Qt与Dlib和CUDA

Question

I'm trying to run Qt with Dlib. What happens is that every algorithm from Dlib that requires CUDA crashes without errors and if i run the same code on visual studio it works perfectly. Qt and Dlib was built with Visual Studio 2015 x64 and CUDA version is 8.0.

The code is some example from Dlib that can use CUDA for better performance:

    #include <iostream>
    #include <dlib/dnn.h>
    #include <dlib/data_io.h>
    #include <dlib/image_processing.h>
    #include <dlib/gui_widgets.h>


    using namespace std;
    using namespace dlib;

    // ----------------------------------------------------------------------------------------

    template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>;
    template <long num_filters, typename SUBNET> using con5  = con<num_filters,5,5,1,1,SUBNET>;

    template <typename SUBNET> using downsampler  = relu<affine<con5d<32, relu<affine<con5d<32, relu<affine<con5d<16,SUBNET>>>>>>>>>;
    template <typename SUBNET> using rcon5  = relu<affine<con5<45,SUBNET>>>;

    using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;

    // ----------------------------------------------------------------------------------------


int main(int argc, char** argv) try
{
    if (argc == 1)
    {
        cout << "Call this program like this:" << endl;
        cout << "./dnn_mmod_face_detection_ex mmod_human_face_detector.dat faces/*.jpg" << endl;
        cout << "\nYou can get the mmod_human_face_detector.dat file from:\n";
        cout << "http://dlib.net/files/mmod_human_face_detector.dat.bz2" << endl;
        return 0;
    }


    net_type net;
    deserialize(argv[1]) >> net;  

    image_window win;
    for (int i = 2; i < argc; ++i)
    {
        matrix<rgb_pixel> img;
        load_image(img, argv[i]);

        // Upsampling the image will allow us to detect smaller faces but will cause the
        // program to use more RAM and run longer.
        while(img.size() < 1800*1800)
            pyramid_up(img);

        // Note that you can process a bunch of images in a std::vector at once and it runs
        // much faster, since this will form mini-batches of images and therefore get
        // better parallelism out of your GPU hardware.  However, all the images must be
        // the same size.  To avoid this requirement on images being the same size we
        // process them individually in this example.
        auto dets = net(img);
        win.clear_overlay();
        win.set_image(img);
        for (auto&& d : dets)
            win.add_overlay(d);

        cout << "Hit enter to process the next image." << endl;
        cin.get();
    }
}
catch(std::exception& e)
{
    cout << e.what() << endl;
}

the program crashes on line "auto dets = net(img);"

my .pro file:

INCLUDEPATH += C:\dlib\dlib-19.4
LIBS += -LC:\dlib\dlib-19.4\mybuild\dlib_build\Release -ldlib

INCLUDEPATH += "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include"
LIBS +="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\curand.lib"
LIBS +="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cublas.lib"
LIBS +="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cublas_device.lib"
LIBS +="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cudnn.lib"
LIBS +="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cudart_static.lib"

Thanks for the attention.

Answer 1

Try this:

LIBS += L"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64"

LIBS += -lcurand -lcublas -lcublas_device -lcudnn -lcudart_static

Answer 2

我只需要在我的项目上定义DLIB_USE_CUDA，它就可以正常工作。