Server-side C++ inference

In this tutorial, we will introduce the detailed steps of deploying PaddleClas models on the server side.

1. Prepare the environment

Environment

• Linux, docker is recommended.
• Windows, compilation based on Visual Studio 2019 Community is supported. In addition, you can refer to How to use PaddleDetection to make a complete project to compile by generating the sln solution.
• This document mainly introduces the compilation and inference of PaddleClas C++ in Linux environment.
• If you need to use the Inference Library in Windows environment, please refer to The compilation tutorial in Windows for detailed information.

1.1 Compile opencv

• First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download and uncompress command are as follows.

wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
tar -xf 3.4.7.tar.gz

Finally, you can see the folder of opencv-3.4.7/ in the current directory.

• Compile opencv, the opencv source path (root_path) and installation path (install_path) should be set by yourself. Among them, root_path is the downloaded opencv source code path, and install_path is the installation path of opencv. In this case, the opencv source is ./opencv-3.4.7.

cd ./opencv-3.4.7
export root_path=$PWD
export install_path=${root_path}/opencv3

• After entering the opencv source code path, you can compile it in the following way.

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

• After make install is completed, the opencv header file and library file will be generated in this folder for later PaddleClas source code compilation.

Take opencv3.4.7 for example, the final file structure under the opencv installation path is as follows. NOTICE:The following file structure may be different for different Versions of Opencv.

opencv3/
|-- bin
|-- include
|-- lib64
|-- share

1.2 Compile or download the Paddle Inference Library

• There are 2 ways to obtain the Paddle Inference Library, described in detail below.

1.2.1 Compile from the source code

• If you want to get the latest Paddle Inference Library features, you can download the latest code from Paddle GitHub repository and compile the inference library from the source code.
• You can refer to [Paddle Inference Library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows.

git clone https://github.com/PaddlePaddle/Paddle.git

• After entering the Paddle directory, the compilation method is as follows.

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

For more compilation parameter options, please refer to the official website of the Paddle C++ inference library:https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html.

• After the compilation process, you can see the following files in the folder of build/fluid_inference_install_dir/.

build/fluid_inference_install_dir/
|-- CMakeCache.txt
|-- paddle
|-- third_party
|-- version.txt

Among them, paddle is the Paddle library required for C++ prediction later, and version.txt contains the version information of the current inference library.

1.2.2 Direct download and installation

• Different cuda versions of the Linux inference library (based on GCC 4.8.2) are provided on the Paddle Inference Library official website. You can view and select the appropriate version of the inference library on the official website.

• After downloading, use the following method to uncompress.

tar -xf fluid_inference.tgz

Finally you can see the following files in the folder of fluid_inference/.

2. Compile and run the demo

2.1 Export the inference model

• You can refer to Model inference，export the inference model. After the model is exported, assuming it is placed in the inference directory, the directory structure is as follows.

inference/
|--model
|--params

NOTICE: Among them, model file stores the model structure information and the params file stores the model parameter information.Therefore, you could rename the files name exported by Model inference.

2.2 Compile PaddleClas C++ inference demo

• The compilation commands are as follows. The addresses of Paddle C++ inference library, opencv and other Dependencies need to be replaced with the actual addresses on your own machines.

sh tools/build.sh

Specifically, the content in tools/build.sh is as follows.

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=ocr_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

In the above parameters of command:

• OPENCV_DIR is the opencv installation path;

• LIB_DIR is the download (fluid_inference folder) or the generated Paddle Inference Library path (build/fluid_inference_install_dir folder);

• CUDA_LIB_DIR is the cuda library file path, in docker; it is /usr/local/cuda/lib64;

• CUDNN_LIB_DIR is the cudnn library file path, in docker it is /usr/lib/x86_64-linux-gnu/.

After the compilation is completed, an executable file named ocr_system will be generated in the build folder.

Run the demo

• Execute the following command to complete the classification of an image.

sh tools/run.sh

• The detection results will be shown on the screen, which is as follows.

• In the above results,class id represents the id corresponding to the category with the highest confidence, and score represents the probability that the image belongs to that category.