[Model] Add FSANet model (#448)

* add yolov5cls

* fixed bugs

* fixed bugs

* fixed preprocess bug

* add yolov5cls readme

* deal with comments

* Add YOLOv5Cls Note

* add yolov5cls test

* add rvm support

* support rvm model

* add rvm demo

* fixed bugs

* add rvm readme

* add TRT support

* add trt support

* add rvm test

* add EXPORT.md

* rename export.md

* rm poros doxyen

* deal with comments

* deal with comments

* add rvm video_mode note

* add fsanet

* fixed bug

* update readme

* fixed for ci

* deal with comments

* deal with comments

* deal with comments

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

examples/vision/headpose/fsanet/python/README.md

@@ -0,0 +1,67 @@
+# FSANet Python部署示例
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+本目录下提供`infer.py`快速完成FSANet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例，保证 FastDeploy 版本 >= 0.6.0 支持FSANet模型。执行如下脚本即可完成
+
+```bash
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/headpose/fsanet/python
+
+# 下载FSANet模型文件和测试图片
+## 原版ONNX模型
+wget https://bj.bcebos.com/paddlehub/fastdeploy/fsanet-var.onnx
+wget https://bj.bcebos.com/paddlehub/fastdeploy/headpose_input.png
+# CPU推理
+python infer.py --model fsanet-var.onnx --image headpose_input.png --device cpu
+# GPU推理
+python infer.py --model fsanet-var.onnx --image headpose_input.png --device gpu
+# TRT推理
+python infer.py --model fsanet-var.onnx --image headpose_input.png --device gpu --backend trt
+```
+
+运行完成可视化结果如下图所示
+
+<div width="520">
+<img width="500" height="514" float="left" src="https://user-images.githubusercontent.com/19977378/198279932-3eee424e-98a2-4249-bdeb-0f79127cbc9d.png">
+</div>
+
+## FSANet Python接口
+
+```python
+fd.vision.headpose.FSANet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
+```
+
+FSANet 模型加载和初始化，其中model_file为导出的ONNX模型格式
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
+> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
+> * **model_format**(ModelFormat): 模型格式，默认为ONNX
+### predict函数
+
+> ```python
+> FSANet.predict(input_image)
+> ```
+>
+> 模型预测结口，输入图像直接输出头部姿态预测结果。
+>
+> **参数**
+>
+> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
+> **返回**
+>
+> > 返回`fastdeploy.vision.HeadPoseResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
+
+## 其它文档
+
+- [FSANet 模型介绍](..)
+- [FSANet C++部署](../cpp)
+- [模型预测结果说明](../../../../../docs/api/vision_results/)
+- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)