参考博客：
https://blog.csdn.net/muyouhang/article/details/54773265
https://blog.csdn.net/hhh0209/article/details/79830988
新建caffe的属性表，caffe_x64_release.props
将NugetPackages，caffe，CUDA中的头文件加进去
属性-C/C++-附加包含目录：
D:\caffe20190311\NugetPackages\OpenCV.2.4.10\build\native\include D:\caffe20190311\NugetPackages\OpenBLAS.0.2.14.1\lib\native\include D:\caffe20190311\NugetPackages\protobuf-v120.2.6.1\build\native\include D:\caffe20190311\NugetPackages\glog.0.3.3.0\build\native\include D:\caffe20190311\NugetPackages\gflags.2.1.2.1\build\native\include D:\caffe20190311\NugetPackages\boost.1.59.0.0\lib\native\include D:\caffe20190311\caffe-master\include D:\caffe20190311\caffe-master\include\caffe C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include
将NugetPackages，caffe生成的，CUDA中静态库加入进去
属性-链接器-常规-附加库目录：
D:\caffe20190311\NugetPackages\OpenCV.2.4.10\build\native\lib\x64\v120\Release D:\caffe20190311\NugetPackages\hdf5-v120-complete.1.8.15.2\lib\native\lib\x64 D:\caffe20190311\NugetPackages\OpenBLAS.0.2.14.1\lib\native\lib\x64 D:\caffe20190311\NugetPackages\gflags.2.1.2.1\build\native\x64\v120\dynamic\Lib D:\caffe20190311\NugetPackages\glog.0.3.3.0\build\native\lib\x64\v120\Release\dynamic D:\caffe20190311\NugetPackages\protobuf-v120.2.6.1\build\native\lib\x64\v120\Release D:\caffe20190311\NugetPackages\boost_chrono-vc120.1.59.0.0\lib\native\address-model-64\lib D:\caffe20190311\NugetPackages\boost_system-vc120.1.59.0.0\lib\native\address-model-64\lib D:\caffe20190311\NugetPackages\boost_thread-vc120.1.59.0.0\lib\native\address-model-64\lib D:\caffe20190311\NugetPackages\boost_filesystem-vc120.1.59.0.0\lib\native\address-model-64\lib D:\caffe20190311\NugetPackages\boost_date_time-vc120.1.59.0.0\lib\native\address-model-64\lib D:\caffe20190311\caffe-master\Build\x64\Release C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64
将一些动态库加入进去
属性-链接器-输入-附加依赖项：
libcaffe.lib
libprotobuf.lib
libglog.lib
gflags.lib
libopenblas.dll.a
hdf5.lib
hdf5_hl.lib
cublas.lib
cublas_device.lib
cuda.lib
cudadevrt.lib
cudnn.lib
cudart.lib
cufft.lib
cudart_static.lib
cufftw.lib
cusparse.lib
cusolver.lib
curand.lib
nppc.lib
opencv_highgui2410.lib
opencv_core2410.lib
opencv_imgproc2410.lib
kernel32.lib
user32.lib
gdi32.lib
winspool.lib
comdlg32.lib
advapi32.lib
shell32.lib
ole32.lib
oleaut32.lib
uuid.lib
odbc32.lib
odbccp32.lib
即可配置好caffe_x64_release.props的属性表
调用caffe，输入prototxt和caffemodel文件，输出每个层的名字：
caffe_layer.h
#include<caffe/common.hpp>
#include<caffe/proto/caffe.pb.h>
#include<caffe/layers/batch_norm_layer.hpp>
#include<caffe/layers/bias_layer.hpp>
#include <caffe/layers/concat_layer.hpp>
#include <caffe/layers/conv_layer.hpp>
#include <caffe/layers/dropout_layer.hpp>
#include<caffe/layers/input_layer.hpp>
#include <caffe/layers/inner_product_layer.hpp>
#include "caffe/layers/lrn_layer.hpp"
#include <caffe/layers/pooling_layer.hpp>
#include <caffe/layers/relu_layer.hpp>
#include "caffe/layers/softmax_layer.hpp"
#include<caffe/layers/scale_layer.hpp>
#include<caffe/layers/prelu_layer.hpp>
namespace caffe
{
extern INSTANTIATE_CLASS(BatchNormLayer);
extern INSTANTIATE_CLASS(BiasLayer);
extern INSTANTIATE_CLASS(InputLayer);
extern INSTANTIATE_CLASS(InnerProductLayer);
extern INSTANTIATE_CLASS(DropoutLayer);
extern INSTANTIATE_CLASS(ConvolutionLayer);
REGISTER_LAYER_CLASS(Convolution);
extern INSTANTIATE_CLASS(ReLULayer);
REGISTER_LAYER_CLASS(ReLU);
extern INSTANTIATE_CLASS(PoolingLayer);
REGISTER_LAYER_CLASS(Pooling);
extern INSTANTIATE_CLASS(LRNLayer);
REGISTER_LAYER_CLASS(LRN);
extern INSTANTIATE_CLASS(SoftmaxLayer);
REGISTER_LAYER_CLASS(Softmax);
extern INSTANTIATE_CLASS(ScaleLayer);
extern INSTANTIATE_CLASS(ConcatLayer);
extern INSTANTIATE_CLASS(PReLULayer);
}
main.cpp
#include<caffe.hpp>
#include <string>
#include <vector>
#include "caffe_layer.h"
using namespace caffe;
using namespace std;
int main() {
string net_file = "./infrared_mbfnet/antispoof-infrared.prototxt"; //prototxt文件
string weight_file = "./infrared_mbfnet/antispoof-infrared.caffemodel"; //caffemodel文件
Caffe::set_mode(Caffe::CPU);
//Caffe::SetDevice(0);
Phase phase = TEST;
boost::shared_ptr<Net<float>> net(new caffe::Net<float>(net_file, phase));
net->CopyTrainedLayersFrom(weight_file);
vector<string> blob_names = net->blob_names();
for (int i = 0; i < blob_names.size(); i++){
cout << blob_names.at(i) << endl;
}
system("pause");
return 0;
}
注意：若模型中用到了，prelu层，需要在caffe_layer.h中加入prelu_layer.hpp的头文件，并在代码中声明一下，extern INSTANTIATE_CLASS(PReLULayer)，否则，会报错，找不到该层。
运行结果：
用c++调用caffe做前向：
在caffe目录中，./caffe-master/examples/cpp_classification/classification.cpp 用该文件做前向。