运行 两次后 ONNXRuntime 程序崩溃

Question

我正在编写一个应该在 VS2013 中 运行 的程序，它接收两张图像，运行 通过带有 C++ 的 ONNX 模型，return 模型的输出

因为我用的是图片，所以我用VS2019写程序，创建一个DLL，在VS2013运行

我能够将它用于一张图片并且效果很好。当我尝试使用两个图像时，运行ning 程序一次运行良好，但如果我尝试连续执行两次，它会在尝试删除 std::wstring 时因断言而崩溃。我试图追踪它到原点，但我能得到的最接近的是在 Microsoft Visual Studio19\Enterprise\VC\Tools\MSVC.29.30133\crt\src\vcruntime 中，在函数

中

_CRT_SECURITYCRITICAL_ATTRIBUTE
void __CRTDECL operator delete(void* const block, size_t const) noexcept
{
    operator delete(block);
}

VS2019中的代码为：

#include <iostream>
#include <fstream>
#include <Windows.h>

Mat MeanOverChannels(Mat m) {
    /*
    * input: cv::Mat with more than one channel (=3)
    * output: cv::Mat with one channel, that is the average over channels
    */
    Size size = m.size();
    int channels = m.channels();
    Mat res(size, CV_64FC1);

    for (int i = 0; i < size.height; i++) {
        for (int j = 0; j < size.width; j++) {
            double avg = 0;
            auto cur = m.at<Vec3b>(i, j);
            for (int c = 0; c < channels; c++)
            {
                avg += cur[c];
            }
            avg /= channels;
            res.at<double>(i, j) = avg;
        }
    }

    return res;
}

cv::Mat GetInputNormalized(string imgpath
    , int& original_height, int& original_width
    , int input_height, int input_width) {
    /*
    * input: path to image, and refernces to save the origianl size
    * output: image from the path after resizing and normalizing
    */

    //read input
    Mat img = imread(imgpath, IMREAD_COLOR);  // can use IMREAD_UNCHANGED
    Size s = img.size();
    original_height = s.height;
    original_width = s.width;

    //mean over axis=2, you can comment out if it is not needed for you
    Mat img_mean = MeanOverChannels(img);


    //resize down
    int down_width = input_width;
    int down_height = input_height;

    Mat resized_down;
    resize(img_mean, resized_down, Size(down_width, down_height), INTER_LINEAR);
    //can return resized_down, from here you can customize the input

    Mat img2float;
    resized_down.convertTo(img2float, CV_64FC1);

    //normalize pixels : p->(p-127.5)/127.5
    Mat imgNorm = (img2float - 127.5) / 127.5;

    return imgNorm;

}

bool Net::RunNet(std::wstring modelName, std::string inPath1, std::string inPath2, float& iRes) {
    /*********this model assumes two inputs and one output************/
    /////path to the onnx model/////
    wchar_t buffer[MAX_PATH];
    GetModuleFileNameW(NULL, buffer, MAX_PATH);
    const wchar_t* endChar = LR"(\/)";
    std::wstring::size_type pos = std::wstring(buffer).find_last_of(endChar);

    std::wstring modelPath;
    modelPath = std::wstring(buffer).substr(0, pos) + endChar + modelName;


    bool success = false;
    /////variables to run the model/////
    Ort::Env env;
    Ort::Session session{ env,  modelPath.c_str(), Ort::SessionOptions{} };
    Ort::AllocatorWithDefaultOptions allocator;
    auto memoryInfo = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
    auto* inputName1 = session.GetInputName(0, allocator);
    auto* inputName2 = session.GetInputName(1, allocator);
    auto* outputName = session.GetOutputName(0, allocator);
    array inputNames = { inputName1, inputName2 };
    array outputNames = { outputName };

    auto inputShape1 = session.GetInputTypeInfo(0).GetTensorTypeAndShapeInfo().GetShape();
    auto inputShape2 = session.GetInputTypeInfo(1).GetTensorTypeAndShapeInfo().GetShape();

    for (int i = 0;i < inputShape1.size(); ++i) {
        inputShape1[i] = inputShape1[i] > 0 ? inputShape1[i] : 1; //in case inputShape[0] is -1 (None in python)
    }
    for (int i = 0;i < inputShape2.size(); ++i) {
        inputShape2[i] = inputShape2[i] > 0 ? inputShape2[i] : 1; //in case inputShape[0] is -1 (None in python)
    }
    int input_height1 = inputShape1[1], input_width1 = inputShape1[2];
    int input_height2 = inputShape2[1], input_width2 = inputShape2[2];

    /////getting input to the net/////

    Mat img1, img2;
    int origHeight1, origWidth1
        , origHeight2, origWidth2;

    img1 = GetInputNormalized(inPath1
        , origHeight1, origWidth1
        , input_height1, input_width1
    );
    img2 = GetInputNormalized(inPath2
        , origHeight2, origWidth2
        , input_height2, input_width2
    );

    vf inputValues1 = MatTo1DVector(img1);
    vf inputValues2 = MatTo1DVector(img2);

    //// create the input tensor (this is not a deep copy!)
    auto inputOnnxTensor1 = Ort::Value::CreateTensor<float>(memoryInfo,
        inputValues1.data(), inputValues1.size(),
        inputShape1.data(), inputShape1.size());
    auto inputOnnxTensor2 = Ort::Value::CreateTensor<float>(memoryInfo,
        inputValues2.data(), inputValues2.size(),
        inputShape2.data(), inputShape2.size());

    array input_tensor = { std::move(inputOnnxTensor1), std::move(inputOnnxTensor2) };
    
    ///////Executing the model/////
    auto outputValues = session.Run(
        Ort::RunOptions{ nullptr }, // e.g. set a verbosity level only for this run
        inputNames.data(), input_tensor.data(), input_tensor.size(), // input to set
        outputNames.data(), 1);                 // output to take 

    auto& output1 = outputValues[0];

    const auto* floats = output1.GetTensorMutableData<float>();
    const auto floatsCount = output1.GetTensorTypeAndShapeInfo().GetElementCount();
    float res = *floats;
    iRes = res;
    
    allocator.Free(inputName1);
    allocator.Free(inputName2);
    allocator.Free(outputName);

    success = true;
    return success;
}

我导出的是NetFactory的一个函数，创建一个unique_ptr的Net，通过这个工厂我们创建一个Net的实例，_net，我执行_net->RunNetSimCompare(modelName, inPath1, inPath2, res);

和returnres执行后

问题出在哪里？

Answer 1

对于那些遇到同样问题的人，似乎将函数的签名更改为以下内容：

bool Net::RunNet(std::wstring& modelName, std::string& inPath1, std::string& inPath2, float& iRes)

修复它