Question

我有一个这样的示例文件，我们称之为dnn_mmod_face_detection_ex.cpp

#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;
}

我有一个像这样的Makefile

CC=g++

CFLAGS=-c -Wall -std=c++11 -v
LDFLAGS=-L/usr/local/cuda/lib64 -ldlib -lcudnn -lpthread -ldl -lrt -lX11 -lcublas -lcudnn -lcurand -lcusolver -lstdc++ -lm -lgcc_s -lc -lxcb -lXau -lXdmcp
SOURCES=dnn_mmod_face_detection_ex.cpp
OBJECTS=$(SOURCES:.cpp=.o)
EXECUTABLE=dnn_mmod_face_detection_ex
INCLUDE=
all: $(SOURCES) $(EXECUTABLE)

$(EXECUTABLE): $(OBJECTS)
    $(CC) $(LDFLAGS) $(OBJECTS) -o $@

.cpp.o:
    $(CC) $(CFLAGS) $(INCLUDE) $< -o $@

但是我得到几乎每个dlib库的未定义引用。即

dnn_mmod_face_detection_ex.cpp:(.text+0x267): undefined reference to `dlib::image_window::image_window()'
dnn_mmod_face_detection_ex.cpp:(.text._ZNK4dlib8gpu_data4hostEv[_ZNK4dlib8gpu_data4hostEv]+0x14): undefined reference to `dlib::gpu_data::copy_to_host() const'
dnn_mmod_face_detection_ex.cpp:(.text._ZN4dlib16resizable_tensorC2Ev[_ZN4dlib16resizable_tensorC5Ev]+0x31): undefined reference to `dlib::cuda::tensor_descriptor::tensor_descriptor()'

我知道以下

编译同样的例子并在/dlib/build/test...目录（dlib-19.9）
我通过发出命令LDFLAGS

ldd /dlib/build/test.../dnn_mmod_face_detection_ex

找出哪些库缺失的正确方法是什么？我试图跟踪dlib提供的Cmake文件，但它比粒子加速器更复杂。

Answer 1

错误放置pip install jf curl "https://api.github.com/repos/alhoo/jf/issues" | jf 'filter(age(x.created_at) < age("1 week"))'是一个简单的错误。如果放在最后就可以了。

LDFLAGS

即使包含库，g ++也会抱怨未定义的引用

1 个答案: