EfficientNetB3 not working in Debug for some PCs

[cesarpgouveia]

The EfficientNetB3 model, running with OpenCV and OpenVINO frameworks, crashes during inference when the code is compiled in Debug mode. This issue is reproducible only on certain PCs and does not occur when the code is compiled in Release mode. The crash specifically happens when using the CPU plugin for inference.

Steps to Reproduce:

Use the provided example code to set up and run inference with the EfficientNetB3 model.

Compile the code in Debug mode.

Execute the program.

Expected Behavior:
The program should successfully complete the inference process without any crashes, as it does in Release mode.

Actual Behavior:
The program crashes during the net.forward() call when attempting inference in Debug mode.

Environment:

OpenCV version: 4.9.1

OpenVINO version: OpenVINO 2023.3

Operating System: Windows 10

Model: EfficientNetB3 (attached as EfficientNetB3_from_scratch.onnx)

CPU that crashes in Debug: 11th Gen Intel(R) Core(TM) i7-11850H @ 2.50GHz

#include <iostream>
#include <chrono>
#include <fstream>

#include <opencv2/highgui.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/core.hpp>


std::string imageFilename = "C:\\development\\ColorImage.png";
std::string modelFilename = "C:\\development\\EfficientNetB3_from_scratch.onnx";
cv::dnn::Backend targetBackend = cv::dnn::DNN_BACKEND_INFERENCE_ENGINE;
cv::dnn::Target targetDevice = cv::dnn::DNN_TARGET_CPU;


unsigned int numInferences = 100;
cv::Size modelInputSize = cv::Size(300, 300);
cv::ImreadModes imReadMode = cv::IMREAD_COLOR;
std::vector<std::string> inputLayerNames = {"data"};
bool swapRBChannels = false;
unsigned int numChannels = 3;

int main()
{
    cv::dnn::Net net = cv::dnn::readNetFromONNX(modelFilename);

    net.setPreferableBackend(targetBackend);
    net.setPreferableTarget(targetDevice);

    cv::Mat img = cv::imread(imageFilename, imReadMode);

    cv::Mat imgResized;
    cv::resize(img, imgResized, modelInputSize);

    std::vector<cv::Mat> imgBatch = { imgResized };

    cv::Mat blob = cv::dnn::blobFromImages(imgBatch, 1.0, cv::Size(), cv::Scalar(), swapRBChannels, false, CV_32F);

    std::cout << "Blob size: " << blob.size[0] << "x" << blob.size[1] << "x" << blob.size[2] << "x" << blob.size[3] << std::endl;

    for (auto inputLayerName : inputLayerNames)
        net.setInput(blob, inputLayerName);

    std::vector<cv::String> unconnectedOutLayerNames = net.getUnconnectedOutLayersNames();

    std::vector<cv::Mat> outputs;
    outputs.clear();

    std::chrono::high_resolution_clock::time_point timeLoadModelPlusInference1 = std::chrono::high_resolution_clock::now();

    net.forward(outputs, unconnectedOutLayerNames);

    std::chrono::high_resolution_clock::time_point timeLoadModelPlusInference2 = std::chrono::high_resolution_clock::now();

    std::chrono::duration<double, std::milli> ms_doubleTimeLoadModelPlusInference = timeLoadModelPlusInference2 - timeLoadModelPlusInference1;

    std::cout << "Execution time (load model + inference): " << ms_doubleTimeLoadModelPlusInference.count() << std::endl; // in ms

    std::chrono::high_resolution_clock::time_point time1 = std::chrono::high_resolution_clock::now();

    try
    {
        for (size_t i = 0; i < numInferences; i++)
            net.forward(outputs, unconnectedOutLayerNames);
    }
    catch (std::exception& ex)
    {
        std::cout << ex.what() << std::endl;
    }
    
    std::chrono::high_resolution_clock::time_point time2 = std::chrono::high_resolution_clock::now();

    std::chrono::duration<double, std::milli> ms_double = time2 - time1;

    std::cout << "Execution time inference only: " << ms_double.count() / numInferences << std::endl; // in ms
}

[cesarpgouveia]

Model was too big, added a link for wetransfer here: https://we.tl/t-eLI8VYZajj