OpenGait/docs/3.detailed_config.md

Configuration item

data_cfg

• Data configuration

• Args
  • dataset_name: Only support CASIA-B and OUMVLP now.
  • dataset_root: The path of storing your dataset.
  • num_workers: The number of workers to collect data.
  • dataset_partition: The path of storing your dataset partition file. It splits the dataset to two parts, including train set and test set.
  • cache: If True, load all data to memory during buiding dataset.
  • test_dataset_name: The name of test dataset.

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loss_cfg

• Loss function

• Args
  • type: Loss function type, support TripletLoss and CrossEntropyLoss.
  • loss_term_weight: loss weight.
  • log_prefix: the prefix of loss log.

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optimizer_cfg

• Optimizer

• Args
  • solver: Optimizer type, example: SGD, Adam.
  • others: Please refer to torch.optim.

scheduler_cfg

• Learning rate scheduler

• Args
  • scheduler : Learning rate scheduler, example: MultiStepLR.
  • others : Please refer to torch.optim.lr_scheduler.

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model_cfg

• Model to be trained

• Args
  • model : Model type, please refer to Model Library for the supported values.
  • others : Please refer to the Training Configuration File of Corresponding Model.

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evaluator_cfg

• Evaluator configuration

• Args
  • enable_float16: If True, enable the auto mixed precision mode.
  • restore_ckpt_strict: If True, check whether the checkpoint is the same as the defined model.
  • restore_hint: int value indicates the iteration number of restored checkpoint; str value indicates the path to restored checkpoint.
  • save_name: The name of the experiment.
  • eval_func: The function name of evaluation. For CASIA-B, choose identification.
  • sampler:
    • type: The name of sampler. Choose InferenceSampler.
    • sample_type: In general, we use all_ordered to input all frames by its natural order, which makes sure the tests are consistent.
    • batch_size: int values.
    • others: Please refer to data.sampler and data.collate_fn
  • transform: Support BaseSilCuttingTransform, BaseSilTransform. The difference between them is BaseSilCuttingTransform cut out the black pixels on both sides horizontally.
  • metric: euc or cos, generally, euc performs better.

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trainer_cfg

• Trainer configuration

• Args
  • restore_hint: int value indicates the iteration number of restored checkpoint; str value indicates the path to restored checkpoint. Use latest to restore the latest rolling resume checkpoint. The option is often used to finetune on new dataset or restore the interrupted training process.
  • auto_resume_latest: If True and restore_hint==0, automatically resume from output/.../checkpoints/latest.pt when it exists.
  • fix_BN: If True, we fix the weight of all BatchNorm layers.
  • log_iter: Log the information per log_iter iterations.
  • resume_every_iter: Save a rolling resume checkpoint every resume_every_iter iterations. These checkpoints update checkpoints/latest.pt and are intended for crash recovery.
  • resume_keep: Number of rolling resume checkpoints retained under checkpoints/resume/. Set 0 to keep all of them.
  • save_iter: Save the checkpoint per save_iter iterations.
  • with_test: If True, we test the model every save_iter iterations. A bit of performance impact.(Disable in Default)
  • optimizer_reset: If True and restore_hint!=0, reset the optimizer while restoring the model.
  • scheduler_reset: If True and restore_hint!=0, reset the scheduler while restoring the model.
  • sync_BN: If True, applies Batch Normalization synchronously.
  • total_iter: The total training iterations, int values.
  • sampler:
    • type: The name of sampler. Choose TripletSampler.
    • sample_type: [all, fixed, unfixed] indicates the number of frames used to test, while [unordered, ordered] means whether input sequence by its natural order. Example: fixed_unordered means selecting fixed number of frames randomly.
    • batch_size: [P,K] where P denotes the subjects in training batch while the K represents the sequences every subject owns. Example:
      • 8
      • 16
    • others: Please refer to data.sampler and data.collate_fn.
  • others: Please refer to evaluator_cfg.

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Note:

• All the config items will be merged into default.yaml, and the current config is preferable.
• The output directory, which includes the log, checkpoint and summary files, is depended on the defined dataset_name, model and save_name settings, like output/${dataset_name}/${model}/${save_name}.

Example

data_cfg:
  dataset_name: CASIA-B
  dataset_root:  your_path
  dataset_partition: ./datasets/CASIA-B/CASIA-B.json
  num_workers: 1
  remove_no_gallery: false # Remove probe if no gallery for it
  test_dataset_name: CASIA-B

evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: 60000
  save_name: Baseline
  eval_func: evaluate_indoor_dataset
  sampler:
    batch_shuffle: false
    batch_size: 16
    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: 64

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:
    in_channels: 1
    layers_cfg: # Layers configuration for automatically model construction
      - BC-64
      - BC-64
      - M
      - BC-128
      - BC-128
      - M
      - BC-256
      - BC-256
      # - M
      # - BC-512
      # - BC-512
    type: Plain
  SeparateFCs:
    in_channels: 256
    out_channels: 256
    parts_num: 31
  SeparateBNNecks:
    class_num: 74
    in_channels: 256
    parts_num: 31
  bin_num:
    - 16
    - 8
    - 4
    - 2
    - 1

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_layers: false
  log_iter: 100
  restore_ckpt_strict: true
  restore_hint: 0
  auto_resume_latest: false
  resume_every_iter: 500
  resume_keep: 3
  save_iter: 10000
  save_name: Baseline
  sync_BN: true
  total_iter: 60000
  sampler:
    batch_shuffle: true
    batch_size:
      - 8 # TripletSampler, batch_size[0] indicates Number of Identity
      - 16 #                 batch_size[1] indicates Samples sequqnce for each Identity
    frames_num_fixed: 30 # fixed frames number for training
    frames_num_max: 50 # max frames number for unfixed training
    frames_num_min: 25 # min frames number for unfixed traing
    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: 64