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环境变量 + - 在系统变量中找到Path(如没有,自行创建),并双击编辑 + - 新建,将OpenCV路径填入并保存,如 `D:\projects\opencv\build\x64\vc14\bin` + +### Step3: 使用Visual Studio 2019直接编译CMake + +1. 打开Visual Studio 2019 Community,点击 `继续但无需代码` + +![step2](./imgs/vs2019_step1.png) + +2. 点击: `文件`->`打开`->`CMake` + +![step2.1](./imgs/vs2019_step2.png) + +选择项目代码所在路径,并打开`CMakeList.txt`: + +![step2.2](./imgs/vs2019_step3.png) + +3. 点击:`项目`->`cpp_inference_demo的CMake设置` + +![step3](./imgs/vs2019_step4.png) + +4. 请设置以下参数的值 + + +| 名称 | 值 | 保存到 JSON | +| ----------------------------- | ------------------ | ----------- | +| CMAKE_BACKWARDS_COMPATIBILITY | 3.17 | [√] | +| CMAKE_BUILD_TYPE | RelWithDebInfo | [√] | +| CUDA_LIB | CUDA的库路径 | [√] | +| CUDNN_LIB | CUDNN的库路径 | [√] | +| OPENCV_DIR | OpenCV的安装路径 | [√] | +| PADDLE_LIB | Paddle预测库的路径 | [√] | +| WITH_GPU | [√] | [√] | +| WITH_MKL | [√] | [√] | +| WITH_STATIC_LIB | [√] | [√] | + +**注意**: + +1. `CMAKE_BACKWARDS_COMPATIBILITY` 的值请根据自己 `cmake` 版本设置,`cmake` 版本可以通过命令:`cmake --version` 查询; +2. `CUDA_LIB` 、 `CUDNN_LIB` 的值仅需在使用**GPU版本**预测库时指定,其中CUDA库版本尽量对齐,**使用9.0、10.0版本,不使用9.2、10.1等版本CUDA库**; +3. 在设置 `CUDA_LIB`、`CUDNN_LIB`、`OPENCV_DIR`、`PADDLE_LIB` 时,点击 `浏览`,分别设置相应的路径; +4. 在使用`CPU`版预测库时,请把 `WITH_GPU` 的勾去掉。 + +![step4](./imgs/vs2019_step5.png) + +**设置完成后**, 点击上图中 `保存并生成CMake缓存以加载变量` 。 + +5. 点击`生成`->`全部生成` + +![step6](./imgs/vs2019_step6.png) + + +### Step4: 预测及可视化 + +在完成上述操作后,`Visual Studio 2019` 编译产出的可执行文件 `clas_system.exe` 在 `out\build\x64-Release`目录下,打开`cmd`,并切换到该目录: + +``` +cd D:\projects\PaddleClas\deploy\cpp_infer\out\build\x64-Release +``` +可执行文件`clas_system.exe`即为编译产出的的预测程序,其使用方法如下: + +```shell +#预测图片 `.\docs\ILSVRC2012_val_00008306.JPEG` +.\clas_system.exe D:\projects\PaddleClas\deploy\cpp_infer\tools\config.txt .\docs\ILSVRC2012_val_00008306.JPEG +``` + +上述命令中,第一个参数为配置文件路径,第二个参数为需要预测的图片路径。 + + +### 注意 +* 在Windows下的终端中执行文件exe时,可能会发生乱码的现象,此时需要在终端中输入`CHCP 65001`,将终端的编码方式由GBK编码(默认)改为UTF-8编码,更加具体的解释可以参考这篇博客:[https://blog.csdn.net/qq_35038153/article/details/78430359](https://blog.csdn.net/qq_35038153/article/details/78430359)。 +* 如果需要使用CPU预测,PaddlePaddle在Windows上仅支持avx的CPU预测,目前不支持noavx的CPU预测。 diff --git a/deploy/cpp_infer/readme.md b/deploy/cpp_infer/readme.md new file mode 100644 index 00000000..b9f670be --- /dev/null +++ b/deploy/cpp_infer/readme.md @@ -0,0 +1,214 @@ +# 服务器端C++预测 + +本教程将介绍在服务器端部署PaddleClas模型的详细步骤。 + + +## 1. 准备环境 + +### 运行准备 +- Linux环境,推荐使用docker。 +- Windows环境,目前支持基于`Visual Studio 2019 Community`进行编译;此外,如果您希望通过生成`sln解决方案`的方式进行编译,可以参考该文档:[https://zhuanlan.zhihu.com/p/145446681](https://zhuanlan.zhihu.com/p/145446681) + +* 该文档主要介绍基于Linux环境下的PaddleClas C++预测流程,如果需要在Windows环境下使用预测库进行C++预测,具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md)。 + +### 1.1 编译opencv库 + +* 首先需要从opencv官网上下载在Linux环境下源码编译的包,以3.4.7版本为例,下载及解压缩命令如下: + +``` +wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz +tar -xvf 3.4.7.tar.gz +``` + +最终可以在当前目录下看到`opencv-3.4.7/`的文件夹。 + +* 编译opencv,首先设置opencv源码路径(`root_path`)以及安装路径(`install_path`),`root_path`为下载的opencv源码路径,`install_path`为opencv的安装路径。在本例中,源码路径即为当前目录下的`opencv-3.4.7/`。 + +```shell +cd ./opencv-3.4.7 +export root_path=$PWD +export install_path=${root_path}/opencv3 +``` + +然后在opencv源码路径下,按照下面的方式进行编译。 + +```shell +rm -rf build +mkdir build +cd build + +cmake .. \ + -DCMAKE_INSTALL_PREFIX=${install_path} \ + -DCMAKE_BUILD_TYPE=Release \ + -DBUILD_SHARED_LIBS=OFF \ + -DWITH_IPP=OFF \ + -DBUILD_IPP_IW=OFF \ + -DWITH_LAPACK=OFF \ + -DWITH_EIGEN=OFF \ + -DCMAKE_INSTALL_LIBDIR=lib64 \ + -DWITH_ZLIB=ON \ + -DBUILD_ZLIB=ON \ + -DWITH_JPEG=ON \ + -DBUILD_JPEG=ON \ + -DWITH_PNG=ON \ + -DBUILD_PNG=ON \ + -DWITH_TIFF=ON \ + -DBUILD_TIFF=ON + +make -j +make install +``` + +`make install`完成之后,会在该文件夹下生成opencv头文件和库文件,用于后面的PaddleClas代码编译。 + +以opencv3.4.7版本为例,最终在安装路径下的文件结构如下所示。**注意**:不同的opencv版本,下述的文件结构可能不同。 + +``` +opencv3/ +|-- bin +|-- include +|-- lib64 +|-- share +``` + +### 1.2 下载或者编译Paddle预测库 + +* 有2种方式获取Paddle预测库,下面进行详细介绍。 + +#### 1.2.1 预测库源码编译 +* 如果希望获取最新预测库特性,可以从Paddle github上克隆最新代码,源码编译预测库。 +* 可以参考[Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)的说明,从github上获取Paddle代码,然后进行编译,生成最新的预测库。使用git获取代码方法如下。 + +```shell +git clone https://github.com/PaddlePaddle/Paddle.git +``` + +* 进入Paddle目录后,使用如下方法编译。 + +```shell +rm -rf build +mkdir build +cd build + +cmake .. \ + -DWITH_CONTRIB=OFF \ + -DWITH_MKL=ON \ + -DWITH_MKLDNN=ON \ + -DWITH_TESTING=OFF \ + -DCMAKE_BUILD_TYPE=Release \ + -DWITH_INFERENCE_API_TEST=OFF \ + -DON_INFER=ON \ + -DWITH_PYTHON=ON +make -j +make inference_lib_dist +``` + +更多编译参数选项可以参考Paddle C++预测库官网:[https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)。 + + +* 编译完成之后,可以在`build/fluid_inference_install_dir/`文件下看到生成了以下文件及文件夹。 + +``` +build/fluid_inference_install_dir/ +|-- CMakeCache.txt +|-- paddle +|-- third_party +|-- version.txt +``` + +其中`paddle`就是之后进行C++预测时所需的Paddle库,`version.txt`中包含当前预测库的版本信息。 + +#### 1.2.2 直接下载安装 + +* [Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)上提供了不同cuda版本的Linux预测库,可以在官网查看并选择合适的预测库版本。 + + 以`ubuntu14.04_cuda9.0_cudnn7_avx_mkl`的`1.8.4`版本为例,使用下述命令下载并解压: + + +```shell +wget https://paddle-inference-lib.bj.bcebos.com/1.8.4-gpu-cuda9-cudnn7-avx-mkl/fluid_inference.tgz + +tar -xvf fluid_inference.tgz +``` + + +最终会在当前的文件夹中生成`fluid_inference/`的子文件夹。 + + +## 2 开始运行 + +### 2.1 将模型导出为inference model + +* 可以参考[模型导出](../../tools/export_model.py),导出`inference model`,用于模型预测。得到预测模型后,假设模型文件放在`inference`目录下,则目录结构如下。 + +``` +inference/ +|--model +|--params +``` +**注意**:上述文件中,`model`文件存储了模型结构信息,`params`文件存储了模型参数信息。因此,在使用模型导出时,需将导出的`__model__`文件重命名为`model`,`__variables__`文件重命名为`params`。 + + +### 2.2 编译PaddleClas C++预测demo + +* 编译命令如下,其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。 + + +```shell +sh tools/build.sh +``` + +具体地,`tools/build.sh`中内容如下。 + +```shell +OPENCV_DIR=your_opencv_dir +LIB_DIR=your_paddle_inference_dir +CUDA_LIB_DIR=your_cuda_lib_dir +CUDNN_LIB_DIR=your_cudnn_lib_dir + +BUILD_DIR=build +rm -rf ${BUILD_DIR} +mkdir ${BUILD_DIR} +cd ${BUILD_DIR} +cmake .. \ + -DPADDLE_LIB=${LIB_DIR} \ + -DWITH_MKL=ON \ + -DDEMO_NAME=clas_system \ + -DWITH_GPU=OFF \ + -DWITH_STATIC_LIB=OFF \ + -DUSE_TENSORRT=OFF \ + -DOPENCV_DIR=${OPENCV_DIR} \ + -DCUDNN_LIB=${CUDNN_LIB_DIR} \ + -DCUDA_LIB=${CUDA_LIB_DIR} \ + +make -j +``` + +上述命令中, + +* `OPENCV_DIR`为opencv编译安装的地址(本例中为`opencv-3.4.7/opencv3`文件夹的路径); + +* `LIB_DIR`为下载的Paddle预测库(`fluid_inference`文件夹),或编译生成的Paddle预测库(`build/fluid_inference_install_dir`文件夹)的路径; + +* `CUDA_LIB_DIR`为cuda库文件地址,在docker中为`/usr/local/cuda/lib64`; + +* `CUDNN_LIB_DIR`为cudnn库文件地址,在docker中为`/usr/lib/x86_64-linux-gnu/`。 + +在执行上述命令,编译完成之后,会在当前路径下生成`build`文件夹,其中生成一个名为`clas_system`的可执行文件。 + + +### 运行demo +* 执行以下命令,完成对一幅图像的分类。 + +```shell +sh tools/run.sh +``` + + +最终屏幕上会输出检测结果如下。 + +

+ +其中`class id`表示置信度最高的类别对应的id,score表示图片属于该类别的 diff --git a/deploy/cpp_infer/src/cls.cpp b/deploy/cpp_infer/src/cls.cpp index 60f92e25..95c043a7 100644 --- a/deploy/cpp_infer/src/cls.cpp +++ b/deploy/cpp_infer/src/cls.cpp @@ -93,7 +93,7 @@ void Classifier::Run(cv::Mat &img) { int maxPosition = max_element(out_data.begin(), out_data.end()) - out_data.begin(); std::cout << "result: " << std::endl; - std::cout << "\tclass: " << maxPosition << std::endl; + std::cout << "\tclass id: " << maxPosition << std::endl; std::cout << std::fixed << std::setprecision(10) << "\tscore: " << double(out_data[maxPosition]) << std::endl; } diff --git a/deploy/cpp_infer/tools/run.sh b/deploy/cpp_infer/tools/run.sh index 2c4b5434..7972bc26 100755 --- a/deploy/cpp_infer/tools/run.sh +++ b/deploy/cpp_infer/tools/run.sh @@ -1,2 +1,2 @@ -./build/clas_system ./tools/config.txt ./ILSVRC2012_val_00000001.JPEG +./build/clas_system ./tools/config.txt ./docs/imgs/ILSVRC2012_val_00000666.JPEG -- GitLab