[Model] Refactor insightface models (#919)

* 重构insightface代码

* 重写insightface example代码

* 重写insightface example代码

* 删除多余代码

* 修改预处理代码

* 修改文档

* 修改文档

* 恢复误删除的文件

* 修改cpp example

* 修改cpp example

* 测试python代码

* 测试python代码

* 测试python代码

* 测试python代码

* 测试python代码

* 测试python代码

* 测试python代码

* 跑通python代码

* 修复重复初始化的bug

* 更新adaface的python代码

* 修复c++重复初始化的问题

* 修复c++重复初始化的问题

* 修复Python重复初始化的问题

* 新增preprocess的几个参数的获取方式

* 修复注释的错误

* 按照要求修改

* 修改文档中的图片为图片压缩包

* 修改编译完成后程序的提示

* 更新错误include

* 删除无用文件

* 更新文档

cmake/rknpu2.cmake

@@ -10,10 +10,10 @@ download_and_decompress(${RKNPU2_URL} ${CMAKE_CURRENT_BINARY_DIR}/${RKNPU2_FILE}
 # set path
 set(RKNPU_RUNTIME_PATH ${THIRD_PARTY_PATH}/install/rknpu2_runtime)
 
-if (${CMAKE_SYSTEM_NAME} STREQUAL "Linux")
+#if (${CMAKE_SYSTEM_NAME} STREQUAL "Linux")
-else ()
+#else ()
-    message(FATAL_ERROR "[rknpu2.cmake] Only support build rknpu2 in Linux")
+#    message(FATAL_ERROR "[rknpu2.cmake] Only support build rknpu2 in Linux")
-endif ()
+#endif ()
 
 
 if (EXISTS ${RKNPU_RUNTIME_PATH})

examples/vision/faceid/README.md

@@ -1,6 +1,8 @@
 # 人脸识别模型
 
 
+## 模型支持列表
+
 FastDeploy目前支持如下人脸识别模型部署
 
 | 模型                                     | 说明             | 模型格式       | 版本                                                                            |
@@ -10,3 +12,7 @@ FastDeploy目前支持如下人脸识别模型部署
 | [deepinsight/PartialFC](./insightface) | PartialFC 系列模型 | ONNX       | [CommitID:babb9a5](https://github.com/deepinsight/insightface/commit/babb9a5) |
 | [deepinsight/VPL](./insightface)       | VPL 系列模型       | ONNX       | [CommitID:babb9a5](https://github.com/deepinsight/insightface/commit/babb9a5) |
 | [paddleclas/AdaFace](./adaface)        | AdaFace 系列模型   | PADDLE     | [CommitID:babb9a5](https://github.com/PaddlePaddle/PaddleClas/tree/v2.4.0)    |
+
+## 模型demo简介
+
+ArcFace,CosFace,PartialFC,VPL同属于deepinsight系列，因此demo使用ONNX作为推理框架。AdaFace则采用PaddleInference作为推理框架。

examples/vision/faceid/adaface/cpp/CMakeLists.txt

@@ -1,4 +1,4 @@
-PROJECT(infer_demo C CXX)
+PROJECT(infer_adaface_demo C CXX)
 CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
 
 # 指定下载解压后的fastdeploy库路径
@@ -9,5 +9,5 @@ include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
 # 添加FastDeploy依赖头文件
 include_directories(${FASTDEPLOY_INCS})
 
-add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+add_executable(infer_adaface_demo ${PROJECT_SOURCE_DIR}/infer.cc)
-target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
+target_link_libraries(infer_adaface_demo ${FASTDEPLOY_LIBS})

examples/vision/faceid/adaface/cpp/README.md

@@ -8,56 +8,43 @@
 - 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. 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/cn/build_and_install/download_prebuilt_libraries.md)
 
-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，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
+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
 
 ```bash
+# “如果预编译库不包含本模型，请从最新代码编译SDK”
 mkdir build
 cd build
-# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
 wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
 tar xvf fastdeploy-linux-x64-x.x.x.tgz
 cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
 #下载测试图片
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_0.JPG
+wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_1.JPG
+unzip face_demo.zip
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_2.JPG
 
 # 如果为Paddle模型，运行以下代码
 wget https://bj.bcebos.com/paddlehub/fastdeploy/mobilefacenet_adaface.tgz
 tar zxvf mobilefacenet_adaface.tgz -C ./
 # CPU推理
-./infer_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
+./infer_adaface_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
               mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
-              test_lite_focal_arcface_0.JPG \
+              face_0.jpg face_1.jpg face_2.jpg 0
-              test_lite_focal_arcface_1.JPG \
-              test_lite_focal_arcface_2.JPG \
-              0
 
 # GPU推理
-./infer_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
+./infer_adaface_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
               mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
-              test_lite_focal_arcface_0.JPG \
+              face_0.jpg face_1.jpg face_2.jpg 1
-              test_lite_focal_arcface_1.JPG \
-              test_lite_focal_arcface_2.JPG \
-              1
 
 # GPU上TensorRT推理
-./infer_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
+./infer_adaface_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
               mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
-              test_lite_focal_arcface_0.JPG \
+              face_0.jpg face_1.jpg face_2.jpg 2
-              test_lite_focal_arcface_1.JPG \
-              test_lite_focal_arcface_2.JPG \
-              2
 
 # XPU推理
 ./infer_demo mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
               mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
-              test_lite_focal_arcface_0.JPG \
+              face_0.jpg face_1.jpg face_2.jpg 3
-              test_lite_focal_arcface_1.JPG \
-              test_lite_focal_arcface_2.JPG \
-              3
 ```
 
 运行完成可视化结果如下图所示
@@ -101,16 +88,22 @@ AdaFace模型加载和初始化，如果使用PaddleInference推理，model_file
 > > * **im**: 输入图像，注意需为HWC，BGR格式
 > > * **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/)
 
-### 类成员变量
+### 修改预处理以及后处理的参数
-#### 预处理参数
+预处理和后处理的参数的需要通过修改AdaFacePostprocessor，AdaFacePreprocessor的成员变量来进行修改。
-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
 
+#### AdaFacePreprocessor成员变量(预处理参数)
+> > * **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],
+      通过AdaFacePreprocessor::SetSize(std::vector<int>& size)来进行修改
+> > * **alpha**(vector&lt;float&gt;): 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],
+      通过AdaFacePreprocessor::SetAlpha(std::vector<float>& alpha)来进行修改
+> > * **beta**(vector&lt;float&gt;): 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],
+      通过AdaFacePreprocessor::SetBeta(std::vector<float>& beta)来进行修改
+> > * **permute**(bool): 预处理是否将BGR转换成RGB，默认true,
+      通过AdaFacePreprocessor::SetPermute(bool permute)来进行修改
 
-> > * **size**(vector&lt;int&gt;): 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]
+#### AdaFacePostprocessor成员变量(后处理参数)
-> > * **alpha**(vector&lt;float&gt;): 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]
+> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认false,
-> > * **beta**(vector&lt;float&gt;): 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]
+      AdaFacePostprocessor::SetL2Normalize(bool& l2_normalize)来进行修改
-> > * **swap_rb**(bool): 预处理是否将BGR转换成RGB，默认true
-> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认false
 
 - [模型介绍](../../)
 - [Python部署](../python)

examples/vision/faceid/adaface/cpp/infer.cc

@@ -1,14 +1,17 @@
-/***************************************************************************
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
- *
+//
- * Copyright (c) 2021 Baidu.com, Inc. All Rights Reserved
+// Licensed under the Apache License, Version 2.0 (the "License");
- *
+// you may not use this file except in compliance with the License.
- **************************************************************************/
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
 
-/**
- * @author Baidu
- * @brief demo_image_inference
- *
- **/
 #include "fastdeploy/vision.h"
 
 void CpuInfer(const std::string &model_file, const std::string &params_file,
@@ -28,8 +31,8 @@ void CpuInfer(const std::string &model_file, const std::string &params_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -40,9 +43,11 @@ void CpuInfer(const std::string &model_file, const std::string &params_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -65,8 +70,8 @@ void XpuInfer(const std::string &model_file, const std::string &params_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -77,9 +82,11 @@ void XpuInfer(const std::string &model_file, const std::string &params_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -103,8 +110,8 @@ void GpuInfer(const std::string &model_file, const std::string &params_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -115,9 +122,11 @@ void GpuInfer(const std::string &model_file, const std::string &params_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -143,8 +152,8 @@ void TrtInfer(const std::string &model_file, const std::string &params_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -155,9 +164,11 @@ void TrtInfer(const std::string &model_file, const std::string &params_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }

examples/vision/faceid/adaface/python/README.md

@@ -15,9 +15,8 @@ cd examples/vision/faceid/adaface/python/
 
 #下载AdaFace模型文件和测试图片
 #下载测试图片
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_0.JPG
+wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_1.JPG
+unzip face_demo.zip
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_2.JPG
 
 # 如果为Paddle模型，运行以下代码
 wget https://bj.bcebos.com/paddlehub/fastdeploy/mobilefacenet_adaface.tgz
@@ -26,25 +25,25 @@ tar zxvf mobilefacenet_adaface.tgz -C ./
 # CPU推理
 python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
                 --params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
-                --face test_lite_focal_arcface_0.JPG \
+                --face face_0.jpg \
-                --face_positive test_lite_focal_arcface_1.JPG \
+                --face_positive face_1.jpg \
-                --face_negative test_lite_focal_arcface_2.JPG \
+                --face_negative face_2.jpg \
                 --device cpu
 # GPU推理
 python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
                 --params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
-                --face test_lite_focal_arcface_0.JPG \
+                --face face_0.jpg \
-                --face_positive test_lite_focal_arcface_1.JPG \
+                --face_positive face_1.jpg \
-                --face_negative test_lite_focal_arcface_2.JPG \
+                --face_negative face_2.jpg \
                 --device gpu
 # GPU上使用TensorRT推理
 python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
                 --params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
-                --face test_lite_focal_arcface_0.JPG \
+                --face face_0.jpg \
-                --face_positive test_lite_focal_arcface_1.JPG \
+                --face_positive face_1.jpg \
-                --face_negative test_lite_focal_arcface_2.JPG \
+                --face_negative face_2.jpg \
-                 --device gpu \
+                --device gpu \
-                 --use_trt True
+                --use_trt True
 
 # XPU推理
 python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
@@ -106,11 +105,15 @@ AdaFace模型加载和初始化，其中model_file为导出的ONNX模型格式
 #### 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
 
-
+#### 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
+
+#### 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
 
 

examples/vision/faceid/insightface/cpp/README.md

@@ -7,12 +7,11 @@
 - 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. 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/cn/build_and_install/download_prebuilt_libraries.md)
 
-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，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
+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
 
 ```bash
 mkdir build
 cd build
-# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
 wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
 tar xvf fastdeploy-linux-x64-x.x.x.tgz
 cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
@@ -20,17 +19,15 @@ make -j
 
 #下载官方转换好的ArcFace模型文件和测试图片
 wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_0.JPG
+wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_1.JPG
+unzip face_demo.zip
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_2.JPG
-
 
 # CPU推理
-./infer_arcface_demo ms1mv3_arcface_r100.onnx test_lite_focal_arcface_0.JPG test_lite_focal_arcface_1.JPG test_lite_focal_arcface_2.JPG 0
+./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 0
 # GPU推理
-./infer_arcface_demo ms1mv3_arcface_r100.onnx test_lite_focal_arcface_0.JPG test_lite_focal_arcface_1.JPG test_lite_focal_arcface_2.JPG 1
+./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 1
 # GPU上TensorRT推理
-./infer_arcface_demo ms1mv3_arcface_r100.onnx test_lite_focal_arcface_0.JPG test_lite_focal_arcface_1.JPG test_lite_focal_arcface_2.JPG 2
+./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 2
 ```
 
 运行完成可视化结果如下图所示
@@ -113,16 +110,22 @@ VPL模型加载和初始化，其中model_file为导出的ONNX模型格式。
 > > * **im**: 输入图像，注意需为HWC，BGR格式
 > > * **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/)
 
-### 类成员变量
+### 修改预处理以及后处理的参数
-#### 预处理参数
+预处理和后处理的参数的需要通过修改InsightFaceRecognitionPostprocessor，InsightFaceRecognitionPreprocessor的成员变量来进行修改。
-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
 
+#### InsightFaceRecognitionPreprocessor成员变量(预处理参数)
+> > * **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],
+      通过InsightFaceRecognitionPreprocessor::SetSize(std::vector<int>& size)来进行修改
+> > * **alpha**(vector&lt;float&gt;): 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],
+      通过InsightFaceRecognitionPreprocessor::SetAlpha(std::vector<float>& alpha)来进行修改
+> > * **beta**(vector&lt;float&gt;): 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],
+      通过InsightFaceRecognitionPreprocessor::SetBeta(std::vector<float>& beta)来进行修改
+> > * **permute**(bool): 预处理是否将BGR转换成RGB，默认true,
+      通过InsightFaceRecognitionPreprocessor::SetPermute(bool permute)来进行修改
 
-> > * **size**(vector&lt;int&gt;): 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]
+#### InsightFaceRecognitionPostprocessor成员变量(后处理参数)
-> > * **alpha**(vector&lt;float&gt;): 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]
+> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认false,
-> > * **beta**(vector&lt;float&gt;): 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]
+      InsightFaceRecognitionPostprocessor::SetL2Normalize(bool& l2_normalize)来进行修改
-> > * **swap_rb**(bool): 预处理是否将BGR转换成RGB，默认true
-> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认false
 
 - [模型介绍](../../)
 - [Python部署](../python)

examples/vision/faceid/insightface/cpp/infer_arcface.cc

@@ -16,11 +16,7 @@
 
 void CpuInfer(const std::string& model_file,
               const std::vector<std::string>& image_file) {
-  auto model = fastdeploy::vision::faceid::ArcFace(model_file);
+  auto model = fastdeploy::vision::faceid::ArcFace(model_file, "");
-  if (!model.Initialized()) {
-    std::cerr << "Failed to initialize." << std::endl;
-    return;
-  }
 
   cv::Mat face0 = cv::imread(image_file[0]);
   cv::Mat face1 = cv::imread(image_file[1]);
@@ -30,8 +26,8 @@ void CpuInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -42,9 +38,11 @@ void CpuInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -67,8 +65,8 @@ void GpuInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -79,9 +77,11 @@ void GpuInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -106,8 +106,8 @@ void TrtInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -118,9 +118,11 @@ void TrtInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -129,8 +131,7 @@ int main(int argc, char* argv[]) {
   if (argc < 6) {
     std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
                  "e.g ./infer_arcface_demo ms1mv3_arcface_r100.onnx "
-                 "test_lite_focal_arcface_0.JPG test_lite_focal_arcface_1.JPG "
+                 "face_0.jpg face_1.jpg face_2.jpg 0"
-                 "test_lite_focal_arcface_2.JPG 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
                  "with gpu; 2: run with gpu and use tensorrt backend."

examples/vision/faceid/insightface/cpp/infer_cosface.cc

@@ -16,11 +16,7 @@
 
 void CpuInfer(const std::string& model_file,
               const std::vector<std::string>& image_file) {
-  auto model = fastdeploy::vision::faceid::CosFace(model_file);
+  auto model = fastdeploy::vision::faceid::CosFace(model_file, "");
-  if (!model.Initialized()) {
-    std::cerr << "Failed to initialize." << std::endl;
-    return;
-  }
 
   cv::Mat face0 = cv::imread(image_file[0]);
   cv::Mat face1 = cv::imread(image_file[1]);
@@ -30,8 +26,8 @@ void CpuInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -42,9 +38,11 @@ void CpuInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -67,8 +65,8 @@ void GpuInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -79,9 +77,11 @@ void GpuInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -106,8 +106,8 @@ void TrtInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -118,9 +118,11 @@ void TrtInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -128,9 +130,8 @@ void TrtInfer(const std::string& model_file,
 int main(int argc, char* argv[]) {
   if (argc < 6) {
     std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
-                 "e.g ./infer_arcface_demo ms1mv3_arcface_r100.onnx "
+                 "e.g ./infer_cosface_demo ms1mv3_cosface_r100.onnx "
-                 "test_lite_focal_arcface_0.JPG test_lite_focal_arcface_1.JPG "
+                 "face_0.jpg face_1.jpg face_2.jpg 0"
-                 "test_lite_focal_arcface_2.JPG 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
                  "with gpu; 2: run with gpu and use tensorrt backend."

examples/vision/faceid/insightface/cpp/infer_partial_fc.cc

@@ -16,11 +16,7 @@
 
 void CpuInfer(const std::string& model_file,
               const std::vector<std::string>& image_file) {
-  auto model = fastdeploy::vision::faceid::PartialFC(model_file);
+  auto model = fastdeploy::vision::faceid::PartialFC(model_file, "");
-  if (!model.Initialized()) {
-    std::cerr << "Failed to initialize." << std::endl;
-    return;
-  }
 
   cv::Mat face0 = cv::imread(image_file[0]);
   cv::Mat face1 = cv::imread(image_file[1]);
@@ -30,8 +26,8 @@ void CpuInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -42,9 +38,11 @@ void CpuInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -67,8 +65,8 @@ void GpuInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -79,9 +77,11 @@ void GpuInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -106,8 +106,8 @@ void TrtInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -118,9 +118,11 @@ void TrtInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -128,9 +130,8 @@ void TrtInfer(const std::string& model_file,
 int main(int argc, char* argv[]) {
   if (argc < 6) {
     std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
-                 "e.g ./infer_arcface_demo ms1mv3_arcface_r100.onnx "
+                 "e.g ./infer_arcface_demo ms1mv3_partial_fc_r100.onnx "
-                 "test_lite_focal_arcface_0.JPG test_lite_focal_arcface_1.JPG "
+                 "face_0.jpg face_1.jpg face_2.jpg 0"
-                 "test_lite_focal_arcface_2.JPG 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
                  "with gpu; 2: run with gpu and use tensorrt backend."

examples/vision/faceid/insightface/cpp/infer_vpl.cc

@@ -16,11 +16,7 @@
 
 void CpuInfer(const std::string& model_file,
               const std::vector<std::string>& image_file) {
-  auto model = fastdeploy::vision::faceid::VPL(model_file);
+  auto model = fastdeploy::vision::faceid::VPL(model_file, "");
-  if (!model.Initialized()) {
-    std::cerr << "Failed to initialize." << std::endl;
-    return;
-  }
 
   cv::Mat face0 = cv::imread(image_file[0]);
   cv::Mat face1 = cv::imread(image_file[1]);
@@ -30,8 +26,8 @@ void CpuInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -42,9 +38,11 @@ void CpuInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -67,8 +65,8 @@ void GpuInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -79,9 +77,11 @@ void GpuInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -106,8 +106,8 @@ void TrtInfer(const std::string& model_file,
   fastdeploy::vision::FaceRecognitionResult res1;
   fastdeploy::vision::FaceRecognitionResult res2;
 
-  if ((!model.Predict(&face0, &res0)) || (!model.Predict(&face1, &res1)) ||
+  if ((!model.Predict(face0, &res0)) || (!model.Predict(face1, &res1)) ||
-      (!model.Predict(&face2, &res2))) {
+      (!model.Predict(face2, &res2))) {
     std::cerr << "Prediction Failed." << std::endl;
   }
 
@@ -118,9 +118,11 @@ void TrtInfer(const std::string& model_file,
   std::cout << "--- [Face 2]:" << res2.Str();
 
   float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res1.embedding, model.l2_normalize);
+      res0.embedding, res1.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
-      res0.embedding, res2.embedding, model.l2_normalize);
+      res0.embedding, res2.embedding,
+      model.GetPostprocessor().GetL2Normalize());
   std::cout << "Detect Done! Cosine 01: " << cosine01
             << ", Cosine 02:" << cosine02 << std::endl;
 }
@@ -128,9 +130,8 @@ void TrtInfer(const std::string& model_file,
 int main(int argc, char* argv[]) {
   if (argc < 6) {
     std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
-                 "e.g ./infer_arcface_demo ms1mv3_arcface_r100.onnx "
+                 "e.g ./infer_arcface_demo ms1mv3_vpl_r100.onnx "
-                 "test_lite_focal_arcface_0.JPG test_lite_focal_arcface_1.JPG "
+                 "face_0.jpg face_1.jpg face_2.jpg 0"
-                 "test_lite_focal_arcface_2.JPG 0"
               << std::endl;
     std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
                  "with gpu; 2: run with gpu and use tensorrt backend."

examples/vision/faceid/insightface/python/README.md

@@ -15,16 +15,28 @@ cd examples/vision/faceid/insightface/python/
 
 #下载ArcFace模型文件和测试图片
 wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_0.JPG
+wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_1.JPG
+unzip face_demo.zip
-wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_2.JPG
 
 # CPU推理
-python infer_arcface.py --model ms1mv3_arcface_r100.onnx --face test_lite_focal_arcface_0.JPG --face_positive test_lite_focal_arcface_1.JPG --face_negative test_lite_focal_arcface_2.JPG --device cpu
+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
-python infer_arcface.py --model ms1mv3_arcface_r100.onnx --face test_lite_focal_arcface_0.JPG --face_positive test_lite_focal_arcface_1.JPG --face_negative test_lite_focal_arcface_2.JPG --device gpu
+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
-python infer_arcface.py --model ms1mv3_arcface_r100.onnx --face test_lite_focal_arcface_0.JPG --face_positive test_lite_focal_arcface_1.JPG --face_negative test_lite_focal_arcface_2.JPG --device gpu --use_trt True
+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 \
+                        --use_trt True
 ```
 
 运行完成可视化结果如下图所示
@@ -82,11 +94,15 @@ ArcFace模型加载和初始化，其中model_file为导出的ONNX模型格式
 #### 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
 
-
+#### 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
+
+#### 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
 
 

examples/vision/faceid/insightface/python/infer_arcface.py

@@ -66,7 +66,7 @@ face1 = cv2.imread(args.face_positive)
 face2 = cv2.imread(args.face_negative)  # 0,2 不同的人
 
 # 设置 l2 normalize
-model.l2_normalize = True
+model.postprocessor.l2_normalize = True
 
 # 预测图片检测结果
 result0 = model.predict(face0)

examples/vision/faceid/insightface/python/infer_cosface.py

@@ -66,7 +66,7 @@ face1 = cv2.imread(args.face_positive)
 face2 = cv2.imread(args.face_negative)  # 0,2 不同的人
 
 # 设置 l2 normalize
-model.l2_normalize = True
+model.postprocessor.l2_normalize = True
 
 # 预测图片检测结果
 result0 = model.predict(face0)

examples/vision/faceid/insightface/python/infer_partial_fc.py

@@ -66,7 +66,7 @@ face1 = cv2.imread(args.face_positive)
 face2 = cv2.imread(args.face_negative)  # 0,2 不同的人
 
 # 设置 l2 normalize
-model.l2_normalize = True
+model.postprocessor.l2_normalize = True
 
 # 预测图片检测结果
 result0 = model.predict(face0)

examples/vision/faceid/insightface/python/infer_vpl.py

@@ -66,7 +66,7 @@ face1 = cv2.imread(args.face_positive)
 face2 = cv2.imread(args.face_negative)  # 0,2 不同的人
 
 # 设置 l2 normalize
-model.l2_normalize = True
+model.postprocessor.l2_normalize = True
 
 # 预测图片检测结果
 result0 = model.predict(face0)

fastdeploy/vision.h

@@ -38,12 +38,8 @@
 #include "fastdeploy/vision/facedet/contrib/ultraface.h"
 #include "fastdeploy/vision/facedet/contrib/yolov5face.h"
 #include "fastdeploy/vision/facedet/contrib/yolov7face/yolov7face.h"
-#include "fastdeploy/vision/faceid/contrib/adaface.h"
+#include "fastdeploy/vision/faceid/contrib/insightface/model.h"
-#include "fastdeploy/vision/faceid/contrib/arcface.h"
+#include "fastdeploy/vision/faceid/contrib/adaface/adaface.h"
-#include "fastdeploy/vision/faceid/contrib/cosface.h"
-#include "fastdeploy/vision/faceid/contrib/insightface_rec.h"
-#include "fastdeploy/vision/faceid/contrib/partial_fc.h"
-#include "fastdeploy/vision/faceid/contrib/vpl.h"
 #include "fastdeploy/vision/headpose/contrib/fsanet.h"
 #include "fastdeploy/vision/keypointdet/pptinypose/pptinypose.h"
 #include "fastdeploy/vision/matting/contrib/modnet.h"

fastdeploy/vision/faceid/contrib/adaface.cc

@@ -1,74 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/vision/faceid/contrib/adaface.h"
-
-#include "fastdeploy/utils/perf.h"
-#include "fastdeploy/vision/utils/utils.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-
-AdaFace::AdaFace(const std::string& model_file, const std::string& params_file,
-                 const RuntimeOption& custom_option,
-                 const ModelFormat& model_format)
-    : InsightFaceRecognitionModel(model_file, params_file, custom_option,
-                                  model_format) {
-  initialized = Initialize();
-}
-
-bool AdaFace::Initialize() {
-  // (1) if parent class initialed backend
-  if (initialized) {
-    // (1.1) re-init parameters for specific sub-classes
-    size = {112, 112};
-    alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-    beta = {-1.f, -1.f, -1.f};  // RGB
-    swap_rb = true;
-    l2_normalize = false;
-    return true;
-  }
-  // (2) if parent class not initialed backend
-  if (!InsightFaceRecognitionModel::Initialize()) {
-    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
-    return false;
-  }
-  // (2.1) re-init parameters for specific sub-classes
-  size = {112, 112};
-  alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-  beta = {-1.f, -1.f, -1.f};  // RGB
-  swap_rb = true;
-  l2_normalize = false;
-  return true;
-}
-
-bool AdaFace::Preprocess(Mat* mat, FDTensor* output) {
-  return InsightFaceRecognitionModel::Preprocess(mat, output);
-}
-
-bool AdaFace::Postprocess(std::vector<FDTensor>& infer_result,
-                          FaceRecognitionResult* result) {
-  return InsightFaceRecognitionModel::Postprocess(infer_result, result);
-}
-
-bool AdaFace::Predict(cv::Mat* im, FaceRecognitionResult* result) {
-  return InsightFaceRecognitionModel::Predict(im, result);
-}
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/adaface.h

@@ -1,65 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#pragma once
-#include "fastdeploy/fastdeploy_model.h"
-#include "fastdeploy/vision/common/processors/transform.h"
-#include "fastdeploy/vision/common/result.h"
-#include "fastdeploy/vision/faceid/contrib/insightface_rec.h"
-
-namespace fastdeploy {
-
-namespace vision {
-/** \brief All object face recognition model APIs are defined inside this namespace
- *
- */
-namespace faceid {
-/*! @brief AdaFace model object used when to load a AdaFace model exported by AdaFacePaddleCLas.
- */
-class FASTDEPLOY_DECL AdaFace : public InsightFaceRecognitionModel {
- public:
-  /** \brief  Set path of model file and the configuration of runtime.
-   *
-   * \param[in] model_file Path of model file, e.g ./adaface.onnx
-   * \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
-   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
-   * \param[in] model_format Model format of the loaded model, default is PADDLE format
-   */
-  AdaFace(const std::string& model_file, const std::string& params_file = "",
-          const RuntimeOption& custom_option = RuntimeOption(),
-          const ModelFormat& model_format = ModelFormat::PADDLE);
-
-  std::string ModelName() const override {
-    return "Zheng-Bicheng/AdaFacePaddleCLas";
-  }
-  /** \brief Predict the face recognition result for an input image
-   *
-   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
-   * \param[in] result The output face recognition result will be writen to this structure
-   * \return true if the prediction successed, otherwise false
-   */
-  bool Predict(cv::Mat* im, FaceRecognitionResult* result) override;
-
- private:
-  bool Initialize() override;
-
-  bool Preprocess(Mat* mat, FDTensor* output) override;
-
-  bool Postprocess(std::vector<FDTensor>& infer_result,
-                   FaceRecognitionResult* result) override;
-};
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/adaface/adaface.cc

@@ -0,0 +1,84 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision/faceid/contrib/adaface/adaface.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace faceid {
+
+AdaFace::AdaFace(
+    const std::string& model_file, const std::string& params_file,
+    const fastdeploy::RuntimeOption& custom_option,
+    const fastdeploy::ModelFormat& model_format) {
+
+  if (model_format == ModelFormat::ONNX) {
+    valid_cpu_backends = {Backend::ORT};
+    valid_gpu_backends = {Backend::ORT, Backend::TRT};
+  } else {
+    valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
+    valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
+  }
+  runtime_option = custom_option;
+  runtime_option.model_format = model_format;
+  runtime_option.model_file = model_file;
+  runtime_option.params_file = params_file;
+  initialized = Initialize();
+}
+
+bool AdaFace::Initialize() {
+  if (!InitRuntime()) {
+    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
+    return false;
+  }
+  return true;
+}
+
+bool AdaFace::Predict(const cv::Mat& im,
+                      FaceRecognitionResult* result) {
+  std::vector<FaceRecognitionResult> results;
+  if (!BatchPredict({im}, &results)) {
+    return false;
+  }
+  if(!results.empty()){
+    *result = std::move(results[0]);
+  }
+  return true;
+}
+
+bool AdaFace::BatchPredict(const std::vector<cv::Mat>& images,
+                           std::vector<FaceRecognitionResult>* results){
+  std::vector<FDMat> fd_images = WrapMat(images);
+  FDASSERT(images.size() == 1, "Only support batch = 1 now.");
+  if (!preprocessor_.Run(&fd_images, &reused_input_tensors_)) {
+    FDERROR << "Failed to preprocess the input image." << std::endl;
+    return false;
+  }
+
+  reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
+  if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
+    FDERROR << "Failed to inference by runtime." << std::endl;
+    return false;
+  }
+
+  if (!postprocessor_.Run(reused_output_tensors_, results)){
+    FDERROR << "Failed to postprocess the inference results by runtime." << std::endl;
+    return false;
+  }
+  return true;
+}
+
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/adaface/adaface.h

@@ -0,0 +1,77 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.  //NOLINT
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "fastdeploy/fastdeploy_model.h"
+#include "fastdeploy/vision/faceid/contrib/adaface/postprocessor.h"
+#include "fastdeploy/vision/faceid/contrib/adaface/preprocessor.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace faceid {
+/*! @brief AdaFace model object used when to load a AdaFace model exported by AdaFace.
+ */
+class FASTDEPLOY_DECL AdaFace : public FastDeployModel {
+ public:
+  /** \brief  Set path of model file and the configuration of runtime.
+   *
+   * \param[in] model_file Path of model file, e.g ./adaface.onnx
+   * \param[in] params_file Path of parameter file, e.g adaface/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
+   * \param[in] model_format Model format of the loaded model, default is ONNX format
+   */
+  AdaFace(
+      const std::string& model_file, const std::string& params_file = "",
+      const RuntimeOption& custom_option = RuntimeOption(),
+      const ModelFormat& model_format = ModelFormat::ONNX);
+
+  std::string ModelName() const { return "insightface_rec"; }
+
+  /** \brief Predict the detection result for an input image
+   *
+   * \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * \param[in] result The output FaceRecognitionResult will be writen to this structure
+   * \return true if the prediction successed, otherwise false
+   */
+  virtual bool Predict(const cv::Mat& im, FaceRecognitionResult* result);
+
+  /** \brief Predict the detection results for a batch of input images
+   *
+   * \param[in] imgs, The input image list, each element comes from cv::imread()
+   * \param[in] results The output FaceRecognitionResult list
+   * \return true if the prediction successed, otherwise false
+   */
+  virtual bool BatchPredict(const std::vector<cv::Mat>& images,
+                            std::vector<FaceRecognitionResult>* results);
+
+  /// Get preprocessor reference of AdaFace
+  virtual AdaFacePreprocessor& GetPreprocessor() {
+    return preprocessor_;
+  }
+
+  /// Get postprocessor reference of AdaFace
+  virtual AdaFacePostprocessor& GetPostprocessor() {
+    return postprocessor_;
+  }
+
+ protected:
+  bool Initialize();
+  AdaFacePreprocessor preprocessor_;
+  AdaFacePostprocessor postprocessor_;
+};
+
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/adaface/adaface_pybind.cc

@@ -0,0 +1,89 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/pybind/main.h"
+
+namespace fastdeploy {
+void BindAdaFace(pybind11::module& m) {
+  pybind11::class_<vision::faceid::AdaFacePreprocessor>(
+      m, "AdaFacePreprocessor")
+      .def(pybind11::init())
+      .def("run", [](vision::faceid::AdaFacePreprocessor& self,
+                     std::vector<pybind11::array>& im_list) {
+        std::vector<vision::FDMat> images;
+        for (size_t i = 0; i < im_list.size(); ++i) {
+          images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
+        }
+        std::vector<FDTensor> outputs;
+        if (!self.Run(&images, &outputs)) {
+          throw std::runtime_error("Failed to preprocess the input data in AdaFacePreprocessor.");
+        }
+        for (size_t i = 0; i < outputs.size(); ++i) {
+          outputs[i].StopSharing();
+        }
+        return outputs;
+      })
+      .def_property("permute", &vision::faceid::AdaFacePreprocessor::GetPermute,
+                    &vision::faceid::AdaFacePreprocessor::SetPermute)
+      .def_property("alpha", &vision::faceid::AdaFacePreprocessor::GetAlpha,
+                    &vision::faceid::AdaFacePreprocessor::SetAlpha)
+      .def_property("beta", &vision::faceid::AdaFacePreprocessor::GetBeta,
+                    &vision::faceid::AdaFacePreprocessor::SetBeta)
+      .def_property("size", &vision::faceid::AdaFacePreprocessor::GetSize,
+                    &vision::faceid::AdaFacePreprocessor::SetSize);
+
+  pybind11::class_<vision::faceid::AdaFacePostprocessor>(
+      m, "AdaFacePostprocessor")
+      .def(pybind11::init())
+      .def("run", [](vision::faceid::AdaFacePostprocessor& self, std::vector<FDTensor>& inputs) {
+        std::vector<vision::FaceRecognitionResult> results;
+        if (!self.Run(inputs, &results)) {
+          throw std::runtime_error("Failed to postprocess the runtime result in AdaFacePostprocessor.");
+        }
+        return results;
+      })
+      .def("run", [](vision::faceid::AdaFacePostprocessor& self, std::vector<pybind11::array>& input_array) {
+        std::vector<vision::FaceRecognitionResult> results;
+        std::vector<FDTensor> inputs;
+        PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
+        if (!self.Run(inputs, &results)) {
+          throw std::runtime_error("Failed to postprocess the runtime result in AdaFacePostprocessor.");
+        }
+        return results;
+      })
+      .def_property("l2_normalize", &vision::faceid::AdaFacePostprocessor::GetL2Normalize,
+                    &vision::faceid::AdaFacePostprocessor::SetL2Normalize);
+
+  pybind11::class_<vision::faceid::AdaFace, FastDeployModel>(
+      m, "AdaFace")
+      .def(pybind11::init<std::string, std::string, RuntimeOption, ModelFormat>())
+      .def("predict", [](vision::faceid::AdaFace& self, pybind11::array& data) {
+        cv::Mat im = PyArrayToCvMat(data);
+        vision::FaceRecognitionResult result;
+        self.Predict(im, &result);
+        return result;
+      })
+      .def("batch_predict", [](vision::faceid::AdaFace& self, std::vector<pybind11::array>& data) {
+        std::vector<cv::Mat> images;
+        for (size_t i = 0; i < data.size(); ++i) {
+          images.push_back(PyArrayToCvMat(data[i]));
+        }
+        std::vector<vision::FaceRecognitionResult> results;
+        self.BatchPredict(images, &results);
+        return results;
+      })
+      .def_property_readonly("preprocessor", &vision::faceid::AdaFace::GetPreprocessor)
+      .def_property_readonly("postprocessor", &vision::faceid::AdaFace::GetPostprocessor);
+}
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/adaface/postprocessor.cc

@@ -0,0 +1,67 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision/faceid/contrib/adaface/postprocessor.h"
+#include "fastdeploy/vision/utils/utils.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace faceid {
+
+AdaFacePostprocessor::AdaFacePostprocessor() {
+  l2_normalize_ = false;
+}
+
+bool AdaFacePostprocessor::Run(std::vector<FDTensor>& infer_result,
+                               std::vector<FaceRecognitionResult>* results) {
+  if (infer_result[0].dtype != FDDataType::FP32) {
+    FDERROR << "Only support post process with float32 data." << std::endl;
+    return false;
+  }
+  if(infer_result.size() != 1){
+    FDERROR << "The default number of output tensor "
+               "must be 1 according to insightface." << std::endl;
+  }
+  int batch = infer_result[0].shape[0];
+  results->resize(batch);
+  for (size_t bs = 0; bs < batch; ++bs) {
+    FDTensor& embedding_tensor = infer_result.at(bs);
+    FDASSERT((embedding_tensor.shape[0] == 1), "Only support batch = 1 now.");
+    if (embedding_tensor.dtype != FDDataType::FP32) {
+      FDERROR << "Only support post process with float32 data." << std::endl;
+      return false;
+    }
+    (*results)[bs].Clear();
+    (*results)[bs].Resize(embedding_tensor.Numel());
+
+    // Copy the raw embedding vector directly without L2 normalize
+    // post process. Let the user decide whether to normalize or not.
+    // Will call utils::L2Normlize() method to perform L2
+    // normalize if l2_normalize was set as 'true'.
+    std::memcpy((*results)[bs].embedding.data(),
+                embedding_tensor.Data(),
+                embedding_tensor.Nbytes());
+    if (l2_normalize_) {
+      auto norm_embedding = utils::L2Normalize((*results)[bs].embedding);
+      std::memcpy((*results)[bs].embedding.data(),
+                  norm_embedding.data(),
+                  embedding_tensor.Nbytes());
+    }
+  }
+  return true;
+}
+
+}  // namespace detection
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/adaface/postprocessor.h

@@ -0,0 +1,50 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "fastdeploy/vision/common/processors/transform.h"
+#include "fastdeploy/vision/common/result.h"
+
+namespace fastdeploy {
+namespace vision {
+
+namespace faceid {
+/*! @brief Postprocessor object for AdaFace serials model.
+ */
+class FASTDEPLOY_DECL AdaFacePostprocessor {
+ public:
+  /** \brief Create a postprocessor instance for AdaFace serials model
+   */
+  AdaFacePostprocessor();
+
+  /** \brief Process the result of runtime and fill to FaceRecognitionResult structure
+   *
+   * \param[in] tensors The inference result from runtime
+   * \param[in] result The output result of FaceRecognitionResult
+   * \return true if the postprocess successed, otherwise false
+   */
+  bool Run(std::vector<FDTensor>& infer_result,
+           std::vector<FaceRecognitionResult>* results);
+
+  void SetL2Normalize(bool& l2_normalize) { l2_normalize_ = l2_normalize; }
+
+  bool GetL2Normalize() { return l2_normalize_; }
+
+ private:
+  bool l2_normalize_;
+};
+
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/adaface/preprocessor.cc

@@ -0,0 +1,76 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision/faceid/contrib/adaface/preprocessor.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace faceid {
+
+AdaFacePreprocessor::AdaFacePreprocessor() {
+  // parameters for preprocess
+  size_ = {112, 112};
+  alpha_ = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
+  beta_ = {-1.f, -1.f, -1.f};  // RGB
+  permute_ = true;
+}
+
+bool AdaFacePreprocessor::Preprocess(FDMat * mat, FDTensor* output) {
+
+  // face recognition model's preprocess steps in insightface
+  // reference: insightface/recognition/arcface_torch/inference.py
+  // 1. Resize
+  // 2. BGR2RGB
+  // 3. Convert(opencv style) or Normalize
+  // 4. HWC2CHW
+  int resize_w = size_[0];
+  int resize_h = size_[1];
+  if (resize_h != mat->Height() || resize_w != mat->Width()) {
+    Resize::Run(mat, resize_w, resize_h);
+  }
+  if (permute_) {
+    BGR2RGB::Run(mat);
+  }
+
+  Convert::Run(mat, alpha_, beta_);
+  HWC2CHW::Run(mat);
+  Cast::Run(mat, "float");
+
+  mat->ShareWithTensor(output);
+  output->ExpandDim(0);  // reshape to n, h, w, c
+  return true;
+}
+
+bool AdaFacePreprocessor::Run(std::vector<FDMat>* images,
+                              std::vector<FDTensor>* outputs) {
+  if (images->empty()) {
+    FDERROR << "The size of input images should be greater than 0." << std::endl;
+    return false;
+  }
+  FDASSERT(images->size() == 1, "Only support batch = 1 now.");
+  outputs->resize(1);
+  // Concat all the preprocessed data to a batch tensor
+  std::vector<FDTensor> tensors(images->size());
+  for (size_t i = 0; i < images->size(); ++i) {
+    if (!Preprocess(&(*images)[i], &tensors[i])) {
+      FDERROR << "Failed to preprocess input image." << std::endl;
+      return false;
+    }
+  }
+  (*outputs)[0] = std::move(tensors[0]);
+  return true;
+}
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/adaface/preprocessor.h

@@ -0,0 +1,80 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "fastdeploy/vision/common/processors/transform.h"
+#include "fastdeploy/vision/common/result.h"
+
+namespace fastdeploy {
+namespace vision {
+
+namespace faceid {
+/*! @brief Preprocessor object for AdaFace serials model.
+ */
+class FASTDEPLOY_DECL AdaFacePreprocessor {
+ public:
+  /** \brief Create a preprocessor instance for AdaFace serials model
+   */
+  AdaFacePreprocessor();
+
+  /** \brief Process the input image and prepare input tensors for runtime
+   *
+   * \param[in] images The input image data list, all the elements are returned by cv::imread()
+   * \param[in] outputs The output tensors which will feed in runtime
+   * \return true if the preprocess successed, otherwise false
+   */
+  bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs);
+
+  /// Get Size
+  std::vector<int> GetSize() { return size_; }
+
+  /// Set size.
+  void SetSize(std::vector<int>& size) { size_ = size; }
+
+  /// Get alpha
+  std::vector<float> GetAlpha() { return alpha_; }
+
+  /// Set alpha.
+  void SetAlpha(std::vector<float>& alpha) { alpha_ = alpha; }
+
+  /// Get beta
+  std::vector<float> GetBeta() { return beta_; }
+
+  /// Set beta.
+  void SetBeta(std::vector<float>& beta) { beta_ = beta; }
+
+  bool GetPermute() { return permute_; }
+
+  /// Set permute.
+  void SetPermute(bool permute) { permute_ = permute; }
+
+ protected:
+  bool Preprocess(FDMat* mat, FDTensor* output);
+  // Argument for image preprocessing step, tuple of (width, height),
+  // decide the target size after resize, default (112, 112)
+  std::vector<int> size_;
+  // Argument for image preprocessing step, alpha values for normalization,
+  // default alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
+  std::vector<float> alpha_;
+  // Argument for image preprocessing step, beta values for normalization,
+  // default beta = {-1.f, -1.f, -1.f}
+  std::vector<float> beta_;
+  // Argument for image preprocessing step, whether to swap the B and R channel,
+  // such as BGR->RGB, default true.
+  bool permute_;
+};
+
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/adaface_pybind.cc

@@ -1,38 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/pybind/main.h"
-
-namespace fastdeploy {
-void BindAdaFace(pybind11::module& m) {
-  // Bind AdaFace
-  pybind11::class_<vision::faceid::AdaFace,
-                   vision::faceid::InsightFaceRecognitionModel>(m, "AdaFace")
-      .def(pybind11::init<std::string, std::string, RuntimeOption,
-                          ModelFormat>())
-      .def("predict",
-           [](vision::faceid::AdaFace& self, pybind11::array& data) {
-             auto mat = PyArrayToCvMat(data);
-             vision::FaceRecognitionResult res;
-             self.Predict(&mat, &res);
-             return res;
-           })
-      .def_readwrite("size", &vision::faceid::AdaFace::size)
-      .def_readwrite("alpha", &vision::faceid::AdaFace::alpha)
-      .def_readwrite("beta", &vision::faceid::AdaFace::beta)
-      .def_readwrite("swap_rb", &vision::faceid::AdaFace::swap_rb)
-      .def_readwrite("l2_normalize", &vision::faceid::AdaFace::l2_normalize);
-}
-
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/arcface.cc

@@ -1,74 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/vision/faceid/contrib/arcface.h"
-#include "fastdeploy/utils/perf.h"
-#include "fastdeploy/vision/utils/utils.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-
-ArcFace::ArcFace(const std::string& model_file, const std::string& params_file,
-                 const RuntimeOption& custom_option,
-                 const ModelFormat& model_format)
-    : InsightFaceRecognitionModel(model_file, params_file, custom_option,
-                                  model_format) {
-  initialized = Initialize();
-}
-
-bool ArcFace::Initialize() {
-  
-  // (1) if parent class initialed backend
-  if (initialized) {
-    // (1.1) re-init parameters for specific sub-classes
-    size = {112, 112};
-    alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-    beta = {-1.f, -1.f, -1.f};  // RGB
-    swap_rb = true;
-    l2_normalize = false;
-    return true;
-  }
-  // (2) if parent class not initialed backend
-  if (!InsightFaceRecognitionModel::Initialize()) {
-    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
-    return false;
-  }
-  // (2.1) re-init parameters for specific sub-classes
-  size = {112, 112};
-  alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-  beta = {-1.f, -1.f, -1.f};  // RGB
-  swap_rb = true;
-  l2_normalize = false;
-  return true;
-}
-
-bool ArcFace::Preprocess(Mat* mat, FDTensor* output) {
-  return InsightFaceRecognitionModel::Preprocess(mat, output);
-}
-
-bool ArcFace::Postprocess(std::vector<FDTensor>& infer_result,
-                          FaceRecognitionResult* result) {
-  return InsightFaceRecognitionModel::Postprocess(infer_result, result);
-}
-
-bool ArcFace::Predict(cv::Mat* im, FaceRecognitionResult* result) {
-  return InsightFaceRecognitionModel::Predict(im, result);
-}
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/arcface.h

@@ -1,63 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#pragma once
-#include "fastdeploy/fastdeploy_model.h"
-#include "fastdeploy/vision/common/processors/transform.h"
-#include "fastdeploy/vision/common/result.h"
-#include "fastdeploy/vision/faceid/contrib/insightface_rec.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-/*! @brief ArcFace model object used when to load a ArcFace model exported by IngsightFace.
- */
-class FASTDEPLOY_DECL ArcFace : public InsightFaceRecognitionModel {
- public:
-  /** \brief  Set path of model file and the configuration of runtime.
-   *
-   * \param[in] model_file Path of model file, e.g ./arcface.onnx
-   * \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
-   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
-   * \param[in] model_format Model format of the loaded model, default is ONNX format
-   */
-  ArcFace(const std::string& model_file, const std::string& params_file = "",
-          const RuntimeOption& custom_option = RuntimeOption(),
-          const ModelFormat& model_format = ModelFormat::ONNX);
-
-  std::string ModelName() const override {
-    return "deepinsight/insightface/recognition/arcface_pytorch";
-  }
-  /** \brief Predict the face recognition result for an input image
-   *
-   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
-   * \param[in] result The output face recognition result will be writen to this structure
-   * \return true if the prediction successed, otherwise false
-   */
-  bool Predict(cv::Mat* im, FaceRecognitionResult* result) override;
-
- private:
-  bool Initialize() override;
-
-  bool Preprocess(Mat* mat, FDTensor* output) override;
-
-  bool Postprocess(std::vector<FDTensor>& infer_result,
-                   FaceRecognitionResult* result) override;
-};
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/arcface_pybind.cc

@@ -1,38 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/pybind/main.h"
-
-namespace fastdeploy {
-void BindArcFace(pybind11::module& m) {
-  // Bind ArcFace
-  pybind11::class_<vision::faceid::ArcFace,
-                   vision::faceid::InsightFaceRecognitionModel>(m, "ArcFace")
-      .def(pybind11::init<std::string, std::string, RuntimeOption,
-                          ModelFormat>())
-      .def("predict",
-           [](vision::faceid::ArcFace& self, pybind11::array& data) {
-             auto mat = PyArrayToCvMat(data);
-             vision::FaceRecognitionResult res;
-             self.Predict(&mat, &res);
-             return res;
-           })
-      .def_readwrite("size", &vision::faceid::ArcFace::size)
-      .def_readwrite("alpha", &vision::faceid::ArcFace::alpha)
-      .def_readwrite("beta", &vision::faceid::ArcFace::beta)
-      .def_readwrite("swap_rb", &vision::faceid::ArcFace::swap_rb)
-      .def_readwrite("l2_normalize", &vision::faceid::ArcFace::l2_normalize);
-}
-
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/cosface.cc

@@ -1,72 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/vision/faceid/contrib/cosface.h"
-#include "fastdeploy/utils/perf.h"
-#include "fastdeploy/vision/utils/utils.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-
-CosFace::CosFace(const std::string& model_file, const std::string& params_file,
-                 const RuntimeOption& custom_option,
-                 const ModelFormat& model_format)
-    : InsightFaceRecognitionModel(model_file, params_file, custom_option,
-                                  model_format) {
-  initialized = Initialize();
-}
-
-bool CosFace::Initialize() {
- 
-  if (initialized) {
-    // (1.1) re-init parameters for specific sub-classes
-    size = {112, 112};
-    alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-    beta = {-1.f, -1.f, -1.f};  // RGB
-    swap_rb = true;
-    l2_normalize = false;
-    return true;
-  }
-  if (!InsightFaceRecognitionModel::Initialize()) {
-    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
-    return false;
-  }
-  // (2.1) re-init parameters for specific sub-classes
-  size = {112, 112};
-  alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-  beta = {-1.f, -1.f, -1.f};  // RGB
-  swap_rb = true;
-  l2_normalize = false;
-  return true;
-}
-
-bool CosFace::Preprocess(Mat* mat, FDTensor* output) {
-  return InsightFaceRecognitionModel::Preprocess(mat, output);
-}
-
-bool CosFace::Postprocess(std::vector<FDTensor>& infer_result,
-                          FaceRecognitionResult* result) {
-  return InsightFaceRecognitionModel::Postprocess(infer_result, result);
-}
-
-bool CosFace::Predict(cv::Mat* im, FaceRecognitionResult* result) {
-  return InsightFaceRecognitionModel::Predict(im, result);
-}
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/cosface.h

@@ -1,63 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#pragma once
-#include "fastdeploy/fastdeploy_model.h"
-#include "fastdeploy/vision/common/processors/transform.h"
-#include "fastdeploy/vision/common/result.h"
-#include "fastdeploy/vision/faceid/contrib/insightface_rec.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-/*! @brief CosFace model object used when to load a CosFace model exported by IngsightFace.
- */
-class FASTDEPLOY_DECL CosFace : public InsightFaceRecognitionModel {
- public:
-  /** \brief  Set path of model file and the configuration of runtime.
-   *
-   * \param[in] model_file Path of model file, e.g ./cosface.onnx
-   * \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
-   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
-   * \param[in] model_format Model format of the loaded model, default is ONNX format
-   */
-  CosFace(const std::string& model_file, const std::string& params_file = "",
-          const RuntimeOption& custom_option = RuntimeOption(),
-          const ModelFormat& model_format = ModelFormat::ONNX);
-
-  std::string ModelName() const override {
-    return "deepinsight/insightface/recognition/arcface_pytorch";
-  }
-  /** \brief Predict the face recognition result for an input image
-   *
-   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
-   * \param[in] result The output face recognition result will be writen to this structure
-   * \return true if the prediction successed, otherwise false
-   */
-  bool Predict(cv::Mat* im, FaceRecognitionResult* result) override;
-
- private:
-  bool Initialize() override;
-
-  bool Preprocess(Mat* mat, FDTensor* output) override;
-
-  bool Postprocess(std::vector<FDTensor>& infer_result,
-                   FaceRecognitionResult* result) override;
-};
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/cosface_pybind.cc

@@ -1,38 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/pybind/main.h"
-
-namespace fastdeploy {
-void BindCosFace(pybind11::module& m) {
-  // Bind CosFace
-  pybind11::class_<vision::faceid::CosFace,
-                   vision::faceid::InsightFaceRecognitionModel>(m, "CosFace")
-      .def(pybind11::init<std::string, std::string, RuntimeOption,
-                          ModelFormat>())
-      .def("predict",
-           [](vision::faceid::CosFace& self, pybind11::array& data) {
-             auto mat = PyArrayToCvMat(data);
-             vision::FaceRecognitionResult res;
-             self.Predict(&mat, &res);
-             return res;
-           })
-      .def_readwrite("size", &vision::faceid::CosFace::size)
-      .def_readwrite("alpha", &vision::faceid::CosFace::alpha)
-      .def_readwrite("beta", &vision::faceid::CosFace::beta)
-      .def_readwrite("swap_rb", &vision::faceid::CosFace::swap_rb)
-      .def_readwrite("l2_normalize", &vision::faceid::CosFace::l2_normalize);
-}
-
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface/base.cc

@@ -0,0 +1,82 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision/faceid/contrib/insightface/base.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace faceid {
+
+InsightFaceRecognitionBase::InsightFaceRecognitionBase(
+    const std::string& model_file, const std::string& params_file,
+    const fastdeploy::RuntimeOption& custom_option,
+    const fastdeploy::ModelFormat& model_format) {
+
+  if (model_format == ModelFormat::ONNX) {
+    valid_cpu_backends = {Backend::ORT};
+    valid_gpu_backends = {Backend::ORT, Backend::TRT};
+  } else {
+    valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
+    valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
+    valid_xpu_backends = {Backend::LITE};
+  }
+  runtime_option = custom_option;
+  runtime_option.model_format = model_format;
+  runtime_option.model_file = model_file;
+  runtime_option.params_file = params_file;
+}
+
+bool InsightFaceRecognitionBase::Initialize() {
+  if (!InitRuntime()) {
+    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
+    return false;
+  }
+  return true;
+}
+
+bool InsightFaceRecognitionBase::Predict(const cv::Mat& im,
+                                         FaceRecognitionResult* result) {
+  std::vector<FaceRecognitionResult> results;
+  if (!BatchPredict({im}, &results)) {
+    return false;
+  }
+  *result = std::move(results[0]);
+  return true;
+}
+
+bool InsightFaceRecognitionBase::BatchPredict(const std::vector<cv::Mat>& images,
+                                              std::vector<FaceRecognitionResult>* results){
+  std::vector<FDMat> fd_images = WrapMat(images);
+  FDASSERT(images.size() == 1, "Only support batch = 1 now.");
+  if (!preprocessor_.Run(&fd_images, &reused_input_tensors_)) {
+    FDERROR << "Failed to preprocess the input image." << std::endl;
+    return false;
+  }
+
+  reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
+  if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
+    FDERROR << "Failed to inference by runtime." << std::endl;
+    return false;
+  }
+
+  if (!postprocessor_.Run(reused_output_tensors_, results)){
+    FDERROR << "Failed to postprocess the inference results by runtime." << std::endl;
+    return false;
+  }
+  return true;
+}
+
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface/base.h

@@ -0,0 +1,77 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.  //NOLINT
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "fastdeploy/fastdeploy_model.h"
+#include "fastdeploy/vision/faceid/contrib/insightface/postprocessor.h"
+#include "fastdeploy/vision/faceid/contrib/insightface/preprocessor.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace faceid {
+/*! @brief InsightFaceRecognition model object used when to load a InsightFaceRecognition model exported by InsightFaceRecognition.
+ */
+class FASTDEPLOY_DECL InsightFaceRecognitionBase : public FastDeployModel {
+ public:
+  /** \brief  Set path of model file and the configuration of runtime.
+   *
+   * \param[in] model_file Path of model file, e.g ./arcface.onnx
+   * \param[in] params_file Path of parameter file, e.g arcface/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
+   * \param[in] model_format Model format of the loaded model, default is ONNX format
+   */
+  InsightFaceRecognitionBase(
+      const std::string& model_file, const std::string& params_file = "",
+      const RuntimeOption& custom_option = RuntimeOption(),
+      const ModelFormat& model_format = ModelFormat::ONNX);
+
+  std::string ModelName() const { return "insightface_rec"; }
+
+  /** \brief Predict the detection result for an input image
+   *
+   * \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * \param[in] result The output FaceRecognitionResult will be writen to this structure
+   * \return true if the prediction successed, otherwise false
+   */
+  virtual bool Predict(const cv::Mat& im, FaceRecognitionResult* result);
+
+  /** \brief Predict the detection results for a batch of input images
+   *
+   * \param[in] imgs, The input image list, each element comes from cv::imread()
+   * \param[in] results The output FaceRecognitionResult list
+   * \return true if the prediction successed, otherwise false
+   */
+  virtual bool BatchPredict(const std::vector<cv::Mat>& images,
+                            std::vector<FaceRecognitionResult>* results);
+
+  /// Get preprocessor reference of InsightFaceRecognition
+  virtual InsightFaceRecognitionPreprocessor& GetPreprocessor() {
+    return preprocessor_;
+  }
+
+  /// Get postprocessor reference of InsightFaceRecognition
+  virtual InsightFaceRecognitionPostprocessor& GetPostprocessor() {
+    return postprocessor_;
+  }
+
+ protected:
+  bool Initialize();
+  InsightFaceRecognitionPreprocessor preprocessor_;
+  InsightFaceRecognitionPostprocessor postprocessor_;
+};
+
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface/insightface_pybind.cc

@@ -0,0 +1,101 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/pybind/main.h"
+
+namespace fastdeploy {
+void BindInsightFace(pybind11::module& m) {
+  pybind11::class_<vision::faceid::InsightFaceRecognitionPreprocessor>(
+      m, "InsightFaceRecognitionPreprocessor")
+      .def(pybind11::init())
+      .def("run", [](vision::faceid::InsightFaceRecognitionPreprocessor& self,
+                     std::vector<pybind11::array>& im_list) {
+        std::vector<vision::FDMat> images;
+        for (size_t i = 0; i < im_list.size(); ++i) {
+          images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
+        }
+        std::vector<FDTensor> outputs;
+        if (!self.Run(&images, &outputs)) {
+          throw std::runtime_error("Failed to preprocess the input data in InsightFaceRecognitionPreprocessor.");
+        }
+        for (size_t i = 0; i < outputs.size(); ++i) {
+          outputs[i].StopSharing();
+        }
+        return outputs;
+      })
+      .def_property("permute", &vision::faceid::InsightFaceRecognitionPreprocessor::GetPermute,
+                    &vision::faceid::InsightFaceRecognitionPreprocessor::SetPermute)
+      .def_property("alpha", &vision::faceid::InsightFaceRecognitionPreprocessor::GetAlpha,
+                    &vision::faceid::InsightFaceRecognitionPreprocessor::SetAlpha)
+      .def_property("beta", &vision::faceid::InsightFaceRecognitionPreprocessor::GetBeta,
+                    &vision::faceid::InsightFaceRecognitionPreprocessor::SetBeta)
+      .def_property("size", &vision::faceid::InsightFaceRecognitionPreprocessor::GetSize,
+                    &vision::faceid::InsightFaceRecognitionPreprocessor::SetSize);
+
+  pybind11::class_<vision::faceid::InsightFaceRecognitionPostprocessor>(
+      m, "InsightFaceRecognitionPostprocessor")
+      .def(pybind11::init())
+      .def("run", [](vision::faceid::InsightFaceRecognitionPostprocessor& self, std::vector<FDTensor>& inputs) {
+        std::vector<vision::FaceRecognitionResult> results;
+        if (!self.Run(inputs, &results)) {
+          throw std::runtime_error("Failed to postprocess the runtime result in InsightFaceRecognitionPostprocessor.");
+        }
+        return results;
+      })
+      .def("run", [](vision::faceid::InsightFaceRecognitionPostprocessor& self, std::vector<pybind11::array>& input_array) {
+        std::vector<vision::FaceRecognitionResult> results;
+        std::vector<FDTensor> inputs;
+        PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
+        if (!self.Run(inputs, &results)) {
+          throw std::runtime_error("Failed to postprocess the runtime result in InsightFaceRecognitionPostprocessor.");
+        }
+        return results;
+      })
+      .def_property("l2_normalize", &vision::faceid::InsightFaceRecognitionPostprocessor::GetL2Normalize,
+                    &vision::faceid::InsightFaceRecognitionPostprocessor::SetL2Normalize);
+
+  pybind11::class_<vision::faceid::InsightFaceRecognitionBase, FastDeployModel>(
+      m, "InsightFaceRecognitionBase")
+      .def(pybind11::init<std::string, std::string, RuntimeOption, ModelFormat>())
+      .def("predict", [](vision::faceid::InsightFaceRecognitionBase& self, pybind11::array& data) {
+        cv::Mat im = PyArrayToCvMat(data);
+        vision::FaceRecognitionResult result;
+        self.Predict(im, &result);
+        return result;
+      })
+      .def("batch_predict", [](vision::faceid::InsightFaceRecognitionBase& self, std::vector<pybind11::array>& data) {
+        std::vector<cv::Mat> images;
+        for (size_t i = 0; i < data.size(); ++i) {
+          images.push_back(PyArrayToCvMat(data[i]));
+        }
+        std::vector<vision::FaceRecognitionResult> results;
+        self.BatchPredict(images, &results);
+        return results;
+      })
+      .def_property_readonly("preprocessor", &vision::faceid::InsightFaceRecognitionBase::GetPreprocessor)
+      .def_property_readonly("postprocessor", &vision::faceid::InsightFaceRecognitionBase::GetPostprocessor);
+
+  pybind11::class_<vision::faceid::ArcFace, vision::faceid::InsightFaceRecognitionBase>(m, "ArcFace")
+      .def(pybind11::init<std::string, std::string, RuntimeOption,ModelFormat>());
+
+  pybind11::class_<vision::faceid::CosFace, vision::faceid::InsightFaceRecognitionBase>(m, "CosFace")
+      .def(pybind11::init<std::string, std::string, RuntimeOption,ModelFormat>());
+
+  pybind11::class_<vision::faceid::PartialFC, vision::faceid::InsightFaceRecognitionBase>(m, "PartialFC")
+      .def(pybind11::init<std::string, std::string, RuntimeOption,ModelFormat>());
+
+  pybind11::class_<vision::faceid::VPL, vision::faceid::InsightFaceRecognitionBase>(m, "VPL")
+      .def(pybind11::init<std::string, std::string, RuntimeOption,ModelFormat>());
+}
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface/model.h

@@ -0,0 +1,133 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.  //NOLINT
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "fastdeploy/vision/faceid/contrib/insightface/base.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace faceid {
+class FASTDEPLOY_DECL ArcFace : public InsightFaceRecognitionBase {
+ public:
+  /** \brief Set path of model file and configuration file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g ArcFace/model.pdmodel
+   * \param[in] params_file Path of parameter file, e.g ArcFace/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
+   * \param[in] model_format Model format of the loaded model, default is Paddle format
+   */
+  ArcFace(const std::string& model_file, const std::string& params_file = "",
+          const RuntimeOption& custom_option = RuntimeOption(),
+          const ModelFormat& model_format = ModelFormat::ONNX)
+      : InsightFaceRecognitionBase(model_file, params_file, custom_option,
+                                   model_format) {
+    if (model_format == ModelFormat::ONNX) {
+      valid_cpu_backends = {Backend::ORT};
+      valid_gpu_backends = {Backend::ORT, Backend::TRT};
+    } else {
+      valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
+      valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
+      valid_xpu_backends = {Backend::LITE};
+    }
+    initialized = Initialize();
+  }
+
+  virtual std::string ModelName() const { return "ArcFace"; }
+};
+
+class FASTDEPLOY_DECL CosFace : public InsightFaceRecognitionBase {
+ public:
+  /** \brief Set path of model file and configuration file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g CosFace/model.pdmodel
+   * \param[in] params_file Path of parameter file, e.g CosFace/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
+   * \param[in] model_format Model format of the loaded model, default is Paddle format
+   */
+  CosFace(const std::string& model_file, const std::string& params_file = "",
+          const RuntimeOption& custom_option = RuntimeOption(),
+          const ModelFormat& model_format = ModelFormat::ONNX)
+      : InsightFaceRecognitionBase(model_file, params_file, custom_option,
+                                   model_format) {
+    if (model_format == ModelFormat::ONNX) {
+      valid_cpu_backends = {Backend::ORT};
+      valid_gpu_backends = {Backend::ORT, Backend::TRT};
+    } else {
+      valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
+      valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
+      valid_xpu_backends = {Backend::LITE};
+    }
+    initialized = Initialize();
+  }
+
+  virtual std::string ModelName() const { return "CosFace"; }
+};
+class FASTDEPLOY_DECL PartialFC : public InsightFaceRecognitionBase {
+ public:
+  /** \brief Set path of model file and configuration file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g PartialFC/model.pdmodel
+   * \param[in] params_file Path of parameter file, e.g PartialFC/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
+   * \param[in] model_format Model format of the loaded model, default is Paddle format
+   */
+  PartialFC(const std::string& model_file, const std::string& params_file = "",
+          const RuntimeOption& custom_option = RuntimeOption(),
+          const ModelFormat& model_format = ModelFormat::ONNX)
+      : InsightFaceRecognitionBase(model_file, params_file, custom_option,
+                                   model_format) {
+    if (model_format == ModelFormat::ONNX) {
+      valid_cpu_backends = {Backend::ORT};
+      valid_gpu_backends = {Backend::ORT, Backend::TRT};
+    } else {
+      valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
+      valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
+      valid_xpu_backends = {Backend::LITE};
+    }
+    initialized = Initialize();
+  }
+
+  virtual std::string ModelName() const { return "PartialFC"; }
+};
+class FASTDEPLOY_DECL VPL : public InsightFaceRecognitionBase {
+ public:
+  /** \brief Set path of model file and configuration file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g VPL/model.pdmodel
+   * \param[in] params_file Path of parameter file, e.g VPL/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
+   * \param[in] model_format Model format of the loaded model, default is Paddle format
+   */
+  VPL(const std::string& model_file, const std::string& params_file = "",
+            const RuntimeOption& custom_option = RuntimeOption(),
+            const ModelFormat& model_format = ModelFormat::ONNX)
+      : InsightFaceRecognitionBase(model_file, params_file, custom_option,
+                                   model_format) {
+    if (model_format == ModelFormat::ONNX) {
+      valid_cpu_backends = {Backend::ORT};
+      valid_gpu_backends = {Backend::ORT, Backend::TRT};
+    } else {
+      valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
+      valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
+      valid_xpu_backends = {Backend::LITE};
+    }
+    initialized = Initialize();
+  }
+
+  virtual std::string ModelName() const { return "VPL"; }
+};
+
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface/postprocessor.cc

@@ -0,0 +1,67 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision/faceid/contrib/insightface/postprocessor.h"
+#include "fastdeploy/vision/utils/utils.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace faceid {
+
+InsightFaceRecognitionPostprocessor::InsightFaceRecognitionPostprocessor() {
+  l2_normalize_ = false;
+}
+
+bool InsightFaceRecognitionPostprocessor::Run(std::vector<FDTensor>& infer_result,
+                                              std::vector<FaceRecognitionResult>* results) {
+  if (infer_result[0].dtype != FDDataType::FP32) {
+    FDERROR << "Only support post process with float32 data." << std::endl;
+    return false;
+  }
+  if(infer_result.size() != 1){
+    FDERROR << "The default number of output tensor "
+               "must be 1 according to insightface." << std::endl;
+  }
+  int batch = infer_result[0].shape[0];
+  results->resize(batch);
+  for (size_t bs = 0; bs < batch; ++bs) {
+    FDTensor& embedding_tensor = infer_result.at(bs);
+    FDASSERT((embedding_tensor.shape[0] == 1), "Only support batch = 1 now.");
+    if (embedding_tensor.dtype != FDDataType::FP32) {
+      FDERROR << "Only support post process with float32 data." << std::endl;
+      return false;
+    }
+    (*results)[bs].Clear();
+    (*results)[bs].Resize(embedding_tensor.Numel());
+
+    // Copy the raw embedding vector directly without L2 normalize
+    // post process. Let the user decide whether to normalize or not.
+    // Will call utils::L2Normlize() method to perform L2
+    // normalize if l2_normalize was set as 'true'.
+    std::memcpy((*results)[bs].embedding.data(),
+                embedding_tensor.Data(),
+                embedding_tensor.Nbytes());
+    if (l2_normalize_) {
+      auto norm_embedding = utils::L2Normalize((*results)[bs].embedding);
+      std::memcpy((*results)[bs].embedding.data(),
+                  norm_embedding.data(),
+                  embedding_tensor.Nbytes());
+    }
+  }
+  return true;
+}
+
+}  // namespace detection
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface/postprocessor.h

@@ -0,0 +1,50 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "fastdeploy/vision/common/processors/transform.h"
+#include "fastdeploy/vision/common/result.h"
+
+namespace fastdeploy {
+namespace vision {
+
+namespace faceid {
+/*! @brief Postprocessor object for InsightFaceRecognition serials model.
+ */
+class FASTDEPLOY_DECL InsightFaceRecognitionPostprocessor {
+ public:
+  /** \brief Create a postprocessor instance for InsightFaceRecognition serials model
+   */
+  InsightFaceRecognitionPostprocessor();
+
+  /** \brief Process the result of runtime and fill to FaceRecognitionResult structure
+   *
+   * \param[in] tensors The inference result from runtime
+   * \param[in] result The output result of FaceRecognitionResult
+   * \return true if the postprocess successed, otherwise false
+   */
+  bool Run(std::vector<FDTensor>& infer_result,
+           std::vector<FaceRecognitionResult>* results);
+
+  void SetL2Normalize(bool& l2_normalize) { l2_normalize_ = l2_normalize; }
+
+  bool GetL2Normalize() { return l2_normalize_; }
+
+ private:
+  bool l2_normalize_;
+};
+
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface/preprocessor.cc

@@ -0,0 +1,76 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision/faceid/contrib/insightface/preprocessor.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace faceid {
+
+InsightFaceRecognitionPreprocessor::InsightFaceRecognitionPreprocessor() {
+  // parameters for preprocess
+  size_ = {112, 112};
+  alpha_ = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
+  beta_ = {-1.f, -1.f, -1.f};  // RGB
+  permute_ = true;
+}
+ 
+bool InsightFaceRecognitionPreprocessor::Preprocess(FDMat * mat, FDTensor* output) {
+
+  // face recognition model's preprocess steps in insightface
+  // reference: insightface/recognition/arcface_torch/inference.py
+  // 1. Resize
+  // 2. BGR2RGB
+  // 3. Convert(opencv style) or Normalize
+  // 4. HWC2CHW
+  int resize_w = size_[0];
+  int resize_h = size_[1];
+  if (resize_h != mat->Height() || resize_w != mat->Width()) {
+    Resize::Run(mat, resize_w, resize_h);
+  }
+  if (permute_) {
+    BGR2RGB::Run(mat);
+  }
+
+  Convert::Run(mat, alpha_, beta_);
+  HWC2CHW::Run(mat);
+  Cast::Run(mat, "float");
+
+  mat->ShareWithTensor(output);
+  output->ExpandDim(0);  // reshape to n, h, w, c
+  return true;
+}
+
+bool InsightFaceRecognitionPreprocessor::Run(std::vector<FDMat>* images,
+                                             std::vector<FDTensor>* outputs) {
+  if (images->empty()) {
+    FDERROR << "The size of input images should be greater than 0." << std::endl;
+    return false;
+  }
+  FDASSERT(images->size() == 1, "Only support batch = 1 now.");
+  outputs->resize(1);
+  // Concat all the preprocessed data to a batch tensor
+  std::vector<FDTensor> tensors(images->size());
+  for (size_t i = 0; i < images->size(); ++i) {
+    if (!Preprocess(&(*images)[i], &tensors[i])) {
+      FDERROR << "Failed to preprocess input image." << std::endl;
+      return false;
+    }
+  }
+  (*outputs)[0] = std::move(tensors[0]);
+  return true;
+}
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface/preprocessor.h

@@ -0,0 +1,80 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "fastdeploy/vision/common/processors/transform.h"
+#include "fastdeploy/vision/common/result.h"
+
+namespace fastdeploy {
+namespace vision {
+
+namespace faceid {
+/*! @brief Preprocessor object for InsightFaceRecognition serials model.
+ */
+class FASTDEPLOY_DECL InsightFaceRecognitionPreprocessor {
+ public:
+  /** \brief Create a preprocessor instance for InsightFaceRecognition serials model
+   */
+  InsightFaceRecognitionPreprocessor();
+
+  /** \brief Process the input image and prepare input tensors for runtime
+   *
+   * \param[in] images The input image data list, all the elements are returned by cv::imread()
+   * \param[in] outputs The output tensors which will feed in runtime
+   * \return true if the preprocess successed, otherwise false
+   */
+  bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs);
+
+  /// Get Size
+  std::vector<int> GetSize() { return size_; }
+
+  /// Set size.
+  void SetSize(std::vector<int>& size) { size_ = size; }
+
+  /// Get alpha
+  std::vector<float> GetAlpha() { return alpha_; }
+
+  /// Set alpha.
+  void SetAlpha(std::vector<float>& alpha) { alpha_ = alpha; }
+
+  /// Get beta
+  std::vector<float> GetBeta() { return beta_; }
+
+  /// Set beta.
+  void SetBeta(std::vector<float>& beta) { beta_ = beta; }
+
+  bool GetPermute() { return permute_; }
+
+  /// Set permute.
+  void SetPermute(bool permute) { permute_ = permute; }
+
+ protected:
+  bool Preprocess(FDMat* mat, FDTensor* output);
+  // Argument for image preprocessing step, tuple of (width, height),
+  // decide the target size after resize, default (112, 112)
+  std::vector<int> size_;
+  // Argument for image preprocessing step, alpha values for normalization,
+  // default alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
+  std::vector<float> alpha_;
+  // Argument for image preprocessing step, beta values for normalization,
+  // default beta = {-1.f, -1.f, -1.f}
+  std::vector<float> beta_;
+  // Argument for image preprocessing step, whether to swap the B and R channel,
+  // such as BGR->RGB, default true.
+  bool permute_;
+};
+
+}  // namespace faceid
+}  // namespace vision
+}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface_rec.cc

@@ -1,138 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/vision/faceid/contrib/insightface_rec.h"
-
-#include "fastdeploy/utils/perf.h"
-#include "fastdeploy/vision/utils/utils.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-
-InsightFaceRecognitionModel::InsightFaceRecognitionModel(
-    const std::string& model_file, const std::string& params_file,
-    const RuntimeOption& custom_option, const ModelFormat& model_format) {
-  if (model_format == ModelFormat::ONNX) {
-    valid_cpu_backends = {Backend::ORT};
-    valid_gpu_backends = {Backend::ORT, Backend::TRT};
-  } else {
-    valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
-    valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
-    valid_xpu_backends = {Backend::LITE};
-  }
-  runtime_option = custom_option;
-  runtime_option.model_format = model_format;
-  runtime_option.model_file = model_file;
-  runtime_option.params_file = params_file;
-  initialized = Initialize();
-}
-
-bool InsightFaceRecognitionModel::Initialize() {
-  // parameters for preprocess
-  size = {112, 112};
-  alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-  beta = {-1.f, -1.f, -1.f};  // RGB
-  swap_rb = true;
-  l2_normalize = false;
-
-  if (!InitRuntime()) {
-    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
-    return false;
-  }
-  return true;
-}
-
-bool InsightFaceRecognitionModel::Preprocess(Mat* mat, FDTensor* output) {
-  // face recognition model's preprocess steps in insightface
-  // reference: insightface/recognition/arcface_torch/inference.py
-  // 1. Resize
-  // 2. BGR2RGB
-  // 3. Convert(opencv style) or Normalize
-  // 4. HWC2CHW
-  int resize_w = size[0];
-  int resize_h = size[1];
-  if (resize_h != mat->Height() || resize_w != mat->Width()) {
-    Resize::Run(mat, resize_w, resize_h);
-  }
-  if (swap_rb) {
-    BGR2RGB::Run(mat);
-  }
-
-  Convert::Run(mat, alpha, beta);
-  HWC2CHW::Run(mat);
-  Cast::Run(mat, "float");
-
-  mat->ShareWithTensor(output);
-  output->shape.insert(output->shape.begin(), 1);  // reshape to n, h, w, c
-  return true;
-}
-
-bool InsightFaceRecognitionModel::Postprocess(
-    std::vector<FDTensor>& infer_result, FaceRecognitionResult* result) {
-  FDASSERT((infer_result.size() == 1),
-           "The default number of output tensor must be 1 according to "
-           "insightface.");
-  FDTensor& embedding_tensor = infer_result.at(0);
-  FDASSERT((embedding_tensor.shape[0] == 1), "Only support batch =1 now.");
-  if (embedding_tensor.dtype != FDDataType::FP32) {
-    FDERROR << "Only support post process with float32 data." << std::endl;
-    return false;
-  }
-
-  result->Clear();
-  result->Resize(embedding_tensor.Numel());
-  // Copy the raw embedding vector directly without L2 normalize
-  // post process. Let the user decide whether to normalize or not.
-  // Will call utils::L2Normlize() method to perform L2
-  // normalize if l2_normalize was set as 'true'.
-  std::memcpy(result->embedding.data(), embedding_tensor.Data(),
-              embedding_tensor.Nbytes());
-  if (l2_normalize) {
-    auto norm_embedding = utils::L2Normalize(result->embedding);
-    std::memcpy(result->embedding.data(), norm_embedding.data(),
-                embedding_tensor.Nbytes());
-  }
-  return true;
-}
-
-bool InsightFaceRecognitionModel::Predict(cv::Mat* im,
-                                          FaceRecognitionResult* result) {
-  Mat mat(*im);
-  std::vector<FDTensor> input_tensors(1);
-
-  if (!Preprocess(&mat, &input_tensors[0])) {
-    FDERROR << "Failed to preprocess input image." << std::endl;
-    return false;
-  }
-
-  input_tensors[0].name = InputInfoOfRuntime(0).name;
-  std::vector<FDTensor> output_tensors;
-  if (!Infer(input_tensors, &output_tensors)) {
-    FDERROR << "Failed to inference." << std::endl;
-    return false;
-  }
-
-  if (!Postprocess(output_tensors, result)) {
-    FDERROR << "Failed to post process." << std::endl;
-    return false;
-  }
-  return true;
-}
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface_rec.h

@@ -1,81 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#pragma once
-#include "fastdeploy/fastdeploy_model.h"
-#include "fastdeploy/vision/common/processors/transform.h"
-#include "fastdeploy/vision/common/result.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-/*! @brief CosFace model object used when to load a CosFace model exported by IngsightFace.
- */
-class FASTDEPLOY_DECL InsightFaceRecognitionModel : public FastDeployModel {
- public:
-  /** \brief  Set path of model file and the configuration of runtime.
-   *
-   * \param[in] model_file Path of model file, e.g ./arcface.onnx
-   * \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
-   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
-   * \param[in] model_format Model format of the loaded model, default is ONNX format
-   */
-  InsightFaceRecognitionModel(
-      const std::string& model_file, const std::string& params_file = "",
-      const RuntimeOption& custom_option = RuntimeOption(),
-      const ModelFormat& model_format = ModelFormat::ONNX);
-
-  virtual std::string ModelName() const { return "deepinsight/insightface"; }
-
-  /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default (112, 112)
-  */
-  std::vector<int> size;
-  /*! @brief
-  Argument for image preprocessing step, alpha values for normalization, default alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-  */
-  std::vector<float> alpha;
-  /*! @brief
-  Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
-  */
-  std::vector<float> beta;
-  /*! @brief
-  Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default true.
-  */
-  bool swap_rb;
-  /*! @brief
-  Argument for image postprocessing step, whether to apply l2 normalize to embedding values, default false;
-  */
-  bool l2_normalize;
-  /** \brief Predict the face recognition result for an input image
-   *
-   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
-   * \param[in] result The output face recognition result will be writen to this structure
-   * \return true if the prediction successed, otherwise false
-   */
-  virtual bool Predict(cv::Mat* im, FaceRecognitionResult* result);
-
-  virtual bool Initialize();
-
-  virtual bool Preprocess(Mat* mat, FDTensor* output);
-
-  virtual bool Postprocess(std::vector<FDTensor>& infer_result,
-                           FaceRecognitionResult* result);
-};
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/insightface_rec_pybind.cc

@@ -1,43 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/pybind/main.h"
-
-namespace fastdeploy {
-void BindInsightFaceRecognitionModel(pybind11::module& m) {
-  // Bind InsightFaceRecognitionModel
-  pybind11::class_<vision::faceid::InsightFaceRecognitionModel,
-                   FastDeployModel>(m, "InsightFaceRecognitionModel")
-      .def(pybind11::init<std::string, std::string, RuntimeOption,
-                          ModelFormat>())
-      .def("predict",
-           [](vision::faceid::InsightFaceRecognitionModel& self,
-              pybind11::array& data) {
-             auto mat = PyArrayToCvMat(data);
-             vision::FaceRecognitionResult res;
-             self.Predict(&mat, &res);
-             return res;
-           })
-      .def_readwrite("size", &vision::faceid::InsightFaceRecognitionModel::size)
-      .def_readwrite("alpha",
-                     &vision::faceid::InsightFaceRecognitionModel::alpha)
-      .def_readwrite("beta", &vision::faceid::InsightFaceRecognitionModel::beta)
-      .def_readwrite("swap_rb",
-                     &vision::faceid::InsightFaceRecognitionModel::swap_rb)
-      .def_readwrite(
-          "l2_normalize",
-          &vision::faceid::InsightFaceRecognitionModel::l2_normalize);
-}
-
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/partial_fc.cc

@@ -1,73 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/vision/faceid/contrib/partial_fc.h"
-#include "fastdeploy/utils/perf.h"
-#include "fastdeploy/vision/utils/utils.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-
-PartialFC::PartialFC(const std::string& model_file,
-                     const std::string& params_file,
-                     const RuntimeOption& custom_option,
-                     const ModelFormat& model_format)
-    : InsightFaceRecognitionModel(model_file, params_file, custom_option,
-                                  model_format) {
-  initialized = Initialize();
-}
-
-bool PartialFC::Initialize() {
- 
-  if (initialized) {
-    // (1.1) re-init parameters for specific sub-classes
-    size = {112, 112};
-    alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-    beta = {-1.f, -1.f, -1.f};  // RGB
-    swap_rb = true;
-    l2_normalize = false;
-    return true;
-  }
-  if (!InsightFaceRecognitionModel::Initialize()) {
-    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
-    return false;
-  }
-  // (2.1) re-init parameters for specific sub-classes
-  size = {112, 112};
-  alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-  beta = {-1.f, -1.f, -1.f};  // RGB
-  swap_rb = true;
-  l2_normalize = false;
-  return true;
-}
-
-bool PartialFC::Preprocess(Mat* mat, FDTensor* output) {
-  return InsightFaceRecognitionModel::Preprocess(mat, output);
-}
-
-bool PartialFC::Postprocess(std::vector<FDTensor>& infer_result,
-                            FaceRecognitionResult* result) {
-  return InsightFaceRecognitionModel::Postprocess(infer_result, result);
-}
-
-bool PartialFC::Predict(cv::Mat* im, FaceRecognitionResult* result) {
-  return InsightFaceRecognitionModel::Predict(im, result);
-}
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/partial_fc.h

@@ -1,63 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#pragma once
-#include "fastdeploy/fastdeploy_model.h"
-#include "fastdeploy/vision/common/processors/transform.h"
-#include "fastdeploy/vision/common/result.h"
-#include "fastdeploy/vision/faceid/contrib/insightface_rec.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-/*! @brief PartialFC model object used when to load a PartialFC model exported by IngsightFace.
- */
-class FASTDEPLOY_DECL PartialFC : public InsightFaceRecognitionModel {
- public:
-  /** \brief  Set path of model file and the configuration of runtime.
-   *
-   * \param[in] model_file Path of model file, e.g ./partial_fc.onnx
-   * \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
-   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
-   * \param[in] model_format Model format of the loaded model, default is ONNX format
-   */
-  PartialFC(const std::string& model_file, const std::string& params_file = "",
-            const RuntimeOption& custom_option = RuntimeOption(),
-            const ModelFormat& model_format = ModelFormat::ONNX);
-
-  std::string ModelName() const override {
-    return "deepinsight/insightface/recognition/partial_fc";
-  }
-  /** \brief Predict the face recognition result for an input image
-   *
-   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
-   * \param[in] result The output face recognition result will be writen to this structure
-   * \return true if the prediction successed, otherwise false
-   */
-  bool Predict(cv::Mat* im, FaceRecognitionResult* result) override;
-
- private:
-  bool Initialize() override;
-
-  bool Preprocess(Mat* mat, FDTensor* output) override;
-
-  bool Postprocess(std::vector<FDTensor>& infer_result,
-                   FaceRecognitionResult* result) override;
-};
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/partial_fc_pybind.cc

@@ -1,38 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/pybind/main.h"
-
-namespace fastdeploy {
-void BindPartialFC(pybind11::module& m) {
-  // Bind Partial FC
-  pybind11::class_<vision::faceid::PartialFC,
-                   vision::faceid::InsightFaceRecognitionModel>(m, "PartialFC")
-      .def(pybind11::init<std::string, std::string, RuntimeOption,
-                          ModelFormat>())
-      .def("predict",
-           [](vision::faceid::PartialFC& self, pybind11::array& data) {
-             auto mat = PyArrayToCvMat(data);
-             vision::FaceRecognitionResult res;
-             self.Predict(&mat, &res);
-             return res;
-           })
-      .def_readwrite("size", &vision::faceid::PartialFC::size)
-      .def_readwrite("alpha", &vision::faceid::PartialFC::alpha)
-      .def_readwrite("beta", &vision::faceid::PartialFC::beta)
-      .def_readwrite("swap_rb", &vision::faceid::PartialFC::swap_rb)
-      .def_readwrite("l2_normalize", &vision::faceid::PartialFC::l2_normalize);
-}
-
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/vpl.cc

@@ -1,71 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/vision/faceid/contrib/vpl.h"
-#include "fastdeploy/utils/perf.h"
-#include "fastdeploy/vision/utils/utils.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-
-VPL::VPL(const std::string& model_file, const std::string& params_file,
-         const RuntimeOption& custom_option, const ModelFormat& model_format)
-    : InsightFaceRecognitionModel(model_file, params_file, custom_option,
-                                  model_format) {
-  initialized = Initialize();
-}
-
-bool VPL::Initialize() {
- 
-  if (initialized) {
-    // (1.1) re-init parameters for specific sub-classes
-    size = {112, 112};
-    alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-    beta = {-1.f, -1.f, -1.f};  // RGB
-    swap_rb = true;
-    l2_normalize = false;
-    return true;
-  }
-  if (!InsightFaceRecognitionModel::Initialize()) {
-    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
-    return false;
-  }
-  // (2.1) re-init parameters for specific sub-classes
-  size = {112, 112};
-  alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
-  beta = {-1.f, -1.f, -1.f};  // RGB
-  swap_rb = true;
-  l2_normalize = false;
-  return true;
-}
-
-bool VPL::Preprocess(Mat* mat, FDTensor* output) {
-  return InsightFaceRecognitionModel::Preprocess(mat, output);
-}
-
-bool VPL::Postprocess(std::vector<FDTensor>& infer_result,
-                      FaceRecognitionResult* result) {
-  return InsightFaceRecognitionModel::Postprocess(infer_result, result);
-}
-
-bool VPL::Predict(cv::Mat* im, FaceRecognitionResult* result) {
-  return InsightFaceRecognitionModel::Predict(im, result);
-}
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/vpl.h

@@ -1,63 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#pragma once
-#include "fastdeploy/fastdeploy_model.h"
-#include "fastdeploy/vision/common/processors/transform.h"
-#include "fastdeploy/vision/common/result.h"
-#include "fastdeploy/vision/faceid/contrib/insightface_rec.h"
-
-namespace fastdeploy {
-
-namespace vision {
-
-namespace faceid {
-/*! @brief VPL model object used when to load a VPL model exported by IngsightFace.
- */
-class FASTDEPLOY_DECL VPL : public InsightFaceRecognitionModel {
- public:
-  /** \brief  Set path of model file and the configuration of runtime.
-   *
-   * \param[in] model_file Path of model file, e.g ./vpl.onnx
-   * \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
-   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
-   * \param[in] model_format Model format of the loaded model, default is ONNX format
-   */
-  VPL(const std::string& model_file, const std::string& params_file = "",
-      const RuntimeOption& custom_option = RuntimeOption(),
-      const ModelFormat& model_format = ModelFormat::ONNX);
-
-  std::string ModelName() const override {
-    return "deepinsight/insightface/recognition/vpl";
-  }
-  /** \brief Predict the face recognition result for an input image
-   *
-   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
-   * \param[in] result The output face recognition result will be writen to this structure
-   * \return true if the prediction successed, otherwise false
-   */
-  bool Predict(cv::Mat* im, FaceRecognitionResult* result) override;
-
- private:
-  bool Initialize() override;
-
-  bool Preprocess(Mat* mat, FDTensor* output) override;
-
-  bool Postprocess(std::vector<FDTensor>& infer_result,
-                   FaceRecognitionResult* result) override;
-};
-
-}  // namespace faceid
-}  // namespace vision
-}  // namespace fastdeploy

fastdeploy/vision/faceid/contrib/vpl_pybind.cc

@@ -1,38 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "fastdeploy/pybind/main.h"
-
-namespace fastdeploy {
-void BindVPL(pybind11::module& m) {
-  // Bind VPL
-  pybind11::class_<vision::faceid::VPL,
-                   vision::faceid::InsightFaceRecognitionModel>(m, "VPL")
-      .def(pybind11::init<std::string, std::string, RuntimeOption,
-                          ModelFormat>())
-      .def("predict",
-           [](vision::faceid::VPL& self, pybind11::array& data) {
-             auto mat = PyArrayToCvMat(data);
-             vision::FaceRecognitionResult res;
-             self.Predict(&mat, &res);
-             return res;
-           })
-      .def_readwrite("size", &vision::faceid::VPL::size)
-      .def_readwrite("alpha", &vision::faceid::VPL::alpha)
-      .def_readwrite("beta", &vision::faceid::VPL::beta)
-      .def_readwrite("swap_rb", &vision::faceid::VPL::swap_rb)
-      .def_readwrite("l2_normalize", &vision::faceid::VPL::l2_normalize);
-}
-
-}  // namespace fastdeploy

fastdeploy/vision/faceid/faceid_pybind.cc

@@ -15,20 +15,11 @@
 #include "fastdeploy/pybind/main.h"
 
 namespace fastdeploy {
+void BindInsightFace(pybind11::module& m);
 void BindAdaFace(pybind11::module& m);
-void BindArcFace(pybind11::module& m);
-void BindInsightFaceRecognitionModel(pybind11::module& m);
-void BindCosFace(pybind11::module& m);
-void BindPartialFC(pybind11::module& m);
-void BindVPL(pybind11::module& m);
-
 void BindFaceId(pybind11::module& m) {
   auto faceid_module = m.def_submodule("faceid", "Face recognition models.");
-  BindInsightFaceRecognitionModel(faceid_module);
+  BindInsightFace(faceid_module);
   BindAdaFace(faceid_module);
-  BindArcFace(faceid_module);
-  BindCosFace(faceid_module);
-  BindPartialFC(faceid_module);
-  BindVPL(faceid_module);
 }
 }  // namespace fastdeploy

python/fastdeploy/vision/faceid/__init__.py

@@ -13,9 +13,4 @@
 # limitations under the License.
 
 from __future__ import absolute_import
-from .contrib.adaface import AdaFace
+from .contrib import *
-from .contrib.arcface import ArcFace
-from .contrib.cosface import CosFace
-from .contrib.insightface_rec import InsightFaceRecognitionModel
-from .contrib.partial_fc import PartialFC
-from .contrib.vpl import VPL

python/fastdeploy/vision/faceid/contrib/__init__.py

@@ -13,3 +13,5 @@
 # limitations under the License.
 
 from __future__ import absolute_import
+from .insightface import *
+from .adaface import *

python/fastdeploy/vision/faceid/contrib/adaface.py

@@ -1,126 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-from .... import FastDeployModel, ModelFormat
-from .... import c_lib_wrap as C
-
-
-class AdaFace(FastDeployModel):
-    def __init__(self,
-                 model_file,
-                 params_file="",
-                 runtime_option=None,
-                 model_format=ModelFormat.PADDLE):
-        """Load a AdaFace model exported by InsigtFace.
-
-        :param model_file: (str)Path of model file, e.g ./adaface.onnx
-        :param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
-        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
-        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
-        """
-        # 调用基函数进行backend_option的初始化
-        # 初始化后的option保存在self._runtime_option
-        super(AdaFace, self).__init__(runtime_option)
-
-        self._model = C.vision.faceid.AdaFace(
-            model_file, params_file, self._runtime_option, model_format)
-        # 通过self.initialized判断整个模型的初始化是否成功
-        assert self.initialized, "AdaFace initialize failed."
-
-    def predict(self, input_image):
-        """ Predict the face recognition result for an input image
-
-        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
-        :return: FaceRecognitionResult
-        """
-        return self._model.predict(input_image)
-
-    # 一些跟模型有关的属性封装
-    # 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
-    @property
-    def size(self):
-        """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
-        """
-        return self._model.size
-
-    @property
-    def alpha(self):
-        """
-        Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
-        """
-        return self._model.alpha
-
-    @property
-    def beta(self):
-        """
-        Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
-
-        """
-        return self._model.beta
-
-    @property
-    def swap_rb(self):
-        """
-        Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
-        """
-        return self._model.swap_rb
-
-    @property
-    def l2_normalize(self):
-        """
-        Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
-        """
-        return self._model.l2_normalize
-
-    @size.setter
-    def size(self, wh):
-        assert isinstance(wh, (list, tuple)), \
-            "The value to set `size` must be type of tuple or list."
-        assert len(wh) == 2, \
-            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
-                len(wh))
-        self._model.size = wh
-
-    @alpha.setter
-    def alpha(self, value):
-        assert isinstance(value, (list, tuple)), \
-            "The value to set `alpha` must be type of tuple or list."
-        assert len(value) == 3, \
-            "The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-                len(value))
-        self._model.alpha = value
-
-    @beta.setter
-    def beta(self, value):
-        assert isinstance(value, (list, tuple)), \
-            "The value to set `beta` must be type of tuple or list."
-        assert len(value) == 3, \
-            "The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-                len(value))
-        self._model.beta = value
-
-    @swap_rb.setter
-    def swap_rb(self, value):
-        assert isinstance(
-            value, bool), "The value to set `swap_rb` must be type of bool."
-        self._model.swap_rb = value
-
-    @l2_normalize.setter
-    def l2_normalize(self, value):
-        assert isinstance(
-            value,
-            bool), "The value to set `l2_normalize` must be type of bool."
-        self._model.l2_normalize = value

python/fastdeploy/vision/faceid/contrib/adaface/__init__.py

@@ -0,0 +1,109 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from ..... import FastDeployModel, ModelFormat
+from ..... import c_lib_wrap as C
+
+
+class AdaFacePreprocessor:
+    def __init__(self):
+        """Create a preprocessor for AdaFace Model
+        """
+        self._preprocessor = C.vision.faceid.AdaFacePreprocessor()
+
+    def run(self, input_ims):
+        """Preprocess input images for AdaFace Model
+
+        :param: input_ims: (list of numpy.ndarray)The input image
+        :return: list of FDTensor, include image, scale_factor, im_shape
+        """
+        return self._preprocessor.run(input_ims)
+
+
+class AdaFacePostprocessor:
+    def __init__(self):
+        """Create a postprocessor for AdaFace Model
+
+        """
+        self._postprocessor = C.vision.faceid.AdaFacePostprocessor()
+
+    def run(self, runtime_results):
+        """Postprocess the runtime results for PaddleClas Model
+
+        :param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
+        :return: list of FaceRecognitionResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
+        """
+        return self._postprocessor.run(runtime_results)
+
+    @property
+    def l2_normalize(self):
+        """
+        confidence threshold for postprocessing, default is 0.5
+        """
+        return self._postprocessor.l2_normalize
+
+
+class AdaFace(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a AdaFace model exported by PaddleClas.
+
+        :param model_file: (str)Path of model file, e.g adaface/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g adaface/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+        super(AdaFace, self).__init__(runtime_option)
+        self._model = C.vision.faceid.AdaFace(
+            model_file, params_file, self._runtime_option, model_format)
+        assert self.initialized, "AdaFace model initialize failed."
+
+    def predict(self, im):
+        """Detect an input image
+
+        :param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :return: DetectionResult
+        """
+
+        assert im is not None, "The input image data is None."
+        return self._model.predict(im)
+
+    def batch_predict(self, images):
+        """Detect a batch of input image list
+
+        :param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
+        :return list of DetectionResult
+        """
+
+        return self._model.batch_predict(images)
+
+    @property
+    def preprocessor(self):
+        """Get AdaFacePreprocessor object of the loaded model
+
+        :return AdaFacePreprocessor
+        """
+        return self._model.preprocessor
+
+    @property
+    def postprocessor(self):
+        """Get AdaFacePostprocessor object of the loaded model
+
+        :return AdaFacePostprocessor
+        """
+        return self._model.postprocessor

python/fastdeploy/vision/faceid/contrib/arcface.py

@@ -1,127 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-import logging
-from .... import FastDeployModel, ModelFormat
-from .... import c_lib_wrap as C
-from ..contrib.insightface_rec import InsightFaceRecognitionModel
-
-
-class ArcFace(FastDeployModel):
-    def __init__(self,
-                 model_file,
-                 params_file="",
-                 runtime_option=None,
-                 model_format=ModelFormat.ONNX):
-        """Load a ArcFace model exported by InsigtFace.
-
-        :param model_file: (str)Path of model file, e.g ./arcface.onnx
-        :param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
-        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
-        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
-        """
-        # 调用基函数进行backend_option的初始化
-        # 初始化后的option保存在self._runtime_option
-        super(ArcFace, self).__init__(runtime_option)
-
-        self._model = C.vision.faceid.ArcFace(
-            model_file, params_file, self._runtime_option, model_format)
-        # 通过self.initialized判断整个模型的初始化是否成功
-        assert self.initialized, "ArcFace initialize failed."
-
-    def predict(self, input_image):
-        """ Predict the face recognition result for an input image
-
-        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
-        :return: FaceRecognitionResult
-        """
-        return self._model.predict(input_image)
-
-    # 一些跟模型有关的属性封装
-    # 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
-    @property
-    def size(self):
-        """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
-        """
-        return self._model.size
-
-    @property
-    def alpha(self):
-        """
-        Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
-        """
-        return self._model.alpha
-
-    @property
-    def beta(self):
-        """
-        Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
-        """
-        return self._model.beta
-
-    @property
-    def swap_rb(self):
-        """
-        Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
-        """
-        return self._model.swap_rb
-
-    @property
-    def l2_normalize(self):
-        """
-        Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
-        """
-        return self._model.l2_normalize
-
-    @size.setter
-    def size(self, wh):
-        assert isinstance(wh, (list, tuple)),\
-            "The value to set `size` must be type of tuple or list."
-        assert len(wh) == 2,\
-            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
-            len(wh))
-        self._model.size = wh
-
-    @alpha.setter
-    def alpha(self, value):
-        assert isinstance(value, (list, tuple)),\
-            "The value to set `alpha` must be type of tuple or list."
-        assert len(value) == 3,\
-            "The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-            len(value))
-        self._model.alpha = value
-
-    @beta.setter
-    def beta(self, value):
-        assert isinstance(value, (list, tuple)),\
-            "The value to set `beta` must be type of tuple or list."
-        assert len(value) == 3,\
-            "The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-            len(value))
-        self._model.beta = value
-
-    @swap_rb.setter
-    def swap_rb(self, value):
-        assert isinstance(
-            value, bool), "The value to set `swap_rb` must be type of bool."
-        self._model.swap_rb = value
-
-    @l2_normalize.setter
-    def l2_normalize(self, value):
-        assert isinstance(
-            value,
-            bool), "The value to set `l2_normalize` must be type of bool."
-        self._model.l2_normalize = value

python/fastdeploy/vision/faceid/contrib/cosface.py

@@ -1,126 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-import logging
-from .... import FastDeployModel, ModelFormat
-from .... import c_lib_wrap as C
-
-
-class CosFace(FastDeployModel):
-    def __init__(self,
-                 model_file,
-                 params_file="",
-                 runtime_option=None,
-                 model_format=ModelFormat.ONNX):
-        """Load a CosFace model exported by InsigtFace.
-
-        :param model_file: (str)Path of model file, e.g ./cosface.onnx
-        :param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
-        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
-        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
-        """
-        # 调用基函数进行backend_option的初始化
-        # 初始化后的option保存在self._runtime_option
-        super(CosFace, self).__init__(runtime_option)
-
-        self._model = C.vision.faceid.CosFace(
-            model_file, params_file, self._runtime_option, model_format)
-        # 通过self.initialized判断整个模型的初始化是否成功
-        assert self.initialized, "CosFace initialize failed."
-
-    def predict(self, input_image):
-        """ Predict the face recognition result for an input image
-
-        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
-        :return: FaceRecognitionResult
-        """
-        return self._model.predict(input_image)
-
-    # 一些跟模型有关的属性封装
-    # 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
-    @property
-    def size(self):
-        """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
-        """
-        return self._model.size
-
-    @property
-    def alpha(self):
-        """
-        Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
-        """
-        return self._model.alpha
-
-    @property
-    def beta(self):
-        """
-        Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
-        """
-        return self._model.beta
-
-    @property
-    def swap_rb(self):
-        """
-        Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
-        """
-        return self._model.swap_rb
-
-    @property
-    def l2_normalize(self):
-        """
-        Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
-        """
-        return self._model.l2_normalize
-
-    @size.setter
-    def size(self, wh):
-        assert isinstance(wh, (list, tuple)),\
-            "The value to set `size` must be type of tuple or list."
-        assert len(wh) == 2,\
-            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
-            len(wh))
-        self._model.size = wh
-
-    @alpha.setter
-    def alpha(self, value):
-        assert isinstance(value, (list, tuple)),\
-            "The value to set `alpha` must be type of tuple or list."
-        assert len(value) == 3,\
-            "The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-            len(value))
-        self._model.alpha = value
-
-    @beta.setter
-    def beta(self, value):
-        assert isinstance(value, (list, tuple)),\
-            "The value to set `beta` must be type of tuple or list."
-        assert len(value) == 3,\
-            "The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-            len(value))
-        self._model.beta = value
-
-    @swap_rb.setter
-    def swap_rb(self, value):
-        assert isinstance(
-            value, bool), "The value to set `swap_rb` must be type of bool."
-        self._model.swap_rb = value
-
-    @l2_normalize.setter
-    def l2_normalize(self, value):
-        assert isinstance(
-            value,
-            bool), "The value to set `l2_normalize` must be type of bool."
-        self._model.l2_normalize = value

python/fastdeploy/vision/faceid/contrib/insightface/__init__.py

@@ -0,0 +1,222 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from ..... import FastDeployModel, ModelFormat
+from ..... import c_lib_wrap as C
+
+
+class InsightFaceRecognitionPreprocessor:
+    def __init__(self):
+        """Create a preprocessor for InsightFaceRecognition Model
+        """
+        self._preprocessor = C.vision.faceid.InsightFaceRecognitionPreprocessor(
+        )
+
+    def run(self, input_ims):
+        """Preprocess input images for InsightFaceRecognition Model
+
+        :param: input_ims: (list of numpy.ndarray)The input image
+        :return: list of FDTensor, include image, scale_factor, im_shape
+        """
+        return self._preprocessor.run(input_ims)
+
+    @property
+    def size(self):
+        """
+        Argument for image preprocessing step, tuple of (width, height),
+        decide the target size after resize, default (112, 112)
+        """
+        return self._preprocessor.size
+
+    @property
+    def alpha(self):
+        """
+        Argument for image preprocessing step, alpha values for normalization,
+        default alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
+        """
+        return self._preprocessor.alpha
+
+    @property
+    def beta(self):
+        """
+        Argument for image preprocessing step, beta values for normalization,
+        default beta = {-1.f, -1.f, -1.f}
+        """
+        return self._preprocessor.beta
+
+    @property
+    def permute(self):
+        """
+        Argument for image preprocessing step, whether to swap the B and R channel,
+        such as BGR->RGB, default true.
+        """
+        return self._preprocessor.permute
+
+
+class InsightFaceRecognitionPostprocessor:
+    def __init__(self):
+        """Create a postprocessor for InsightFaceRecognition Model
+        """
+        self._postprocessor = C.vision.faceid.InsightFaceRecognitionPostprocessor(
+        )
+
+    def run(self, runtime_results):
+        """Postprocess the runtime results for PaddleClas Model
+
+        :param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
+        :return: list of FaceRecognitionResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
+        """
+        return self._postprocessor.run(runtime_results)
+
+    @property
+    def l2_normalize(self):
+        """
+        confidence threshold for postprocessing, default is 0.5
+        """
+        return self._postprocessor.l2_normalize
+
+
+class InsightFaceRecognitionBase(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a InsightFaceRecognitionBase model exported by PaddleClas.
+
+        :param model_file: (str)Path of model file, e.g InsightFaceRecognitionBase/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g InsightFaceRecognitionBase/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+        super(InsightFaceRecognitionBase, self).__init__(runtime_option)
+        self._model = C.vision.faceid.InsightFaceRecognitionBase(
+            model_file, params_file, self._runtime_option, model_format)
+        assert self.initialized, "InsightFaceRecognitionBase model initialize failed."
+
+    def predict(self, im):
+        """Detect an input image
+
+        :param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :return: DetectionResult
+        """
+
+        assert im is not None, "The input image data is None."
+        return self._model.predict(im)
+
+    def batch_predict(self, images):
+        """Detect a batch of input image list
+
+        :param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
+        :return list of DetectionResult
+        """
+
+        return self._model.batch_predict(images)
+
+    @property
+    def preprocessor(self):
+        """Get InsightFaceRecognitionPreprocessor object of the loaded model
+
+        :return InsightFaceRecognitionPreprocessor
+        """
+        return self._model.preprocessor
+
+    @property
+    def postprocessor(self):
+        """Get InsightFaceRecognitionPostprocessor object of the loaded model
+
+        :return InsightFaceRecognitionPostprocessor
+        """
+        return self._model.postprocessor
+
+
+class ArcFace(InsightFaceRecognitionBase):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a ArcFace model exported by PaddleClas.
+        :param model_file: (str)Path of model file, e.g ArcFace/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g ArcFace/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+
+        super(InsightFaceRecognitionBase, self).__init__(runtime_option)
+
+        self._model = C.vision.faceid.ArcFace(
+            model_file, params_file, self._runtime_option, model_format)
+        assert self.initialized, "ArcFace model initialize failed."
+
+
+class CosFace(InsightFaceRecognitionBase):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a CosFace model exported by PaddleClas.
+        :param model_file: (str)Path of model file, e.g CosFace/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g CosFace/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+
+        super(InsightFaceRecognitionBase, self).__init__(runtime_option)
+
+        self._model = C.vision.faceid.CosFace(
+            model_file, params_file, self._runtime_option, model_format)
+        assert self.initialized, "CosFace model initialize failed."
+
+
+class PartialFC(InsightFaceRecognitionBase):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a PartialFC model exported by PaddleClas.
+        :param model_file: (str)Path of model file, e.g PartialFC/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g PartialFC/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+
+        super(InsightFaceRecognitionBase, self).__init__(runtime_option)
+
+        self._model = C.vision.faceid.PartialFC(
+            model_file, params_file, self._runtime_option, model_format)
+        assert self.initialized, "PartialFC model initialize failed."
+
+
+class VPL(InsightFaceRecognitionBase):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a VPL model exported by PaddleClas.
+        :param model_file: (str)Path of model file, e.g VPL/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g VPL/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+
+        super(InsightFaceRecognitionBase, self).__init__(runtime_option)
+
+        self._model = C.vision.faceid.VPL(model_file, params_file,
+                                          self._runtime_option, model_format)
+        assert self.initialized, "VPL model initialize failed."

python/fastdeploy/vision/faceid/contrib/insightface_rec.py

@@ -1,126 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-import logging
-from .... import FastDeployModel, ModelFormat
-from .... import c_lib_wrap as C
-
-
-class InsightFaceRecognitionModel(FastDeployModel):
-    def __init__(self,
-                 model_file,
-                 params_file="",
-                 runtime_option=None,
-                 model_format=ModelFormat.ONNX):
-        """Load a InsightFace model exported by InsigtFace.
-
-        :param model_file: (str)Path of model file, e.g ./arcface.onnx
-        :param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
-        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
-        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
-        """
-        # 调用基函数进行backend_option的初始化
-        # 初始化后的option保存在self._runtime_option
-        super(InsightFaceRecognitionModel, self).__init__(runtime_option)
-
-        self._model = C.vision.faceid.InsightFaceRecognitionModel(
-            model_file, params_file, self._runtime_option, model_format)
-        # 通过self.initialized判断整个模型的初始化是否成功
-        assert self.initialized, "InsightFaceRecognitionModel initialize failed."
-
-    def predict(self, input_image):
-        """ Predict the face recognition result for an input image
-
-        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
-        :return: FaceRecognitionResult
-        """
-        return self._model.predict(input_image)
-
-    # 一些跟InsightFaceRecognitionModel模型有关的属性封装
-    # 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
-    @property
-    def size(self):
-        """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
-        """
-        return self._model.size
-
-    @property
-    def alpha(self):
-        """
-        Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
-        """
-        return self._model.alpha
-
-    @property
-    def beta(self):
-        """
-        Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
-        """
-        return self._model.beta
-
-    @property
-    def swap_rb(self):
-        """
-        Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
-        """
-        return self._model.swap_rb
-
-    @property
-    def l2_normalize(self):
-        """
-        Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
-        """
-        return self._model.l2_normalize
-
-    @size.setter
-    def size(self, wh):
-        assert isinstance(wh, (list, tuple)),\
-            "The value to set `size` must be type of tuple or list."
-        assert len(wh) == 2,\
-            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
-            len(wh))
-        self._model.size = wh
-
-    @alpha.setter
-    def alpha(self, value):
-        assert isinstance(value, (list, tuple)),\
-            "The value to set `alpha` must be type of tuple or list."
-        assert len(value) == 3,\
-            "The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-            len(value))
-        self._model.alpha = value
-
-    @beta.setter
-    def beta(self, value):
-        assert isinstance(value, (list, tuple)),\
-            "The value to set `beta` must be type of tuple or list."
-        assert len(value) == 3,\
-            "The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-            len(value))
-        self._model.beta = value
-
-    @swap_rb.setter
-    def swap_rb(self, value):
-        assert isinstance(
-            value, bool), "The value to set `swap_rb` must be type of bool."
-        self._model.swap_rb = value
-
-    @l2_normalize.setter
-    def l2_normalize(self, value):
-        assert isinstance(
-            value,
-            bool), "The value to set `l2_normalize` must be type of bool."
-        self._model.l2_normalize = value

python/fastdeploy/vision/faceid/contrib/partial_fc.py

@@ -1,126 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-import logging
-from .... import FastDeployModel, ModelFormat
-from .... import c_lib_wrap as C
-
-
-class PartialFC(FastDeployModel):
-    def __init__(self,
-                 model_file,
-                 params_file="",
-                 runtime_option=None,
-                 model_format=ModelFormat.ONNX):
-        """Load a PartialFC model exported by InsigtFace.
-
-        :param model_file: (str)Path of model file, e.g ./partial_fc.onnx
-        :param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
-        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
-        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
-        """
-        # 调用基函数进行backend_option的初始化
-        # 初始化后的option保存在self._runtime_option
-        super(PartialFC, self).__init__(runtime_option)
-
-        self._model = C.vision.faceid.PartialFC(
-            model_file, params_file, self._runtime_option, model_format)
-        # 通过self.initialized判断整个模型的初始化是否成功
-        assert self.initialized, "PartialFC initialize failed."
-
-    def predict(self, input_image):
-        """ Predict the face recognition result for an input image
-
-        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
-        :return: FaceRecognitionResult
-        """
-        return self._model.predict(input_image)
-
-    # 一些跟模型有关的属性封装
-    # 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
-    @property
-    def size(self):
-        """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
-        """
-        return self._model.size
-
-    @property
-    def alpha(self):
-        """
-        Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
-        """
-        return self._model.alpha
-
-    @property
-    def beta(self):
-        """
-        Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
-        """
-        return self._model.beta
-
-    @property
-    def swap_rb(self):
-        """
-        Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
-        """
-        return self._model.swap_rb
-
-    @property
-    def l2_normalize(self):
-        """
-        Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
-        """
-        return self._model.l2_normalize
-
-    @size.setter
-    def size(self, wh):
-        assert isinstance(wh, (list, tuple)),\
-            "The value to set `size` must be type of tuple or list."
-        assert len(wh) == 2,\
-            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
-            len(wh))
-        self._model.size = wh
-
-    @alpha.setter
-    def alpha(self, value):
-        assert isinstance(value, (list, tuple)),\
-            "The value to set `alpha` must be type of tuple or list."
-        assert len(value) == 3,\
-            "The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-            len(value))
-        self._model.alpha = value
-
-    @beta.setter
-    def beta(self, value):
-        assert isinstance(value, (list, tuple)),\
-            "The value to set `beta` must be type of tuple or list."
-        assert len(value) == 3,\
-            "The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-            len(value))
-        self._model.beta = value
-
-    @swap_rb.setter
-    def swap_rb(self, value):
-        assert isinstance(
-            value, bool), "The value to set `swap_rb` must be type of bool."
-        self._model.swap_rb = value
-
-    @l2_normalize.setter
-    def l2_normalize(self, value):
-        assert isinstance(
-            value,
-            bool), "The value to set `l2_normalize` must be type of bool."
-        self._model.l2_normalize = value

python/fastdeploy/vision/faceid/contrib/vpl.py

@@ -1,126 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-import logging
-from .... import FastDeployModel, ModelFormat
-from .... import c_lib_wrap as C
-
-
-class VPL(FastDeployModel):
-    def __init__(self,
-                 model_file,
-                 params_file="",
-                 runtime_option=None,
-                 model_format=ModelFormat.ONNX):
-        """Load a VPL model exported by InsigtFace.
-
-        :param model_file: (str)Path of model file, e.g ./vpl.onnx
-        :param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
-        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
-        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
-        """
-        # 调用基函数进行backend_option的初始化
-        # 初始化后的option保存在self._runtime_option
-        super(VPL, self).__init__(runtime_option)
-
-        self._model = C.vision.faceid.VPL(model_file, params_file,
-                                          self._runtime_option, model_format)
-        # 通过self.initialized判断整个模型的初始化是否成功
-        assert self.initialized, "VPL initialize failed."
-
-    def predict(self, input_image):
-        """ Predict the face recognition result for an input image
-
-        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
-        :return: FaceRecognitionResult
-        """
-        return self._model.predict(input_image)
-
-    # 一些跟模型有关的属性封装
-    # 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
-    @property
-    def size(self):
-        """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
-        """
-        return self._model.size
-
-    @property
-    def alpha(self):
-        """
-        Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
-        """
-        return self._model.alpha
-
-    @property
-    def beta(self):
-        """
-        Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
-        """
-        return self._model.beta
-
-    @property
-    def swap_rb(self):
-        """
-        Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
-        """
-        return self._model.swap_rb
-
-    @property
-    def l2_normalize(self):
-        """
-        Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
-        """
-        return self._model.l2_normalize
-
-    @size.setter
-    def size(self, wh):
-        assert isinstance(wh, (list, tuple)),\
-            "The value to set `size` must be type of tuple or list."
-        assert len(wh) == 2,\
-            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
-            len(wh))
-        self._model.size = wh
-
-    @alpha.setter
-    def alpha(self, value):
-        assert isinstance(value, (list, tuple)),\
-            "The value to set `alpha` must be type of tuple or list."
-        assert len(value) == 3,\
-            "The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-            len(value))
-        self._model.alpha = value
-
-    @beta.setter
-    def beta(self, value):
-        assert isinstance(value, (list, tuple)),\
-            "The value to set `beta` must be type of tuple or list."
-        assert len(value) == 3,\
-            "The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
-            len(value))
-        self._model.beta = value
-
-    @swap_rb.setter
-    def swap_rb(self, value):
-        assert isinstance(
-            value, bool), "The value to set `swap_rb` must be type of bool."
-        self._model.swap_rb = value
-
-    @l2_normalize.setter
-    def l2_normalize(self, value):
-        assert isinstance(
-            value,
-            bool), "The value to set `l2_normalize` must be type of bool."
-        self._model.l2_normalize = value