data_cfg:

  dataset_name: HID

  dataset_root: your_path

  dataset_partition: ./datasets/HID/HID2023.json

  num_workers: 1

  remove_no_gallery: false # Remove probe if no gallery for it

evaluator_cfg:

  enable_float16: true

  restore_ckpt_strict: true

  restore_hint: 60000

  save_name: GaitBase

  eval_func: HID_submission

  rerank: False

  sampler:

    batch_shuffle: false

    batch_size: 8

    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: BaseSilCuttingTransform

      img_w: 128

loss_cfg:

  - loss_term_weight: 1.0

    margin: 0.2

    type: TripletLoss

    log_prefix: triplet

  - loss_term_weight: 0.1

    scale: 16

    type: CrossEntropyLoss

    log_prefix: softmax

    log_accuracy: true

model_cfg:

  model: Baseline

  backbone_cfg:

    type: ResNet9

    block: BasicBlock

    channels: # Layers configuration for automatically model construction

      - 64

      - 128

      - 256

      - 512

    layers:

      - 1

      - 1

      - 1

      - 1

    strides:

      - 1

      - 2

      - 2

      - 1

    maxpool: false

  SeparateFCs:

    in_channels: 512

    out_channels: 256

    parts_num: 16

  SeparateBNNecks:

    class_num: 500 # you need to change

    in_channels: 256

    parts_num: 16

  bin_num:

    - 16

optimizer_cfg:

  lr: 0.1

  momentum: 0.9

  solver: SGD

  weight_decay: 0.0005

scheduler_cfg:

  gamma: 0.1

  milestones: # Learning Rate Reduction at each milestones

    - 20000

    - 40000

  scheduler: MultiStepLR

trainer_cfg:

  enable_float16: true # half_percesion float for memory reduction and speedup

  fix_BN: false

  log_iter: 100

  restore_ckpt_strict: true

  restore_hint: 0

  save_iter: 10000

  save_name: GaitBase

  sync_BN: true

  total_iter: 60000

  sampler:

    batch_shuffle: true

    batch_size:

      - 16 # TripletSampler, batch_size[0] indicates Number of Identity

      - 8 #                 batch_size[1] indicates Samples sequqnce for each Identity

    frames_num_fixed: 30 # 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

  transform:

    - type: BaseSilCuttingTransform

      img_w: 128