voxel_size = [0.25, 0.25, 8]
model = dict(
    type='MVXFasterRCNN',
    pts_voxel_layer=dict(
        max_num_points=64,
        point_cloud_range=[-50, -50, -5, 50, 50, 3],
        voxel_size=[0.25, 0.25, 8],
        max_voxels=(30000, 40000)),
    pts_voxel_encoder=dict(
        type='HardVFE',
        in_channels=4,
        feat_channels=[64, 64],
        with_distance=False,
        voxel_size=[0.25, 0.25, 8],
        with_cluster_center=True,
        with_voxel_center=True,
        point_cloud_range=[-50, -50, -5, 50, 50, 3],
        norm_cfg=dict(type='naiveSyncBN1d', eps=0.001, momentum=0.01)),
    pts_middle_encoder=dict(
        type='PointPillarsScatter', in_channels=64, output_shape=[400, 400]),
    pts_backbone=dict(
        type='SECOND',
        in_channels=64,
        norm_cfg=dict(type='naiveSyncBN2d', eps=0.001, momentum=0.01),
        layer_nums=[3, 5, 5],
        layer_strides=[2, 2, 2],
        out_channels=[64, 128, 256]),
    pts_neck=dict(
        type='FPN',
        norm_cfg=dict(type='naiveSyncBN2d', eps=0.001, momentum=0.01),
        act_cfg=dict(type='ReLU'),
        in_channels=[64, 128, 256],
        out_channels=256,
        start_level=0,
        num_outs=3),
    pts_bbox_head=dict(
        type='Anchor3DHead',
        num_classes=10,
        in_channels=256,
        feat_channels=256,
        use_direction_classifier=True,
        anchor_generator=dict(
            type='AlignedAnchor3DRangeGenerator',
            ranges=[[-50, -50, -1.8, 50, 50, -1.8]],
            scales=[1, 2, 4],
            sizes=[[0.866, 2.5981, 1.0], [0.5774, 1.7321, 1.0],
                   [1.0, 1.0, 1.0], [0.4, 0.4, 1]],
            custom_values=[0, 0],
            rotations=[0, 1.57],
            reshape_out=True),
        assigner_per_size=False,
        diff_rad_by_sin=True,
        dir_offset=0.7854,
        dir_limit_offset=0,
        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=9),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(
            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0),
        loss_dir=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
    train_cfg=dict(
        pts=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                iou_calculator=dict(type='BboxOverlapsNearest3D'),
                pos_iou_thr=0.6,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                ignore_iof_thr=-1),
            allowed_border=0,
            code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2],
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        pts=dict(
            use_rotate_nms=True,
            nms_across_levels=False,
            nms_pre=1000,
            nms_thr=0.2,
            score_thr=0.05,
            min_bbox_size=0,
            max_num=500)))
point_cloud_range = [-50, -50, -5, 50, 50, 3]
class_names = [
    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
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)
file_client_args = dict(backend='disk')
train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        file_client_args=dict(backend='disk')),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=dict(backend='disk')),
    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-0.3925, 0.3925],
        scale_ratio_range=[0.95, 1.05],
        translation_std=[0, 0, 0]),
    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
    dict(
        type='PointsRangeFilter', point_cloud_range=[-50, -50, -5, 50, 50, 3]),
    dict(
        type='ObjectRangeFilter', point_cloud_range=[-50, -50, -5, 50, 50, 3]),
    dict(
        type='ObjectNameFilter',
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ]),
    dict(type='PointShuffle'),
    dict(
        type='DefaultFormatBundle3D',
        class_names=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ]),
    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        file_client_args=dict(backend='disk')),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=dict(backend='disk')),
    dict(
        type='MultiScaleFlipAug3D',
        img_scale=(1333, 800),
        pts_scale_ratio=1,
        flip=False,
        transforms=[
            dict(
                type='GlobalRotScaleTrans',
                rot_range=[0, 0],
                scale_ratio_range=[1.0, 1.0],
                translation_std=[0, 0, 0]),
            dict(type='RandomFlip3D'),
            dict(
                type='PointsRangeFilter',
                point_cloud_range=[-50, -50, -5, 50, 50, 3]),
            dict(
                type='DefaultFormatBundle3D',
                class_names=[
                    'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                    'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                    'barrier'
                ],
                with_label=False),
            dict(type='Collect3D', keys=['points'])
        ])
]
eval_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        file_client_args=dict(backend='disk')),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=dict(backend='disk')),
    dict(
        type='DefaultFormatBundle3D',
        class_names=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        with_label=False),
    dict(type='Collect3D', keys=['points'])
]
data = dict(
    samples_per_gpu=1,
    workers_per_gpu=4,
    train=dict(
        type='NuScenesDataset',
        data_root='data/nuscenes/',
        ann_file='data/nuscenes/nuscenes_infos_train.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=5,
                use_dim=5,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadPointsFromMultiSweeps',
                sweeps_num=10,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadAnnotations3D',
                with_bbox_3d=True,
                with_label_3d=True),
            dict(
                type='GlobalRotScaleTrans',
                rot_range=[-0.3925, 0.3925],
                scale_ratio_range=[0.95, 1.05],
                translation_std=[0, 0, 0]),
            dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
            dict(
                type='PointsRangeFilter',
                point_cloud_range=[-50, -50, -5, 50, 50, 3]),
            dict(
                type='ObjectRangeFilter',
                point_cloud_range=[-50, -50, -5, 50, 50, 3]),
            dict(
                type='ObjectNameFilter',
                classes=[
                    'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                    'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                    'barrier'
                ]),
            dict(type='PointShuffle'),
            dict(
                type='DefaultFormatBundle3D',
                class_names=[
                    'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                    'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                    'barrier'
                ]),
            dict(
                type='Collect3D',
                keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
        ],
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        modality=dict(
            use_lidar=True,
            use_camera=False,
            use_radar=False,
            use_map=False,
            use_external=False),
        test_mode=False,
        box_type_3d='LiDAR'),
    val=dict(
        type='NuScenesDataset',
        data_root='data/nuscenes/',
        ann_file='data/nuscenes/nuscenes_infos_val.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=5,
                use_dim=5,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadPointsFromMultiSweeps',
                sweeps_num=10,
                file_client_args=dict(backend='disk')),
            dict(
                type='MultiScaleFlipAug3D',
                img_scale=(1333, 800),
                pts_scale_ratio=1,
                flip=False,
                transforms=[
                    dict(
                        type='GlobalRotScaleTrans',
                        rot_range=[0, 0],
                        scale_ratio_range=[1.0, 1.0],
                        translation_std=[0, 0, 0]),
                    dict(type='RandomFlip3D'),
                    dict(
                        type='PointsRangeFilter',
                        point_cloud_range=[-50, -50, -5, 50, 50, 3]),
                    dict(
                        type='DefaultFormatBundle3D',
                        class_names=[
                            'car', 'truck', 'trailer', 'bus',
                            'construction_vehicle', 'bicycle', 'motorcycle',
                            'pedestrian', 'traffic_cone', 'barrier'
                        ],
                        with_label=False),
                    dict(type='Collect3D', keys=['points'])
                ])
        ],
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        modality=dict(
            use_lidar=True,
            use_camera=False,
            use_radar=False,
            use_map=False,
            use_external=False),
        test_mode=True,
        box_type_3d='LiDAR'),
    test=dict(
        type='NuScenesDataset',
        data_root='data/nuscenes/',
        ann_file='data/nuscenes/nuscenes_infos_val.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=5,
                use_dim=5,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadPointsFromMultiSweeps',
                sweeps_num=10,
                file_client_args=dict(backend='disk')),
            dict(
                type='MultiScaleFlipAug3D',
                img_scale=(1333, 800),
                pts_scale_ratio=1,
                flip=False,
                transforms=[
                    dict(
                        type='GlobalRotScaleTrans',
                        rot_range=[0, 0],
                        scale_ratio_range=[1.0, 1.0],
                        translation_std=[0, 0, 0]),
                    dict(type='RandomFlip3D'),
                    dict(
                        type='PointsRangeFilter',
                        point_cloud_range=[-50, -50, -5, 50, 50, 3]),
                    dict(
                        type='DefaultFormatBundle3D',
                        class_names=[
                            'car', 'truck', 'trailer', 'bus',
                            'construction_vehicle', 'bicycle', 'motorcycle',
                            'pedestrian', 'traffic_cone', 'barrier'
                        ],
                        with_label=False),
                    dict(type='Collect3D', keys=['points'])
                ])
        ],
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        modality=dict(
            use_lidar=True,
            use_camera=False,
            use_radar=False,
            use_map=False,
            use_external=False),
        test_mode=True,
        box_type_3d='LiDAR'))
evaluation = dict(
    interval=24,
    pipeline=[
        dict(
            type='LoadPointsFromFile',
            coord_type='LIDAR',
            load_dim=5,
            use_dim=5,
            file_client_args=dict(backend='disk')),
        dict(
            type='LoadPointsFromMultiSweeps',
            sweeps_num=10,
            file_client_args=dict(backend='disk')),
        dict(
            type='DefaultFormatBundle3D',
            class_names=[
                'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                'barrier'
            ],
            with_label=False),
        dict(type='Collect3D', keys=['points'])
    ])
optimizer = dict(type='AdamW', lr=0.001, weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=1000,
    warmup_ratio=0.001,
    step=[20, 23])
momentum_config = None
runner = dict(type='EpochBasedRunner', max_epochs=24)
checkpoint_config = dict(interval=1)
log_config = dict(
    interval=50,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d'
load_from = None
resume_from = None
workflow = [('train', 1)]
gpu_ids = range(0, 1)

voxel_size = [0.25, 0.25, 8]
model = dict(
    type='MVXFasterRCNN',
    pts_voxel_layer=dict(
        max_num_points=64,
        point_cloud_range=[-50, -50, -5, 50, 50, 3],
        voxel_size=[0.25, 0.25, 8],
        max_voxels=(30000, 40000)),
    pts_voxel_encoder=dict(
        type='HardVFE',
        in_channels=4,
        feat_channels=[64, 64],
        with_distance=False,
        voxel_size=[0.25, 0.25, 8],
        with_cluster_center=True,
        with_voxel_center=True,
        point_cloud_range=[-50, -50, -5, 50, 50, 3],
        norm_cfg=dict(type='naiveSyncBN1d', eps=0.001, momentum=0.01)),
    pts_middle_encoder=dict(
        type='PointPillarsScatter', in_channels=64, output_shape=[400, 400]),
    pts_backbone=dict(
        type='SECOND',
        in_channels=64,
        norm_cfg=dict(type='naiveSyncBN2d', eps=0.001, momentum=0.01),
        layer_nums=[3, 5, 5],
        layer_strides=[2, 2, 2],
        out_channels=[64, 128, 256]),
    pts_neck=dict(
        type='FPN',
        norm_cfg=dict(type='naiveSyncBN2d', eps=0.001, momentum=0.01),
        act_cfg=dict(type='ReLU'),
        in_channels=[64, 128, 256],
        out_channels=256,
        start_level=0,
        num_outs=3),
    pts_bbox_head=dict(
        type='Anchor3DHead',
        num_classes=10,
        in_channels=256,
        feat_channels=256,
        use_direction_classifier=True,
        anchor_generator=dict(
            type='AlignedAnchor3DRangeGenerator',
            ranges=[[-50, -50, -1.8, 50, 50, -1.8]],
            scales=[1, 2, 4],
            sizes=[[0.866, 2.5981, 1.0], [0.5774, 1.7321, 1.0],
                   [1.0, 1.0, 1.0], [0.4, 0.4, 1]],
            custom_values=[0, 0],
            rotations=[0, 1.57],
            reshape_out=True),
        assigner_per_size=False,
        diff_rad_by_sin=True,
        dir_offset=0.7854,
        dir_limit_offset=0,
        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=9),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(
            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0),
        loss_dir=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
    train_cfg=dict(
        pts=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                iou_calculator=dict(type='BboxOverlapsNearest3D'),
                pos_iou_thr=0.6,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                ignore_iof_thr=-1),
            allowed_border=0,
            code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2],
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        pts=dict(
            use_rotate_nms=True,
            nms_across_levels=False,
            nms_pre=1000,
            nms_thr=0.2,
            score_thr=0.05,
            min_bbox_size=0,
            max_num=500)))
point_cloud_range = [-50, -50, -5, 50, 50, 3]
class_names = [
    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
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)
file_client_args = dict(backend='disk')
train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        file_client_args=dict(backend='disk')),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=dict(backend='disk')),
    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-0.3925, 0.3925],
        scale_ratio_range=[0.95, 1.05],
        translation_std=[0, 0, 0]),
    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
    dict(
        type='PointsRangeFilter', point_cloud_range=[-50, -50, -5, 50, 50, 3]),
    dict(
        type='ObjectRangeFilter', point_cloud_range=[-50, -50, -5, 50, 50, 3]),
    dict(
        type='ObjectNameFilter',
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ]),
    dict(type='PointShuffle'),
    dict(
        type='DefaultFormatBundle3D',
        class_names=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ]),
    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        file_client_args=dict(backend='disk')),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=dict(backend='disk')),
    dict(
        type='MultiScaleFlipAug3D',
        img_scale=(1333, 800),
        pts_scale_ratio=1,
        flip=False,
        transforms=[
            dict(
                type='GlobalRotScaleTrans',
                rot_range=[0, 0],
                scale_ratio_range=[1.0, 1.0],
                translation_std=[0, 0, 0]),
            dict(type='RandomFlip3D'),
            dict(
                type='PointsRangeFilter',
                point_cloud_range=[-50, -50, -5, 50, 50, 3]),
            dict(
                type='DefaultFormatBundle3D',
                class_names=[
                    'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                    'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                    'barrier'
                ],
                with_label=False),
            dict(type='Collect3D', keys=['points'])
        ])
]
eval_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        file_client_args=dict(backend='disk')),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=dict(backend='disk')),
    dict(
        type='DefaultFormatBundle3D',
        class_names=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        with_label=False),
    dict(type='Collect3D', keys=['points'])
]
data = dict(
    samples_per_gpu=1,
    workers_per_gpu=4,
    train=dict(
        type='NuScenesDataset',
        data_root='data/nuscenes/',
        ann_file='data/nuscenes/nuscenes_infos_train.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=5,
                use_dim=5,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadPointsFromMultiSweeps',
                sweeps_num=10,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadAnnotations3D',
                with_bbox_3d=True,
                with_label_3d=True),
            dict(
                type='GlobalRotScaleTrans',
                rot_range=[-0.3925, 0.3925],
                scale_ratio_range=[0.95, 1.05],
                translation_std=[0, 0, 0]),
            dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
            dict(
                type='PointsRangeFilter',
                point_cloud_range=[-50, -50, -5, 50, 50, 3]),
            dict(
                type='ObjectRangeFilter',
                point_cloud_range=[-50, -50, -5, 50, 50, 3]),
            dict(
                type='ObjectNameFilter',
                classes=[
                    'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                    'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                    'barrier'
                ]),
            dict(type='PointShuffle'),
            dict(
                type='DefaultFormatBundle3D',
                class_names=[
                    'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                    'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                    'barrier'
                ]),
            dict(
                type='Collect3D',
                keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
        ],
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        modality=dict(
            use_lidar=True,
            use_camera=False,
            use_radar=False,
            use_map=False,
            use_external=False),
        test_mode=False,
        box_type_3d='LiDAR'),
    val=dict(
        type='NuScenesDataset',
        data_root='data/nuscenes/',
        ann_file='data/nuscenes/nuscenes_infos_val.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=5,
                use_dim=5,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadPointsFromMultiSweeps',
                sweeps_num=10,
                file_client_args=dict(backend='disk')),
            dict(
                type='MultiScaleFlipAug3D',
                img_scale=(1333, 800),
                pts_scale_ratio=1,
                flip=False,
                transforms=[
                    dict(
                        type='GlobalRotScaleTrans',
                        rot_range=[0, 0],
                        scale_ratio_range=[1.0, 1.0],
                        translation_std=[0, 0, 0]),
                    dict(type='RandomFlip3D'),
                    dict(
                        type='PointsRangeFilter',
                        point_cloud_range=[-50, -50, -5, 50, 50, 3]),
                    dict(
                        type='DefaultFormatBundle3D',
                        class_names=[
                            'car', 'truck', 'trailer', 'bus',
                            'construction_vehicle', 'bicycle', 'motorcycle',
                            'pedestrian', 'traffic_cone', 'barrier'
                        ],
                        with_label=False),
                    dict(type='Collect3D', keys=['points'])
                ])
        ],
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        modality=dict(
            use_lidar=True,
            use_camera=False,
            use_radar=False,
            use_map=False,
            use_external=False),
        test_mode=True,
        box_type_3d='LiDAR'),
    test=dict(
        type='NuScenesDataset',
        data_root='data/nuscenes/',
        ann_file='data/nuscenes/nuscenes_infos_val.pkl',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=5,
                use_dim=5,
                file_client_args=dict(backend='disk')),
            dict(
                type='LoadPointsFromMultiSweeps',
                sweeps_num=10,
                file_client_args=dict(backend='disk')),
            dict(
                type='MultiScaleFlipAug3D',
                img_scale=(1333, 800),
                pts_scale_ratio=1,
                flip=False,
                transforms=[
                    dict(
                        type='GlobalRotScaleTrans',
                        rot_range=[0, 0],
                        scale_ratio_range=[1.0, 1.0],
                        translation_std=[0, 0, 0]),
                    dict(type='RandomFlip3D'),
                    dict(
                        type='PointsRangeFilter',
                        point_cloud_range=[-50, -50, -5, 50, 50, 3]),
                    dict(
                        type='DefaultFormatBundle3D',
                        class_names=[
                            'car', 'truck', 'trailer', 'bus',
                            'construction_vehicle', 'bicycle', 'motorcycle',
                            'pedestrian', 'traffic_cone', 'barrier'
                        ],
                        with_label=False),
                    dict(type='Collect3D', keys=['points'])
                ])
        ],
        classes=[
            'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
            'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
        ],
        modality=dict(
            use_lidar=True,
            use_camera=False,
            use_radar=False,
            use_map=False,
            use_external=False),
        test_mode=True,
        box_type_3d='LiDAR'))
evaluation = dict(
    interval=24,
    pipeline=[
        dict(
            type='LoadPointsFromFile',
            coord_type='LIDAR',
            load_dim=5,
            use_dim=5,
            file_client_args=dict(backend='disk')),
        dict(
            type='LoadPointsFromMultiSweeps',
            sweeps_num=10,
            file_client_args=dict(backend='disk')),
        dict(
            type='DefaultFormatBundle3D',
            class_names=[
                'car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                'barrier'
            ],
            with_label=False),
        dict(type='Collect3D', keys=['points'])
    ])
optimizer = dict(type='AdamW', lr=0.001, weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=1000,
    warmup_ratio=0.001,
    step=[20, 23])
momentum_config = None
runner = dict(type='EpochBasedRunner', max_epochs=24)
checkpoint_config = dict(interval=1)
log_config = dict(
    interval=50,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d'
load_from = None
resume_from = None
workflow = [('train', 1)]
gpu_ids = range(0, 1)
