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README.md

English | 简体中文

YOLOX C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of YOLOX on CPU/GPU and GPU accelerated by TensorRT.

Two steps before deployment

Taking the CPU 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.

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 official converted YOLOX model files and test images 
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolox_s.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg


# CPU inference
./infer_demo yolox_s.onnx 000000014439.jpg 0
# GPU inference
./infer_demo yolox_s.onnx 000000014439.jpg 1
# TensorRT inference on GPU
./infer_demo yolox_s.onnx 000000014439.jpg 2

The visualized result after running is as follows

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

YOLOX C++ Interface

YOLOX Class

fastdeploy::vision::detection::YOLOX(
        const string& model_file,
        const string& params_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::ONNX)

YOLOX model loading and initialization, among which model_file is the exported ONNX model format.

Parameter

  • model_file(str): Model file path
  • params_file(str): Parameter file path. Merely passing an empty string when the model is in ONNX format
  • runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
  • model_format(ModelFormat): Model format. ONNX format by default

Predict Function

YOLOX::Predict(cv::Mat* im, DetectionResult* result,
             float conf_threshold = 0.25,
             float nms_iou_threshold = 0.5)

Model prediction interface. Input images and output detection results.

Parameter

  • im: Input images in HWC or BGR format
  • result: Detection results, including detection box and confidence of each box. Refer to Vision Model Prediction Result for DetectionResult
  • conf_threshold: Filtering threshold of detection box confidence
  • nms_iou_threshold: iou threshold during NMS processing

Class Member Variable

Pre-processing Parameter

Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results

  • size(vector<int>): This parameter changes resize used during preprocessing, containing two integer elements for [width, height] with default value [640, 640]
  • padding_value(vector<float>): This parameter is used to change the padding value of images during resize, containing three floating-point elements that represent the value of three channels. Default value [114, 114, 114]
  • is_no_pad(bool): Specify whether to resize the image through padding. is_no_pad=ture represents no paddling. Default is_no_pad=false
  • is_decode_exported(bool): Whether the decode part with coordinate inversion is contained in the exported YOLOX onnx model files. Default is_decode_exported=false. The default export doesn’t cover this part. Set this parameter to true if your model is decode exported