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[vision] Add AdaFace model support (#301)
* 新增adaface模型 * 新增adaface模型python代码 * 新增adaface模型example代码 * 删除无用的import * update * 修正faceid文档的错误 * 修正faceid文档的错误 * 删除无用文件 * 新增adaface模型paddleinference推理代码,模型文件先提交方便测试后期会删除 * 新增adaface模型paddleinference推理代码,模型文件先提交方便测试后期会删除 * 按照要求修改并跑通cpp example * 测试python example * python cpu测试通过,修改了文档 * 修正文档,替换了模型下载地址 * 修正文档 * 修正文档 Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
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examples/vision/faceid/adaface/cpp/README.md
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# AdaFace C++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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以AdaFace为例提供`infer.cc`快速完成AdaFace在CPU/GPU,以及GPU上通过TensorRT加速部署的示例。
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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/environment.md)
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- 2. 根据开发环境,下载预编译部署库和samples代码,参考[FastDeploy预编译库](../../../../../docs/quick_start)
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以Linux上CPU推理为例,在本目录执行如下命令即可完成编译测试
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```bash
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# “如果预编译库不包含本模型,请从最新代码编译SDK”
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mkdir build
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cd build
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.2.1.tgz
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tar xvf fastdeploy-linux-x64-0.2.1.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.2.1
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make -j
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#下载测试图片
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wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_0.JPG
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wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_1.JPG
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wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_2.JPG
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# 如果为Paddle模型,运行以下代码
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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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./infer_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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test_lite_focal_arcface_0.JPG \
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test_lite_focal_arcface_1.JPG \
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test_lite_focal_arcface_2.JPG \
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0
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# GPU推理
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./infer_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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test_lite_focal_arcface_0.JPG \
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test_lite_focal_arcface_1.JPG \
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test_lite_focal_arcface_2.JPG \
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1
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# GPU上TensorRT推理
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./infer_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
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mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
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test_lite_focal_arcface_0.JPG \
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test_lite_focal_arcface_1.JPG \
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test_lite_focal_arcface_2.JPG \
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2
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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="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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<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184322004-a551e6e4-6f47-454e-95d6-f8ba2f47b516.JPG">
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<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321622-d9a494c3-72f3-47f1-97c5-8a2372de491f.JPG">
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</div>
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以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/compile/how_to_use_sdk_on_windows.md)
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## AdaFace C++接口
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### AdaFace类
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```c++
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fastdeploy::vision::faceid::AdaFace(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE)
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```
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AdaFace模型加载和初始化,如果使用PaddleInference推理,model_file和params_file为PaddleInference模型格式;
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如果使用ONNXRuntime推理,model_file为ONNX模型格式,params_file为空。
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#### Predict函数
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> ```c++
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> AdaFace::Predict(cv::Mat* im, FaceRecognitionResult* result)
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> ```
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>
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> 模型预测接口,输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **im**: 输入图像,注意需为HWC,BGR格式
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> > * **result**: 检测结果,包括检测框,各个框的置信度, FaceRecognitionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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### 类成员变量
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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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> > * **size**(vector<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**(vector<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**(vector<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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> > * **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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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md)
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