196 lines
7.6 KiB
Markdown
196 lines
7.6 KiB
Markdown
# Configuration item
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### data_cfg
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* Data configuration
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>
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> * Args
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> * dataset_name: Only support `CASIA-B` and `OUMVLP` now.
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> * dataset_root: The path of storing your dataset.
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> * num_workers: The number of workers to collect data.
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> * dataset_partition: The path of storing your dataset partition file. It splits the dataset to two parts, including train set and test set.
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> * cache: If `True`, load all data to memory during buiding dataset.
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> * test_dataset_name: The name of test dataset.
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----
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### loss_cfg
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* Loss function
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> * Args
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> * type: Loss function type, support `TripletLoss` and `CrossEntropyLoss`.
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> * loss_term_weight: loss weight.
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> * log_prefix: the prefix of loss log.
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----
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### optimizer_cfg
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* Optimizer
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> * Args
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> * solver: Optimizer type, example: `SGD`, `Adam`.
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> * **others**: Please refer to `torch.optim`.
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### scheduler_cfg
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* Learning rate scheduler
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> * Args
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> * scheduler : Learning rate scheduler, example: `MultiStepLR`.
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> * **others** : Please refer to `torch.optim.lr_scheduler`.
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----
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### model_cfg
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* Model to be trained
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> * Args
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> * model : Model type, please refer to [Model Library](../opengait/modeling/models) for the supported values.
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> * **others** : Please refer to the [Training Configuration File of Corresponding Model](../configs).
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----
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### evaluator_cfg
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* Evaluator configuration
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> * Args
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> * enable_float16: If `True`, enable the auto mixed precision mode.
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> * restore_ckpt_strict: If `True`, check whether the checkpoint is the same as the defined model.
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> * restore_hint: `int` value indicates the iteration number of restored checkpoint; `str` value indicates the path to restored checkpoint.
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> * save_name: The name of the experiment.
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> * eval_func: The function name of evaluation. For `CASIA-B`, choose `identification`.
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> * sampler:
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> - type: The name of sampler. Choose `InferenceSampler`.
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> - sample_type: In general, we use `all_ordered` to input all frames by its natural order, which makes sure the tests are consistent.
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> - batch_size: `int` values.
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> - **others**: Please refer to [data.sampler](../opengait/data/sampler.py) and [data.collate_fn](../opengait/data/collate_fn.py)
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> * transform: Support `BaseSilCuttingTransform`, `BaseSilTransform`. The difference between them is `BaseSilCuttingTransform` cut out the black pixels on both sides horizontally.
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> * metric: `euc` or `cos`, generally, `euc` performs better.
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----
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### trainer_cfg
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* Trainer configuration
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> * Args
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> * 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.
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> * auto_resume_latest: If `True` and `restore_hint==0`, automatically resume from `output/.../checkpoints/latest.pt` when it exists.
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> * fix_BN: If `True`, we fix the weight of all `BatchNorm` layers.
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> * log_iter: Log the information per `log_iter` iterations.
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> * 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.
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> * resume_keep: Number of rolling resume checkpoints retained under `checkpoints/resume/`. Set `0` to keep all of them.
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> * save_iter: Save the checkpoint per `save_iter` iterations.
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> * with_test: If `True`, we test the model every `save_iter` iterations. A bit of performance impact.(*Disable in Default*)
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> * optimizer_reset: If `True` and `restore_hint!=0`, reset the optimizer while restoring the model.
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> * scheduler_reset: If `True` and `restore_hint!=0`, reset the scheduler while restoring the model.
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> * sync_BN: If `True`, applies Batch Normalization synchronously.
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> * total_iter: The total training iterations, `int` values.
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> * sampler:
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> - type: The name of sampler. Choose `TripletSampler`.
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> - 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.
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> - batch_size: *[P,K]* where `P` denotes the subjects in training batch while the `K` represents the sequences every subject owns. **Example**:
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> - 8
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> - 16
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> - **others**: Please refer to [data.sampler](../opengait/data/sampler.py) and [data.collate_fn](../opengait/data/collate_fn.py).
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> * **others**: Please refer to `evaluator_cfg`.
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---
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**Note**:
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- All the config items will be merged into [default.yaml](../configs/default.yaml), and the current config is preferable.
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- 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}`.
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# Example
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```yaml
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data_cfg:
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dataset_name: CASIA-B
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dataset_root: your_path
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dataset_partition: ./datasets/CASIA-B/CASIA-B.json
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num_workers: 1
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remove_no_gallery: false # Remove probe if no gallery for it
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test_dataset_name: CASIA-B
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evaluator_cfg:
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enable_float16: true
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restore_ckpt_strict: true
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restore_hint: 60000
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save_name: Baseline
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eval_func: evaluate_indoor_dataset
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sampler:
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batch_shuffle: false
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batch_size: 16
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sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered
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frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
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metric: euc # cos
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transform:
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- type: BaseSilCuttingTransform
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img_w: 64
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loss_cfg:
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- loss_term_weight: 1.0
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margin: 0.2
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type: TripletLoss
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log_prefix: triplet
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- loss_term_weight: 0.1
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scale: 16
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type: CrossEntropyLoss
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log_prefix: softmax
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log_accuracy: true
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model_cfg:
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model: Baseline
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backbone_cfg:
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in_channels: 1
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layers_cfg: # Layers configuration for automatically model construction
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- BC-64
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- BC-64
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- M
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- BC-128
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- BC-128
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- M
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- BC-256
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- BC-256
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# - M
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# - BC-512
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# - BC-512
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type: Plain
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SeparateFCs:
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in_channels: 256
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out_channels: 256
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parts_num: 31
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SeparateBNNecks:
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class_num: 74
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in_channels: 256
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parts_num: 31
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bin_num:
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- 16
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- 8
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- 4
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- 2
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- 1
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optimizer_cfg:
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lr: 0.1
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momentum: 0.9
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solver: SGD
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weight_decay: 0.0005
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scheduler_cfg:
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gamma: 0.1
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milestones: # Learning Rate Reduction at each milestones
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- 20000
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- 40000
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scheduler: MultiStepLR
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trainer_cfg:
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enable_float16: true # half_percesion float for memory reduction and speedup
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fix_layers: false
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log_iter: 100
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restore_ckpt_strict: true
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restore_hint: 0
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auto_resume_latest: false
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resume_every_iter: 500
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resume_keep: 3
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save_iter: 10000
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save_name: Baseline
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sync_BN: true
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total_iter: 60000
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sampler:
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batch_shuffle: true
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batch_size:
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- 8 # TripletSampler, batch_size[0] indicates Number of Identity
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- 16 # batch_size[1] indicates Samples sequqnce for each Identity
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frames_num_fixed: 30 # fixed frames number for training
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frames_num_max: 50 # max frames number for unfixed training
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frames_num_min: 25 # min frames number for unfixed traing
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sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
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type: TripletSampler
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transform:
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- type: BaseSilCuttingTransform
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img_w: 64
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```
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