# Advanced Usages

### Cross-Dataset Evalution

> You can conduct cross-dataset evalution by just modifying several arguments in your [data_cfg](../configs/baseline/baseline.yaml#L1).

>

>  Take [baseline.yaml](../configs/baseline/baseline.yaml) as an 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

> ```

> Now, suppose we get the model trained on [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp), and then we want to test it on [OUMVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html).

> 

> We should alter the `dataset_root`, `dataset_partition` and `test_dataset_name`, just like:

> ```yaml

> data_cfg:

>   dataset_name: CASIA-B

>   dataset_root:  your_OUMVLP_path

>   dataset_partition: ./datasets/OUMVLP/OUMVLP.json

>   num_workers: 1

>   remove_no_gallery: false # Remove probe if no gallery for it

>   test_dataset_name: OUMVLP

> ```

---

>

<!-- ### Identification Function

> Sometime, your test dataset may be neither the popular [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) nor the largest [OUMVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html). Meanwhile, you need to customize a special identification function to fit your dataset. 

> 

> * If your path structure is similar to [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) (the 3-flod style: `id-type-view`), we recommand you to  -->

### Data Augmentation

> In OpenGait, there is a basic transform class almost called by all the models, this is [BaseSilCuttingTransform](../opengait/data/transform.py#L20), which is used to cut the input silhouettes.

>

> Accordingly, by referring to this implementation, you can easily customize the data agumentation in just two steps:

> * *Step1*: Define the transform function or class in [transform.py](../opengait/data/transform.py), and make sure it callable. The style of [torchvision.transforms](https://pytorch.org/vision/stable/_modules/torchvision/transforms/transforms.html) is recommanded, and following shows a demo;

>> ```python

>> import torchvision.transforms as T

>> class demo1():

>>     def __init__(self, args):

>>         pass

>>     

>>     def __call__(self, seqs):

>>         '''

>>             seqs: with dimension of [sequence, height, width]

>>         '''

>>         pass

>>         return seqs

>> 

>> class demo2():

>>     def __init__(self, args):

>>         pass

>>     

>>     def __call__(self, seqs):

>>         pass

>>         return seqs

>> 

>>  def TransformDemo(base_args, demo1_args, demo2_args):

>>     transform = T.Compose([

>>         BaseSilCuttingTransform(**base_args), 

>>         demo1(args=demo1_args), 

>>         demo2(args=demo2_args)

>>     ])

>>     return transform

>> ```

> * *Step2*: Reset the [`transform`](../configs/baseline.yaml#L100) arguments in your config file:

>> ```yaml

>> transform:

>> - type: TransformDemo

>>     base_args: {'img_w': 64}

>>     demo1_args: false

>>     demo2_args: false

>> ```

### Visualization

> To learn how does the model work, sometimes, you need to visualize the intermediate result.

> 

> For this purpose, we have defined a built-in instantiation of [`torch.utils.tensorboard.SummaryWriter`](https://pytorch.org/docs/stable/tensorboard.html), that is [`self.msg_mgr.writer`](../opengait/utils/msg_manager.py#L24), to make sure you can log the middle information everywhere you want.

> 

> Demo: if we want to visualize the output feature of [baseline's backbone](../opengait/modeling/models/baseline.py#L27), we could just insert the following codes at [baseline.py#L28](../opengait/modeling/models/baseline.py#L28):

>> ```python

>> summary_writer = self.msg_mgr.writer

>> if torch.distributed.get_rank() == 0 and self.training and self.iteration % 100==0:

>>     summary_writer.add_video('outs', outs.mean(2).unsqueeze(2), self.iteration)

>> ```

> Note that this example requires the [`moviepy`](https://github.com/Zulko/moviepy) package, and hence you should run `pip install moviepy` first.