caffe c++API特征提取

作为caffe初学者，特征提取python官方给的教程比较好用；但是关于c++API的使用，找了一些资料，说的并不是特征完善；项目要求，得到ImageNet图像在CaffeNet网络结构中第fc7层的特征。

环境：window7+caffe
语言：C++
目的：测试图像某层特征，并以保存
可以直接执行下面的命令，（注意官方给的案例是保存为leveldb 格式，数据库格式不够直观，解析也比较麻烦，直接保存为文本形式多好呀~另外是7个参数哦，直接弄清楚对应的参数信息）

extract_features.exe D:/caffe-windows/examples/_temp/caffe_reference_imagenet_model D:/caffe-windows/examples/_temp/imagenet_val.prototxt fc7 
D:/caffe-windows/examples/_temp/features_fc7 10 leveldb GPU

然后通过解决方案中的extract_features工程源码extract_features.cpp进行了改进,代码更改区域块如下

 ofstream *fout = new ofstream[num_features];

  for (size_t i = 0; i < num_features; ++i) {
    LOG(INFO)<< "Opening dataset " << dataset_names[i];
    //boost::shared_ptr db(db::GetDB(db_type));
  //  db->Open(dataset_names.at(i), db::NEW);
   // feature_dbs.push_back(db);
   // boost::shared_ptr txn(db->NewTransaction());
    //txns.push_back(txn);
    string feat_file_name = dataset_names.at(i);
    feat_file_name = feat_file_name.append("/").append(blob_names[i]).append(".txt");
    fout[i].open(feat_file_name);
  }

  LOG(ERROR)<< "Extacting Features";
  //Datum datum;
  std::vector<int> image_indices(num_features, 0);
  for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {  
      feature_extraction_net->Forward();

    for (int i = 0; i < num_features; ++i) {
      const boost::shared_ptr > feature_blob =
        feature_extraction_net->blob_by_name(blob_names[i]);
        //blob是一个四维的数据：n*c*w*h,“fc7”层50*1*1*4960；“conv1”层50*96*55*55；
       // “conv2”层50*256*27*27。
      int batch_size = feature_blob->num();
      //batch_size 就是blob中的n，表示feature-map的个数

      LOG(ERROR) << "总的特征块数量:" << batch_size*num_mini_batches;//50*10

      int dim_features = feature_blob->count() / batch_size;

      LOG(ERROR) << "dim_features:" << dim_features;//4096

      const Dtype* feature_blob_data;
      for (int n = 0; n < batch_size; ++n) {
       // datum.set_height(feature_blob->height());
       // datum.set_width(feature_blob->width());
       // datum.set_channels(feature_blob->channels());
       // datum.clear_data();
       // datum.clear_float_data();
        feature_blob_data = feature_blob->cpu_data() +
            feature_blob->offset(n);    

      //  for (int d = 0; d < dim_features; ++d) {
         // datum.add_float_data(feature_blob_data[d]);
//写入到文本文件中，fc7总共有4096维
        if (n == 0)
        {
            for (int d = 0; d < dim_features; ++d)
                fout[i] << feature_blob_data[d] << " ";
        }   
       // string key_str = caffe::format_int(image_indices[i], 10);

        //string out;
       // CHECK(datum.SerializeToString(&out));
       // txns.at(i)->Put(key_str, out);
       // ++image_indices[i];
       // if (image_indices[i] % 1000 == 0) {
      //    txns.at(i)->Commit();
       //   txns.at(i).reset(feature_dbs.at(i)->NewTransaction());
       //   LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
      //        " query images for feature blob " << blob_names[i];
       // }
      }  // for (int n = 0; n < batch_size; ++n)

      fout[i].close();


    }  // for (int i = 0; i < num_features; ++i)
  }  // for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)
  // write the last batch
 // for (int i = 0; i < num_features; ++i) {
   // if (image_indices[i] % 1000 != 0) {
    //  txns.at(i)->Commit();
   // }
   // LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
        //" query images for feature blob " << blob_names[i];
    //feature_dbs.at(i)->Close();
  //}

代码改写后，得到的结果就是有文档形式的特征了，可以继续接下来的项目任务了~~

fc7.txt保存的就是4096维的float数据啦~
配置文件上传出现问题，不能超过60M,晕~~