Caffe使用教程
jopen
9年前
by Shicai Yang（@星空下的巫师）on 2015/08/06
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net boost::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: 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);