rename lib to opengait

docs/1.detailed_config.md

@@ -36,7 +36,7 @@
 ### model_cfg
 * Model to be trained
 >  * Args
->     * model : Model type, please refer to [Model Library](../lib/modeling/models) for the supported values.
+>     * 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
@@ -51,7 +51,7 @@
 >       - 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](../lib/data/sampler.py) and [data.collate_fn](../lib/data/collate_fn.py)
+>       - **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.
 
@@ -73,7 +73,7 @@
 >       - 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](../lib/data/sampler.py) and [data.collate_fn](../lib/data/collate_fn.py).
+>       - **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**: 

docs/2.how_to_create_your_model.md

@@ -50,11 +50,11 @@ class NewModel(BaseModel):
 > 
 > `embeddings`, `logits` and `labels` are the input arguments of the loss function.
 
-More information should be seen in [base_model.py](../lib/modeling/base_model.py) and [loss_aggregator.py](../lib/modeling/loss_aggregator.py).
+More information should be seen in [base_model.py](../opengait/modeling/base_model.py) and [loss_aggregator.py](../opengait/modeling/loss_aggregator.py).
 
 After finishing the model file, you have two steps left to do:
 
-**Step 1**: Put your newmodel.py under `lib/modeling/models`.
+**Step 1**: Put your newmodel.py under `opengait/modeling/models`.
 
 **Step 2**: Specify the model name in a yaml file:
 ```yaml
@@ -67,7 +67,7 @@ model_cfg:
 
 
 ## A new loss
-If you want to write a new loss, you need to write a class inherited from `lib/modeling/losses`, like this
+If you want to write a new loss, you need to write a class inherited from `opengait/modeling/losses`, like this
 ```python
 from .base import BaseLoss
 
@@ -81,6 +81,6 @@ class NewLoss(BaseLoss):
 ```
 Remember to use `gather_and_scale_wrapper` to wrap your forward function if your loss is computed by pairs like `triplet`. By this, we gather all features to one GPU card and scale the loss by the number of GPUs.
 
-Then, put your loss in `lib/modeling/losses` so that you can use it in config file. 
+Then, put your loss in `opengait/modeling/losses` so that you can use it in config file. 
 
-Moreover, refer to [loss_aggregator.py](../lib/modeling/loss_aggregator.py) to explore how does your defined loss work in the model.
+Moreover, refer to [loss_aggregator.py](../opengait/modeling/loss_aggregator.py) to explore how does your defined loss work in the model.

docs/3.advanced_usages.md

@@ -32,10 +32,10 @@
 > * 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: