OpenGait/doc/detailed_config.md

Configuration item

data_cfg

• Data configuration

• Args
  • dataset_name: Dataset name. Only support CASIA-B.
  • 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_weights: 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 Training Configuration File of Corresponding Model

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evaluator_cfg

• Evaluator configuration

• Args
  • enable_float16: If True, enable auto mixed precision.
  • restore_ckpt_strict: If True, check whether the checkpoint is the same as the model.
  • restore_hint: int value indicates the iteration number of restored checkpoint; str value indicates the path of 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: In general, it should equal to the number of utilized GPU.
    • others: Please refer to data.sampler and data.collate_fn
  • transform: support BaseSilCuttingTransform, BaseSilTransform. The difference between them is BaseSilCuttingTransform cut the pixels on both sides horizontally.
  • metric: euc or cos, generally, euc performs better.

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trainer_cfg

• Trainer configuration

• Args
  • fix_BN: If True, we fix the weight of all BatchNorm layers.
  • log_iter: Every log_iter iterations, log the information.
  • save_iter: Every save_iter iterations, save the model.
  • with_test: If True, we test the model every save_iter iterations. A bit of performance impact.(To Be Fixed)
  • 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 as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.
  • total_iter: The total number of training iterations.
  • 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]
      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 configuatrarion items will merged into default.yaml, and the current configuration is preferable.

Example

data_cfg:
  dataset_name: CASIA-B
  dataset_root:  your_path
  dataset_partition: ./misc/partitions/CASIA-B_include_005.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: identification
  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


loss_cfg:
  - loss_term_weights: 1.0
    margin: 0.2
    type: TripletLoss
    log_prefix: triplet
  - loss_term_weights: 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
    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_BN: false
  log_iter: 100
  restore_ckpt_strict: true
  restore_hint: 0
  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