C++:onnxruntime调用FasterRCNN模型
2021/12/16 22:11:32
本文主要是介绍C++:onnxruntime调用FasterRCNN模型,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!
背景:
最近由于项目原因,需要用C++做一些目标检测的任务,就捣鼓一下YOLOv5,发现部署确实很方便,将YOLOv5模型转为onnx模型后,可以用OpenCV的dnn.readNetFromONNX读取该模型,接着就是输入预处理和输出结果解析的事情。
然而,当我将tf15训练得到的FasterRCNN模型并利用tf2onnx成功转为onnx模型后,却不能用OpenCV读取,报出以下错误,而onnxruntime可以成功调用该模型。
cv2.error: OpenCV(4.5.4) D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\onnx\onnx_graph_simplifier.cpp:692: error: (-210:Unsupported format or combination of formats) Unsupported data type: BOOL in function 'cv::dnn::dnn4_v20211004::getMatFromTensor'
大概意思可能是:不支持的数据类型从而导致不支持该操作吧
程序:
所以,只好采用onnxruntime的C++接口进行模型调用,废话不多说,直接上代码:
//FRCNN.h #pragma once #include<iostream> #include<fstream> #include<numeric> #include<opencv.hpp> #include"../commonStruct.h" #include"../BaseShipDetectionModel.h" #include <onnxruntime_cxx_api.h> # class FRCNN:public BaseShipDetectionModel { public: FRCNN(); ~FRCNN(); bool readModel(std::string &netPath, bool isCuda=false); bool DetectShip(cv::Mat &SrcImg, std::vector<Output> &output); void drawPredShip(cv::Mat &img, std::vector<Output>& result); private: enum OutputFlag { //NOTHING, BOXES, SCORES, CLSIDS }; Ort::Env *OnnxEnv; Ort::SessionOptions OnnxSessionOp; Ort::Session* OnnxSession; Ort::AllocatorWithDefaultOptions allocator; Ort::MemoryInfo *memory_info; cv::Size2f factor; const int netWidth = 1067; const int netHeight = 600; float nmsThreshold = 0.45; float boxThreshold = 0.31; float classThreshold = 0.25; size_t num_input_nodes, num_output_nodes; std::vector<const char*> input_node_names, output_node_names; std::vector<OutputFlag> output_node_namesFlag; //Ort::Value *input_tensor; std::vector<int64_t> input_node_dims = { netHeight, netWidth,3 }; size_t input_tensor_size = 3 * netHeight * netWidth; void parseOnnxOutput(std::vector<Ort::Value>&inputTensors, std::vector<Output> &results); };
//FRCNN.cpp #include "FRCNN.h" using namespace std; using namespace cv; using namespace dnn; #if 1 FRCNN::FRCNN() { num_input_nodes = 0; num_output_nodes = 0; //Ort::Env env(ORT_LOGGING_LEVEL_VERBOSE, "test"); OnnxEnv = new Ort::Env(ORT_LOGGING_LEVEL_WARNING, "test"); //Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault); memory_info=new Ort::MemoryInfo(Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault)); //memory_info =new Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault); if (OnnxEnv == nullptr) { std::cout << "new Error for " << VNAME(OnnxEnv) << std::endl; throw OnnxEnv; } if (memory_info == nullptr) { std::cout << "new Error for " << VNAME(memory_info) << std::endl; throw memory_info; } OnnxSessionOp.SetIntraOpNumThreads(5); OnnxSessionOp.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL); flag_onnx = true; /*cout << OnnxEnv << '\t' << *OnnxEnv << endl; cout << &env << '\t' << env << endl;*/ //test(*OnnxEnv, env, OnnxEnv); //std::cout<<env.operator const OrtEnv * } FRCNN::~FRCNN() { if(OnnxEnv!=nullptr) delete OnnxEnv; if (OnnxSession != nullptr) delete OnnxSession; } bool FRCNN::readModel(string &netPath, bool isCuda) { try { std::ifstream f(netPath.c_str()); std::cout << f.good() << std::endl; std::wstring wnetPath = std::wstring(netPath.begin(), netPath.end()); OnnxSession=new Ort::Session((*OnnxEnv), wnetPath.c_str(), OnnxSessionOp); if (OnnxSession == nullptr) { std::cout << "new Error for " << VNAME(OnnxSession) << std::endl; throw OnnxSession; } // print model input layer (node names, types, shape etc.) // print number of model input nodes num_input_nodes = OnnxSession->GetInputCount(); num_output_nodes = OnnxSession->GetOutputCount(); for (int i = 0; i < num_input_nodes; ++i) { input_node_names.push_back(OnnxSession->GetInputName(0, allocator)); //= { "image:0"}; std::cout << input_node_names[i] << std::endl; } for (int i = 0; i < num_output_nodes; ++i) { char* name = OnnxSession->GetOutputName(i, allocator); output_node_names.push_back(name); if (strstr(name, "boxes") != nullptr) { output_node_namesFlag.push_back(BOXES); } else if (strstr(name, "scores") != nullptr) { output_node_namesFlag.push_back(SCORES); } else if (strstr(name, "labels") != nullptr) { output_node_namesFlag.push_back(CLSIDS); } else { //output_node_namesFlag.push_back(NOTHING); throw(name); } //= { "output/boxes:0", "output/scores:0","output/labels:0"}; std::cout << output_node_names[i] << std::endl; } } catch (const std::exception& e) { return false; } return true; } bool FRCNN::DetectShip(cv::Mat &SrcImg, std::vector<Output> &results) { if (SrcImg.empty()) { std::cout << "empty image error!" << std::endl; return false; } int col = SrcImg.cols; int row = SrcImg.rows; int i, j; results.clear(); Mat netInputImg,Img; std::vector<int> indices; Output result; //netInputImg.create(SrcImg.size,SrcImg.depth()); //SrcImg.copyTo(netInputImg); cv::resize(SrcImg, Img, cv::Size(netWidth, netHeight), 0.0, 0.0, cv::INTER_LINEAR); factor= cv::Size2f((float)SrcImg.cols / netWidth, (float)SrcImg.rows / netHeight); try { netInputImg.create(cv::Size(netWidth, netHeight), CV_32FC3);//allocate the continuous Mat Img.convertTo(netInputImg, CV_32F); assert(netInputImg.isContinuous());// Ort::Value input_tensor = Ort::Value::CreateTensor<float>(*memory_info, (float*)netInputImg.data, input_tensor_size, input_node_dims.data(), 3); assert(input_tensor.IsTensor()); std::vector<Ort::Value> ort_inputs; ort_inputs.push_back(std::move(input_tensor)); //Run the Detection std::vector<Ort::Value> output_tensors = OnnxSession->Run(Ort::RunOptions{ nullptr }, input_node_names.data(), ort_inputs.data(), ort_inputs.size(), output_node_names.data(), 3); parseOnnxOutput(output_tensors, results); } catch (...) { std::cout << "prediction error!" << std::endl; return false; } if (results.size()) return true; else return false; } void FRCNN::drawPredShip(cv::Mat & img, std::vector<Output>& result) { BaseShipDetectionModel::drawPredShip(img, result); } void FRCNN::parseOnnxOutput(std::vector<Ort::Value>& inputTensors, std::vector<Output>& results) { std::vector<int64_t> classIds; std::vector<float> confidences; std::vector<cv::Rect> boxes; int i, j; std::vector<int64_t> shape; size_t eleCount; size_t DimCount; int xmin, xmax, ymin, ymax; for (i = 0; i < num_output_nodes; ++i) { Ort::TensorTypeAndShapeInfo Info = inputTensors[i].GetTensorTypeAndShapeInfo(); //std::cout << ":GetDimensionsCount:" << Info.GetDimensionsCount() << '\t'; shape = Info.GetShape(); DimCount = shape.size(); //std::cout << i << "shape:"; //for (int j = 0; j < shape.size(); ++j) { // std::cout << shape[j] << '\t'; //} eleCount = Info.GetElementCount(); //std::cout << ":GetElementCount:" << eleCount << '\t'; ONNXTensorElementDataType onnxType = Info.GetElementType(); void* ptr = nullptr; //std::cout << "GetElementType:" << onnxType << '\t' << std::endl; switch (onnxType) { case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT:// maps to c type float { ptr = inputTensors[i].GetTensorMutableData<float>(); } break; case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64:// maps to c type int64_t { ptr = inputTensors[i].GetTensorMutableData<int64_t>(); } break; default: throw("Unknown DataType!"); break; } //std::cout << sizeof(ptr) << sizeof((float*)ptr) << sizeof((int64_t*)ptr)<<sizeof((uint8_t*)ptr)<< sizeof((uint16_t*)ptr)<< sizeof((int32_t*)ptr) << std::endl; //= { "output/boxes:0", "output/scores:0","output/labels:0"}; /* output[0]//(44->(11,4)) output[1]//(11->(11)) output[2]//(11->(11)) */ switch (output_node_namesFlag[i]) { case BOXES://xmin,ymin,xmax,ymax { float* p_boxes = (float*)ptr; for (j = 0; j < eleCount; j += 4) { xmin = p_boxes[j] *factor.width; ymin = p_boxes[j + 1] * factor.height; xmax = p_boxes[j + 2] *factor.width; ymax = p_boxes[j + 3] *factor.height; boxes.push_back(cv::Rect(xmin, ymin, xmax - xmin, ymax - ymin)); } break; } case SCORES: { float* p_scores = (float*)ptr; for (j = 0; j < eleCount; j++) { confidences.push_back(p_scores[j]); } break; } case CLSIDS: { int64_t* p_clsids = (int64_t*)ptr; for (j = 0; j < eleCount; j++) { classIds.push_back(p_clsids[j]); } break; } } } Output result; assert((boxes.size() == classIds.size())&&(boxes.size()==confidences.size())); for (i = 0; i < boxes.size(); ++i) { //j = indices[i]; if (confidences[i] > boxThreshold) { result.ClsId = classIds[i]-1;//except background result.confidence = confidences[i]; result.box = boxes[i]; results.push_back(result); } } } #endif
代码调用顺序:
1.readModel//读取模型
2.DetectShip//检测目标,函数名根据需要修改
3.drawPredShip//画图,函数名根据需要修改
该代码对应FasterRCNN模型下载链接: fasterRCNN.model-深度学习文档类资源-CSDN文库
后记:
本文仅为onnxruntime的C++调用作个笔记,特别是对输入数据准备与输出数据解析这两部分,如有疑问,请不吝指教!
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