nuscenes_unet32_spherical_transformer.yaml

DATA:
  data_name: nuscenes
  data_root: YOUR_DATA_ROOT # Fill in the data path 
  label_mapping: util/nuscenes.yaml
  classes: 16 
  fea_dim: 6
  voxel_size: [0.1, 0.1, 0.1]
  voxel_max: 120000 

TRAIN:
  # arch
  arch: unet_spherical_transformer
  input_c: 4
  m: 32
  block_reps: 2
  block_residual: True
  layers: [32, 64, 128, 256, 256]
  quant_size_scale: 24
  patch_size: 1 
  window_size: 6
  use_xyz: True
  sync_bn: True  # adopt sync_bn or not
  rel_query: True
  rel_key: True
  rel_value: True
  drop_path_rate: 0.3
  max_batch_points: 250000

  xyz_norm: False
  window_size_sphere: [2, 2, 120]
  window_size_scale: [2.0, 2.0]
  sphere_layers: [1,2,3,4,5]
  grad_checkpoint_layers: []
  a: 0.0125
  loss_name: ce_loss
  use_tta: False
  vote_num: 4

  # training
  aug: True
  transformer_lr_scale: 0.1 
  scheduler_update: step 
  scheduler: Poly
  power: 0.9
  use_amp: True
  train_gpu: [0,1,2,3] 
  workers: 16  # data loader workers
  batch_size: 16  # batch size for training
  batch_size_val: 8  # batch size for validation during training, memory and speed tradeoff
  base_lr: 0.006
  epochs: 50
  start_epoch: 0
  momentum: 0.9
  weight_decay: 0.01
  drop_rate: 0.5

  ignore_label: 255
  manual_seed: 123
  print_freq: 10
  save_freq: 1
  save_path: runs/nuscenes_unet32_spherical_transformer
  weight:  # path to initial weight (default: none)
  resume:  # path to latest checkpoint (default: none)
  evaluate: True  # evaluate on validation set, extra gpu memory needed and small batch_size_val is recommend
  eval_freq: 1
  val: False
  
Distributed:
  dist_url: tcp://127.0.0.1:6789
  dist_backend: 'nccl'
  multiprocessing_distributed: True
  world_size: 1
  rank: 0