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深圳外贸网站设计公司,公司网站建设工作方案,南京网站设计制作套餐,商标代理公司心路历程#xff08;可略过#xff09; 为了能在arm64上跑通yolov8#xff0c;我试过很多很多代码#xff0c;太多对库版本的要求太高了#xff1b; 比如说有一个是需要依赖onnx库的#xff0c;#xff08;https://github.com/UNeedCryDear/yolov8-opencv-onnxruntime-…心路历程可略过 为了能在arm64上跑通yolov8我试过很多很多代码太多对库版本的要求太高了 比如说有一个是需要依赖onnx库的https://github.com/UNeedCryDear/yolov8-opencv-onnxruntime-cpp 运行成功了报错error: IOrtSessionOptionsAppendExecutionProvider CUDA’ was not declare d in this scope一查是不仅需要onnx库还需要gpu版本的onnx库 因为这个函数是onnxgpu里才有的函数OrtStatus* status OrtSessionOptionsAppendExecutionProvider_CUDA(_OrtSessionOptions, cudaID); 而onnxruntime的官方下载地址https://github.com/microsoft/onnxruntime/releases/ 只有这个版本可以用但是这个并不是onnxruntime的gpu版本我在论坛上上搜到onnx官方是不提供nvidia gpu的库的所以需要自己编译。 我就尝试自己编译结果有各种各样的版本不匹配的问题先是说opencv版本低然后又是杂七杂八的。我都按照要求升级了最后来一个gcc版本也得升级那我真是得放弃了因为当前硬件得这些基础环境是不能改变的我只能放弃上面这个关于onnxruntime的yolov8代码所以得到一个经验这种大型的库最好直接下载官方现成的自己编译真的非常麻烦不到万不得已的时候建议直接换代码这种版本匹配与编译的问题是最难解决的 好在很幸运找到了一个轻量级的能在nvidia arm64硬件上成功运行的轻量级cyolov8代码非常简洁好用不需要依赖杂七杂八的库可以说直接用jetpack默认的库就能可以简单编译而成能找到非常不容易下面是全部代码。 jetpack版本 文件结构 main.cpp
// // Created by triple-Mu on 24-1-2023. // Modified by Q-engineering on 6-3-2024 //#include chrono #include opencv2/opencv.hpp #include yolov8.hppusing namespace std; using namespace cv;//#define VIDEOcv::Size im_size(640, 640); const int num_labels 80; const int topk 100; const float score_thres 0.25f; const float iou_thres 0.65f;int main(int argc, char** argv) {float f;float FPS[16];int i, Fcnt0;cv::Mat image;std::chrono::steady_clock::time_point Tbegin, Tend;if (argc 3) {fprintf(stderr,Usage: ./YoloV8_RT [model_trt.engine] [image or video path] \n);return -1;}const string engine_file_path argv[1];const string imagepath argv[2];for(i0;i16;i) FPS[i]0.0;cout Set CUDA…\n endl;//wjp// cudaSetDevice(0);cudaStream_t(0);cout Loading TensorRT model engine_file_path endl;cout \nWait a second…. std::flush;auto yolov8 new YOLOv8(engine_file_path);cout \rLoading the pipe… string(10, ) \n\r ;cout endl;yolov8-MakePipe(true);#ifdef VIDEOVideoCapture cap(imagepath);if (!cap.isOpened()) {cerr ERROR: Unable to open the stream imagepath endl;return 0;} #endif // VIDEOwhile(1){ #ifdef VIDEOcap image;if (image.empty()) {cerr ERROR: Unable to grab from the camera endl;break;} #elseimage cv::imread(imagepath); #endifyolov8-CopyFromMat(image, im_size);std::vectorObject objs;Tbegin std::chrono::steady_clock::now();yolov8-Infer();Tend std::chrono::steady_clock::now();yolov8-PostProcess(objs, score_thres, iou_thres, topk, num_labels);yolov8-DrawObjects(image, objs);//calculate frame ratef std::chrono::duration_cast std::chrono::milliseconds (Tend - Tbegin).count();cout Infer time f endl;if(f0.0) FPS[((Fcnt)0x0F)]1000.0/f;for(f0.0, i0;i16;i){ fFPS[i]; }putText(image, cv::format(FPS %0.2f, f/16),cv::Point(10,20),cv::FONT_HERSHEY_SIMPLEX,0.6, cv::Scalar(0, 0, 255));//show output// imshow(Jetson Orin Nano- 8 Mb RAM, image);// char esc cv::waitKey(1);// if(esc 27) break;imwrite(./out.jpg, image);return 0;}cv::destroyAllWindows();delete yolov8;return 0; } yolov8.cpp // // Created by triple-Mu on 24-1-2023. // Modified by Q-engineering on 6-3-2024 //#include yolov8.hpp #include cuda_runtime_api.h #include cuda.h//—————————————————————————————- //using namespace det; //—————————————————————————————- const char* class_names[] {person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light,fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow,elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee,skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard,tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple,sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch,potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone,microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear,hair drier, toothbrush }; //—————————————————————————————- YOLOv8::YOLOv8(const std::string engine_file_path) {std::ifstream file(engine_file_path, std::ios::binary);assert(file.good());file.seekg(0, std::ios::end);auto size file.tellg();file.seekg(0, std::ios::beg);char* trtModelStream new char[size];assert(trtModelStream);file.read(trtModelStream, size);file.close();initLibNvInferPlugins(this-gLogger, );this-runtime nvinfer1::createInferRuntime(this-gLogger);assert(this-runtime ! nullptr);this-engine this-runtime-deserializeCudaEngine(trtModelStream, size);assert(this-engine ! nullptr);delete[] trtModelStream;this-context this-engine-createExecutionContext();assert(this-context ! nullptr);cudaStreamCreate(this-stream);this-num_bindings this-engine-getNbBindings();for (int i 0; i this-num_bindings; i) {Binding binding;nvinfer1::Dims dims;nvinfer1::DataType dtype this-engine-getBindingDataType(i);std::string name this-engine-getBindingName(i);binding.name name;binding.dsize type_to_size(dtype);bool IsInput engine-bindingIsInput(i);if (IsInput) {this-num_inputs 1;dims this-engine-getProfileDimensions(i, 0, nvinfer1::OptProfileSelector::kMAX);binding.size get_size_by_dims(dims);binding.dims dims;this-input_bindings.push_back(binding);// set max opt shapethis-context-setBindingDimensions(i, dims);}else {dims this-context-getBindingDimensions(i);binding.size get_size_by_dims(dims);binding.dims dims;this-output_bindings.push_back(binding);this-num_outputs 1;}} } //—————————————————————————————- YOLOv8::~YOLOv8() {this-context-destroy();this-engine-destroy();this-runtime-destroy();cudaStreamDestroy(this-stream);for (auto ptr : this-device_ptrs) {CHECK(cudaFree(ptr));}for (auto ptr : this-host_ptrs) {CHECK(cudaFreeHost(ptr));} } //—————————————————————————————- void YOLOv8::MakePipe(bool warmup) { #ifndef CUDART_VERSION #error CUDART_VERSION Undefined! #endiffor (auto bindings : this-input_bindings) {void* d_ptr; #if(CUDART_VERSION 11000)CHECK(cudaMalloc(d_ptr, bindings.size * bindings.dsize)); #elseCHECK(cudaMallocAsync(d_ptr, bindings.size * bindings.dsize, this-stream)); #endifthis-device_ptrs.push_back(d_ptr);}for (auto bindings : this-output_bindings) {void * d_ptr, *h_ptr;size_t size bindings.size * bindings.dsize; #if(CUDART_VERSION 11000)CHECK(cudaMalloc(d_ptr, bindings.size * bindings.dsize)); #elseCHECK(cudaMallocAsync(d_ptr, bindings.size * bindings.dsize, this-stream)); #endifCHECK(cudaHostAlloc(h_ptr, size, 0));this-device_ptrs.push_back(d_ptr);this-host_ptrs.push_back(h_ptr);}if (warmup) {for (int i 0; i 10; i) {for (auto bindings : this-input_bindings) {size_t size bindings.size * bindings.dsize;void* h_ptr malloc(size);memset(h_ptr, 0, size);CHECK(cudaMemcpyAsync(this-device_ptrs[0], h_ptr, size, cudaMemcpyHostToDevice, this-stream));free(h_ptr);}this-Infer();}} } //—————————————————————————————- void YOLOv8::Letterbox(const cv::Mat image, cv::Mat out, cv::Size size) {const float inp_h size.height;const float inp_w size.width;float height image.rows;float width image.cols;float r std::min(inp_h / height, inp_w / width);int padw std::round(width * r);int padh std::round(height * r);cv::Mat tmp;if ((int)width ! padw || (int)height ! padh) {cv::resize(image, tmp, cv::Size(padw, padh));}else {tmp image.clone();}float dw inp_w - padw;float dh inp_h - padh;dw / 2.0f;dh / 2.0f;int top int(std::round(dh - 0.1f));int bottom int(std::round(dh 0.1f));int left int(std::round(dw - 0.1f));int right int(std::round(dw 0.1f));cv::copyMakeBorder(tmp, tmp, top, bottom, left, right, cv::BORDER_CONSTANT, {114, 114, 114});cv::dnn::blobFromImage(tmp, out, 1 / 255.f, cv::Size(), cv::Scalar(0, 0, 0), true, false, CV_32F);this-pparam.ratio 1 / r;this-pparam.dw dw;this-pparam.dh dh;this-pparam.height height;this-pparam.width width;; } //—————————————————————————————- void YOLOv8::CopyFromMat(const cv::Mat image) {cv::Mat nchw;auto in_binding this-input_bindings[0];auto width in_binding.dims.d[3];auto height in_binding.dims.d[2];cv::Size size{width, height};this-Letterbox(image, nchw, size);this-context-setBindingDimensions(0, nvinfer1::Dims{4, {1, 3, height, width}});CHECK(cudaMemcpyAsync(this-device_ptrs[0], nchw.ptrfloat(), nchw.total() * nchw.elemSize(), cudaMemcpyHostToDevice, this-stream)); } //—————————————————————————————- void YOLOv8::CopyFromMat(const cv::Mat image, cv::Size size) {cv::Mat nchw;this-Letterbox(image, nchw, size);this-context-setBindingDimensions(0, nvinfer1::Dims{4, {1, 3, size.height, size.width}});CHECK(cudaMemcpyAsync(this-device_ptrs[0], nchw.ptrfloat(), nchw.total() * nchw.elemSize(), cudaMemcpyHostToDevice, this-stream)); } //—————————————————————————————- void YOLOv8::Infer() {this-context-enqueueV2(this-device_ptrs.data(), this-stream, nullptr);for (int i 0; i this-num_outputs; i) {size_t osize this-output_bindings[i].size * this-output_bindings[i].dsize;CHECK(cudaMemcpyAsync(this-host_ptrs[i], this-device_ptrs[i this-num_inputs], osize, cudaMemcpyDeviceToHost, this-stream));}cudaStreamSynchronize(this-stream); } //—————————————————————————————- void YOLOv8::PostProcess(std::vectorObject objs, float score_thres, float iou_thres, int topk, int num_labels) {objs.clear();auto num_channels this-output_bindings[0].dims.d[1];auto num_anchors this-output_bindings[0].dims.d[2];auto dw this-pparam.dw;auto dh this-pparam.dh;auto width this-pparam.width;auto height this-pparam.height;auto ratio this-pparam.ratio;std::vectorcv::Rect bboxes;std::vectorfloat scores;std::vectorint labels;std::vectorint indices;cv::Mat output cv::Mat(num_channels, num_anchors, CV_32F, static_castfloat*(this-host_ptrs[0]));output output.t();for (int i 0; i num_anchors; i) {auto row_ptr output.row(i).ptrfloat();auto bboxes_ptr row_ptr;auto scores_ptr row_ptr 4;auto max_s_ptr std::max_element(scores_ptr, scores_ptr num_labels);float score *max_s_ptr;if (score score_thres) {float x *bboxes_ptr - dw;float y *bboxes_ptr - dh;float w *bboxes_ptr;float h *bboxes_ptr;float x0 clamp((x - 0.5f * w) * ratio, 0.f, width);float y0 clamp((y - 0.5f * h) * ratio, 0.f, height);float x1 clamp((x 0.5f * w) * ratio, 0.f, width);float y1 clamp((y 0.5f * h) * ratio, 0.f, height);int label max_s_ptr - scores_ptr;cv::Rect_float bbox;bbox.x x0;bbox.y y0;bbox.width x1 - x0;bbox.height y1 - y0;bboxes.push_back(bbox);labels.push_back(label);scores.push_back(score);}}#ifdef BATCHED_NMScv::dnn::NMSBoxesBatched(bboxes, scores, labels, score_thres, iou_thres, indices); #elsecv::dnn::NMSBoxes(bboxes, scores, score_thres, iou_thres, indices); #endifint cnt 0;for (auto i : indices) {if (cnt topk) {break;}Object obj;obj.rect bboxes[i];obj.prob scores[i];obj.label labels[i];objs.push_back(obj);cnt 1;} } //—————————————————————————————- void YOLOv8::DrawObjects(cv::Mat bgr, const std::vectorObject objs) {char text[256];for (auto obj : objs) {cv::rectangle(bgr, obj.rect, cv::Scalar(255, 0, 0));sprintf(text, %s %.1f%%, class_names[obj.label], obj.prob * 100);int baseLine 0;cv::Size label_size cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, baseLine);int x (int)obj.rect.x;int y (int)obj.rect.y - label_size.height - baseLine;if (y 0) y 0;if (y bgr.rows) y bgr.rows;if (x label_size.width bgr.cols) x bgr.cols - label_size.width;cv::rectangle(bgr, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height baseLine)), cv::Scalar(255, 255, 255), -1);cv::putText(bgr, text, cv::Point(x, y label_size.height), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));} } //—————————————————————————————- common.hpp // // Created by triple-Mu on 24-1-2023. // Modified by Q-engineering on 6-3-2024 //#ifndef DETECT_NORMAL_COMMON_HPP #define DETECT_NORMAL_COMMON_HPP #include NvInfer.h #include opencv2/opencv.hpp#define CHECK(call) \do { \const cudaError_t error_code call; \if (error_code ! cudaSuccess) { \printf(CUDA Error:\n); \printf( File: %s\n, FILE); \printf( Line: %d\n, LINE); \printf( Error code: %d\n, error_code); \printf( Error text: %s\n, cudaGetErrorString(error_code)); \exit(1); } } while (0)class Logger: public nvinfer1::ILogger { public:nvinfer1::ILogger::Severity reportableSeverity;explicit Logger(nvinfer1::ILogger::Severity severity nvinfer1::ILogger::Severity::kINFO):reportableSeverity(severity){}void log(nvinfer1::ILogger::Severity severity, const char* msg) noexcept override{if (severity reportableSeverity) {return;}switch (severity) {case nvinfer1::ILogger::Severity::kINTERNAL_ERROR:std::cerr INTERNAL_ERROR: ;break;case nvinfer1::ILogger::Severity::kERROR:std::cerr ERROR: ;break;case nvinfer1::ILogger::Severity::kWARNING:std::cerr WARNING: ;break;case nvinfer1::ILogger::Severity::kINFO:std::cerr INFO: ;break;default:std::cerr VERBOSE: ;break;}std::cerr msg std::endl;} };inline int get_size_by_dims(const nvinfer1::Dims dims) {int size 1;for (int i 0; i dims.nbDims; i) {size * dims.d[i];}return size; }inline int type_to_size(const nvinfer1::DataType dataType) {switch (dataType) {case nvinfer1::DataType::kFLOAT:return 4;case nvinfer1::DataType::kHALF:return 2;case nvinfer1::DataType::kINT32:return 4;case nvinfer1::DataType::kINT8:return 1;case nvinfer1::DataType::kBOOL:return 1;default:return 4;} }inline static float clamp(float val, float min, float max) {return val min ? (val max ? val : max) : min; }namespace det { struct Binding {size_t size 1;size_t dsize 1;nvinfer1::Dims dims;std::string name; };struct Object {cv::Rect_float rect;int label 0;float prob 0.0; };struct PreParam {float ratio 1.0f;float dw 0.0f;float dh 0.0f;float height 0;float width 0; }; } // namespace det #endif // DETECT_NORMAL_COMMON_HPP yolov8.hpp // // Created by triple-Mu on 24-1-2023. // Modified by Q-engineering on 6-3-2024 // #ifndef DETECT_NORMAL_YOLOV8_HPP #define DETECT_NORMAL_YOLOV8_HPP #include NvInferPlugin.h #include common.hpp #include fstreamusing namespace det;class YOLOv8 { private:nvinfer1::ICudaEngine* engine nullptr;nvinfer1::IRuntime* runtime nullptr;nvinfer1::IExecutionContext* context nullptr;cudaStream_t stream nullptr;Logger gLogger{nvinfer1::ILogger::Severity::kERROR}; public:int num_bindings;int num_inputs 0;int num_outputs 0;std::vectorBinding input_bindings;std::vectorBinding output_bindings;std::vectorvoid* host_ptrs;std::vectorvoid* device_ptrs;PreParam pparam;public:explicit YOLOv8(const std::string engine_file_path);~YOLOv8();void MakePipe(bool warmup true);void CopyFromMat(const cv::Mat image);void CopyFromMat(const cv::Mat image, cv::Size size);void Letterbox(const cv::Mat image, cv::Mat out, cv::Size size);void Infer();void PostProcess(std::vectorObject objs, float score_thres, float iou_thres, int topk, int num_labels 80);void DrawObjects(cv::Mat bgr, const std::vectorObject objs); }; #endif // DETECT_NORMAL_YOLOV8_HPP CMakeLists.txt cmake_minimum_required(VERSION 3.1)set(CMAKE_CUDA_ARCHITECTURES 60 61 62 70 72 75 86 89 90) set(CMAKE_CUDA_COMPILER /usr/local/cuda/bin/nvcc)project(YoloV8rt LANGUAGES CXX CUDA)set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -stdc14 -O3) set(CMAKE_CXX_STANDARD 14) set(CMAKE_BUILD_TYPE Release) option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)# CUDA include_directories(/usr/local/cuda-10.2/targets/aarch64-linux/include) link_directories(/usr/local/cuda-10.2/targets/aarch64-linux/lib)

find_package(CUDA REQUIRED)

message(STATUS CUDA Libs: \n${CUDA_LIBRARIES}\n)

get_filename_component(CUDA_LIB_DIR ${CUDA_LIBRARIES} DIRECTORY)

message(STATUS CUDA Headers: \n${CUDA_INCLUDE_DIRS}\n)# OpenCV

find_package(OpenCV REQUIRED)# TensorRT set(TensorRT_INCLUDE_DIRS /usr/include /usr/include/aarch-linux-gnu) set(TensorRT_LIBRARIES /usr/lib/aarch64-linux-gnu)message(STATUS TensorRT Libs: \n\n\({TensorRT_LIBRARIES}\n) message(STATUS TensorRT Headers: \n\){TensorRT_INCLUDE_DIRS}\n)list(APPEND INCLUDE_DIRS\({CUDA_INCLUDE_DIRS}\){OpenCV_INCLUDE_DIRS}\({TensorRT_INCLUDE_DIRS}include)list(APPEND ALL_LIBS\){CUDA_LIBRARIES}\({CUDA_LIB_DIR}\){OpenCV_LIBRARIES}\({TensorRT_LIBRARIES})include_directories(\){INCLUDE_DIRS})add_executable(\({PROJECT_NAME}src/main.cppsrc/yolov8.cppinclude/yolov8.hppinclude/common.hpp)target_link_libraries(\){PROJECT_NAME} PUBLIC \({ALL_LIBS}) target_link_libraries(\){PROJECT_NAME} PRIVATE nvinfer nvinfer_plugin cudart \({OpenCV_LIBS})#place exe in parent folder set(EXECUTABLE_OUTPUT_PATH ./)if (\){OpenCV_VERSION} VERSION_GREATER_EQUAL 4.7.0)message(STATUS Build with -DBATCHED_NMS)add_definitions(-DBATCHED_NMS) endif () 原项目地址 https://github.com/Qengineering/YoloV8-TensorRT-Jetson_Nano