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rename lib to opengait

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darkliang
2022-04-12 11:28:09 +08:00
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docs/3.advanced_usages.md
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> * 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](../lib/data/transform.py#L20), which is used to cut the input silhouettes.
> 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](../lib/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;
> * *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():
@@ -77,9 +77,9 @@
### 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`](../lib/utils/msg_manager.py#L24), to make sure you can log the middle information everywhere you want.
> 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](../lib/modeling/models/baseline.py#L27), we could just insert the following codes at [baseline.py#L28](../lib/modeling/models/baseline.py#L28):
> 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: