rename lib to opengait

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.