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This commit is contained in:
Hu Chuqi
2023-01-06 09:34:28 +08:00
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examples/vision/faceid/insightface/python/README.md
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@@ -1,36 +1,36 @@
# InsightFace Python部署示例
本目录下提供infer_xxx.py快速完成InsighFace模型包括ArcFace\CosFace\VPL\Partial_FC在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
English | [简体中文](README_CN.md)
# InsightFace Python Deployment Example
This directory provides examples that `infer_xxx.py` fast finishes the deployment of InsighFace, including ArcFace\CosFace\VPL\Partial_FC on CPU/GPU and GPU accelerated by TensorRT.
在部署前，需确认以下两个步骤
Before deployment, two steps require confirmation
- 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)
以ArcFace为例子, 提供`infer_arcface.py`快速完成ArcFace在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
Taking ArcFace as an example, we demonstrate how `infer_arcface.py` fast finishes the deployment of ArcFace on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
```bash
#下载部署示例代码
# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/faceid/insightface/python/
#下载ArcFace模型文件和测试图片
# Download ArcFace model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
unzip face_demo.zip
# CPU推理
# CPU inference
python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
--face face_0.jpg \
--face_positive face_1.jpg \
--face_negative face_2.jpg \
--device cpu
# GPU推理
# GPU inference
python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
--face face_0.jpg \
--face_positive face_1.jpg \
--face_negative face_2.jpg \
--device gpu
# GPU上使用TensorRT推理
# TensorRT inference on GPU
python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
--face face_0.jpg \
--face_positive face_1.jpg \
@@ -39,7 +39,7 @@ python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
--use_trt True
```
运行完成可视化结果如下图所示
The visualized result after running is as follows
<div width="700">
<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321537-860bf857-0101-4e92-a74c-48e8658d838c.JPG">
@@ -56,7 +56,7 @@ Detect Done! Cosine 01: 0.814385, Cosine 02:-0.059388
```
## InsightFace Python接口
## InsightFace Python Interface
```python
fastdeploy.vision.faceid.ArcFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
@@ -65,50 +65,50 @@ fastdeploy.vision.faceid.PartialFC(model_file, params_file=None, runtime_option=
fastdeploy.vision.faceid.VPL(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
```
ArcFace模型加载和初始化，其中model_file为导出的ONNX模型格式
ArcFace model loading and initialization, among which model_file is the exported ONNX model format
**参数**
**Parameter**
> * **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
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
> * **model_format**(ModelFormat): Model format. ONNX format by default
### predict函数
### predict function
> ```python
> ArcFace.predict(image_data)
> ```
>
> 模型预测结口，输入图像直接输出检测结果。
> Model prediction interface. Input images and output detection results.
>
> **参数**
> **Parameter**
>
> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> > * **image_data**(np.ndarray): Input data in HWC or BGR format
> **返回**
> **Return**
>
> > 返回`fastdeploy.vision.FaceRecognitionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
> > Return `fastdeploy.vision.FaceRecognitionResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for its description.
### 类成员属性
#### 预处理参数
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
### Class Member Property
#### Pre-processing Parameter
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
#### AdaFacePreprocessor的成员变量
以下变量为AdaFacePreprocessor的成员变量
> > * **size**(list[int]): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[112, 112]
> > * **alpha**(list[float]): 预处理归一化的alpha值，计算公式为`x'=x*alpha+beta`，alpha默认为[1. / 127.5, 1.f / 127.5, 1. / 127.5]
> > * **beta**(list[float]): 预处理归一化的beta值，计算公式为`x'=x*alpha+beta`，beta默认为[-1.f, -1.f, -1.f]
> > * **swap_rb**(bool): 预处理是否将BGR转换成RGB，默认True
#### Member variables of AdaFacePreprocessor
Member variables of AdaFacePreprocessor are as follows
> > * **size**(list[int]): This parameter changes the size of the resize during preprocessing, containing two integer elements for [width, height] with default value [112, 112]
> > * **alpha**(list[float]): Preprocess normalized alpha, and calculated as `x'=x*alpha+beta`. alpha defaults to [1. / 127.5, 1.f / 127.5, 1. / 127.5]
> > * **beta**(list[float]): Preprocess normalized beta, and calculated as `x'=x*alpha+beta`，beta defaults to [-1.f, -1.f, -1.f]
> > * **swap_rb**(bool): Whether to convert BGR to RGB in pre-processing. Default True
#### AdaFacePostprocessor的成员变量
以下变量为AdaFacePostprocessor的成员变量
> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认False
#### Member variables of AdaFacePostprocessor
Member variables of AdaFacePostprocessor are as follows
> > * **l2_normalize**(bool): Whether to perform l2 normalization before outputting the face vector. Default False.
## 其它文档
## Other Documents
- [InsightFace 模型介绍](..)
- [InsightFace 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)
- [InsightFace Model Description](..)
- [InsightFace C++ Deployment](../cpp)
- [Model Prediction Results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)