PROSAGA码农传奇-Caffe-如何在C ++中可视化Caffe深度学习过程的单个层输出？

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  <P>
    根据Shai的建议，我所做的就是跟随内部
     <code>
      ForwardDebugInfo()
    </code>
    。
  </p>
   <pre>
    <code>
      for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
        Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];    
        const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
            string name = blob_name;
            for (int i = 0; i < name.length(); ++i) {
               if (name[i] == '/')
                   name[i] = '_';
            }
            string foldname = "images/"+name;
            if (stat(foldname.c_str(), &st) == -1) {
                mkdir(foldname.c_str(), 0700);
            }
        //cout<<"blob_name " << blob_name << " layer_id is " << layer_id << " blob.num() " << blob.num() << " blob.channels() " << blob.channels() << " blob.height() " << blob.height() << " blob.width() " << blob.width() << endl;

///////Plotting output of individual layer
        if(blob.height()>1 && blob.width()>1){
            cv::Size ss(blob.width(), blob.height());
            Dtype* data = blob.mutable_cpu_data();
            for(int k=0; k < blob.channels(); k++)
            {
              cv::Mat channel(ss, CV_32FC1, data);
              stringstream s;
              s << k;
              cv::imwrite(foldname+"/"+s.str()+".jpg",channel*255.0);         
              channel.release();
              data += ss.area();
            }
        }

// mainImg.release();

/////////////////////////////////////////
        const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
        LOG_IF(INFO, Caffe::root_solver())
            << "    [Forward] "
            << "Layer " << layer_names_[layer_id]
            << ", top blob " << blob_name
            << " data: " << data_abs_val_mean;
  }

</code>
  </pre>
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