[Feature Request] A model with dynamic input and dynamic output。 will have a memory leak after inference with Openvino.

[ZzhangYyan]

Describe the feature request

Application: Lightglue model for image registration, the input is a fixed-size image, the output is a dynamic shape. Openvino has a memory leak, I hope to solve this problem

Model source：https://github.com/fabio-sim/LightGlue-ONNX/releases/tag/v1.0.0

Openvino version is 2024.3.0.

code:
std::vector ApplyTransform1(const cv::Mat& image, float& mean, float& std)
{
cv::Mat resized, floatImage;
image.convertTo(floatImage, CV_32FC1);
mean = 0.0f;
std = 0.0f;
cv::Scalar meanScalar, stdScalar;
meanStdDev(floatImage, meanScalar, stdScalar);
mean = static_cast(meanScalar.val[0]);
std = static_cast(stdScalar.val[0]);

std::vector<float> imgData;
for (int h = 0; h < image.rows; h++)
{
    for (int w = 0; w < image.cols; w++)
    {
        imgData.push_back((floatImage.at<float>(h, w) - mean) / std);
    }
}
return imgData;

}

void MatchEnd2EndInf::SetImgInTensor(const cv::Mat& img, ov::Tensor& tensor)
{
std::vector imgData;
cv::Mat grayImg;
float mean, std;

if (img.channels() == 3)
{
    cv::cvtColor(img, grayImg, cv::COLOR_BGR2GRAY);
}
else
{
    /*      grayImg.copyTo(img);*/
    grayImg = img.clone();
}

imgData = ApplyTransform1(grayImg, mean, std);

float* data = tensor.data<float>();
for (size_t i = 0; i < imgData.size(); i++)
{
    data[i] = imgData[i];
}

}

void MatchEnd2EndInf::Infer(const cv::Mat& baseImg, const cv::Mat& testImg, cv::detail::MatchesInfo& matches_info)
{
std::chrono::steady_clock::time_point startTime;
std::chrono::steady_clock::time_point endTime;
std::chrono::duration<double, std::milli> duration;
int nElapsedTime;
int hh = 0, ww = 0;
ov::Shape shape1 = ov::Shape(4);
ov::Shape shape2 = ov::Shape(4);

hh = baseImg.rows; ww = baseImg.cols;
shape1[0] = 1;
shape1[1] = 1;
shape1[2] = hh;
shape1[3] = ww;

hh = testImg.rows; ww = testImg.cols;
shape2[0] = 1;
shape2[1] = 1;
shape2[2] = hh;
shape2[3] = ww;

startTime = std::chrono::steady_clock::now();
{
    // -------- Step 3. Create an Inference Request --------
    std::unique_ptr<ov::InferRequest>   inferRequest = std::make_unique<ov::InferRequest>(this->m_pModel->create_infer_request());
    ov::Tensor                          baseTensor(ov::element::f32, shape1);
    ov::Tensor                          testTensor(ov::element::f32, shape2);

    SetImgInTensor(baseImg, baseTensor);
    SetImgInTensor(testImg, testTensor);

    inferRequest->set_tensor("image0", baseTensor);
    inferRequest->set_tensor("image1", testTensor);

    std::cout << "The shape of output image0:" << baseTensor.get_shape() << std::endl;
    std::cout << "The shape of output image1:" << testTensor.get_shape() << std::endl;

    inferRequest->infer();

    ov::Tensor output = inferRequest->get_output_tensor(0);
    ov::Shape output_shape = output.get_shape();
    ov::Tensor output1 = inferRequest->get_output_tensor(1);
    ov::Shape output_shape1 = output1.get_shape();
    ov::Tensor output2 = inferRequest->get_output_tensor(2);
    ov::Shape output_shape2 = output2.get_shape();
    ov::Tensor output3 = inferRequest->get_output_tensor(3);
    ov::Shape output_shape3 = output2.get_shape();
    std::cout << "The shape of output kpts0:" << output_shape << std::endl;
    std::cout << "The shape of output kpts1:" << output_shape1 << std::endl;
    std::cout << "The shape of output matches0:" << output_shape2 << std::endl;
    std::cout << "The shape of output matches1:" << output_shape2 << std::endl;

}

Describe scenario use case

Application: Lightglue model for image registration, the input is a fixed-size image, the output is a dynamic shape. Openvino has a memory leak, I hope to solve this problem

[sfatimar]

Hello are you using OpenVINO Directly. If so please raise issue in OpenVINO Repo https://github.com/openvinotoolkit/openvino/issues