caffe模型weights&featureMap 可视化（c++）

caffe模型在训练完成后，会生成一个*.caffemodel的文件，在运行的时候，直接调用caffe就可以读取其中的相应权值参数。但是如果用一个第三方软件打开这个，却是不可以可视化的二值乱码。

 

将模型中的参数导出，可编辑化后能有哪些好处呢，

（1）方便进行fpga平台的移植

（2）可以基于别人训练好的模型，0数据训练自己的模型，使用自己的模型拟合别人模型的权值分布，达到用模型训模型的目的。

（3）可以对网络进行剪支，加速等操作。

将模型中的特征图和权值可视化有哪些好处呢，

（1）方便对卷积网络的特征有所了解，训练好的特征总是有规则的特征图，可以侧面辅助训练过程。 

这里分析lenet5这样的网络结构，所有其他网络都通用。

核心程序：

（1）只导出weights，不进行显示

void parse_caffemodel(string caffemodel, string outtxt){printf("%s\n", caffemodel.c_str());NetParameter net;bool success = loadCaffemodel(caffemodel.c_str(), &net);if (!success){printf("读取错误啦:%s\n", caffemodel.c_str());return;}FILE* fmodel = fopen(outtxt.c_str(), "wb");for (int i = 0; i < net.layer_size(); ++i){LayerParameter& param = *net.mutable_layer(i);int n = param.mutable_blobs()->size();if (n){const BlobProto& blob = param.blobs(0);printf("layer: %s weight(%d)", param.name().c_str(), blob.data_size());fprintf(fmodel, "\nlayer: %s weight(%d)\n", param.name().c_str(), blob.data_size());writeData(fmodel, blob.data().data(), blob.data_size());if (n > 1){const BlobProto& bais = param.blobs(1);printf(" bais(%d)", bais.data_size());fprintf(fmodel, "\nlayer: %s bais(%d)\n", param.name().c_str(), bais.data_size());writeData(fmodel, bais.data().data(), bais.data_size());}printf("\n");}}fclose(fmodel);}

（2）weights可视化

cv::Mat visualize_weights(string prototxt, string caffemodel, int weights_layer_num){::google::InitGoogleLogging("0");#ifdef CPU_ONLYCaffe::set_mode(Caffe::CPU);#elseCaffe::set_mode(Caffe::GPU);#endifNet<float> net(prototxt, TEST);net.CopyTrainedLayersFrom(caffemodel);   vector<shared_ptr<Blob<float> > > params = net.params();    std::cout << "各层参数的维度信息为：\n";for (int i = 0; i<params.size(); ++i)std::cout << params[i]->shape_string() << std::endl;int width = params[weights_layer_num]->shape(3);     //宽度int height = params[weights_layer_num]->shape(2);    //高度int channel = params[weights_layer_num]->shape(1);//通道数int num = params[weights_layer_num]->shape(0);       //个数int imgHeight = (int)(1 + sqrt(num))*height;  int imgWidth = (int)(1 + sqrt(num))*width;Mat img(imgHeight, imgWidth, CV_8UC3, Scalar(0, 0, 0));float maxValue = -1000, minValue = 10000;const float* tmpValue = params[weights_layer_num]->cpu_data();  for (int i = 0; i<params[weights_layer_num]->count(); i++){        maxValue = std::max(maxValue, tmpValue[i]);minValue = std::min(minValue, tmpValue[i]);}int kk = 0;                        for (int y = 0; y<imgHeight; y += height){for (int x = 0; x<imgWidth; x += width){if (kk >= num)continue;Mat roi = img(Rect(x, y, width, height));for (int i = 0; i<height; i++){for (int j = 0; j<width; j++){for (int k = 0; k<channel; k++){float value = params[weights_layer_num]->data_at(kk, k, i, j);roi.at<Vec3b>(i, j)[k] = (value - minValue) / (maxValue - minValue) * 255;   }}}++kk;}}return img;}

（3）featuremap可视化

cv::Mat Classifier::visualize_featuremap(const cv::Mat& img,string layer_name){Blob<float>* input_layer = net_->input_blobs()[0];input_layer->Reshape(1, num_channels_, input_geometry_.height, input_geometry_.width);net_->Reshape();std::vector<cv::Mat> input_channels;WrapInputLayer(&input_channels);Preprocess(img, &input_channels);net_->Forward(); std::cout << "网络中的Blobs名称为：\n";vector<shared_ptr<Blob<float> > > blobs = net_->blobs();   vector<string> blob_names = net_->blob_names();           std::cout << blobs.size() << " " << blob_names.size() << std::endl;for (int i = 0; i<blobs.size(); i++){std::cout << blob_names[i] << " " << blobs[i]->shape_string() << std::endl;}std::cout << std::endl;assert(net_->has_blob(layer_name));    shared_ptr<Blob<float> >  conv1Blob = net_->blob_by_name(layer_name);  std::cout << "测试图片的特征响应图的形状信息为：" << conv1Blob->shape_string() << std::endl;   float maxValue = -10000000, minValue = 10000000;const float* tmpValue = conv1Blob->cpu_data();for (int i = 0; i<conv1Blob->count(); i++){maxValue = std::max(maxValue, tmpValue[i]);minValue = std::min(minValue, tmpValue[i]);}int width = conv1Blob->shape(3);  //响应图的高度int height = conv1Blob->shape(2);  //响应图的宽度int channel = conv1Blob->shape(1);  //通道数int num = conv1Blob->shape(0);      //个数int imgHeight = (int)(1 + sqrt(channel))*height;int imgWidth = (int)(1 + sqrt(channel))*width;cv::Mat img(imgHeight, imgWidth, CV_8UC1, cv::Scalar(0));   int kk = 0;for (int x = 0; x<imgHeight; x += height){for (int y = 0; y<imgWidth; y += width){if (kk >= channel)continue;cv::Mat roi = img(cv::Rect(y, x, width, height));for (int i = 0; i<height; i++){for (int j = 0; j<width; j++){float value = conv1Blob->data_at(0, kk, i, j);roi.at<uchar>(i, j) = (value - minValue) / (maxValue - minValue) * 255;}}kk++;}}return img;}

运行结果：

（1）

string caffemodel = "lenet_iter_10000.caffemodel";;string outtxt = "lenet.txt";parse_caffemodel(caffemodel, outtxt);

（2）

string prototxt = "lenet.prototxt";string caffemodel = "lenet_iter_10000.caffemodel";int weights_layer_num = 0;Mat image=visualize_weights(prototxt, caffemodel, weights_layer_num);imshow("weights", image);          waitKey(0);

（3）

::google::InitGoogleLogging(argv[0]);string model_file = "lenet.prototxt";string trained_file = "lenet_iter_10000.caffemodel";Classifier classifier(model_file, trained_file);string file = "5.jpg";cv::Mat img = cv::imread(file, -1);CHECK(!img.empty()) << "Unable to decode image " << file;cv::Mat feature_map = classifier.visualize_featuremap(img,"conv2");imshow("feature_map", feature_map);cv::waitKey(0);

将权值导入matlab中，可以看到权值基本都是服从均值为0，方差很小的分布。

完整程序下载链接：http://download.csdn.net/detail/qq_14845119/9895412