opt.yaml

GENERAL:
  task: train  # train, valid, ssc_pretrain, ssc_valid, visualize
  dataset_dir: ../dataset
  logging_root: ./logs
  eval_logging_root: ./eval_logs
  debug: True


DATA_IO:
  num_workers: 10
  train_batch_size: 2
  valid_batch_size: 1 # get discrete sdf for every single sample

  use_off_vertex: True
  use_on_surround: False
  off_count_scale: 2

  ignore_off_label: False

  augmentation: True
  augmentation_flip: False
  augmentation_angle: 45

  complt_num_per_class: [7632350044, 15783539,  125136, 118809, 646799, 821951, 262978, 283696, 204750, 61688703, 4502961, 44883650, 2269923, 56840218, 15719652, 158442623, 2061623, 36970522, 1151988, 334146]  # 20

  
TRAIN:
  steps_til_summary: 10
  steps_til_eval: 50
  epochs_til_ckpt: 5

  lr: 0.0001
  lr_scheduler: True
  num_epochs: 200
  loss_weights: [3000, 100, 100, 50, 100, 100, 100]
  clip_grad: True
  moo: False

  eval_threshold: [0.015, 0.012, 0.01, 0.008, 0.006]

  chunk_size: 4 # scale size

  shape_embedding_size: 256
  shape_normalize: True
  shape_sample_strategy: trilinear # trilinear or nearest

  encode_xyz: True
  encode_levels: 10
  inc_input: True

  class_count: 20 # semantic kitti ssc classes

  use_ssc_pretrain: False
  ssc_pretrain_path: /

  resume: False
  resume_path: /

  D_TRAIN:
    D_input: occupancy # occupancy or radial or radial_height

    pruning_choice: [True, True, True, True, True]
    output_layers: 2 # 2 or 4
    nonlinearity: elu # elu or relu

  G_TRAIN:
    on_surface_size: 16000

    nonlinearity: sine # sine or relu
    hidden_features: 256
    num_hidden_layers: 3 # hidden layers: n; total layers: n+1


EVAL:
  checkpoint_path: /
  ssc_pretrain_path: /

  eval_threshold: [0.015, 0.012, 0.011, 0.01, 0.008, 0.006, 0.004, 0.002]

  eval_cd: False

  save_predict_point: True
  mesh:
    create_mesh: True
    mesh_level: 0.015
    ratio: [0.5, 1.0, 2.0]  # multi-resolution