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Paddle Inference Deployment (C++)

1. Description

This document introduces an example of deploying a segmentation model on a Linux server (NV GPU or X86 CPU) using Paddle Inference's C++ interface. The main steps include:

  • Prepare the environment
  • Prepare models and pictures
  • Compile and execute

PaddlePaddle provides multiple prediction engine deployment models (as shown in the figure below) for different scenarios. For details, please refer to document.

inference_ecosystem

In addition, PaddleX also provides examples and documentation for C++ deployment, refer to link.

2. Prepare the environment

Prepare Paddle Inference C++ prediction library

You can download the Paddle Inference C++ prediction library from link.

Pay attention to select the exact version according to the machine's CUDA version, cudnn version, using MKLDNN or OpenBlas, whether to use TenorRT and other information. It is recommended to choose a prediction library with version >= 2.0.1.

Download the paddle_inference.tgz compressed file and decompress it, and save the decompressed paddle_inference file to PaddleSeg/deploy/cpp/.

If you need to compile the Paddle Inference C++ prediction library, you can refer to the document, which will not be repeated here.

Prepare OpenCV

This example uses OpenCV to read images, so OpenCV needs to be prepared.

Run the following commands to download, compile, and install OpenCV.

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

mkdir -p opencv-3.4.7/build
cd opencv-3.4.7/build

install_path=/usr/local/opencv3
cmake .. -DCMAKE_INSTALL_PREFIX=${install_path} -DCMAKE_BUILD_TYPE=Release
make -j
make install

cd ../..

Install Yaml

This example uses Yaml to read configuration file information.

Run the following commands to download, compile, and install Yaml.

wget https://github.com/jbeder/yaml-cpp/archive/refs/tags/yaml-cpp-0.7.0.zip
unzip yaml-cpp-0.7.0.zip
mkdir -p yaml-cpp-yaml-cpp-0.7.0/build
cd yaml-cpp-yaml-cpp-0.7.0/build
cmake -DYAML_BUILD_SHARED_LIBS=ON ..
make -j
make install

3. Prepare models and pictures

Execute the following command in the PaddleSeg/deploy/cpp/ directory to download the test model for testing. If you need to test other models, please refer to documentation to export the prediction model.

wget https://paddleseg.bj.bcebos.com/dygraph/demo/bisenet_demo_model.tar.gz
tar xzf bisenet_demo_model.tar.gz

Download one image from the validation set of cityscapes.

wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png

4. Compile and execute

Please check that PaddleSeg/deploy/cpp/ stores prediction libraries, models, and pictures, as follows.

PaddleSeg/deploy/cpp
|-- paddle_inference # prediction library
|-- bisenetv2_demo_model # model
|-- cityscapes_demo.png # image

Execute sh run_seg_cpu.sh, it will compile and then perform prediction on X86 CPU.

Execute sh run_seg_gpu.sh, it will compile and then perform prediction on Nvidia GPU.

The segmentation result will be saved in the "out_img.jpg" image in the current directory, as shown below. Note that this image is using histogram equalization for easy visualization.

out_img