Junhao Liang
190 lines
7.0 KiB
Markdown
190 lines
7.0 KiB
Markdown
# 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](../config).
|
|
----
|
|
### 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](../config/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: ./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
|
|
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
|
|
|
|
```
|