text: # model settings
  voxel_size = [0.16, 0.16, 4]
  point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
  model = dict(
      type='VoxelNet',
      voxel_layer=dict(
          max_num_points=64,
          point_cloud_range=point_cloud_range,
          voxel_size=voxel_size,
          max_voxels=(12000, 20000)),
      voxel_encoder=dict(
          type='PillarFeatureNet',
          in_channels=4,
          feat_channels=[64],
          with_distance=False,
          voxel_size=voxel_size,
          point_cloud_range=point_cloud_range),
      middle_encoder=dict(
          type='PointPillarsScatter', in_channels=64, output_shape=[496, 432]),
      backbone=dict(
          type='SECOND',
          in_channels=64,
          layer_nums=[3, 5, 5],
          layer_strides=[2, 2, 2],
          out_channels=[64, 128, 256]),
      neck=dict(
          type='SECONDFPN',
          in_channels=[64, 128, 256],
          upsample_strides=[1, 2, 4],
          out_channels=[128, 128, 128]),
      bbox_head=dict(
          type='Anchor3DHead',
          num_classes=1,
          in_channels=384,
          feat_channels=384,
          use_direction_classifier=True,
          anchor_generator=dict(
              type='Anchor3DRangeGenerator',
              ranges=[[0, -39.68, -1.78, 69.12, 39.68, -1.78]],
              sizes=[[1.6, 3.9, 1.56]],
              rotations=[0, 1.57],
              reshape_out=True),
          diff_rad_by_sin=True,
          bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
          loss_cls=dict(
              type='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=2.0),
          loss_dir=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
      # model training and testing settings
      train_cfg=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              iou_calculator=dict(type='BboxOverlapsNearest3D'),
              pos_iou_thr=0.6,
              neg_iou_thr=0.45,
              min_pos_iou=0.45,
              ignore_iof_thr=-1),
          allowed_border=0,
          pos_weight=-1,
          debug=False),
      test_cfg=dict(
          use_rotate_nms=True,
          nms_across_levels=False,
          nms_thr=0.01,
          score_thr=0.1,
          min_bbox_size=0,
          nms_pre=100,
          max_num=50))
  
  # dataset settings
  dataset_type = 'KittiDataset'
  data_root = '/data/Dataset/KITTI/'
  class_names = ['Car']
  input_modality = dict(use_lidar=True, use_camera=False)
  db_sampler = dict(
      data_root=data_root,
      info_path=data_root + 'kitti_dbinfos_train.pkl',
      rate=1.0,
      prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
      sample_groups=dict(Car=15),
      classes=class_names)
  
  train_pipeline = [
      dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
      dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
      dict(type='ObjectSample', db_sampler=db_sampler),
      dict(
          type='ObjectNoise',
          num_try=100,
          translation_std=[0.25, 0.25, 0.25],
          global_rot_range=[0.0, 0.0],
          rot_range=[-0.15707963267, 0.15707963267]),
      dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
      dict(
          type='GlobalRotScaleTrans',
          rot_range=[-0.78539816, 0.78539816],
          scale_ratio_range=[0.95, 1.05]),
      dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
      dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
      dict(type='PointShuffle'),
      dict(type='DefaultFormatBundle3D', class_names=class_names),
      dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
  ]
  test_pipeline = [
      dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
      dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
      dict(
          type='DefaultFormatBundle3D',
          class_names=class_names,
          with_label=False),
      dict(type='Collect3D', 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)
  eval_pipeline = [
      dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
      dict(
          type='DefaultFormatBundle3D',
          class_names=class_names,
          with_label=False),
      dict(type='Collect3D', keys=['points'])
  ]
  
  data = dict(
      samples_per_gpu=3,
      workers_per_gpu=3,
      train=dict(
          type='RepeatDataset',
          times=2,
          dataset=dict(
              type=dataset_type,
              data_root=data_root,
              ann_file=data_root + 'kitti_infos_train.pkl',
              split='training',
              pts_prefix='velodyne_reduced',
              pipeline=train_pipeline,
              modality=input_modality,
              classes=class_names,
              test_mode=False)),
      val=dict(
          type=dataset_type,
          data_root=data_root,
          ann_file=data_root + 'kitti_infos_val.pkl',
          split='training',
          pts_prefix='velodyne_reduced',
          pipeline=test_pipeline,
          modality=input_modality,
          classes=class_names,
          test_mode=True),
      test=dict(
          type=dataset_type,
          data_root=data_root,
          ann_file=data_root + 'kitti_infos_val.pkl',
          split='training',
          pts_prefix='velodyne_reduced',
          pipeline=test_pipeline,
          modality=input_modality,
          classes=class_names,
          test_mode=True))
  # optimizer
  lr = 0.001  # max learning rate
  optimizer = dict(
      type='AdamW',
      lr=lr,
      betas=(0.95, 0.99),  # the momentum is change during training
      weight_decay=0.01)
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
  # learning policy
  lr_config = dict(
      policy='cyclic',
      target_ratio=(10, 1e-4),
      cyclic_times=1,
      step_ratio_up=0.4)
  momentum_config = dict(
      policy='cyclic',
      target_ratio=(0.85 / 0.95, 1),
      cyclic_times=1,
      step_ratio_up=0.4)
  checkpoint_config = dict(interval=1)
  evaluation = dict(interval=1, pipeline=eval_pipeline)
  # yapf:disable
  log_config = dict(
      interval=50,
      hooks=[
          dict(type='TextLoggerHook'),
          dict(type='TensorboardLoggerHook')
      ])
  # yapf:enable
  # runtime settings
  runner = dict(type='EpochBasedRunner', max_epochs=50)
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/pp_secfpn_100e'
  load_from = None
  resume_from = None
  workflow = [('train', 50)]
  