def create_nuscenes_infos(root_path,
                          out_path,
                          can_bus_root_path,
                          info_prefix,
                          version='v1.0-trainval',
                          max_sweeps=10):
    """Create info file of nuscene dataset.

    Given the raw data, generate its related info file in pkl format.

    Args:
        root_path (str): Path of the data root.
        info_prefix (str): Prefix of the info file to be generated.
        version (str): Version of the data.
            Default: 'v1.0-trainval'
        max_sweeps (int): Max number of sweeps.
            Default: 10
    """
    from nuscenes.nuscenes import NuScenes
    from nuscenes.can_bus.can_bus_api import NuScenesCanBus
    print(version, root_path)
    nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
    nusc_can_bus = NuScenesCanBus(dataroot=can_bus_root_path)
    from nuscenes.utils import splits
    available_vers = ['v1.0-trainval', 'v1.0-test', 'v1.0-mini']
    assert version in available_vers
    if version == 'v1.0-trainval':
        train_scenes = splits.train
        val_scenes = splits.val
    elif version == 'v1.0-test':
        train_scenes = splits.test
        val_scenes = []
    elif version == 'v1.0-mini':
        train_scenes = splits.mini_train
        val_scenes = splits.mini_val
    else:
        raise ValueError('unknown')

    # filter existing scenes.
    available_scenes = get_available_scenes(nusc)
    available_scene_names = [s['name'] for s in available_scenes]
    train_scenes = list(
        filter(lambda x: x in available_scene_names, train_scenes))
    val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes))
    train_scenes = set([
        available_scenes[available_scene_names.index(s)]['token']
        for s in train_scenes
    ])
    val_scenes = set([
        available_scenes[available_scene_names.index(s)]['token']
        for s in val_scenes
    ])

    test = 'test' in version
    if test:
        print('test scene: {}'.format(len(train_scenes)))
    else:
        print('train scene: {}, val scene: {}'.format(
            len(train_scenes), len(val_scenes)))

    train_nusc_infos, val_nusc_infos = _fill_trainval_infos(
        nusc, nusc_can_bus, train_scenes, val_scenes, test, max_sweeps=max_sweeps)

    metadata = dict(version=version)
    if test:
        print('test sample: {}'.format(len(train_nusc_infos)))
        data = dict(infos=train_nusc_infos, metadata=metadata)
        info_path = osp.join(out_path,
                             '{}_infos_temporal_test.pkl'.format(info_prefix))
        mmcv.dump(data, info_path)
    else:
        print('train sample: {}, val sample: {}'.format(
            len(train_nusc_infos), len(val_nusc_infos)))
        data = dict(infos=train_nusc_infos, metadata=metadata)
        info_path = osp.join(out_path,
                             '{}_infos_temporal_train.pkl'.format(info_prefix))
        mmcv.dump(data, info_path)
        data['infos'] = val_nusc_infos
        info_val_path = osp.join(out_path,
                                 '{}_infos_temporal_val.pkl'.format(info_prefix))
        mmcv.dump(data, info_val_path)
Create info file of nuscene dataset.

    Given the raw data, generate its related info file in pkl format.

    Args:
        root_path (str): Path of the data root.
        info_prefix (str): Prefix of the info file to be generated.
        version (str): Version of the data.
            Default: 'v1.0-trainval'
        max_sweeps (int): Max number of sweeps.
            Default: 10
    
tools/data_converter/vad_nuscenes_converter.py