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如何使用Caffe

Caffe教程（http://robots.princeton.edu/courses/COS598/2015sp/slides/Caffe/caffe_tutorial.pdf）

預備知識

Google Protocol Buffer

https://developers.google.com/protocol-buffers/docs/cpptutorial?
Caffe數(shù)據(jù)的讀取、運算、存儲都是采用Google Protocol Buffer來進行的。PB是一種輕便、高效的結構化數(shù)據(jù)存儲格式，可以用于結構化數(shù)據(jù)串行化，很適合做數(shù)據(jù)存儲或 RPC 數(shù)據(jù)交換格式。它可用于通訊協(xié)議、數(shù)據(jù)存儲等領域的語言無關、平臺無關、可擴展的序列化結構數(shù)據(jù)格式。是一種效率和兼容性都很優(yōu)秀的二進制數(shù)據(jù)傳輸格式，目前提供了 C++、Java、Python 三種語言的 API。Caffe采用的是C++和Python的API。

轉(zhuǎn)載自https://github.com/shicai/Caffe_Manual/blob/master/ReadMe.md

初始化網(wǎng)絡

#include "caffe/caffe.hpp" #include <string> #include <vector> using namespace caffe;char *proto = "H:\\Models\\Caffe\\deploy.prototxt"; /* 加載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，都是這個net boost::shared_ptr< Net<float> > net(new caffe::Net<float>(proto, phase));

加載已訓練好的模型

char *model = "H:\\Models\\Caffe\\bvlc_reference_caffenet.caffemodel"; net->CopyTrainedLayersFrom(model);

讀取模型中的每層的結構配置參數(shù)

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) {// 結構配置參數(shù):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();} }

讀取圖像均值

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(); }

根據(jù)指定數(shù)據(jù)，前向傳播網(wǎng)絡

//! Note: data_ptr指向已經(jīng)處理好（去均值的，符合網(wǎng)絡輸入圖像的長寬和Batch Size）的數(shù)據(jù) 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(); }

根據(jù)Feature層的名字獲取其在網(wǎng)絡中的Index

//! Note: Net的Blob是指，每個層的輸出數(shù)據(jù)，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; }

讀取網(wǎng)絡指定Feature層數(shù)據(jù)

//! Note: 根據(jù)CaffeNet的deploy.prototxt文件，該Net共有15個Blob，從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();

根據(jù)文件列表，獲取特征，并存為二進制文件

詳見get_features.cpp文件：

主要包括三個步驟?
- 生成文件列表，格式與訓練用的類似，每行一個圖像?
包括文件全路徑、空格、標簽（沒有的話，可以置0）?
- 根據(jù)train_val或者deploy的prototxt，改寫生成feat.prototxt?
主要是將輸入層改為image_data層，最后加上prob和argmax（為了輸出概率和Top1/5預測標簽）?
- 根據(jù)指定參數(shù)，運行程序后會生成若干個二進制文件，可以用MATLAB讀取數(shù)據(jù)，進行分析

根據(jù)Layer的名字獲取其在網(wǎng)絡中的Index

//! Note: Layer包括神經(jīng)網(wǎng)絡所有層，比如，CaffeNet共有23層 // 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; }

讀取指定Layer的權重數(shù)據(jù)

//! Note: 不同于Net的Blob是Feature Maps，Layer的Blob是指Conv和FC等層的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<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: 訓練模式下，讀取指定Layer的梯度數(shù)據(jù)，與此相似，唯一的區(qū)別是將cpu_data改為cpu_diff

修改某層的Weight數(shù)據(jù)

const float* data_ptr; /* 指向待寫入數(shù)據(jù)的指針， 源數(shù)據(jù)指針*/ float* weight_ptr = NULL; /* 指向網(wǎng)絡中某層權重的指針，目標數(shù)據(jù)指針*/ unsigned int data_size; /* 待寫入的數(shù)據(jù)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: 訓練模式下，手動修改指定Layer的梯度數(shù)據(jù)，與此相似 // mutable_cpu_data改為mutable_cpu_diff，mutable_gpu_data改為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);

Caffe中添加新的層

https://github.com/BVLC/caffe/wiki/Development?

用預訓練網(wǎng)絡參數(shù)初始化

caffe的參數(shù)初始化是根據(jù)名字從caffemodel讀取的，只要修改名字，自己想要修改的層就能隨機初始化。

• 修改名字，保留前面幾層的參數(shù)，同時后面的參數(shù)設置較高的學習率，基礎學習率大概0.00001左右。

總結

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