model:
  encoders:
    camera: null
    lidar:
      voxelize:
        max_num_points: 10
        point_cloud_range: ${point_cloud_range}
        voxel_size: ${voxel_size}
        max_voxels: [90000, 120000]
      backbone:
        type: SparseEncoder
        in_channels: 5
        sparse_shape: [1024, 1024, 41]
        output_channels: 128
        order:
          - conv
          - norm
          - act
        encoder_channels:
          - [16, 16, 32]
          - [32, 32, 64]
          - [64, 64, 128]
          - [128, 128]
        encoder_paddings:
          - [0, 0, 1]
          - [0, 0, 1]
          - [0, 0, [1, 1, 0]]
          - [0, 0]
        block_type: basicblock
  fuser: null
  decoder:
    backbone:
      type: SECOND
      in_channels: 256
      out_channels: [128, 256]
      layer_nums: [5, 5]
      layer_strides: [1, 2]
      norm_cfg:
        type: BN
        eps: 1.0e-3
        momentum: 0.01
      conv_cfg:
        type: Conv2d
        bias: false
    neck:
      type: SECONDFPN
      in_channels: [128, 256]
      out_channels: [256, 256]
      upsample_strides: [1, 2]
      norm_cfg:
        type: BN
        eps: 1.0e-3
        momentum: 0.01
      upsample_cfg:
        type: deconv
        bias: false
      use_conv_for_no_stride: true
  heads:
    map:
      in_channels: 512

optimizer:
  type: AdamW
  lr: 1.0e-4

optimizer_config:
  grad_clip:
    max_norm: 35
    norm_type: 2

lr_config:
  policy: cyclic

momentum_config:
  policy: cyclic