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Update YOLOv8-ONNXRuntime-CPP example with GPU inference (#4328)

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Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
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>
This commit is contained in:
Onuralp SEZER
2023-08-13 18:34:39 +03:00
committed by GitHub
parent f6b58e9d75
commit b5d1af42d8
6 changed files with 364 additions and 362 deletions
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20
examples/README.md
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@ -4,16 +4,16 @@ This repository features a collection of real-world applications and walkthrough
### Ultralytics YOLO Example Applications
| Title | Format | Contributor |
| -------------------------------------------------------------------------------------------------------------- | ------------------ | --------------------------------------------------- |
| [YOLO ONNX Detection Inference with C++](./YOLOv8-CPP-Inference) | C++/ONNX | [Justas Bartnykas](https://github.com/JustasBart) |
| [YOLO OpenCV ONNX Detection Python](./YOLOv8-OpenCV-ONNX-Python) | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro) |
| [YOLOv8 .NET ONNX ImageSharp](https://github.com/dme-compunet/YOLOv8) | C#/ONNX/ImageSharp | [Compunet](https://github.com/dme-compunet) |
| [YOLO .Net ONNX Detection C#](https://www.nuget.org/packages/Yolov8.Net) | C# .Net | [Samuel Stainback](https://github.com/sstainba) |
| [YOLOv8 on NVIDIA Jetson(TensorRT and DeepStream)](https://wiki.seeedstudio.com/YOLOv8-DeepStream-TRT-Jetson/) | Python | [Lakshantha](https://github.com/lakshanthad) |
| [YOLOv8 ONNXRuntime Python](./YOLOv8-ONNXRuntime) | Python/ONNXRuntime | [Semih Demirel](https://github.com/semihhdemirel) |
| [YOLOv8-ONNXRuntime-CPP](./YOLOv8-ONNXRuntime-CPP) | C++/ONNXRuntime | [DennisJcy](https://github.com/DennisJcy) |
| [RTDETR ONNXRuntime C#](https://github.com/Kayzwer/yolo-cs/blob/master/RTDETR.cs) | C#/ONNX | [Kayzwer](https://github.com/Kayzwer) |
| Title | Format | Contributor |
| -------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------------------------------------------------------------------------------- |
| [YOLO ONNX Detection Inference with C++](./YOLOv8-CPP-Inference) | C++/ONNX | [Justas Bartnykas](https://github.com/JustasBart) |
| [YOLO OpenCV ONNX Detection Python](./YOLOv8-OpenCV-ONNX-Python) | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro) |
| [YOLOv8 .NET ONNX ImageSharp](https://github.com/dme-compunet/YOLOv8) | C#/ONNX/ImageSharp | [Compunet](https://github.com/dme-compunet) |
| [YOLO .Net ONNX Detection C#](https://www.nuget.org/packages/Yolov8.Net) | C# .Net | [Samuel Stainback](https://github.com/sstainba) |
| [YOLOv8 on NVIDIA Jetson(TensorRT and DeepStream)](https://wiki.seeedstudio.com/YOLOv8-DeepStream-TRT-Jetson/) | Python | [Lakshantha](https://github.com/lakshanthad) |
| [YOLOv8 ONNXRuntime Python](./YOLOv8-ONNXRuntime) | Python/ONNXRuntime | [Semih Demirel](https://github.com/semihhdemirel) |
| [YOLOv8-ONNXRuntime-CPP](./YOLOv8-ONNXRuntime-CPP) | C++/ONNXRuntime | [DennisJcy](https://github.com/DennisJcy), [Onuralp Sezer](https://github.com/onuralpszr) |
| [RTDETR ONNXRuntime C#](https://github.com/Kayzwer/yolo-cs/blob/master/RTDETR.cs) | C#/ONNX | [Kayzwer](https://github.com/Kayzwer) |
### How to Contribute

69
examples/YOLOv8-ONNXRuntime-CPP/CMakeLists.txt
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@ -17,58 +17,69 @@ include_directories(${OpenCV_INCLUDE_DIRS})
# -------------- Compile CUDA for FP16 inference if needed ------------------#
find_package(CUDA REQUIRED)
include_directories(${CUDA_INCLUDE_DIRS})
option(USE_CUDA "Enable CUDA support" ON)
if (USE_CUDA)
find_package(CUDA REQUIRED)
include_directories(${CUDA_INCLUDE_DIRS})
add_definitions(-DUSE_CUDA)
endif ()
# ONNXRUNTIME
# Set ONNXRUNTIME_VERSION
set(ONNXRUNTIME_VERSION 1.15.1)
if(WIN32)
# CPU
# set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-win-x64-${ONNXRUNTIME_VERSION}")
# GPU
set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-win-x64-gpu-${ONNXRUNTIME_VERSION}")
elseif(LINUX)
# CPU
# set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}")
# GPU
set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-linux-x64-gpu-${ONNXRUNTIME_VERSION}")
elseif(APPLE)
if (WIN32)
if (USE_CUDA)
set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-win-x64-gpu-${ONNXRUNTIME_VERSION}")
else ()
set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-win-x64-${ONNXRUNTIME_VERSION}")
endif ()
elseif (LINUX)
if (USE_CUDA)
set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-linux-x64-gpu-${ONNXRUNTIME_VERSION}")
else ()
set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}")
endif ()
elseif (APPLE)
set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-osx-arm64-${ONNXRUNTIME_VERSION}")
# Apple X64 binary
# set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-osx-x64-${ONNXRUNTIME_VERSION}")
# Apple Universal binary
# set(ONNXRUNTIME_ROOT "${CMAKE_CURRENT_SOURCE_DIR}/onnxruntime-osx-universal2-${ONNXRUNTIME_VERSION}")
endif()
endif ()
include_directories(${PROJECT_NAME} ${ONNXRUNTIME_ROOT}/include)
set(PROJECT_SOURCES
main.cpp
inference.h
inference.cpp
main.cpp
inference.h
inference.cpp
)
add_executable(${PROJECT_NAME} ${PROJECT_SOURCES})
if(WIN32)
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS} ${ONNXRUNTIME_ROOT}/lib/onnxruntime.lib ${CUDA_LIBRARIES})
elseif(LINUX)
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS} ${ONNXRUNTIME_ROOT}/lib/libonnxruntime.so ${CUDA_LIBRARIES})
elseif(APPLE)
if (WIN32)
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS} ${ONNXRUNTIME_ROOT}/lib/onnxruntime.lib)
if (USE_CUDA)
target_link_libraries(${PROJECT_NAME} ${CUDA_LIBRARIES})
endif ()
elseif (LINUX)
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS} ${ONNXRUNTIME_ROOT}/lib/libonnxruntime.so)
if (USE_CUDA)
target_link_libraries(${PROJECT_NAME} ${CUDA_LIBRARIES})
endif ()
elseif (APPLE)
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS} ${ONNXRUNTIME_ROOT}/lib/libonnxruntime.dylib)
endif()
endif ()
# For windows system, copy onnxruntime.dll to the same folder of the executable file
if(WIN32)
if (WIN32)
add_custom_command(TARGET ${PROJECT_NAME} POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy_if_different
"${ONNXRUNTIME_ROOT}/lib/onnxruntime.dll"
$<TARGET_FILE_DIR:${PROJECT_NAME}>)
endif()
COMMAND ${CMAKE_COMMAND} -E copy_if_different
"${ONNXRUNTIME_ROOT}/lib/onnxruntime.dll"
$<TARGET_FILE_DIR:${PROJECT_NAME}>)
endif ()
# Download https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/datasets/coco.yaml
# and put it in the same folder of the executable file

19
examples/YOLOv8-ONNXRuntime-CPP/README.md
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@ -28,16 +28,23 @@ Alternatively, you can use the following command for exporting the model in the
yolo export model=yolov8n.pt opset=12 simplify=True dynamic=False format=onnx imgsz=640,640
```
## Download COCO.yaml file
In order to run example, you also need to download coco.yaml. You can download the file manually from [here](https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/datasets/coco.yaml)
## Dependencies
| Dependency | Version |
| -------------------------------- | -------- |
| Onnxruntime(linux,windows,macos) | >=1.14.1 |
| OpenCV | >=4.0.0 |
| C++ | >=17 |
| Cmake | >=3.5 |
| Dependency | Version |
| -------------------------------- | ------------- |
| Onnxruntime(linux,windows,macos) | >=1.14.1 |
| OpenCV | >=4.0.0 |
| C++ | >=17 |
| Cmake | >=3.5 |
| Cuda (Optional) | >=11.4,\<12.0 |
| cuDNN (Cuda required) | =8 |
Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature.
Note (2): Due to ONNX Runtime, we need to use CUDA 11 and cuDNN 8. Keep in mind that this requirement might change in the future.
## Usage

451
examples/YOLOv8-ONNXRuntime-CPP/inference.cpp
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@ -3,297 +3,280 @@
#define benchmark
DCSP_CORE::DCSP_CORE()
{
DCSP_CORE::DCSP_CORE() {
}
DCSP_CORE::~DCSP_CORE()
{
delete session;
DCSP_CORE::~DCSP_CORE() {
delete session;
}
#ifdef USE_CUDA
namespace Ort
{
template<>
struct TypeToTensorType<half> { static constexpr ONNXTensorElementDataType type = ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16; };
template<>
struct TypeToTensorType<half> { static constexpr ONNXTensorElementDataType type = ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16; };
}
#endif
template<typename T>
char* BlobFromImage(cv::Mat& iImg, T& iBlob)
{
int channels = iImg.channels();
int imgHeight = iImg.rows;
int imgWidth = iImg.cols;
char *BlobFromImage(cv::Mat &iImg, T &iBlob) {
int channels = iImg.channels();
int imgHeight = iImg.rows;
int imgWidth = iImg.cols;
for (int c = 0; c < channels; c++)
{
for (int h = 0; h < imgHeight; h++)
{
for (int w = 0; w < imgWidth; w++)
{
iBlob[c * imgWidth * imgHeight + h * imgWidth + w] = typename std::remove_pointer<T>::type((iImg.at<cv::Vec3b>(h, w)[c]) / 255.0f);
}
}
}
return RET_OK;
for (int c = 0; c < channels; c++) {
for (int h = 0; h < imgHeight; h++) {
for (int w = 0; w < imgWidth; w++) {
iBlob[c * imgWidth * imgHeight + h * imgWidth + w] = typename std::remove_pointer<T>::type(
(iImg.at<cv::Vec3b>(h, w)[c]) / 255.0f);
}
}
}
return RET_OK;
}
char* PostProcess(cv::Mat& iImg, std::vector<int> iImgSize, cv::Mat& oImg)
{
cv::Mat img = iImg.clone();
char *PostProcess(cv::Mat &iImg, std::vector<int> iImgSize, cv::Mat &oImg) {
cv::Mat img = iImg.clone();
cv::resize(iImg, oImg, cv::Size(iImgSize.at(0), iImgSize.at(1)));
if (img.channels() == 1)
{
cv::cvtColor(oImg, oImg, cv::COLOR_GRAY2BGR);
}
cv::cvtColor(oImg, oImg, cv::COLOR_BGR2RGB);
return RET_OK;
if (img.channels() == 1) {
cv::cvtColor(oImg, oImg, cv::COLOR_GRAY2BGR);
}
cv::cvtColor(oImg, oImg, cv::COLOR_BGR2RGB);
return RET_OK;
}
char* DCSP_CORE::CreateSession(DCSP_INIT_PARAM &iParams)
{
char* Ret = RET_OK;
std::regex pattern("[\u4e00-\u9fa5]");
bool result = std::regex_search(iParams.ModelPath, pattern);
if (result)
{
Ret = "[DCSP_ONNX]:Model path error.Change your model path without chinese characters.";
std::cout << Ret << std::endl;
return Ret;
}
try
{
rectConfidenceThreshold = iParams.RectConfidenceThreshold;
iouThreshold = iParams.iouThreshold;
imgSize = iParams.imgSize;
modelType = iParams.ModelType;
env = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "Yolo");
Ort::SessionOptions sessionOption;
if (iParams.CudaEnable)
{
cudaEnable = iParams.CudaEnable;
OrtCUDAProviderOptions cudaOption;
cudaOption.device_id = 0;
sessionOption.AppendExecutionProvider_CUDA(cudaOption);
}
sessionOption.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
sessionOption.SetIntraOpNumThreads(iParams.IntraOpNumThreads);
sessionOption.SetLogSeverityLevel(iParams.LogSeverityLevel);
char *DCSP_CORE::CreateSession(DCSP_INIT_PARAM &iParams) {
char *Ret = RET_OK;
std::regex pattern("[\u4e00-\u9fa5]");
bool result = std::regex_search(iParams.ModelPath, pattern);
if (result) {
Ret = "[DCSP_ONNX]:Model path error.Change your model path without chinese characters.";
std::cout << Ret << std::endl;
return Ret;
}
try {
rectConfidenceThreshold = iParams.RectConfidenceThreshold;
iouThreshold = iParams.iouThreshold;
imgSize = iParams.imgSize;
modelType = iParams.ModelType;
env = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "Yolo");
Ort::SessionOptions sessionOption;
if (iParams.CudaEnable) {
cudaEnable = iParams.CudaEnable;
OrtCUDAProviderOptions cudaOption;
cudaOption.device_id = 0;
sessionOption.AppendExecutionProvider_CUDA(cudaOption);
}
sessionOption.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
sessionOption.SetIntraOpNumThreads(iParams.IntraOpNumThreads);
sessionOption.SetLogSeverityLevel(iParams.LogSeverityLevel);
#ifdef _WIN32
int ModelPathSize = MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), nullptr, 0);
wchar_t* wide_cstr = new wchar_t[ModelPathSize + 1];
MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), wide_cstr, ModelPathSize);
wide_cstr[ModelPathSize] = L'\0';
const wchar_t* modelPath = wide_cstr;
int ModelPathSize = MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), nullptr, 0);
wchar_t* wide_cstr = new wchar_t[ModelPathSize + 1];
MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), wide_cstr, ModelPathSize);
wide_cstr[ModelPathSize] = L'\0';
const wchar_t* modelPath = wide_cstr;
#else
const char* modelPath = iParams.ModelPath.c_str();
const char *modelPath = iParams.ModelPath.c_str();
#endif // _WIN32
session = new Ort::Session(env, modelPath, sessionOption);
Ort::AllocatorWithDefaultOptions allocator;
size_t inputNodesNum = session->GetInputCount();
for (size_t i = 0; i < inputNodesNum; i++)
{
Ort::AllocatedStringPtr input_node_name = session->GetInputNameAllocated(i, allocator);
char* temp_buf = new char[50];
strcpy(temp_buf, input_node_name.get());
inputNodeNames.push_back(temp_buf);
}
size_t OutputNodesNum = session->GetOutputCount();
for (size_t i = 0; i < OutputNodesNum; i++)
{
Ort::AllocatedStringPtr output_node_name = session->GetOutputNameAllocated(i, allocator);
char* temp_buf = new char[10];
strcpy(temp_buf, output_node_name.get());
outputNodeNames.push_back(temp_buf);
}
options = Ort::RunOptions{ nullptr };
WarmUpSession();
return RET_OK;
}
catch (const std::exception& e)
{
const char* str1 = "[DCSP_ONNX]:";
const char* str2 = e.what();
std::string result = std::string(str1) + std::string(str2);
char* merged = new char[result.length() + 1];
std::strcpy(merged, result.c_str());
std::cout << merged << std::endl;
delete[] merged;
return "[DCSP_ONNX]:Create session failed.";
}
session = new Ort::Session(env, modelPath, sessionOption);
Ort::AllocatorWithDefaultOptions allocator;
size_t inputNodesNum = session->GetInputCount();
for (size_t i = 0; i < inputNodesNum; i++) {
Ort::AllocatedStringPtr input_node_name = session->GetInputNameAllocated(i, allocator);
char *temp_buf = new char[50];
strcpy(temp_buf, input_node_name.get());
inputNodeNames.push_back(temp_buf);
}
size_t OutputNodesNum = session->GetOutputCount();
for (size_t i = 0; i < OutputNodesNum; i++) {
Ort::AllocatedStringPtr output_node_name = session->GetOutputNameAllocated(i, allocator);
char *temp_buf = new char[10];
strcpy(temp_buf, output_node_name.get());
outputNodeNames.push_back(temp_buf);
}
options = Ort::RunOptions{nullptr};
WarmUpSession();
return RET_OK;
}
catch (const std::exception &e) {
const char *str1 = "[DCSP_ONNX]:";
const char *str2 = e.what();
std::string result = std::string(str1) + std::string(str2);
char *merged = new char[result.length() + 1];
std::strcpy(merged, result.c_str());
std::cout << merged << std::endl;
delete[] merged;
return "[DCSP_ONNX]:Create session failed.";
}
}
char* DCSP_CORE::RunSession(cv::Mat &iImg, std::vector<DCSP_RESULT>& oResult)
{
char *DCSP_CORE::RunSession(cv::Mat &iImg, std::vector<DCSP_RESULT> &oResult) {
#ifdef benchmark
clock_t starttime_1 = clock();
clock_t starttime_1 = clock();
#endif // benchmark
char* Ret = RET_OK;
cv::Mat processedImg;
PostProcess(iImg, imgSize, processedImg);
if (modelType < 4)
{
float* blob = new float[processedImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> inputNodeDims = { 1,3,imgSize.at(0),imgSize.at(1) };
TensorProcess(starttime_1, iImg, blob, inputNodeDims, oResult);
}
else
{
half* blob = new half[processedImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> inputNodeDims = { 1,3,imgSize.at(0),imgSize.at(1) };
TensorProcess(starttime_1, iImg, blob, inputNodeDims, oResult);
}
char *Ret = RET_OK;
cv::Mat processedImg;
PostProcess(iImg, imgSize, processedImg);
if (modelType < 4) {
float *blob = new float[processedImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> inputNodeDims = {1, 3, imgSize.at(0), imgSize.at(1)};
TensorProcess(starttime_1, iImg, blob, inputNodeDims, oResult);
} else {
#ifdef USE_CUDA
half* blob = new half[processedImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> inputNodeDims = { 1,3,imgSize.at(0),imgSize.at(1) };
TensorProcess(starttime_1, iImg, blob, inputNodeDims, oResult);
#endif
}
return Ret;
return Ret;
}
template<typename N>
char* DCSP_CORE::TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims, std::vector<DCSP_RESULT>& oResult)
{
Ort::Value inputTensor = Ort::Value::CreateTensor<typename std::remove_pointer<N>::type>(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1), inputNodeDims.data(), inputNodeDims.size());
char *DCSP_CORE::TensorProcess(clock_t &starttime_1, cv::Mat &iImg, N &blob, std::vector<int64_t> &inputNodeDims,
std::vector<DCSP_RESULT> &oResult) {
Ort::Value inputTensor = Ort::Value::CreateTensor<typename std::remove_pointer<N>::type>(
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1),
inputNodeDims.data(), inputNodeDims.size());
#ifdef benchmark
clock_t starttime_2 = clock();
clock_t starttime_2 = clock();
#endif // benchmark
auto outputTensor = session->Run(options, inputNodeNames.data(), &inputTensor, 1, outputNodeNames.data(), outputNodeNames.size());
auto outputTensor = session->Run(options, inputNodeNames.data(), &inputTensor, 1, outputNodeNames.data(),
outputNodeNames.size());
#ifdef benchmark
clock_t starttime_3 = clock();
clock_t starttime_3 = clock();
#endif // benchmark
Ort::TypeInfo typeInfo = outputTensor.front().GetTypeInfo();
auto tensor_info = typeInfo.GetTensorTypeAndShapeInfo();
std::vector<int64_t>outputNodeDims = tensor_info.GetShape();
Ort::TypeInfo typeInfo = outputTensor.front().GetTypeInfo();
auto tensor_info = typeInfo.GetTensorTypeAndShapeInfo();
std::vector<int64_t> outputNodeDims = tensor_info.GetShape();
auto output = outputTensor.front().GetTensorMutableData<typename std::remove_pointer<N>::type>();
delete blob;
switch (modelType)
{
case 1://V8_ORIGIN_FP32
case 4://V8_ORIGIN_FP16
{
int strideNum = outputNodeDims[2];
int signalResultNum = outputNodeDims[1];
std::vector<int> class_ids;
std::vector<float> confidences;
std::vector<cv::Rect> boxes;
cv::Mat rowData(signalResultNum, strideNum, CV_32F, output);
rowData = rowData.t();
delete blob;
switch (modelType) {
case 1://V8_ORIGIN_FP32
case 4://V8_ORIGIN_FP16
{
int strideNum = outputNodeDims[2];
int signalResultNum = outputNodeDims[1];
std::vector<int> class_ids;
std::vector<float> confidences;
std::vector<cv::Rect> boxes;
cv::Mat rowData(signalResultNum, strideNum, CV_32F, output);
rowData = rowData.t();
float* data = (float*)rowData.data;
float *data = (float *) rowData.data;
float x_factor = iImg.cols / 640.;
float y_factor = iImg.rows / 640.;
for (int i = 0; i < strideNum; ++i)
{
float* classesScores = data + 4;
cv::Mat scores(1, this->classes.size(), CV_32FC1, classesScores);
cv::Point class_id;
double maxClassScore;
cv::minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);
if (maxClassScore > rectConfidenceThreshold)
{
confidences.push_back(maxClassScore);
class_ids.push_back(class_id.x);
float x_factor = iImg.cols / 640.;
float y_factor = iImg.rows / 640.;
for (int i = 0; i < strideNum; ++i) {
float *classesScores = data + 4;
cv::Mat scores(1, this->classes.size(), CV_32FC1, classesScores);
cv::Point class_id;
double maxClassScore;
cv::minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);
if (maxClassScore > rectConfidenceThreshold) {
confidences.push_back(maxClassScore);
class_ids.push_back(class_id.x);
float x = data[0];
float y = data[1];
float w = data[2];
float h = data[3];
float x = data[0];
float y = data[1];
float w = data[2];
float h = data[3];
int left = int((x - 0.5 * w) * x_factor);
int top = int((y - 0.5 * h) * y_factor);
int left = int((x - 0.5 * w) * x_factor);
int top = int((y - 0.5 * h) * y_factor);
int width = int(w * x_factor);
int height = int(h * y_factor);
int width = int(w * x_factor);
int height = int(h * y_factor);
boxes.emplace_back(left, top, width, height);
}
data += signalResultNum;
}
boxes.emplace_back(left, top, width, height);
}
data += signalResultNum;
}
std::vector<int> nmsResult;
cv::dnn::NMSBoxes(boxes, confidences, rectConfidenceThreshold, iouThreshold, nmsResult);
std::vector<int> nmsResult;
cv::dnn::NMSBoxes(boxes, confidences, rectConfidenceThreshold, iouThreshold, nmsResult);
for (int i = 0; i < nmsResult.size(); ++i)
{
int idx = nmsResult[i];
DCSP_RESULT result;
result.classId = class_ids[idx];
result.confidence = confidences[idx];
result.box = boxes[idx];
oResult.push_back(result);
}
for (int i = 0; i < nmsResult.size(); ++i) {
int idx = nmsResult[i];
DCSP_RESULT result;
result.classId = class_ids[idx];
result.confidence = confidences[idx];
result.box = boxes[idx];
oResult.push_back(result);
}
#ifdef benchmark
clock_t starttime_4 = clock();
double pre_process_time = (double)(starttime_2 - starttime_1) / CLOCKS_PER_SEC * 1000;
double process_time = (double)(starttime_3 - starttime_2) / CLOCKS_PER_SEC * 1000;
double post_process_time = (double)(starttime_4 - starttime_3) / CLOCKS_PER_SEC * 1000;
if (cudaEnable)
{
std::cout << "[DCSP_ONNX(CUDA)]: " << pre_process_time << "ms pre-process, " << process_time << "ms inference, " << post_process_time << "ms post-process." << std::endl;
}
else
{
std::cout << "[DCSP_ONNX(CPU)]: " << pre_process_time << "ms pre-process, " << process_time << "ms inference, " << post_process_time << "ms post-process." << std::endl;
}
clock_t starttime_4 = clock();
double pre_process_time = (double) (starttime_2 - starttime_1) / CLOCKS_PER_SEC * 1000;
double process_time = (double) (starttime_3 - starttime_2) / CLOCKS_PER_SEC * 1000;
double post_process_time = (double) (starttime_4 - starttime_3) / CLOCKS_PER_SEC * 1000;
if (cudaEnable) {
std::cout << "[DCSP_ONNX(CUDA)]: " << pre_process_time << "ms pre-process, " << process_time
<< "ms inference, " << post_process_time << "ms post-process." << std::endl;
} else {
std::cout << "[DCSP_ONNX(CPU)]: " << pre_process_time << "ms pre-process, " << process_time
<< "ms inference, " << post_process_time << "ms post-process." << std::endl;
}
#endif // benchmark
break;
}
}
return RET_OK;
break;
}
}
return RET_OK;
}
char* DCSP_CORE::WarmUpSession()
{
clock_t starttime_1 = clock();
cv::Mat iImg = cv::Mat(cv::Size(imgSize.at(0), imgSize.at(1)), CV_8UC3);
cv::Mat processedImg;
PostProcess(iImg, imgSize, processedImg);
if (modelType < 4)
{
float* blob = new float[iImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> YOLO_input_node_dims = { 1,3,imgSize.at(0),imgSize.at(1) };
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());
auto output_tensors = session->Run(options, inputNodeNames.data(), &input_tensor, 1, outputNodeNames.data(), outputNodeNames.size());
delete[] blob;
clock_t starttime_4 = clock();
double post_process_time = (double)(starttime_4 - starttime_1) / CLOCKS_PER_SEC * 1000;
if (cudaEnable)
{
std::cout << "[DCSP_ONNX(CUDA)]: " << "Cuda warm-up cost " << post_process_time << " ms. " << std::endl;
}
}
else
{
half* blob = new half[iImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> YOLO_input_node_dims = { 1,3,imgSize.at(0),imgSize.at(1) };
Ort::Value input_tensor = Ort::Value::CreateTensor<half>(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1), YOLO_input_node_dims.data(), YOLO_input_node_dims.size());
auto output_tensors = session->Run(options, inputNodeNames.data(), &input_tensor, 1, outputNodeNames.data(), outputNodeNames.size());
delete[] blob;
clock_t starttime_4 = clock();
double post_process_time = (double)(starttime_4 - starttime_1) / CLOCKS_PER_SEC * 1000;
if (cudaEnable)
{
std::cout << "[DCSP_ONNX(CUDA)]: " << "Cuda warm-up cost " << post_process_time << " ms. " << std::endl;
}
}
return RET_OK;
char *DCSP_CORE::WarmUpSession() {
clock_t starttime_1 = clock();
cv::Mat iImg = cv::Mat(cv::Size(imgSize.at(0), imgSize.at(1)), CV_8UC3);
cv::Mat processedImg;
PostProcess(iImg, imgSize, processedImg);
if (modelType < 4) {
float *blob = new float[iImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> YOLO_input_node_dims = {1, 3, imgSize.at(0), imgSize.at(1)};
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());
auto output_tensors = session->Run(options, inputNodeNames.data(), &input_tensor, 1, outputNodeNames.data(),
outputNodeNames.size());
delete[] blob;
clock_t starttime_4 = clock();
double post_process_time = (double) (starttime_4 - starttime_1) / CLOCKS_PER_SEC * 1000;
if (cudaEnable) {
std::cout << "[DCSP_ONNX(CUDA)]: " << "Cuda warm-up cost " << post_process_time << " ms. " << std::endl;
}
} else {
#ifdef USE_CUDA
half* blob = new half[iImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> YOLO_input_node_dims = { 1,3,imgSize.at(0),imgSize.at(1) };
Ort::Value input_tensor = Ort::Value::CreateTensor<half>(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1), YOLO_input_node_dims.data(), YOLO_input_node_dims.size());
auto output_tensors = session->Run(options, inputNodeNames.data(), &input_tensor, 1, outputNodeNames.data(), outputNodeNames.size());
delete[] blob;
clock_t starttime_4 = clock();
double post_process_time = (double)(starttime_4 - starttime_1) / CLOCKS_PER_SEC * 1000;
if (cudaEnable)
{
std::cout << "[DCSP_ONNX(CUDA)]: " << "Cuda warm-up cost " << post_process_time << " ms. " << std::endl;
}
#endif
}
return RET_OK;
}

96
examples/YOLOv8-ONNXRuntime-CPP/inference.h
View File

@ -1,6 +1,6 @@
#pragma once
#define RET_OK nullptr
#define RET_OK nullptr
#ifdef _WIN32
#include <Windows.h>
@ -13,72 +13,72 @@
#include <cstdio>
#include <opencv2/opencv.hpp>
#include "onnxruntime_cxx_api.h"
#ifdef USE_CUDA
#include <cuda_fp16.h>
#endif
enum MODEL_TYPE
{
//FLOAT32 MODEL
YOLO_ORIGIN_V5 = 0,
YOLO_ORIGIN_V8 = 1,//only support v8 detector currently
YOLO_POSE_V8 = 2,
YOLO_CLS_V8 = 3,
YOLO_ORIGIN_V8_HALF = 4,
YOLO_POSE_V8_HALF = 5,
YOLO_CLS_V8_HALF = 6
enum MODEL_TYPE {
//FLOAT32 MODEL
YOLO_ORIGIN_V5 = 0,
YOLO_ORIGIN_V8 = 1,//only support v8 detector currently
YOLO_POSE_V8 = 2,
YOLO_CLS_V8 = 3,
YOLO_ORIGIN_V8_HALF = 4,
YOLO_POSE_V8_HALF = 5,
YOLO_CLS_V8_HALF = 6
};
typedef struct _DCSP_INIT_PARAM
{
std::string ModelPath;
MODEL_TYPE ModelType = YOLO_ORIGIN_V8;
std::vector<int> imgSize={640, 640};
float RectConfidenceThreshold = 0.6;
float iouThreshold = 0.5;
bool CudaEnable = false;
int LogSeverityLevel = 3;
int IntraOpNumThreads = 1;
}DCSP_INIT_PARAM;
typedef struct _DCSP_INIT_PARAM {
std::string ModelPath;
MODEL_TYPE ModelType = YOLO_ORIGIN_V8;
std::vector<int> imgSize = {640, 640};
float RectConfidenceThreshold = 0.6;
float iouThreshold = 0.5;
bool CudaEnable = false;
int LogSeverityLevel = 3;
int IntraOpNumThreads = 1;
} DCSP_INIT_PARAM;
typedef struct _DCSP_RESULT
{
int classId;
float confidence;
cv::Rect box;
}DCSP_RESULT;
typedef struct _DCSP_RESULT {
int classId;
float confidence;
cv::Rect box;
} DCSP_RESULT;
class DCSP_CORE
{
class DCSP_CORE {
public:
DCSP_CORE();
~DCSP_CORE();
DCSP_CORE();
~DCSP_CORE();
public:
char* CreateSession(DCSP_INIT_PARAM &iParams);
char *CreateSession(DCSP_INIT_PARAM &iParams);
char* RunSession(cv::Mat &iImg, std::vector<DCSP_RESULT>& oResult);
char *RunSession(cv::Mat &iImg, std::vector<DCSP_RESULT> &oResult);
char* WarmUpSession();
char *WarmUpSession();
template<typename N>
char* TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims, std::vector<DCSP_RESULT>& oResult);
template<typename N>
char *TensorProcess(clock_t &starttime_1, cv::Mat &iImg, N &blob, std::vector<int64_t> &inputNodeDims,
std::vector<DCSP_RESULT> &oResult);
std::vector<std::string> classes{};
private:
Ort::Env env;
Ort::Session* session;
bool cudaEnable;
Ort::RunOptions options;
std::vector<const char*> inputNodeNames;
std::vector<const char*> outputNodeNames;
Ort::Env env;
Ort::Session *session;
bool cudaEnable;
Ort::RunOptions options;
std::vector<const char *> inputNodeNames;
std::vector<const char *> outputNodeNames;
MODEL_TYPE modelType;
std::vector<int> imgSize;
float rectConfidenceThreshold;
float iouThreshold;
MODEL_TYPE modelType;
std::vector<int> imgSize;
float rectConfidenceThreshold;
float iouThreshold;
};

71
examples/YOLOv8-ONNXRuntime-CPP/main.cpp
View File

@ -3,42 +3,41 @@
#include <filesystem>
#include <fstream>
void file_iterator(DCSP_CORE*& p)
{
std::filesystem::path current_path = std::filesystem::current_path();
std::filesystem::path imgs_path = current_path/"images";
for (auto& i : std::filesystem::directory_iterator(imgs_path))
{
if (i.path().extension() == ".jpg" || i.path().extension() == ".png")
{
std::string img_path = i.path().string();
cv::Mat img = cv::imread(img_path);
std::vector<DCSP_RESULT> res;
p->RunSession(img, res);
void file_iterator(DCSP_CORE *&p) {
std::filesystem::path current_path = std::filesystem::current_path();
std::filesystem::path imgs_path = current_path / "images";
for (auto &i: std::filesystem::directory_iterator(imgs_path)) {
if (i.path().extension() == ".jpg" || i.path().extension() == ".png" || i.path().extension() == ".jpeg") {
std::string img_path = i.path().string();
cv::Mat img = cv::imread(img_path);
std::vector<DCSP_RESULT> res;
p->RunSession(img, res);
for (auto & re : res)
{
cv::rectangle(img, re.box, cv::Scalar(0, 0 , 255), 3);
std::string label = p->classes[re.classId];
for (auto &re: res) {
cv::RNG rng(cv::getTickCount());
cv::Scalar color(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256));
cv::rectangle(img, re.box, color, 3);
std::string label = p->classes[re.classId] + " " + std::to_string(re.confidence);
cv::putText(
img,
label,
cv::Point(re.box.x, re.box.y - 5),
cv::FONT_HERSHEY_SIMPLEX,
0.75,
cv::Scalar(255, 255, 0),
color,
2
);
}
cv::imshow("Result", img);
cv::waitKey(0);
cv::destroyAllWindows();
}
}
}
std::cout << "Press any key to exit" << std::endl;
cv::imshow("Result of Detection", img);
cv::waitKey(0);
cv::destroyAllWindows();
}
}
}
int read_coco_yaml(DCSP_CORE*& p)
{
int read_coco_yaml(DCSP_CORE *&p) {
// Open the YAML file
std::ifstream file("coco.yaml");
if (!file.is_open()) {
@ -80,17 +79,19 @@ int read_coco_yaml(DCSP_CORE*& p)
}
int main()
{
DCSP_CORE* yoloDetector = new DCSP_CORE;
std::string model_path = "yolov8n.onnx";
int main() {
DCSP_CORE *yoloDetector = new DCSP_CORE;
std::string model_path = "yolov8n.onnx";
read_coco_yaml(yoloDetector);
// GPU FP32 inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, true };
#ifdef USE_CUDA
// GPU FP32 inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, true };
// GPU FP16 inference
// DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8_HALF, {640, 640}, 0.1, 0.5, true };
// CPU inference
// DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, false };
// DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8_HALF, {640, 640}, 0.1, 0.5, true };
#else
// CPU inference
DCSP_INIT_PARAM params{model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, false};
#endif
yoloDetector->CreateSession(params);
file_iterator(yoloDetector);
file_iterator(yoloDetector);
}