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* 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>
68 lines
2.5 KiB
Markdown
68 lines
2.5 KiB
Markdown
# FSANet Python部署示例
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在部署前，需确认以下两个步骤
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- 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)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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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
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/headpose/fsanet/python
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# 下载FSANet模型文件和测试图片
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## 原版ONNX模型
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wget https://bj.bcebos.com/paddlehub/fastdeploy/fsanet-var.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/headpose_input.png
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# CPU推理
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python infer.py --model fsanet-var.onnx --image headpose_input.png --device cpu
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# GPU推理
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python infer.py --model fsanet-var.onnx --image headpose_input.png --device gpu
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# TRT推理
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python infer.py --model fsanet-var.onnx --image headpose_input.png --device gpu --backend trt
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```
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运行完成可视化结果如下图所示
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<div width="520">
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<img width="500" height="514" float="left" src="https://user-images.githubusercontent.com/19977378/198279932-3eee424e-98a2-4249-bdeb-0f79127cbc9d.png">
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</div>
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## FSANet Python接口
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```python
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fd.vision.headpose.FSANet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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FSANet 模型加载和初始化，其中model_file为导出的ONNX模型格式
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **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
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为ONNX
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### predict函数
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> ```python
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> FSANet.predict(input_image)
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> ```
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>
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> 模型预测结口，输入图像直接输出头部姿态预测结果。
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>
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> **参数**
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>
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> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> **返回**
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>
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> > 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/)
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## 其它文档
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- [FSANet 模型介绍](..)
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- [FSANet C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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