ test_pipeline = [
                 translation_std=[0, 0, 0]),
             dict(type='RandomFlip3D'),
             dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
 # construct a pipeline for data and gt loading in show function
 # please keep its loading function consistent with test_pipeline (e.g. client)
@@ -95,48 +90,63 @@ eval_pipeline = [
     dict(
         type='LoadPointsFromMultiSweeps',
         sweeps_num=10,
+        test_mode=True,
         file_client_args=file_client_args),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
-
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    train=dict(
+train_dataloader = dict(
+    batch_size=4,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
         type=dataset_type,
         data_root=data_root,
-        ann_file=data_root + 'nuscenes_infos_train.pkl',
+        ann_file='nuscenes_infos_train.pkl',
         pipeline=train_pipeline,
-        classes=class_names,
+        metainfo=metainfo,
         modality=input_modality,
         test_mode=False,
+        data_prefix=data_prefix,
         # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
         # and box_type_3d='Depth' in sunrgbd and scannet dataset.
-        box_type_3d='LiDAR'),
-    val=dict(
+        box_type_3d='LiDAR'))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
         type=dataset_type,
         data_root=data_root,
-        ann_file=data_root + 'nuscenes_infos_val.pkl',
+        ann_file='nuscenes_infos_val.pkl',
         pipeline=test_pipeline,
-        classes=class_names,
+        metainfo=metainfo,
         modality=input_modality,
+        data_prefix=data_prefix,
         test_mode=True,
-        box_type_3d='LiDAR'),
-    test=dict(
+        box_type_3d='LiDAR'))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
         type=dataset_type,
         data_root=data_root,
-        ann_file=data_root + 'nuscenes_infos_val.pkl',
+        ann_file='nuscenes_infos_val.pkl',
         pipeline=test_pipeline,
-        classes=class_names,
+        metainfo=metainfo,
         modality=input_modality,
         test_mode=True,
+        data_prefix=data_prefix,
         box_type_3d='LiDAR'))
-# For nuScenes dataset, we usually evaluate the model at the end of training.
-# Since the models are trained by 24 epochs by default, we set evaluation
-# interval to be 24. Please change the interval accordingly if you do not
-# use a default schedule.
-evaluation = dict(interval=24, pipeline=eval_pipeline)
+
+val_evaluator = dict(
+    type='NuScenesMetric',
+    data_root=data_root,
+    ann_file=data_root + 'nuscenes_infos_val.pkl',
+    metric='bbox')
+test_evaluator = val_evaluator

configs/_base_/models/hv_pointpillars_fpn_nus.py

@@ -6,6 +6,7 @@
 voxel_size = [0.25, 0.25, 8]
 model = dict(
     type='MVXFasterRCNN',
+    data_preprocessor=dict(type='Det3DDataPreprocessor'),
     pts_voxel_layer=dict(
         max_num_points=64,
         point_cloud_range=[-50, -50, -5, 50, 50, 3],
@@ -62,19 +63,21 @@ model = dict(
         dir_offset=-0.7854,  # -pi / 4
         bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=9),
         loss_cls=dict(
-            type='FocalLoss',
+            type='mmdet.FocalLoss',
             use_sigmoid=True,
             gamma=2.0,
             alpha=0.25,
             loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
+        loss_bbox=dict(
+            type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
         loss_dir=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
+            type='mmdet.CrossEntropyLoss', use_sigmoid=False,
+            loss_weight=0.2)),
     # model training and testing settings
     train_cfg=dict(
         pts=dict(
             assigner=dict(
-                type='MaxIoUAssigner',
+                type='Max3DIoUAssigner',
                 iou_calculator=dict(type='BboxOverlapsNearest3D'),
                 pos_iou_thr=0.6,
                 neg_iou_thr=0.3,

configs/_base_/schedules/schedule_2x.py

 # optimizer
 # This schedule is mainly used by models on nuScenes dataset
-optimizer = dict(type='AdamW', lr=0.001, weight_decay=0.01)
-# max_norm=10 is better for SECOND
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=1.0 / 1000,
-    step=[20, 23])
-momentum_config = None
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=24)
+lr = 0.001
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=lr, weight_decay=0.01),
+    # max_norm=10 is better for SECOND
+    clip_grad=dict(max_norm=35, norm_type=2))
+
+# training schedule for 2x
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0 / 1000,
+        by_epoch=False,
+        begin=0,
+        end=1000),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=24,
+        by_epoch=True,
+        milestones=[20, 23],
+        gamma=0.1)
+]

configs/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py

@@ -3,3 +3,9 @@ _base_ = [
     '../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule_2x.py',
     '../_base_/default_runtime.py'
 ]
+
+# For nuScenes dataset, we usually evaluate the model at the end of training.
+# Since the models are trained by 24 epochs by default, we set evaluation
+# interval to be 24. Please change the interval accordingly if you do not
+# use a default schedule.
+train_cfg = dict(val_interval=24)

configs/pointpillars/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py

 _base_ = './hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py'
-data = dict(samples_per_gpu=2, workers_per_gpu=2)
+train_dataloader = dict(batch_size=2, num_workers=2)
 # fp16 settings, the loss scale is specifically tuned to avoid Nan
 fp16 = dict(loss_scale=32.)

configs/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py

@@ -40,3 +40,9 @@ model = dict(
             custom_values=[0, 0],
             rotations=[0, 1.57],
             reshape_out=True)))
+
+# For nuScenes dataset, we usually evaluate the model at the end of training.
+# Since the models are trained by 24 epochs by default, we set evaluation
+# interval to be 24. Please change the interval accordingly if you do not
+# use a default schedule.
+train_cfg = dict(val_interval=24)

configs/pointpillars/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d.py

 _base_ = './hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py'
-data = dict(samples_per_gpu=2, workers_per_gpu=2)
+train_dataloader = dict(batch_size=2, num_workers=2)
 # fp16 settings, the loss scale is specifically tuned to avoid Nan
 fp16 = dict(loss_scale=32.)

mmdet3d/datasets/det3d_dataset.py

@@ -227,8 +227,12 @@ class Det3DDataset(BaseDataset):
         if self.modality['use_camera']:
             for cam_id, img_info in info['images'].items():
                 if 'img_path' in img_info:
-                    img_info['img_path'] = osp.join(
-                        self.data_prefix.get('img', ''), img_info['img_path'])
+                    if cam_id in self.data_prefix:
+                        cam_prefix = self.data_prefix[cam_id]
+                    else:
+                        cam_prefix = self.data_prefix.get('img', '')
+                    img_info['img_path'] = osp.join(cam_prefix,
+                                                    img_info['img_path'])
 
         if not self.test_mode:
             # used in traing

mmdet3d/datasets/pipelines/loading.py

@@ -101,7 +101,7 @@ class LoadImageFromFileMono3D(LoadImageFromFile):
 
 
 @TRANSFORMS.register_module()
-class LoadPointsFromMultiSweeps(object):
+class LoadPointsFromMultiSweeps(BaseTransform):
     """Load points from multiple sweeps.
 
     This is usually used for nuScenes dataset to utilize previous sweeps.
@@ -186,7 +186,7 @@ class LoadPointsFromMultiSweeps(object):
         not_close = np.logical_not(np.logical_and(x_filt, y_filt))
         return points[not_close]
 
-    def __call__(self, results):
+    def transform(self, results):
         """Call function to load multi-sweep point clouds from files.
 
         Args:
@@ -204,30 +204,35 @@ class LoadPointsFromMultiSweeps(object):
         points.tensor[:, 4] = 0
         sweep_points_list = [points]
         ts = results['timestamp']
-        if self.pad_empty_sweeps and len(results['sweeps']) == 0:
-            for i in range(self.sweeps_num):
-                if self.remove_close:
-                    sweep_points_list.append(self._remove_close(points))
-                else:
-                    sweep_points_list.append(points)
+        if 'lidar_sweeps' not in results:
+            if self.pad_empty_sweeps:
+                for i in range(self.sweeps_num):
+                    if self.remove_close:
+                        sweep_points_list.append(self._remove_close(points))
+                    else:
+                        sweep_points_list.append(points)
         else:
-            if len(results['sweeps']) <= self.sweeps_num:
-                choices = np.arange(len(results['sweeps']))
+            if len(results['lidar_sweeps']) <= self.sweeps_num:
+                choices = np.arange(len(results['lidar_sweeps']))
             elif self.test_mode:
                 choices = np.arange(self.sweeps_num)
             else:
                 choices = np.random.choice(
-                    len(results['sweeps']), self.sweeps_num, replace=False)
+                    len(results['lidar_sweeps']),
+                    self.sweeps_num,
+                    replace=False)
             for idx in choices:
-                sweep = results['sweeps'][idx]
-                points_sweep = self._load_points(sweep['data_path'])
+                sweep = results['lidar_sweeps'][idx]
+                points_sweep = self._load_points(
+                    sweep['lidar_points']['lidar_path'])
                 points_sweep = np.copy(points_sweep).reshape(-1, self.load_dim)
                 if self.remove_close:
                     points_sweep = self._remove_close(points_sweep)
-                sweep_ts = sweep['timestamp'] / 1e6
-                points_sweep[:, :3] = points_sweep[:, :3] @ sweep[
-                    'sensor2lidar_rotation'].T
-                points_sweep[:, :3] += sweep['sensor2lidar_translation']
+                # bc-breaking: Timestamp has divided 1e6 in pkl infos.
+                sweep_ts = sweep['timestamp']
+                lidar2cam = np.array(sweep['lidar_points']['lidar2sensor'])
+                points_sweep[:, :3] = points_sweep[:, :3] @ lidar2cam[:3, :3]
+                points_sweep[:, :3] -= lidar2cam[:3, 3]
                 points_sweep[:, 4] = ts - sweep_ts
                 points_sweep = points.new_point(points_sweep)
                 sweep_points_list.append(points_sweep)

mmdet3d/datasets/utils.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import mmcv
+import numpy as np
 from mmcv.transforms import LoadImageFromFile
+from pyquaternion import Quaternion
 
 # yapf: disable
 from mmdet3d.datasets.pipelines import (LoadAnnotations3D,
@@ -137,3 +139,14 @@ def extract_result_dict(results, key):
     if isinstance(data, mmcv.parallel.DataContainer):
         data = data._data
     return data
+
+
+def convert_quaternion_to_matrix(quaternion: list,
+                                 translation: list = None) -> list:
+    """Compute a transform matrix by given quaternion and translation
+    vector."""
+    result = np.eye(4)
+    result[:3, :3] = Quaternion(quaternion).rotation_matrix
+    if translation is not None:
+        result[:3, 3] = np.array(translation)
+    return result.astype(np.float32).tolist()

tests/test_data/test_datasets/test_nuscenes_dataset.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+from mmcv.transforms.base import BaseTransform
+from mmengine.data import InstanceData
+from mmengine.registry import TRANSFORMS
+
+from mmdet3d.core.bbox import LiDARInstance3DBoxes
+from mmdet3d.core.data_structures import Det3DDataSample
+from mmdet3d.datasets import NuScenesDataset
+
+
+def _generate_nus_dataset_config():
+    data_root = 'tests/data/nuscenes'
+    ann_file = 'nus_info.pkl'
+    classes = [
+        'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+        'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
+    ]
+    if 'Identity' not in TRANSFORMS:
+
+        @TRANSFORMS.register_module()
+        class Identity(BaseTransform):
+
+            def transform(self, info):
+                packed_input = dict(data_sample=Det3DDataSample())
+                if 'ann_info' in info:
+                    packed_input['data_sample'].gt_instances_3d = InstanceData(
+                    )
+                    packed_input[
+                        'data_sample'].gt_instances_3d.labels_3d = info[
+                            'ann_info']['gt_labels_3d']
+                return packed_input
+
+    pipeline = [
+        dict(type='Identity'),
+    ]
+    modality = dict(use_lidar=True, use_camera=True)
+    data_prefix = dict(pts='samples/LIDAR_TOP', img='samples/CAM_BACK_LEFT')
+    return data_root, ann_file, classes, data_prefix, pipeline, modality
+
+
+def test_getitem():
+    np.random.seed(0)
+    data_root, ann_file, classes, data_prefix, pipeline, modality = \
+        _generate_nus_dataset_config()
+
+    nus_dataset = NuScenesDataset(
+        data_root,
+        ann_file,
+        data_prefix=data_prefix,
+        pipeline=pipeline,
+        metainfo=dict(CLASSES=classes),
+        modality=modality)
+
+    nus_dataset.prepare_data(0)
+    input_dict = nus_dataset.get_data_info(0)
+    # assert the the path should contains data_prefix and data_root
+    assert data_prefix['pts'] in input_dict['lidar_points']['lidar_path']
+    assert input_dict['lidar_points'][
+        'lidar_path'] == 'tests/data/nuscenes/samples/LIDAR_TOP/' \
+                         'n015-2018-08-02-17-16-37+0800__LIDAR_TOP__' \
+                         '1533201470948018.pcd.bin'
+    for cam_id, img_info in input_dict['images'].items():
+        if 'img_path' in img_info:
+            assert data_prefix['img'] in img_info['img_path']
+            assert data_root in img_info['img_path']
+
+    ann_info = nus_dataset.parse_ann_info(input_dict)
+
+    # assert the keys in ann_info and the type
+    assert 'gt_labels_3d' in ann_info
+    assert ann_info['gt_labels_3d'].dtype == np.int64
+    assert len(ann_info['gt_labels_3d']) == 37
+
+    assert 'gt_bboxes_3d' in ann_info
+    assert isinstance(ann_info['gt_bboxes_3d'], LiDARInstance3DBoxes)
+
+    assert len(nus_dataset.metainfo['CLASSES']) == 10
+
+    assert input_dict['token'] == 'fd8420396768425eabec9bdddf7e64b6'
+    assert input_dict['timestamp'] == 1533201470.448696

tools/data_converter/update_infos_to_v2.py

@@ -15,6 +15,8 @@ from os import path as osp
 import mmcv
 import numpy as np
 
+from mmdet3d.datasets.utils import convert_quaternion_to_matrix
+
 
 def get_empty_instance():
     """Empty annotation for single instance."""
@@ -156,6 +158,7 @@ def get_empty_standard_data_info():
         radar_points=get_empty_radar_points(),
         # (list[dict], optional): Image sweeps data.
         image_sweeps=[],
+        lidar_sweeps=[],
         instances=[],
         # (list[dict], optional): Required by object
         # detection, instance  to be ignored during training.
@@ -203,6 +206,116 @@ def clear_data_info_unused_keys(data_info):
     return data_info, empty_flag
 
 
+def update_nuscenes_infos(pkl_path, out_dir):
+    print(f'{pkl_path} will be modified.')
+    if out_dir in pkl_path:
+        print(f'Warning, you may overwriting '
+              f'the original data {pkl_path}.')
+    print(f'Reading from input file: {pkl_path}.')
+    data_list = mmcv.load(pkl_path)
+    METAINFO = {
+        'CLASSES':
+        ('car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+         'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'),
+        'DATASET':
+        'Nuscenes',
+        'version':
+        data_list['metadata']['version']
+    }
+    print('Start updating:')
+    converted_list = []
+    for i, ori_info_dict in enumerate(
+            mmcv.track_iter_progress(data_list['infos'])):
+        temp_data_info = get_empty_standard_data_info()
+        temp_data_info['sample_idx'] = i
+        temp_data_info['token'] = ori_info_dict['token']
+        temp_data_info['ego2global'] = convert_quaternion_to_matrix(
+            ori_info_dict['ego2global_rotation'],
+            ori_info_dict['ego2global_translation'])
+        temp_data_info['lidar_points']['lidar_path'] = ori_info_dict[
+            'lidar_path'].split('/')[-1]
+        temp_data_info['lidar_points'][
+            'lidar2ego'] = convert_quaternion_to_matrix(
+                ori_info_dict['lidar2ego_rotation'],
+                ori_info_dict['lidar2ego_translation'])
+        # bc-breaking: Timestamp has divided 1e6 in pkl infos.
+        temp_data_info['timestamp'] = ori_info_dict['timestamp'] / 1e6
+        for ori_sweep in ori_info_dict['sweeps']:
+            temp_lidar_sweep = get_single_lidar_sweep()
+            temp_lidar_sweep['lidar_points'][
+                'lidar2ego'] = convert_quaternion_to_matrix(
+                    ori_sweep['sensor2ego_rotation'],
+                    ori_sweep['sensor2ego_translation'])
+            temp_lidar_sweep['ego2global'] = convert_quaternion_to_matrix(
+                ori_sweep['ego2global_rotation'],
+                ori_sweep['ego2global_translation'])
+            lidar2sensor = np.eye(4)
+            lidar2sensor[:3, :3] = ori_sweep['sensor2lidar_rotation'].T
+            lidar2sensor[:3, 3] = -ori_sweep['sensor2lidar_translation']
+            temp_lidar_sweep['lidar_points'][
+                'lidar2sensor'] = lidar2sensor.astype(np.float32).tolist()
+            temp_lidar_sweep['timestamp'] = ori_sweep['timestamp'] / 1e6
+            temp_lidar_sweep['lidar_points']['lidar_path'] = ori_sweep[
+                'data_path']
+            temp_lidar_sweep['sample_data_token'] = ori_sweep[
+                'sample_data_token']
+            temp_data_info['lidar_sweeps'].append(temp_lidar_sweep)
+        temp_data_info['images'] = {}
+        for cam in ori_info_dict['cams']:
+            empty_img_info = get_empty_img_info()
+            empty_img_info['img_path'] = ori_info_dict['cams'][cam][
+                'data_path'].split('/')[-1]
+            empty_img_info['cam2img'] = ori_info_dict['cams'][cam][
+                'cam_intrinsic'].tolist()
+            empty_img_info['sample_data_token'] = ori_info_dict['cams'][cam][
+                'sample_data_token']
+            # bc-breaking: Timestamp has divided 1e6 in pkl infos.
+            empty_img_info[
+                'timestamp'] = ori_info_dict['cams'][cam]['timestamp'] / 1e6
+            empty_img_info['cam2ego'] = convert_quaternion_to_matrix(
+                ori_info_dict['cams'][cam]['sensor2ego_rotation'],
+                ori_info_dict['cams'][cam]['sensor2ego_translation'])
+            lidar2sensor = np.eye(4)
+            lidar2sensor[:3, :3] = ori_info_dict['cams'][cam][
+                'sensor2lidar_rotation'].T
+            lidar2sensor[:3, 3] = -ori_info_dict['cams'][cam][
+                'sensor2lidar_translation']
+            empty_img_info['lidar2cam'] = lidar2sensor.astype(
+                np.float32).tolist()
+            temp_data_info['images'][cam] = empty_img_info
+        num_instances = ori_info_dict['gt_boxes'].shape[0]
+        ignore_class_name = set()
+        for i in range(num_instances):
+            empty_instance = get_empty_instance()
+            empty_instance['bbox_3d'] = ori_info_dict['gt_boxes'][
+                i, :].tolist()
+            if ori_info_dict['gt_names'][i] in METAINFO['CLASSES']:
+                empty_instance['bbox_label'] = METAINFO['CLASSES'].index(
+                    ori_info_dict['gt_names'][i])
+            else:
+                ignore_class_name.add(ori_info_dict['gt_names'][i])
+                empty_instance['bbox_label'] = -1
+            empty_instance['bbox_label_3d'] = copy.deepcopy(
+                empty_instance['bbox_label'])
+            empty_instance['velocity'] = ori_info_dict['gt_velocity'][
+                i, :].tolist()
+            empty_instance['num_lidar_pts'] = ori_info_dict['num_lidar_pts'][i]
+            empty_instance['num_radar_pts'] = ori_info_dict['num_radar_pts'][i]
+            empty_instance['bbox_3d_isvalid'] = ori_info_dict['valid_flag'][i]
+            empty_instance = clear_instance_unused_keys(empty_instance)
+            temp_data_info['instances'].append(empty_instance)
+        temp_data_info, _ = clear_data_info_unused_keys(temp_data_info)
+        converted_list.append(temp_data_info)
+    pkl_name = pkl_path.split('/')[-1]
+    out_path = osp.join(out_dir, pkl_name)
+    print(f'Writing to output file: {out_path}.')
+    print(f'ignore classes: {ignore_class_name}')
+    converted_data_info = dict(metainfo=METAINFO, data_list=converted_list)
+
+    mmcv.dump(converted_data_info, out_path, 'pkl')
+    return temp_lidar_sweep
+
+
 def update_kitti_infos(pkl_path, out_dir):
     print(f'{pkl_path} will be modified.')
     if out_dir in pkl_path:
@@ -479,6 +592,8 @@ def main():
         update_scannet_infos(pkl_path=args.pkl, out_dir=args.out_dir)
     elif args.dataset.lower() == 'sunrgbd':
         update_sunrgbd_infos(pkl_path=args.pkl, out_dir=args.out_dir)
+    elif args.dataset.lower() == 'nuscenes':
+        update_nuscenes_infos(pkl_path=args.pkl, out_dir=args.out_dir)
     else:
         raise NotImplementedError(
             f'Do not support convert {args.dataset} to v2.')