首先是Net使用的小例子：
#include <vector> #include <iostream> #include <caffe/net.hpp> using namespace std; using namespace caffe; int main() { std::string proto("./bambootry/deploy.prototxt"); Net<float> nn(proto,caffe::TEST); vector<string> bn=nn.blob_names();//获取Net中所有Blob对象名 for(int i=0;i<bn.size();i++) { cout<<"Blob #"<<i<<" : "<<bn[i]<<endl; } return 0; }
linux下编译（bambootry为自己创建的文件夹）
g++ -o ./bambootry/netapp ./bambootry/net.cpp -I ./include -D CPU_ONLY \ -I ./.build_release/src/ -L ./build/lib -lcaffe -lglog -lboost_system \ -lprotobuf
结果：
……中间省略
继续省略……
代码注释
src/caffe/proto/caffe.proto中NetParameter
1 message NetParameter { 2 optional string name = 1; // consider giving the network a name 网络名称 3 // DEPRECATED. See InputParameter. The input blobs to the network. 4 repeated string input = 3;//网络的输入blob名称，可以有多个blob 5 // DEPRECATED. See InputParameter. The shape of the input blobs. 6 repeated BlobShape input_shape = 8;//输入Blob维度信息 7 8 // 4D input dimensions -- deprecated. Use "input_shape" instead. 9 // If specified, for each input blob there should be four 10 // values specifying the num, channels, height and width of the input blob. 11 // Thus, there should be a total of (4 * #input) numbers. 12 repeated int32 input_dim = 4;//旧版维度信息 13 14 // Whether the network will force every layer to carry out backward operation. 15 // If set False, then whether to carry out backward is determined 16 // automatically according to the net structure and learning rates. 17 // 网络是否强制每个层进行反向传播运算，如果设置为false，则由网络结构和学习速率自动确定是否进行反向传播运算 18 optional bool force_backward = 5 [default = false]; 19 // The current "state" of the network, including the phase, level, and stage. 20 // Some layers may be included/excluded depending on this state and the states 21 // specified in the layers' include and exclude fields. 22 optional NetState state = 6;//网络当前状态，phase, level, 和 stage，根据状态可以确定是否包含某些层 23 24 // Print debugging information about results while running Net::Forward, 25 // Net::Backward, and Net::Update. 26 // 运行Net::Forward,Net::Backward, and Net::Update是否打印结果的调试信息 27 optional bool debug_info = 7 [default = false]; 28 29 // The layers that make up the net. Each of their configurations, including 30 // connectivity and behavior, is specified as a LayerParameter. 31 //组成net的所有层。每个层的配置都包括连接属性和行为，由LayerParameter定义。 32 //ID设为100可以保证层描述置于末尾 33 repeated LayerParameter layer = 100; // ID 100 so layers are printed last. 34 35 // DEPRECATED: use 'layer' instead. 36 repeated V1LayerParameter layers = 2;//已淘汰 37 }
include/caffe/net.hpp
1 #ifndef CAFFE_NET_HPP_ 2 #define CAFFE_NET_HPP_ 3 4 #include <map> 5 #include <set> 6 #include <string> 7 #include <utility> 8 #include <vector> 9 10 #include "caffe/blob.hpp" 11 #include "caffe/common.hpp" 12 #include "caffe/layer.hpp" 13 #include "caffe/proto/caffe.pb.h" 14 15 namespace caffe { 16 17 /** 18 * @brief Connects Layer%s together into a directed acyclic graph (DAG) 19 * specified by a NetParameter. 20 * 21 * TODO(dox): more thorough description. 22 */ 23 template <typename Dtype> 24 class Net { 25 public: 26 explicit Net(const NetParameter& param); 27 explicit Net(const string& param_file, Phase phase, 28 const int level = 0, const vector<string>* stages = NULL); 29 virtual ~Net() {} 30 31 /// @brief Initialize a network with a NetParameter. 32 void Init(const NetParameter& param);//用NetParameter对象初始化Net 33 34 /** 35 * @brief Run Forward and return the result. 36 * 37 */ 38 //前向传播 39 const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL); 40 /// @brief DEPRECATED; use Forward() instead.已舍弃 41 const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL) { 42 LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() " 43 << "will be removed in a future version. Use Forward()."; 44 return Forward(loss); 45 } 46 47 /** 48 * The From and To variants of Forward and Backward operate on the 49 * (topological) ordering by which the net is specified. For general DAG 50 * networks, note that (1) computing from one layer to another might entail 51 * extra computation on unrelated branches, and (2) computation starting in 52 * the middle may be incorrect if all of the layers of a fan-in are not 53 * included. 54 */ 55 //前向传播的几种形式 56 Dtype ForwardFromTo(int start, int end); 57 Dtype ForwardFrom(int start); 58 Dtype ForwardTo(int end); 59 /// @brief DEPRECATED; set input blobs then use Forward() instead. 60 //弃用。指定输入Blob返回输出Blob。确定输入Blob后使用Forward（）替代。 61 const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom, 62 Dtype* loss = NULL); 63 64 /** 65 * @brief Zeroes out the diffs of all net parameters. 66 * Should be run before Backward. 67 */ 68 //将diff的所有权值参数清零。应在反向传播之前调用。 69 void ClearParamDiffs(); 70 71 /** 72 * The network backward should take no input and output, since it solely 73 * computes the gradient w.r.t the parameters, and the data has already been 74 * provided during the forward pass. 75 */ 76 //反向传播的几个函数。不需要输入或输出的Blob，数据已经在前向传播时提供。 77 void Backward(); 78 void BackwardFromTo(int start, int end); 79 void BackwardFrom(int start); 80 void BackwardTo(int end); 81 82 /** 83 * @brief Reshape all layers from bottom to top. 84 * 85 * This is useful to propagate changes to layer sizes without running 86 * a forward pass, e.g. to compute output feature size. 87 */ 88 //对Net中所有层自底向上的变形函数。无需进行一次前向传播就可以计算各层所需的Blob尺寸。 89 void Reshape(); 90 //前向传播+反向传播。输入为bottom blob，输出为loss。 91 Dtype ForwardBackward() { 92 Dtype loss; 93 Forward(&loss); 94 Backward(); 95 return loss; 96 } 97 98 /// @brief Updates the network weights based on the diff values computed. 99 void Update();//根据（Solver）准备好的diff更新网络权值 100 /** 101 * @brief Shares weight data of owner blobs with shared blobs. 102 * 103 * Note: this is called by Net::Init, and thus should normally not be 104 * called manually. 105 */ 106 void ShareWeights();//由初始化函数调用，不能随意调用 107 108 /** 109 * @brief For an already initialized net, implicitly copies (i.e., using no 110 * additional memory) the pre-trained layers from another Net. 111 */ 112 void ShareTrainedLayersWith(const Net* other); 113 // For an already initialized net, CopyTrainedLayersFrom() copies the already 114 // trained layers from another net parameter instance. 115 /** 116 * @brief For an already initialized net, copies the pre-trained layers from 117 * another Net. 118 */ 119 void CopyTrainedLayersFrom(const NetParameter& param); 120 void CopyTrainedLayersFrom(const string trained_filename); 121 void CopyTrainedLayersFromBinaryProto(const string trained_filename); 122 void CopyTrainedLayersFromHDF5(const string trained_filename); 123 /// @brief Writes the net to a proto. 124 void ToProto(NetParameter* param, bool write_diff = false) const; 125 /// @brief Writes the net to an HDF5 file. 126 void ToHDF5(const string& filename, bool write_diff = false) const; 127 128 /// @brief returns the network name.返回网络名称 129 inline const string& name() const { return name_; } 130 /// @brief returns the layer names 返回层名 131 inline const vector<string>& layer_names() const { return layer_names_; } 132 /// @brief returns the blob names 133 inline const vector<string>& blob_names() const { return blob_names_; } 134 /// @brief returns the blobs 135 inline const vector<shared_ptr<Blob<Dtype> > >& blobs() const { 136 return blobs_; 137 } 138 /// @brief returns the layers 139 inline const vector<shared_ptr<Layer<Dtype> > >& layers() const { 140 return layers_; 141 } 142 /// @brief returns the phase: TRAIN or TEST 143 inline Phase phase() const { return phase_; } 144 /** 145 * @brief returns the bottom vecs for each layer -- usually you won't 146 * need this unless you do per-layer checks such as gradients. 147 */ 148 inline const vector<vector<Blob<Dtype>*> >& bottom_vecs() const { 149 return bottom_vecs_;//grad check 需要调用返回每一层的输入。平时不需要。 150 } 151 /** 152 * @brief returns the top vecs for each layer -- usually you won't 153 * need this unless you do per-layer checks such as gradients. 154 */ 155 inline const vector<vector<Blob<Dtype>*> >& top_vecs() const { 156 return top_vecs_;//grad check 需要调用返回每一层的输出。平时不需要。 157 } 158 /// @brief returns the ids of the top blobs of layer i 159 inline const vector<int> & top_ids(int i) const { 160 CHECK_GE(i, 0) << "Invalid layer id"; 161 CHECK_LT(i, top_id_vecs_.size()) << "Invalid layer id"; 162 return top_id_vecs_[i]; 163 } 164 /// @brief returns the ids of the bottom blobs of layer i 165 inline const vector<int> & bottom_ids(int i) const { 166 CHECK_GE(i, 0) << "Invalid layer id"; 167 CHECK_LT(i, bottom_id_vecs_.size()) << "Invalid layer id"; 168 return bottom_id_vecs_[i]; 169 } 170 inline const vector<vector<bool> >& bottom_need_backward() const { 171 return bottom_need_backward_; 172 } 173 inline const vector<Dtype>& blob_loss_weights() const { 174 return blob_loss_weights_; 175 } 176 inline const vector<bool>& layer_need_backward() const { 177 return layer_need_backward_; 178 } 179 /// @brief returns the parameters 180 inline const vector<shared_ptr<Blob<Dtype> > >& params() const { 181 return params_; 182 } 183 inline const vector<Blob<Dtype>*>& learnable_params() const { 184 return learnable_params_; 185 } 186 /// @brief returns the learnable parameter learning rate multipliers 187 inline const vector<float>& params_lr() const { return params_lr_; } 188 inline const vector<bool>& has_params_lr() const { return has_params_lr_; } 189 /// @brief returns the learnable parameter decay multipliers 190 inline const vector<float>& params_weight_decay() const { 191 return params_weight_decay_; 192 } 193 inline const vector<bool>& has_params_decay() const { 194 return has_params_decay_; 195 } 196 const map<string, int>& param_names_index() const { 197 return param_names_index_; 198 } 199 inline const vector<int>& param_owners() const { return param_owners_; } 200 inline const vector<string>& param_display_names() const { 201 return param_display_names_; 202 } 203 /// @brief Input and output blob numbers 输入输出的blob数量 204 inline int num_inputs() const { return net_input_blobs_.size(); } 205 inline int num_outputs() const { return net_output_blobs_.size(); } 206 inline const vector<Blob<Dtype>*>& input_blobs() const { 207 return net_input_blobs_;//返回输入blob 208 } 209 inline const vector<Blob<Dtype>*>& output_blobs() const { 210 return net_output_blobs_;//返回输出blob 211 } 212 inline const vector<int>& input_blob_indices() const { 213 return net_input_blob_indices_;//返回输入blob下标 214 } 215 inline const vector<int>& output_blob_indices() const { 216 return net_output_blob_indices_;//返回输出blob下标 217 } 218 bool has_blob(const string& blob_name) const;//查找网络是否包含该blob 219 const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name) const;//如果有则找出 220 bool has_layer(const string& layer_name) const;//查找网络是否包含该层 221 const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name) const;//如果有则找出 222 223 void set_debug_info(const bool value) { debug_info_ = value; } 224 225 // Helpers for Init.以下用于初始化 226 /** 227 * @brief Remove layers that the user specified should be excluded given the current 228 * phase, level, and stage. 229 */ 230 //过滤掉用户指定的在某阶段、级别、状态下不应包含的层 231 static void FilterNet(const NetParameter& param, 232 NetParameter* param_filtered); 233 /// @brief return whether NetState state meets NetStateRule rule 234 //判断网络状态是否满足网络规则 235 static bool StateMeetsRule(const NetState& state, const NetStateRule& rule, 236 const string& layer_name); 237 238 // Invoked at specific points during an iteration 239 class Callback { 240 protected: 241 virtual void run(int layer) = 0; 242 243 template <typename T> 244 friend class Net; 245 }; 246 const vector<Callback*>& before_forward() const { return before_forward_; } 247 void add_before_forward(Callback* value) { 248 before_forward_.push_back(value); 249 } 250 const vector<Callback*>& after_forward() const { return after_forward_; } 251 void add_after_forward(Callback* value) { 252 after_forward_.push_back(value); 253 } 254 const vector<Callback*>& before_backward() const { return before_backward_; } 255 void add_before_backward(Callback* value) { 256 before_backward_.push_back(value); 257 } 258 const vector<Callback*>& after_backward() const { return after_backward_; } 259 void add_after_backward(Callback* value) { 260 after_backward_.push_back(value); 261 } 262 263 protected: 264 // Helpers for Init. 265 /// @brief Append a new top blob to the net.给网络追加新的输出blob 266 void AppendTop(const NetParameter& param, const int layer_id, 267 const int top_id, set<string>* available_blobs, 268 map<string, int>* blob_name_to_idx); 269 /// @brief Append a new bottom blob to the net.给网络追加新的输入blob 270 int AppendBottom(const NetParameter& param, const int layer_id, 271 const int bottom_id, set<string>* available_blobs, 272 map<string, int>* blob_name_to_idx); 273 /// @brief Append a new parameter blob to the net.给网络追加新的权值blob 274 void AppendParam(const NetParameter& param, const int layer_id, 275 const int param_id); 276 //以下几个函数显示调试信息 277 /// @brief Helper for displaying debug info in Forward. 278 void ForwardDebugInfo(const int layer_id); 279 /// @brief Helper for displaying debug info in Backward. 280 void BackwardDebugInfo(const int layer_id); 281 /// @brief Helper for displaying debug info in Update. 282 void UpdateDebugInfo(const int param_id); 283 284 /// @brief The network name网络名 285 string name_; 286 /// @brief The phase: TRAIN or TEST 网络当前阶段（训练 测试） 287 Phase phase_; 288 /// @brief Individual layers in the net 网络中的独立层 289 vector<shared_ptr<Layer<Dtype> > > layers_; 290 vector<string> layer_names_;//层名称 291 map<string, int> layer_names_index_;//层名称与索引映射表 292 vector<bool> layer_need_backward_;//标记某个层是否需要反向传播 293 /// @brief the blobs storing intermediate results between the layer. 294 vector<shared_ptr<Blob<Dtype> > > blobs_;//层与层之间传递数据通道 295 vector<string> blob_names_;//Blob名称 296 map<string, int> blob_names_index_;//Blob名称和索引映射表 297 vector<bool> blob_need_backward_;//标记Blob是否需要反向传播 298 /// bottom_vecs stores the vectors containing the input for each layer. 299 /// They don't actually host the blobs (blobs_ does), so we simply store 300 /// pointers. 301 //存放每个层的输入Blob指针（真正数据所有者为blobs_） 302 vector<vector<Blob<Dtype>*> > bottom_vecs_; 303 vector<vector<int> > bottom_id_vecs_; 304 vector<vector<bool> > bottom_need_backward_; 305 /// top_vecs stores the vectors containing the output for each layer 306 //存放每个层的输出Blob指针（真正数据所有者为blobs_） 307 vector<vector<Blob<Dtype>*> > top_vecs_; 308 vector<vector<int> > top_id_vecs_; 309 /// Vector of weight in the loss (or objective) function of each net blob, 310 /// indexed by blob_id. 311 //每个blob对全局损失（目标函数）的贡献权重 312 vector<Dtype> blob_loss_weights_; 313 vector<vector<int> > param_id_vecs_; 314 vector<int> param_owners_; 315 vector<string> param_display_names_; 316 vector<pair<int, int> > param_layer_indices_; 317 map<string, int> param_names_index_; 318 /// blob indices for the input and the output of the net 319 //网络输入输出blob的索引 320 vector<int> net_input_blob_indices_; 321 vector<int> net_output_blob_indices_; 322 vector<Blob<Dtype>*> net_input_blobs_; 323 vector<Blob<Dtype>*> net_output_blobs_; 324 /// The parameters in the network. 网络权值 325 vector<shared_ptr<Blob<Dtype> > > params_; 326 vector<Blob<Dtype>*> learnable_params_;//可训练的网络权值 327 /** 328 * The mapping from params_ -> learnable_params_: we have 329 * learnable_param_ids_.size() == params_.size(), 330 * and learnable_params_[learnable_param_ids_[i]] == params_[i].get() 331 * if and only if params_[i] is an "owner"; otherwise, params_[i] is a sharer 332 * and learnable_params_[learnable_param_ids_[i]] gives its owner. 333 */ 334 //params_ 到 learnable_params_映射: 335 //当且仅当params_[i]是所有者时，learnable_param_ids_.size() == params_.size(), 336 //learnable_params_[learnable_param_ids_[i]] == params_[i].get() 337 //否则 params_[i] 只是共享者，learnable_params_[learnable_param_ids_[i]] 给出所有者 338 vector<int> learnable_param_ids_; 339 /// the learning rate multipliers for learnable_params_ 340 vector<float> params_lr_;//学习率倍乘因子 341 vector<bool> has_params_lr_; 342 /// the weight decay multipliers for learnable_params_ 343 vector<float> params_weight_decay_;//权值衰减因子 344 vector<bool> has_params_decay_; 345 /// The bytes of memory used by this net 346 size_t memory_used_;//记录网络占用的内存大小 347 /// Whether to compute and display debug info for the net. 348 bool debug_info_;//是否显示调试信息 349 // Callbacks 350 vector<Callback*> before_forward_; 351 vector<Callback*> after_forward_; 352 vector<Callback*> before_backward_; 353 vector<Callback*> after_backward_; 354 355 DISABLE_COPY_AND_ASSIGN(Net);//禁止拷贝构造函数、赋值运算函数 356 }; 357 358 359 } // namespace caffe 360 361 #endif // CAFFE_NET_HPP_
未完待续……
内容来自赵永科《深度学习 21天实战caffe》
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