hednet_transfusion_L_nusc_trainval.py

_base_=['./hednet_transfusion_L_nusc.py']

point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
    'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
    'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
voxel_size = [0.075, 0.075, 0.2]

dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'

input_modality = dict(
    use_lidar=True,
    use_camera=False,
    use_radar=False,
    use_map=False,
    use_external=False)

train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=[0, 1, 2, 3, 4],
    ),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        use_dim=[0, 1, 2, 3, 4],
        remove_close=True,
    ),
    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
    dict(
        type='ObjectSample',
        db_sampler=dict(
            data_root=data_root,
            info_path=data_root + 'nuscenes_dbinfos_trainval.pkl',
            rate=1.0,
            prepare=dict(
                filter_by_difficulty=[-1],
                filter_by_min_points=dict(
                    car=5,
                    truck=5,
                    bus=5,
                    trailer=5,
                    construction_vehicle=5,
                    traffic_cone=5,
                    barrier=5,
                    motorcycle=5,
                    bicycle=5,
                    pedestrian=5)),
            classes=class_names,
            sample_groups=dict(
                car=2,
                truck=3,
                construction_vehicle=7,
                bus=4,
                trailer=6,
                barrier=2,
                motorcycle=6,
                bicycle=6,
                pedestrian=2,
                traffic_cone=2),
            points_loader=dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=5,
                use_dim=[0, 1, 2, 3, 4],
            )),
            max_abled_epoch=12),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-0.3925 * 2, 0.3925 * 2],
        scale_ratio_range=[0.9, 1.1],
        translation_std=[0.5, 0.5, 0.5]),
    dict(
        type='RandomFlip3D',
        sync_2d=False,
        flip_ratio_bev_horizontal=0.5,
        flip_ratio_bev_vertical=0.5),
    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
    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'])
]

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=6,
    train=dict(
        type='CBGSDataset',
        dataset=[
            dict(
                type=dataset_type,
                data_root=data_root,
                ann_file=data_root + '/nuscenes_infos_train.pkl',
                load_interval=1,
                pipeline=train_pipeline,
                classes=class_names,
                modality=input_modality,
                test_mode=False,
                box_type_3d='LiDAR'),
            dict(
                type=dataset_type,
                data_root=data_root,
                ann_file=data_root + '/nuscenes_infos_val.pkl',
                load_interval=1,
                pipeline=train_pipeline,
                classes=class_names,
                modality=input_modality,
                test_mode=False,
                box_type_3d='LiDAR'),
        ]
    )
)

custom_hooks = []