# 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

>  * Args

>     * model : Model type, please refer to [Model Library](../opengait/modeling/models) for the supported values.

>     * **others** : Please refer to the [Training Configuration File of Corresponding Model](../configs).

----

### 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](../opengait/data/sampler.py) and [data.collate_fn](../opengait/data/collate_fn.py)

>     * 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](../opengait/data/sampler.py) and [data.collate_fn](../opengait/data/collate_fn.py).

>     * **others**: Please refer to `evaluator_cfg`.

---

**Note**: 

- All the config items will be merged into [default.yaml](../configs/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

```yaml

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

```