checkpoint_config:
  interval: 1
data:
  samples_per_gpu: 1
  workers_per_gpu: 0
log_config:
  hooks:
  - type: TextLoggerHook
  interval: 50
lr_config:
  min_lr_ratio: 0.001
  policy: CosineAnnealing
  warmup: linear
  warmup_iters: 500
  warmup_ratio: 0.33333333
model:
  encoders:
    lidar:
      backbone:
        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
        in_channels: 5
        order:
        - conv
        - norm
        - act
        output_channels: 128
        sparse_shape:
        - 1440
        - 1440
        - 41
        type: SparseEncoder
      voxelize:
        max_num_points: 10
        max_voxels:
        - 120000
        - 160000
        point_cloud_range:
        - -54.0
        - -54.0
        - -5.0
        - 54.0
        - 54.0
        - 3.0
        voxel_size:
        - 0.075
        - 0.075
        - 0.2
  fuser:
    in_channels:
    - 256
    out_channels: 256
    type: ConvFuser
  heads:
    map:
      classes:
      - drivable_area
      - ped_crossing
      - walkway
      - stop_line
      - carpark_area
      - divider
      deep_supervision: true
      dice_weight: 0.5
      focal_alpha: 0.25
      focal_gamma: 2.0
      grid_transform:
        input_scope:
        - - -54.0
          - 54.0
          - 0.75
        - - -54.0
          - 54.0
          - 0.75
        output_scope:
        - - -50
          - 50
          - 0.167
        - - -50
          - 50
          - 0.167
      in_channels: 256
      loss: focal
      type: EnhancedBEVSegmentationHead
      use_dice_loss: true
  type: BEVFusion
optimizer:
  lr: 0.0001
  type: AdamW
runner:
  max_epochs: 1
  type: EpochBasedRunner