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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.

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.

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.

model_cfg

  • Model to be trained

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.

trainer_cfg

  • Trainer configuration
  • Args
    • restore_hint: int value indicates the iteration number of restored checkpoint; str value indicates the path to restored checkpoint. The option is often used to finetune on new dataset or restore the interrupted training process.
    • fix_BN: If True, we fix the weight of all BatchNorm layers.
    • log_iter: Log the information per log_iter iterations.
    • 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.

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
  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