Update YOLOv8-ONNXRuntime-CPP (#3455)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>single_channel
DennisJ
1 year ago
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GitHub
parent
1c4a363497
commit
69a2d70a78
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# YOLOv8 OnnxRuntime C++
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This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API.
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We recommend using Visual Studio to build the project.
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## Benefits
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- Friendly for deployment in the industrial sector.
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- Faster than OpenCV's DNN inference on both CPU and GPU.
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- Supports CUDA acceleration.
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- Easy to add FP16 inference (using template functions).
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## Exporting YOLOv8 Models
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To export YOLOv8 models, use the following Python script:
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```python
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from ultralytics import YOLO
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# Load a YOLOv8 model
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model = YOLO("yolov8n.pt")
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# Export the model
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model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
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```
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## Dependencies
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| Dependency | Version |
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| ----------------------- | -------- |
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| Onnxruntime-win-x64-gpu | >=1.14.1 |
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| OpenCV | >=4.0.0 |
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| C++ | >=17 |
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Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature.
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## Usage
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```c++
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// CPU inference
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DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, class_num, 0.1, 0.5, false};
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// GPU inference
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DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, class_num, 0.1, 0.5, true};
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// Load your image
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cv::Mat img = cv::imread(img_path);
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char* ret = p1->CreateSession(params);
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ret = p->RunSession(img, res);
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```
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This repository should also work for YOLOv5, which needs a permute operator for the output of the YOLOv5 model, but this has not been implemented yet.
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#include "inference.h"
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#include <regex>
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#define benchmark
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#define ELOG
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DCSP_CORE::DCSP_CORE()
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{
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}
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DCSP_CORE::~DCSP_CORE()
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{
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delete session;
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}
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template<typename T>
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char* BlobFromImage(cv::Mat& iImg, T& iBlob)
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{
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int channels = iImg.channels();
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int imgHeight = iImg.rows;
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int imgWidth = iImg.cols;
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for (int c = 0; c < channels; c++)
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{
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for (int h = 0; h < imgHeight; h++)
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{
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for (int w = 0; w < imgWidth; w++)
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{
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iBlob[c * imgWidth * imgHeight + h * imgWidth + w] = (std::remove_pointer<T>::type)((iImg.at<cv::Vec3b>(h, w)[c]) / 255.0f);
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}
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}
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}
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return RET_OK;
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}
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char* PostProcess(cv::Mat& iImg, std::vector<int> iImgSize, cv::Mat& oImg)
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{
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cv::Mat img = iImg.clone();
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cv::resize(iImg, oImg, cv::Size(iImgSize.at(0), iImgSize.at(1)));
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if (img.channels() == 1)
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{
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cv::cvtColor(oImg, oImg, cv::COLOR_GRAY2BGR);
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}
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cv::cvtColor(oImg, oImg, cv::COLOR_BGR2RGB);
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return RET_OK;
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}
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char* DCSP_CORE::CreateSession(DCSP_INIT_PARAM &iParams)
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{
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char* Ret = RET_OK;
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std::regex pattern("[\u4e00-\u9fa5]");
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bool result = std::regex_search(iParams.ModelPath, pattern);
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if (result)
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{
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Ret = "[DCSP_ONNX]:model path error.change your model path without chinese characters.";
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std::cout << Ret << std::endl;
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return Ret;
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}
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try
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{
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rectConfidenceThreshold = iParams.RectConfidenceThreshold;
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iouThreshold = iParams.iouThreshold;
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imgSize = iParams.imgSize;
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modelType = iParams.ModelType;
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env = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "Yolo");
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Ort::SessionOptions sessionOption;
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if (iParams.CudaEnable)
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{
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cudaEnable = iParams.CudaEnable;
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OrtCUDAProviderOptions cudaOption;
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cudaOption.device_id = 0;
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sessionOption.AppendExecutionProvider_CUDA(cudaOption);
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//OrtOpenVINOProviderOptions ovOption;
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//sessionOption.AppendExecutionProvider_OpenVINO(ovOption);
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}
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sessionOption.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
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sessionOption.SetIntraOpNumThreads(iParams.IntraOpNumThreads);
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sessionOption.SetLogSeverityLevel(iParams.LogSeverityLevel);
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int ModelPathSize = MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), nullptr, 0);
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wchar_t* wide_cstr = new wchar_t[ModelPathSize + 1];
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MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), wide_cstr, ModelPathSize);
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wide_cstr[ModelPathSize] = L'\0';
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const wchar_t* modelPath = wide_cstr;
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session = new Ort::Session(env, modelPath, sessionOption);
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Ort::AllocatorWithDefaultOptions allocator;
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size_t inputNodesNum = session->GetInputCount();
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for (size_t i = 0; i < inputNodesNum; i++)
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{
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Ort::AllocatedStringPtr input_node_name = session->GetInputNameAllocated(i, allocator);
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char* temp_buf = new char[50];
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strcpy(temp_buf, input_node_name.get());
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inputNodeNames.push_back(temp_buf);
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}
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size_t OutputNodesNum = session->GetOutputCount();
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for (size_t i = 0; i < OutputNodesNum; i++)
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{
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Ort::AllocatedStringPtr output_node_name = session->GetOutputNameAllocated(i, allocator);
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char* temp_buf = new char[10];
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strcpy(temp_buf, output_node_name.get());
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outputNodeNames.push_back(temp_buf);
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}
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options = Ort::RunOptions{ nullptr };
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WarmUpSession();
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//std::cout << OrtGetApiBase()->GetVersionString() << std::endl;;
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Ret = RET_OK;
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return Ret;
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}
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catch (const std::exception& e)
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{
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const char* str1 = "[DCSP_ONNX]:";
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const char* str2 = e.what();
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std::string result = std::string(str1) + std::string(str2);
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char* merged = new char[result.length() + 1];
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std::strcpy(merged, result.c_str());
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std::cout << merged << std::endl;
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delete[] merged;
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//return merged;
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return "[DCSP_ONNX]:Create session failed.";
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}
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}
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char* DCSP_CORE::RunSession(cv::Mat &iImg, std::vector<DCSP_RESULT>& oResult)
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{
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#ifdef benchmark
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clock_t starttime_1 = clock();
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#endif // benchmark
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char* Ret = RET_OK;
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cv::Mat processedImg;
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PostProcess(iImg, imgSize, processedImg);
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if (modelType < 4)
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{
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float* blob = new float[processedImg.total() * 3];
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BlobFromImage(processedImg, blob);
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std::vector<int64_t> inputNodeDims = { 1,3,imgSize.at(0),imgSize.at(1) };
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TensorProcess(starttime_1, iImg, blob, inputNodeDims, oResult);
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}
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return Ret;
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}
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template<typename N>
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char* DCSP_CORE::TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims, std::vector<DCSP_RESULT>& oResult)
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{
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Ort::Value inputTensor = Ort::Value::CreateTensor<std::remove_pointer<N>::type>(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1), inputNodeDims.data(), inputNodeDims.size());
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#ifdef benchmark
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clock_t starttime_2 = clock();
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#endif // benchmark
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auto outputTensor = session->Run(options, inputNodeNames.data(), &inputTensor, 1, outputNodeNames.data(), outputNodeNames.size());
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#ifdef benchmark
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clock_t starttime_3 = clock();
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#endif // benchmark
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Ort::TypeInfo typeInfo = outputTensor.front().GetTypeInfo();
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auto tensor_info = typeInfo.GetTensorTypeAndShapeInfo();
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std::vector<int64_t>outputNodeDims = tensor_info.GetShape();
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std::remove_pointer<N>::type* output = outputTensor.front().GetTensorMutableData<std::remove_pointer<N>::type>();
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delete blob;
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switch (modelType)
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{
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case 1:
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{
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int strideNum = outputNodeDims[2];
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int signalResultNum = outputNodeDims[1];
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std::vector<int> class_ids;
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std::vector<float> confidences;
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std::vector<cv::Rect> boxes;
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cv::Mat rowData(signalResultNum, strideNum, CV_32F, output);
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rowData = rowData.t();
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float* data = (float*)rowData.data;
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float x_factor = iImg.cols / 640.;
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float y_factor = iImg.rows / 640.;
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for (int i = 0; i < strideNum; ++i)
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{
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float* classesScores = data + 4;
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cv::Mat scores(1, classesNum, CV_32FC1, classesScores);
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cv::Point class_id;
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double maxClassScore;
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cv::minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);
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if (maxClassScore > rectConfidenceThreshold)
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{
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confidences.push_back(maxClassScore);
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class_ids.push_back(class_id.x);
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float x = data[0];
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float y = data[1];
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float w = data[2];
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float h = data[3];
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int left = int((x - 0.5 * w) * x_factor);
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int top = int((y - 0.5 * h) * y_factor);
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int width = int(w * x_factor);
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int height = int(h * y_factor);
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boxes.push_back(cv::Rect(left, top, width, height));
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}
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data += signalResultNum;
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}
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std::vector<int> nmsResult;
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cv::dnn::NMSBoxes(boxes, confidences, rectConfidenceThreshold, iouThreshold, nmsResult);
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for (int i = 0; i < nmsResult.size(); ++i)
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{
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int idx = nmsResult[i];
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DCSP_RESULT result;
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result.classId = class_ids[idx];
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result.confidence = confidences[idx];
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result.box = boxes[idx];
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oResult.push_back(result);
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}
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#ifdef benchmark
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clock_t starttime_4 = clock();
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double pre_process_time = (double)(starttime_2 - starttime_1) / CLOCKS_PER_SEC * 1000;
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double process_time = (double)(starttime_3 - starttime_2) / CLOCKS_PER_SEC * 1000;
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double post_process_time = (double)(starttime_4 - starttime_3) / CLOCKS_PER_SEC * 1000;
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if (cudaEnable)
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{
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std::cout << "[DCSP_ONNX(CUDA)]: " << pre_process_time << "ms pre-process, " << process_time << "ms inference, " << post_process_time << "ms post-process." << std::endl;
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}
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else
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{
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std::cout << "[DCSP_ONNX(CPU)]: " << pre_process_time << "ms pre-process, " << process_time << "ms inference, " << post_process_time << "ms post-process." << std::endl;
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}
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#endif // benchmark
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break;
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}
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}
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char* Ret = RET_OK;
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return Ret;
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}
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char* DCSP_CORE::WarmUpSession()
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{
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clock_t starttime_1 = clock();
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char* Ret = RET_OK;
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cv::Mat iImg = cv::Mat(cv::Size(imgSize.at(0), imgSize.at(1)), CV_8UC3);
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cv::Mat processedImg;
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PostProcess(iImg, imgSize, processedImg);
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if (modelType < 4)
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{
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float* blob = new float[iImg.total() * 3];
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BlobFromImage(processedImg, blob);
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std::vector<int64_t> YOLO_input_node_dims = { 1,3,imgSize.at(0),imgSize.at(1) };
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Ort::Value input_tensor = Ort::Value::CreateTensor<float>(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1), YOLO_input_node_dims.data(), YOLO_input_node_dims.size());
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auto output_tensors = session->Run(options, inputNodeNames.data(), &input_tensor, 1, outputNodeNames.data(), outputNodeNames.size());
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delete[] blob;
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clock_t starttime_4 = clock();
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double post_process_time = (double)(starttime_4 - starttime_1) / CLOCKS_PER_SEC * 1000;
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if (cudaEnable)
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{
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std::cout << "[DCSP_ONNX(CUDA)]: " << "Cuda warm-up cost " << post_process_time << " ms. " << std::endl;
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}
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}
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return Ret;
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}
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#pragma once
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#define _CRT_SECURE_NO_WARNINGS
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#define RET_OK nullptr
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#include <string>
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#include <vector>
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#include <stdio.h>
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#include "io.h"
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#include "direct.h"
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#include "opencv.hpp"
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#include <Windows.h>
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#include "onnxruntime_cxx_api.h"
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enum MODEL_TYPE
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{
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//FLOAT32 MODEL
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YOLO_ORIGIN_V5 = 0,
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YOLO_ORIGIN_V8 = 1,//only support v8 detector currently
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YOLO_POSE_V8 = 2,
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YOLO_CLS_V8 = 3
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};
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typedef struct _DCSP_INIT_PARAM
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{
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std::string ModelPath;
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MODEL_TYPE ModelType = YOLO_ORIGIN_V8;
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std::vector<int> imgSize={640, 640};
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int classesNum=80;
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float RectConfidenceThreshold = 0.6;
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float iouThreshold = 0.5;
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bool CudaEnable = false;
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int LogSeverityLevel = 3;
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int IntraOpNumThreads = 1;
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}DCSP_INIT_PARAM;
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typedef struct _DCSP_RESULT
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{
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int classId;
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float confidence;
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cv::Rect box;
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}DCSP_RESULT;
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class DCSP_CORE
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{
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public:
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DCSP_CORE();
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~DCSP_CORE();
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public:
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char* CreateSession(DCSP_INIT_PARAM &iParams);
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char* RunSession(cv::Mat &iImg, std::vector<DCSP_RESULT>& oResult);
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char* WarmUpSession();
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template<typename N>
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char* TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims, std::vector<DCSP_RESULT>& oResult);
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private:
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Ort::Env env;
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Ort::Session* session;
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bool cudaEnable;
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Ort::RunOptions options;
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std::vector<const char*> inputNodeNames;
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std::vector<const char*> outputNodeNames;
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int classesNum;
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MODEL_TYPE modelType;
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std::vector<int> imgSize;
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float rectConfidenceThreshold;
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float iouThreshold;
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};
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#include <iostream>
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#include <stdio.h>
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#include "inference.h"
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#include <filesystem>
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void file_iterator(DCSP_CORE*& p)
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{
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std::filesystem::path img_path = R"(E:\project\Project_C++\DCPS_ONNX\TEST_ORIGIN)";
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int k = 0;
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for (auto& i : std::filesystem::directory_iterator(img_path))
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{
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if (i.path().extension() == ".jpg")
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{
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std::string img_path = i.path().string();
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//std::cout << img_path << std::endl;
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cv::Mat img = cv::imread(img_path);
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std::vector<DCSP_RESULT> res;
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char* ret = p->RunSession(img, res);
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for (int i = 0; i < res.size(); i++)
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{
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cv::rectangle(img, res.at(i).box, cv::Scalar(125, 123, 0), 3);
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}
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k++;
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cv::imshow("TEST_ORIGIN", img);
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cv::waitKey(0);
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cv::destroyAllWindows();
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//cv::imwrite("E:\\output\\" + std::to_string(k) + ".png", img);
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}
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}
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}
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int main()
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{
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DCSP_CORE* p1 = new DCSP_CORE;
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std::string model_path = "yolov8n.onnx";
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DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 80, 0.1, 0.5, false };
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char* ret = p1->CreateSession(params);
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file_iterator(p1);
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}
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