data_cfg:
  dataset_name: SUSTech1K
  dataset_root: your_path # download from https://lidargait.github.io/ if you don't have dataset
  dataset_partition: ./datasets/SUSTech1K/SUSTech1K.json
  num_workers: 4
  data_in_use: [true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false]
  remove_no_gallery: false # Remove probe if no gallery for it
  test_dataset_name: SUSTech1K

evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: 40000
  save_name: lidargaitv2
  eval_func: evaluate_indoor_dataset 
  sampler:
    batch_shuffle: false
    points_in_use: # For point-based gait recognition using point clouds
      pointcloud_index: 0 
      points_num: 1024 #2048
    batch_size: 4 
    frames_num_fixed: 10 # fixed frames number for training
    frames_skip_num: 0
    #sample_type: fixed_ordered
    sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options:   fixed_unordered
    frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
  metric: euc # cos
  transform:
      - type: PointCloudsTransform
        xyz_only: true
        scale_aware: true

loss_cfg:
  - loss_term_weight: 1.0
    margin: 0.2
    type: TripletLoss
    log_prefix: triplet
    lazy: False
  - loss_term_weight: 0.1
    scale: 31
    type: CrossEntropyLoss
    log_prefix: softmax
    log_accuracy: true

   
model_cfg:
  model: LidarGaitPlusPlus
  pool: PPP_HAP 
  sampling: knn
  channel: 16 
  npoints: [512, 256, 128]
  nsample: 32
  scale_aware: true 
  normalize_dp: true 
  SeparateFCs:
    in_channels: 256 
    out_channels: 256 
    parts_num: 31
  SeparateBNNecks:
    class_num: 250
    in_channels: 256 
    parts_num: 31 
  scale:
    - 1
    - 2
    - 4
    - 8
    - 16


optimizer_cfg:
  lr: 0.1
  momentum: 0.9
  solver: SGD
  weight_decay: 0.0005

scheduler_cfg:
  T_max: 40000
  eta_min: 0.0001
  scheduler: CosineAnnealingLR


trainer_cfg:
  enable_float16: false #true # half_percesion float for memory reduction and speedup
  fix_BN: false
  with_test: true 
  log_iter: 100
  restore_ckpt_strict: true
  restore_hint: 0
  save_iter: 5000
  save_name: lidargaitv2
  sync_BN: true
  total_iter: 40000
  sampler:
    batch_shuffle: true
    batch_size:
      - 32 # TripletSampler, batch_size[0] indicates Number of Identity
      - 4 #                 batch_size[1] indicates Samples sequqnce for each Identity
    frames_num_fixed: 10 # fixed frames number for training
    sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
    type: TripletSampler
    points_in_use:
      pointcloud_index: 0
      points_num: 1024
  transform:
      - type: PointCloudsTransform
        xyz_only: true
        scale_aware: true
        scale_prob: 1
        flip_prob: 0.15