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[Doc]Add English version of documents in examples (#1070)
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Hu Chuqi
@@ -1,42 +1,42 @@
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# AdaFace Python部署示例
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本目录下提供infer_xxx.py快速完成AdaFace模型在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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English | [简体中文](README_CN.md)
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# AdaFace Python Deployment Example
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This directory provides examples that `infer_xxx.py` fast finishes the deployment of AdaFace on CPU/GPU and GPU accelerated by TensorRT.
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在部署前，需确认以下两个步骤
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Before deployment, two steps require confirmation
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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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以AdaFace为例子, 提供`infer.py`快速完成AdaFace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
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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Taking AdaFace as an example, we demonstrate how `infer.py` fast finishes the deployment of AdaFace on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
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```bash
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#下载部署示例代码
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# Download the example code for deployment
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/faceid/adaface/python/
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#下载AdaFace模型文件和测试图片
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#下载测试图片
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# Download AdaFace model files and test images
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# Download test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
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unzip face_demo.zip
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# 如果为Paddle模型，运行以下代码
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# Run the following code if the model is in Paddle format
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wget https://bj.bcebos.com/paddlehub/fastdeploy/mobilefacenet_adaface.tgz
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tar zxvf mobilefacenet_adaface.tgz -C ./
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# CPU推理
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# CPU inference
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python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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--params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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--face face_0.jpg \
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--face_positive face_1.jpg \
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--face_negative face_2.jpg \
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--device cpu
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# GPU推理
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# GPU inference
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python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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--params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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--face face_0.jpg \
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--face_positive face_1.jpg \
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--face_negative face_2.jpg \
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--device gpu
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# GPU上使用TensorRT推理
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# TensorRT inference on GPU
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python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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--params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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--face face_0.jpg \
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@@ -45,7 +45,7 @@ python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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--device gpu \
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--use_trt True
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# 昆仑芯XPU推理
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# KunlunXin XPU inference
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python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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--params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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--face test_lite_focal_arcface_0.JPG \
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@@ -54,7 +54,7 @@ python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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--device kunlunxin
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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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The visualized result after running is as follows
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<div width="700">
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<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321537-860bf857-0101-4e92-a74c-48e8658d838c.JPG">
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@@ -70,56 +70,56 @@ Cosine 01: 0.7483505506964364
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Cosine 02: -0.09605773855893639
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```
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## AdaFace Python接口
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## AdaFace Python Interface
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```python
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fastdeploy.vision.faceid.AdaFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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AdaFace模型加载和初始化，其中model_file为导出的ONNX模型格式或PADDLE静态图格式
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AdaFace model loading and initialization, among which model_file is the exported ONNX model format or PADDLE static graph format
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**参数**
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**Parameter**
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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PADDLE
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. Paddle format by default
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### predict函数
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### predict function
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> ```python
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> AdaFace.predict(image_data)
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> ```
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>
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> 模型预测结口，输入图像直接输出检测结果。
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> Model prediction interface. Input images and output detection results.
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>
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> **参数**
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> **Parameter**
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>
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> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> > * **image_data**(np.ndarray): Input data in HWC or BGR format
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> **返回**
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> **Return**
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>
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> > 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/)
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> > Return `fastdeploy.vision.FaceRecognitionResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for its description.
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### 类成员属性
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#### 预处理参数
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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
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### Class Member Property
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#### Pre-processing Parameter
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Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
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#### AdaFacePreprocessor的成员变量
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以下变量为AdaFacePreprocessor的成员变量
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> > * **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]
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> > * **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]
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> > * **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]
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> > * **swap_rb**(bool): 预处理是否将BGR转换成RGB，默认True
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#### Member variables of AdaFacePreprocessor
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The member variables of AdaFacePreprocessor are as follows
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> > * **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]
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> > * **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]
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> > * **beta**(list[float]): Preprocess normalized alpha, and calculated as `x'=x*alpha+beta`. beta defaults to [-1.f, -1.f, -1.f]
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> > * **swap_rb**(bool): Whether to convert BGR to RGB in pre-processing. Default true
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#### AdaFacePostprocessor的成员变量
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以下变量为AdaFacePostprocessor的成员变量
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> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认False
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#### Member variables of AdaFacePostprocessor
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The member variables of AdaFacePostprocessor are as follows
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> > * **l2_normalize**(bool): Whether to perform l2 normalization before outputting the face vector. Default false.
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## 其它文档
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## Other Documents
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- [AdaFace 模型介绍](..)
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- [AdaFace 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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- [AdaFace Model Description](..)
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- [AdaFace C++ Deployment](../cpp)
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- [Model Prediction Results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)
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examples/vision/faceid/adaface/python/README_CN.md
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examples/vision/faceid/adaface/python/README_CN.md
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[English](README.md) | 简体中文
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# AdaFace准备部署模型
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- [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/)
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- [官方库](https://github.com/PaddlePaddle/PaddleClas/)中训练过后的Paddle模型导出Paddle静态图模型操作后，可进行部署；
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## 简介
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一直以来，低质量图像的人脸识别都具有挑战性，因为低质量图像的人脸属性是模糊和退化的。将这样的图片输入模型时，将不能很好的实现分类。
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而在人脸识别任务中，我们经常会利用opencv的仿射变换来矫正人脸数据，这时数据会出现低质量退化的现象。如何解决低质量图片的分类问题成为了模型落地时的痛点问题。
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在AdaFace这项工作中，作者在损失函数中引入了另一个因素，即图像质量。作者认为，强调错误分类样本的策略应根据其图像质量进行调整。
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具体来说，简单或困难样本的相对重要性应该基于样本的图像质量来给定。据此作者提出了一种新的损失函数来通过图像质量强调不同的困难样本的重要性。
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由上，AdaFace缓解了低质量图片在输入网络后输出结果精度变低的情况，更加适合在人脸识别任务落地中使用。
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## 导出Paddle静态图模型
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以AdaFace为例:
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训练和导出代码，请参考[AIStudio](https://aistudio.baidu.com/aistudio/projectdetail/4479879?contributionType=1)
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## 下载预训练Paddle静态图模型
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为了方便开发者的测试，下面提供了我转换过的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）其中精度指标来源于AIStudio中对各模型的介绍。
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| 模型 | 大小 | 精度 (AgeDB_30) |
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|:----------------------------------------------------------------------------------------------|:------|:--------------|
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| [AdaFace-MobileFacenet](https://bj.bcebos.com/paddlehub/fastdeploy/mobilefacenet_adaface.tgz) | 3.2MB | 95.5 |
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## 详细部署文档
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- [Python部署](python)
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- [C++部署](cpp)
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