caffe的c++接口

caffe的c++接口：caffe中的c++接口怎么调用呢？有什么方法能够快速调用呢？接下来红黑小编就来介绍一下caffe的c++接口的调用方法，希望对大家有所帮助。
接口可以完全按照官网的分类cpp文件调用学习。

classification.cpp

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

using namespace caffe;  // nolint(build/namespaces)
using std::string;

/* pair (label, confidence) representing a prediction. */
typedef std::pair prediction;

class classifier {
 public:
  classifier(const string& model_file,
             const string& trained_file,
             const string& mean_file,
             const string& label_file);

  std::vector
 classify(const cv::mat& img, int n = 5);

 private:
  void setmean(const string& mean_file);

  std::vector predict(const cv::mat& img);

  void wrapinputlayer(std::vector* input_channels);

  void preprocess(const cv::mat& img,
                  std::vector* input_channels);

 private:
  shared_ptr > net_;
  cv::size input_geometry_;
  int num_channels_;
  cv::mat mean_;
  std::vector labels_;
};

classifier::classifier(const string& model_file,
                       const string& trained_file,
                       const string& mean_file,
                       const string& label_file) {
#ifdef cpu_only
  caffe::set_mode(caffe::cpu);
#else
  caffe::set_mode(caffe::gpu);
#endif

  /* load the network. */
  net_.reset(new net(model_file, test));
  net_->copytrainedlayersfrom(trained_file);

  check_eq(net_->num_inputs(), 1) << "network should have exactly one input.";
  check_eq(net_->num_outputs(), 1) << "network should have exactly one output.";

  blob* input_layer = net_->input_blobs()[0];
  num_channels_ = input_layer->channels();
  check(num_channels_ == 3 || num_channels_ == 1)
    << "input layer should have 1 or 3 channels.";
  input_geometry_ = cv::size(input_layer->width(), input_layer->height());

  /* load the binaryproto mean file. */
  setmean(mean_file);

  /* load labels. */
  std::ifstream labels(label_file.c_str());
  check(labels) << "unable to open labels file " << label_file;
  string line;
  while (std::getline(labels, line))
    labels_.push_back(string(line));

  blob* output_layer = net_->output_blobs()[0];
  check_eq(labels_.size(), output_layer->channels())
    << "number of labels is different from the output layer dimension.";
}

static bool paircompare(const std::pair& lhs,
                        const std::pair& rhs) {
  return lhs.first > rhs.first;
}

/* return the indices of the top n values of vector v. */
static std::vector argmax(const std::vector& v, int n) {
  std::vector > pairs;
  for (size_t i = 0; i < v.size(); ++i)
    pairs.push_back(std::make_pair(v[i], i));
  std::partial_sort(pairs.begin(), pairs.begin() + n, pairs.end(), paircompare);

  std::vector result;
  for (int i = 0; i < n; ++i)
    result.push_back(pairs[i].second);
  return result;
}

/* return the top n predictions. */
std::vector
 classifier::classify(const cv::mat& img, int n) {
  std::vector output = predict(img);

  std::vector maxn = argmax(output, n);
  std::vector
 predictions;
  for (int i = 0; i < n; ++i) {
    int idx = maxn[i];
    predictions.push_back(std::make_pair(labels_[idx], output[idx]));
  }

  return predictions;
}

/* load the mean file in binaryproto format. */
void classifier::setmean(const string& mean_file) {
  blobproto blob_proto;
  readprotofrombinaryfileordie(mean_file.c_str(), &blob_proto);

  /* convert from blobproto to blob */
  blob mean_blob;
  mean_blob.fromproto(blob_proto);
  check_eq(mean_blob.channels(), num_channels_)
    << "number of channels of mean file doesn't match input layer.";

  /* the format of the mean file is planar 32-bit float bgr or grayscale. */
  std::vector channels;
  float* data = mean_blob.mutable_cpu_data();
  for (int i = 0; i < num_channels_; ++i) {
    /* extract an inpidual channel. */
    cv::mat channel(mean_blob.height(), mean_blob.width(), cv_32fc1, data);
    channels.push_back(channel);
    data += mean_blob.height() * mean_blob.width();
  }

  /* merge the separate channels into a single image. */
  cv::mat mean;
  cv::merge(channels, mean);

  /* compute the global mean pixel value and create a mean image
   * filled with this value. */
  cv::scalar channel_mean = cv::mean(mean);
  mean_ = cv::mat(input_geometry_, mean.type(), channel_mean);
}

std::vector classifier::predict(const cv::mat& img) {
  blob* input_layer = net_->input_blobs()[0];
  input_layer->reshape(1, num_channels_,
                       input_geometry_.height, input_geometry_.width);
  /* forward dimension change to all layers. */
  net_->reshape();

  std::vector input_channels;
  wrapinputlayer(&input_channels);

  preprocess(img, &input_channels);

  net_->forwardprefilled();

  /* copy the output layer to a std::vector */
  blob* output_layer = net_->output_blobs()[0];
  const float* begin = output_layer->cpu_data();
  const float* end = begin + output_layer->channels();
  return std::vector(begin, end);
}

/* wrap the input layer of the network in separate cv::mat objects
 * (one per channel). this way we save one memcpy operation and we
 * don't need to rely on cudamemcpy2d. the last preprocessing
 * operation will write the separate channels directly to the input
 * layer. */
void classifier::wrapinputlayer(std::vector* input_channels) {
  blob* input_layer = net_->input_blobs()[0];

  int width = input_layer->width();
  int height = input_layer->height();
  float* input_data = input_layer->mutable_cpu_data();
  for (int i = 0; i < input_layer->channels(); ++i) {
    cv::mat channel(height, width, cv_32fc1, input_data);
    input_channels->push_back(channel);
    input_data += width * height;
  }
}

void classifier::preprocess(const cv::mat& img,
                            std::vector* input_channels) {
  /* convert the input image to the input image format of the network. */
  cv::mat sample;
  if (img.channels() == 3 && num_channels_ == 1)
    cv::cvtcolor(img, sample, cv_bgr2gray);
  else if (img.channels() == 4 && num_channels_ == 1)
    cv::cvtcolor(img, sample, cv_bgra2gray);
  else if (img.channels() == 4 && num_channels_ == 3)
    cv::cvtcolor(img, sample, cv_bgra2bgr);
  else if (img.channels() == 1 && num_channels_ == 3)
    cv::cvtcolor(img, sample, cv_gray2bgr);
  else
    sample = img;

  cv::mat sample_resized;
  if (sample.size() != input_geometry_)
    cv::resize(sample, sample_resized, input_geometry_);
  else
    sample_resized = sample;

  cv::mat sample_float;
  if (num_channels_ == 3)
    sample_resized.convertto(sample_float, cv_32fc3);
  else
    sample_resized.convertto(sample_float, cv_32fc1);

  cv::mat sample_normalized;
  cv::subtract(sample_float, mean_, sample_normalized);

  /* this operation will write the separate bgr planes directly to the
   * input layer of the network because it is wrapped by the cv::mat
   * objects in input_channels. */
  cv::split(sample_normalized, *input_channels);

  check(reinterpret_cast(input_channels->at(0).data)
        == net_->input_blobs()[0]->cpu_data())
    << "input channels are not wrapping the input layer of the network.";
}

int main(int argc, char** argv) {
  if (argc != 6) {
    std::cerr << "usage: " << argv[0]
              << " deploy.prototxt network.caffemodel"
              << " mean.binaryproto labels.txt img.jpg" << std::endl;
    return 1;
  }

  ::google::initgooglelogging(argv[0]);

  string model_file   = argv[1];
  string trained_file = argv[2];
  string mean_file    = argv[3];
  string label_file   = argv[4];
  classifier classifier(model_file, trained_file, mean_file, label_file);

  string file = argv[5];

  std::cout << "---------- prediction for "
            << file << " ----------" << std::endl;

  cv::mat img = cv::imread(file, -1);
  check(!img.empty()) << "unable to decode image " << file;
  std::vector
 predictions = classifier.classify(img);

  /* print the top n predictions. */
  for (size_t i = 0; i < predictions.size(); ++i) {
    prediction p = predictions[i];
    std::cout << std::fixed << std::setprecision(4) << p.second << " - \""
              << p.first << "\"" << std::endl;
  }
}
海康的星空下的巫师一个脚本：
 
/************************初始化网络**************************/
#include "caffe/caffe.hpp"
#include 
#include 
using namespace caffe;

char *proto = "h:\\models\\caffe\\deploy.prototxt"; /* 加载caffenet的配置 */
phase phase = test; /* or train */
caffe::set_mode(caffe::cpu);
// caffe::set_mode(caffe::gpu);
// caffe::setdevice(0);

//! note: 后文所有提到的net，都是这个net
boost::shared_ptr< net > net(new caffe::net(proto, phase));
/************************加载已训练好的模型**************************/
char *model = "h:\\models\\caffe\\bvlc_reference_caffenet.caffemodel";    
net->copytrainedlayersfrom(model);
/*******************读取模型中的每层的结构配置参数*************************/

char *model = "h:\\models\\caffe\\bvlc_reference_caffenet.caffemodel";
netparameter param;
readnetparamsfrombinaryfileordie(model, ¶m);
int num_layers = param.layer_size();
for (int i = 0; i < num_layers; ++i)
{
    // 结构配置参数:name，type，kernel size，pad，stride等
    log(error) << "layer " << i << ":" << param.layer(i).name() << "\t" << param.layer(i).type();
    if (param.layer(i).type() == "convolution")
    {
        convolutionparameter conv_param = param.layer(i).convolution_param();
        log(error) << "\t\tkernel size: " << conv_param.kernel_size()
            << ", pad: " << conv_param.pad()
            << ", stride: " << conv_param.stride();
    }
}
/************************读取图像均值**************************/


char *mean_file = "h:\\models\\caffe\\imagenet_mean.binaryproto";
blob image_mean;
blobproto blob_proto;
const float *mean_ptr;
unsigned int num_pixel;

bool succeed = readprotofrombinaryfile(mean_file, &blob_proto);
if (succeed)
{
    image_mean.fromproto(blob_proto);
    num_pixel = image_mean.count(); /* nchw=1x3x256x256=196608 */
    mean_ptr = (const float *) image_mean.cpu_data();
}
/************************根据指定数据，前向传播网络**************************/


//! note: data_ptr指向已经处理好（去均值的，符合网络输入图像的长宽和batch size）的数据
void caffe_forward(boost::shared_ptr< net > & net, float *data_ptr)
{
    blob* input_blobs = net->input_blobs()[0];
    switch (caffe::mode())
    {
    case caffe::cpu:
        memcpy(input_blobs->mutable_cpu_data(), data_ptr,
            sizeof(float) * input_blobs->count());
        break;
    case caffe::gpu:
        cudamemcpy(input_blobs->mutable_gpu_data(), data_ptr,
            sizeof(float) * input_blobs->count(), cudamemcpyhosttodevice);
        break;
    default:
        log(fatal) << "unknown caffe mode.";
    } 
    net->forwardprefilled();
}
/*********************根据feature层的名字获取其在网络中的index******************/

//! note: net的blob是指，每个层的输出数据，即feature maps
// char *query_blob_name = "conv1";
unsigned int get_blob_index(boost::shared_ptr< net > & net, char *query_blob_name)
{
    std::string str_query(query_blob_name);    
    vector< string > const & blob_names = net->blob_names();
    for( unsigned int i = 0; i != blob_names.size(); ++i ) 
    { 
        if( str_query == blob_names[i] ) 
        { 
            return i;
        } 
    }
    log(fatal) << "unknown blob name: " << str_query;
}
/*********************读取网络指定feature层数据*****************/


//! note: 根据caffenet的deploy.prototxt文件，该net共有15个blob，从data一直到prob    
char *query_blob_name = "conv1"; /* data, conv1, pool1, norm1, fc6, prob, etc */
unsigned int blob_id = get_blob_index(net, query_blob_name);

boost::shared_ptr > blob = net->blobs()[blob_id];
unsigned int num_data = blob->count(); /* nchw=10x96x55x55 */
const float *blob_ptr = (const float *) blob->cpu_data();
/*********************根据文件列表，获取特征，并存为二进制文件*****************/

/*
主要包括三个步骤

生成文件列表，格式与训练用的类似，每行一个图像 包括文件全路径、空格、标签（没有的话，可以置0）
根据train_val或者deploy的prototxt，改写生成feat.prototxt 主要是将输入层改为image_data层，最后加上prob和argmax（为了输出概率和top1/5预测标签）
根据指定参数，运行程序后会生成若干个二进制文件，可以用matlab读取数据，进行分析
根据layer的名字获取其在网络中的index
*/
//! note: layer包括神经网络所有层，比如，caffenet共有23层
// char *query_layer_name = "conv1";
unsigned int get_layer_index(boost::shared_ptr< net > & net, char *query_layer_name)
{
    std::string str_query(query_layer_name);    
    vector< string > const & layer_names = net->layer_names();
    for( unsigned int i = 0; i != layer_names.size(); ++i ) 
    { 
        if( str_query == layer_names[i] ) 
        { 
            return i;
        } 
    }
    log(fatal) << "unknown layer name: " << str_query;
}
/*********************读取指定layer的权重数据*****************/

//! note: 不同于net的blob是feature maps，layer的blob是指conv和fc等层的weight和bias
char *query_layer_name = "conv1";
const float *weight_ptr, *bias_ptr;
unsigned int layer_id = get_layer_index(net, query_layer_name);
boost::shared_ptr > layer = net->layers()[layer_id];
std::vector  >> blobs = layer->blobs();
if (blobs.size() > 0)
{
    weight_ptr = (const float *) blobs[0]->cpu_data();
    bias_ptr = (const float *) blobs[1]->cpu_data();
}

//! note: 训练模式下，读取指定layer的梯度数据，与此相似，唯一的区别是将cpu_data改为cpu_diff
/*********************修改某层的weight数据*****************/


const float* data_ptr;          /* 指向待写入数据的指针， 源数据指针*/
float* weight_ptr = null;       /* 指向网络中某层权重的指针，目标数据指针*/
unsigned int data_size;         /* 待写入的数据量 */
char *layer_name = "conv1";     /* 需要修改的layer名字 */

unsigned int layer_id = get_layer_index(net, query_layer_name);    
boost::shared_ptr > blob = net->layers()[layer_id]->blobs()[0];

check(data_size == blob->count());
switch (caffe::mode())
{
case caffe::cpu:
    weight_ptr = blob->mutable_cpu_data();
    break;
case caffe::gpu:
    weight_ptr = blob->mutable_gpu_data();
    break;
default:
    log(fatal) << "unknown caffe mode";
}
caffe_copy(blob->count(), data_ptr, weight_ptr);

//! note: 训练模式下，手动修改指定layer的梯度数据，与此相似
// mutable_cpu_data改为mutable_cpu_diff，mutable_gpu_data改为mutable_gpu_diff
/*********************保存新的模型*****************/

char* weights_file = "bvlc_reference_caffenet_new.caffemodel";
netparameter net_param;
net->toproto(&net_param, false);
writeprototobinaryfile(net_param, weights_file);