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PaddleDetection C++ Deployment Example

This directory provides examples that infer_xxx.cc fast finishes the deployment of PaddleDetection models, including PPYOLOE/PicoDet/YOLOX/YOLOv3/PPYOLO/FasterRCNN/YOLOv5/YOLOv6/YOLOv7/RTMDet on CPU/GPU and GPU accelerated by TensorRT.

Before deployment, two steps require confirmation

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library

Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.

ppyoloe is taken as an example for inference deployment

mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
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

# Download the PPYOLOE model file and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
tar xvf ppyoloe_crn_l_300e_coco.tgz


# CPU inference
./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 0
# GPU inference
./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 1
# TensorRT Inference on GPU
./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 2
# Kunlunxin XPU Inference
./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 3
# Huawei Ascend Inference
./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 4

The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:

• How to use FastDeploy C++ SDK in Windows

PaddleDetection C++ Interface

Model Class

PaddleDetection currently supports 6 kinds of models, including PPYOLOE, PicoDet, PaddleYOLOX, PPYOLO, FasterRCNN，SSD,PaddleYOLOv5,PaddleYOLOv6,PaddleYOLOv7,RTMDet. The constructors and predictors for all 6 kinds are consistent in terms of parameters. This document takes PPYOLOE as an example to introduce its API

fastdeploy::vision::detection::PPYOLOE(
        const string& model_file,
        const string& params_file,
        const string& config_file
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::PADDLE)

Loading and initializing PaddleDetection PPYOLOE model, where the format of model_file is as the exported ONNX model.

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path
• config_file(str): • Configuration file path, which is the deployment yaml file exported by PaddleDetection
• runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
• model_format(ModelFormat): Model format. Paddle format by default

Predict Function

PPYOLOE::Predict(cv::Mat* im, DetectionResult* result)

Model prediction interface. Input images and output results directly.

Parameter

• im: Input images in HWC or BGR format
• result: Detection result, including detection box and confidence of each box. Refer to Vision Model Prediction Result for DetectionResult

• Model Description
• Python Deployment
• Vision Model prediction results
• How to switch the model inference backend engine