Caffe使用教程【转】

from：https://github.com/shicai/Caffe_Manual

初始化网络

#include "caffe/caffe.hpp"#include <string>#include <vector>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netboost::shared_ptr< Net<float> > net(new caffe::Net<float>(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, &param);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<float> 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<float> > & net, float *data_ptr){    Blob<float>* 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<float> > & 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<float> > 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

详见get_features.cpp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<float> > & 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Biaschar *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<float> > layer = net->layers()[layer_id];std::vector<boost::shared_ptr<Blob<float>  >> 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<float> > 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);