caffe——net.cpp——init()
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上一节讲的是solver的初始化,在其过程中,调用了net.cpp的init函数,下面,来看一下它是
怎么干活的。
template <typename Dtype>void Net<Dtype>::Init(const NetParameter& in_param) {//in_param,接solver.cpp的NetParameter CHECK(Caffe::root_solver() || root_net_) << "root_net_ needs to be set for all non-root solvers"; // Set phase from the state. phase_ = in_param.state().phase(); //phase_ = caffe::TRAIN // Filter layers based on their include/exclude rules and // the current NetState. NetParameter filtered_param; FilterNet(in_param, &filtered_param); //这个函数的作用就是检查in_param,如果in_param的layer符合要求,就赋给filtered_param //否则就不赋给filtered_param,你也可以认为这个函数的作用是移除in_param的指定层,将剩下 //的复制给filtered_param(这里面主要是针对included和exclude) LOG_IF(INFO, Caffe::root_solver()) << "Initializing net from parameters: " << std::endl << filtered_param.DebugString(); // Create a copy of filtered_param with splits added where necessary. NetParameter param; InsertSplits(filtered_param, ¶m); //函数从filtered_param读入新网络到param // Basically, build all the layers and set up their connections. name_ = param.name(); map<string, int> blob_name_to_idx; set<string> available_blobs; //关于set容器,可以看这个网址http://blog.csdn.net/wangran51/article/details/8836160 memory_used_ = 0; // For each layer, set up its input and output bottom_vecs_.resize(param.layer_size());//重置bottom_vecs_的大小,一下是函数前后对比 // bottom_vecs_ = std::vector of length 0, capacity 0// bottom_vecs_ = std::vector of length 9, capacity 9 = {// std::vector of length 0, capacity 0, std::vector of length 0, capacity 0, // std::vector of length 0, capacity 0, std::vector of length 0, capacity 0, // std::vector of length 0, capacity 0, std::vector of length 0, capacity 0, // std::vector of length 0, capacity 0, std::vector of length 0, capacity 0, // std::vector of length 0, capacity 0}//这里面九个元素指的是网络的train layer共有9个所以需要九个参数 top_vecs_.resize(param.layer_size()); bottom_id_vecs_.resize(param.layer_size()); param_id_vecs_.resize(param.layer_size()); top_id_vecs_.resize(param.layer_size()); bottom_need_backward_.resize(param.layer_size()); //差不多参数后面带‘_’的,代表的都是函数运行过程中的中间变量 for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) { //对layer的每一层进行处理 // For non-root solvers, whether this layer is shared from root_net_. bool share_from_root = !Caffe::root_solver() && root_net_->layers_[layer_id]->ShareInParallel(); // Inherit phase from net if unset. if (!param.layer(layer_id).has_phase()) { param.mutable_layer(layer_id)->set_phase(phase_); } // Setup layer. const LayerParameter& layer_param = param.layer(layer_id);//看 caffe.proto去~ 赶紧的 if (layer_param.propagate_down_size() > 0) { //propagate_down:Specifies on which bottoms the backpropagation should //be skipped. The size must be either 0 or equal to the number of bottoms. CHECK_EQ(layer_param.propagate_down_size(), layer_param.bottom_size()) << "propagate_down param must be specified " << "either 0 or bottom_size times "; } if (share_from_root) { LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net"; layers_.push_back(root_net_->layers_[layer_id]); layers_[layer_id]->SetShared(true); } else { layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param)); 创建layer并将layer_param的值赋值给layers_(具体见下篇博客) } layer_names_.push_back(layer_param.name()); LOG_IF(INFO, Caffe::root_solver()) << "Creating Layer " << layer_param.name(); bool need_backward = false; // Figure out this layer's input and output for (int bottom_id = 0; bottom_id < layer_param.bottom_size(); ++bottom_id) //上边创建了层,然后就该对bottom/top进行处理了 { const int blob_id = AppendBottom(param, layer_id, bottom_id, &available_blobs, &blob_name_to_idx); //见附1 // If a blob needs backward, this layer should provide it. need_backward |= blob_need_backward_[blob_id]; } int num_top = layer_param.top_size(); for (int top_id = 0; top_id < num_top; ++top_id) { AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx); //见附2 // Collect Input layer tops as Net inputs. if (layer_param.type() == "Input") { const int blob_id = blobs_.size() - 1; net_input_blob_indices_.push_back(blob_id); net_input_blobs_.push_back(blobs_[blob_id].get()); } } // If the layer specifies that AutoTopBlobs() -> true and the LayerParameter // specified fewer than the required number (as specified by // ExactNumTopBlobs() or MinTopBlobs()), allocate them here. Layer<Dtype>* layer = layers_[layer_id].get(); //vector<shared_ptr<Layer<Dtype> > > layers_; if (layer->AutoTopBlobs()) { const int needed_num_top = std::max(layer->MinTopBlobs(), layer->ExactNumTopBlobs()); for (; num_top < needed_num_top; ++num_top) { // Add "anonymous" top blobs -- do not modify available_blobs or // blob_name_to_idx as we don't want these blobs to be usable as input // to other layers. AppendTop(param, layer_id, num_top, NULL, NULL); } } // After this layer is connected, set it up. if (share_from_root) { // Set up size of top blobs using root_net_ const vector<Blob<Dtype>*>& base_top = root_net_->top_vecs_[layer_id]; const vector<Blob<Dtype>*>& this_top = this->top_vecs_[layer_id]; for (int top_id = 0; top_id < base_top.size(); ++top_id) { this_top[top_id]->ReshapeLike(*base_top[top_id]); LOG(INFO) << "Created top blob " << top_id << " (shape: " << this_top[top_id]->shape_string() << ") for shared layer " << layer_param.name(); } } else { layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]); //调用SetUp这一段的介绍看下一篇啊,要不然东西就太多了 } LOG_IF(INFO, Caffe::root_solver()) << "Setting up " << layer_names_[layer_id]; //更新向量blob_loss_weights for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) { if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) { blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0)); //调整blob_loss_weights_的大小,使其与top_id_vecs_[layer_id][top_id]一样大 } blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id); //loss函数返回loss_weight ——> 在模板类的SetUp方法中会调用SetLossWeights来设置 //其私有数据成员loss_,里面存储的其实是loss_weight LOG_IF(INFO, Caffe::root_solver()) << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string(); // top_vecs_[0][0]->shape_string() = "64 1 28 28 (50176)" if (layer->loss(top_id)) { LOG_IF(INFO, Caffe::root_solver()) << " with loss weight " << layer->loss(top_id); } memory_used_ += top_vecs_[layer_id][top_id]->count(); } LOG_IF(INFO, Caffe::root_solver()) << "Memory required for data: " << memory_used_ * sizeof(Dtype); const int param_size = layer_param.param_size(); const int num_param_blobs = layers_[layer_id]->blobs().size(); //param_size是Layermeter类型对象layer_param中ParamSpec param成员的个数, num_param_blobs是一//个Layer中learnable parameter blob的个数,param_size <= num_param_blobs CHECK_LE(param_size, num_param_blobs) << "Too many params specified for layer " << layer_param.name(); ParamSpec default_param_spec; for (int param_id = 0; param_id < num_param_blobs; ++param_id) { const ParamSpec* param_spec = (param_id < param_size) ? &layer_param.param(param_id) : &default_param_spec; const bool param_need_backward = param_spec->lr_mult() != 0; //是否反反向传播,主要看基础学习率,如果其为0,则不传播 need_backward |= param_need_backward; //由param_need_backward来决定need_backward是否为真,并且,只要有一次遍历使得 //need_backward为真,则这个for循环结束后,need_backward也为真 layers_[layer_id]->set_param_propagate_down(param_id, param_need_backward); } for (int param_id = 0; param_id < num_param_blobs; ++param_id) { AppendParam(param, layer_id, param_id);//附3 } // Finally, set the backward flag layer_need_backward_.push_back(need_backwar d); if (need_backward) { for (int top_id = 0; top_id < top_id_vecs_[layer_id].size(); ++top_id) { blob_need_backward_[top_id_vecs_[layer_id][top_id]] = true; } } } //大循环,对每个层都进行处理。 附4 // Go through the net backwards to determine which blobs contribute to the // loss. We can skip backward computation for blobs that don't contribute // to the loss. // Also checks if all bottom blobs don't need backward computation (possible // because the skip_propagate_down param) and so we can skip bacward // computation for the entire layer set<string> blobs_under_loss; set<string> blobs_skip_backp; //这两个参数你可能不太懂,别着急 往下看 //从上往下,遍历每一层 for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) { bool layer_contributes_loss = false; bool layer_skip_propagate_down = true; //为true,则表示当前layer的bottom blob不需要backward computation,即该层不需要backward computation。 //这个局部变量所表示的意义与caffe.proto里message Layerparameter的propagate_down的定义恰好相反。 //对于每一层的 top for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) { const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]]; if (layers_[layer_id]->loss(top_id) || (blobs_under_loss.find(blob_name) != blobs_under_loss.end())) { //blobs_under_loss的赋值是在下面,也就是上几层 layer_contributes_loss = true; } if (blobs_skip_backp.find(blob_name) == blobs_skip_backp.end()) { layer_skip_propagate_down = false; } if (layer_contributes_loss && !layer_skip_propagate_down) break; } // If this layer can skip backward computation, also all his bottom blobs // don't need backpropagation if (layer_need_backward_[layer_id] && layer_skip_propagate_down) { layer_need_backward_[layer_id] = false; for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size(); ++bottom_id) { bottom_need_backward_[layer_id][bottom_id] = false; } } if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; } if (Caffe::root_solver()) { if (layer_need_backward_[layer_id]) { LOG(INFO) << layer_names_[layer_id] << " needs backward computation."; } else { LOG(INFO) << layer_names_[layer_id] << " does not need backward computation."; } } for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size(); ++bottom_id) { if (layer_contributes_loss) { const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]]; blobs_under_loss.insert(blob_name); //判断当前层是否contributions to loss 是的话 就把名字插入 blobs_under_loss中 } else { bottom_need_backward_[layer_id][bottom_id] = false; } if (!bottom_need_backward_[layer_id][bottom_id]) { const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]]; blobs_skip_backp.insert(blob_name); //若本层不需要反反向传播,将名字插入blobs_skip_backp中。 } } } // Handle force_backward if needed. if (param.force_backward()) { for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) { layer_need_backward_[layer_id] = true; for (int bottom_id = 0; bottom_id < bottom_need_backward_[layer_id].size(); ++bottom_id) { bottom_need_backward_[layer_id][bottom_id] = bottom_need_backward_[layer_id][bottom_id] || layers_[layer_id]->AllowForceBackward(bottom_id); blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] = blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] || bottom_need_backward_[layer_id][bottom_id]; } for (int param_id = 0; param_id < layers_[layer_id]->blobs().size(); ++param_id) { layers_[layer_id]->set_param_propagate_down(param_id, true); } } } // In the end, all remaining blobs are considered output blobs. for (set<string>::iterator it = available_blobs.begin(); it != available_blobs.end(); ++it) { LOG_IF(INFO, Caffe::root_solver()) << "This network produces output " << *it; net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get()); net_output_blob_indices_.push_back(blob_name_to_idx[*it]); }//blob_names_.size() = 9 for (size_t blob_id = 0; blob_id < blob_names_.size(); ++blob_id) { blob_names_index_[blob_names_[blob_id]] = blob_id; //向 blob_names_index_里逐一添加元素 }//layer_names_.size()= 9 for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) { layer_names_index_[layer_names_[layer_id]] = layer_id; }/*(gdb) p blob_names_index_$95 = std::map with 9 elements = {["conv1"] = 2, ["conv2"] = 4, ["data"] = 0, ["ip1"] = 6, ["ip2"] = 7, ["label"] = 1, ["loss"] = 8, ["pool1"] = 3, ["pool2"] = 5}(gdb) p layer_names_index_$96 = std::map with 9 elements = {["conv1"] = 1, ["conv2"] = 3, ["ip1"] = 5, ["ip2"] = 7, ["loss"] = 8, ["mnist"] = 0, ["pool1"] = 2, ["pool2"] = 4, ["relu1"] = 6}*/ ShareWeights(); debug_info_ = param.debug_info(); LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";}template <typename Dtype>void Net<Dtype>::FilterNet(const NetParameter& param, NetParameter* param_filtered) { NetState net_state(param.state()); param_filtered->CopyFrom(param); param_filtered->clear_layer(); for (int i = 0; i < param.layer_size(); ++i) { const LayerParameter& layer_param = param.layer(i); const string& layer_name = layer_param.name(); CHECK(layer_param.include_size() == 0 || layer_param.exclude_size() == 0) << "Specify either include rules or exclude rules; not both."; // If no include rules are specified, the layer is included by default and // only excluded if it meets one of the exclude rules. bool layer_included = (layer_param.include_size() == 0); for (int j = 0; layer_included && j < layer_param.exclude_size(); ++j) { if (StateMeetsRule(net_state, layer_param.exclude(j), layer_name)) { layer_included = false; } } for (int j = 0; !layer_included && j < layer_param.include_size(); ++j) { if (StateMeetsRule(net_state, layer_param.include(j), layer_name)) { layer_included = true; } } if (layer_included) { param_filtered->add_layer()->CopyFrom(layer_param); } }}template <typename Dtype>bool Net<Dtype>::StateMeetsRule(const NetState& state, const NetStateRule& rule, const string& layer_name) { // Check whether the rule is broken due to phase. if (rule.has_phase()) { if (rule.phase() != state.phase()) { LOG_IF(INFO, Caffe::root_solver()) << "The NetState phase (" << state.phase() << ") differed from the phase (" << rule.phase() << ") specified by a rule in layer " << layer_name; return false; } } // Check whether the rule is broken due to min level. if (rule.has_min_level()) { if (state.level() < rule.min_level()) { LOG_IF(INFO, Caffe::root_solver()) << "The NetState level (" << state.level() << ") is above the min_level (" << rule.min_level() << ") specified by a rule in layer " << layer_name; return false; } } // Check whether the rule is broken due to max level. if (rule.has_max_level()) { if (state.level() > rule.max_level()) { LOG_IF(INFO, Caffe::root_solver()) << "The NetState level (" << state.level() << ") is above the max_level (" << rule.max_level() << ") specified by a rule in layer " << layer_name; return false; } } // Check whether the rule is broken due to stage. The NetState must // contain ALL of the rule's stages to meet it. for (int i = 0; i < rule.stage_size(); ++i) { // Check that the NetState contains the rule's ith stage. bool has_stage = false; for (int j = 0; !has_stage && j < state.stage_size(); ++j) { if (rule.stage(i) == state.stage(j)) { has_stage = true; } } if (!has_stage) { LOG_IF(INFO, Caffe::root_solver()) << "The NetState did not contain stage '" << rule.stage(i) << "' specified by a rule in layer " << layer_name; return false; } } // Check whether the rule is broken due to not_stage. The NetState must // contain NONE of the rule's not_stages to meet it. for (int i = 0; i < rule.not_stage_size(); ++i) { // Check that the NetState contains the rule's ith not_stage. bool has_stage = false; for (int j = 0; !has_stage && j < state.stage_size(); ++j) { if (rule.not_stage(i) == state.stage(j)) { has_stage = true; } } if (has_stage) { LOG_IF(INFO, Caffe::root_solver()) << "The NetState contained a not_stage '" << rule.not_stage(i) << "' specified by a rule in layer " << layer_name; return false; } } return true;}附1::AppendBottom// Helper for Net::Init: add a new bottom blob to the net.template <typename Dtype>int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id, const int bottom_id, set<string>* available_blobs, map<string, int>* blob_name_to_idx) { const LayerParameter& layer_param = param.layer(layer_id); const string& blob_name = layer_param.bottom(bottom_id); if (available_blobs->find(blob_name) == available_blobs->end()) { LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '" << layer_param.name() << "', bottom index " << bottom_id << ")"; } const int blob_id = (*blob_name_to_idx)[blob_name]; LOG_IF(INFO, Caffe::root_solver()) << layer_names_[layer_id] << " <- " << blob_name; bottom_vecs_[layer_id].push_back(blobs_[blob_id].get()); //调用shared_ptr类的get()方法提取存储在blobs_中的中间变量 bottom_id_vecs_[layer_id].push_back(blob_id); available_blobs->erase(blob_name); bool need_backward = blob_need_backward_[blob_id]; // Check if the backpropagation on bottom_id should be skipped if (layer_param.propagate_down_size() > 0) { need_backward = layer_param.propagate_down(bottom_id); ////propagate_down为true,则表示参与BP;否则,skip bp } bottom_need_backward_[layer_id].push_back(need_backward); return blob_id;}附2:AppendTop// Helper for Net::Init: add a new top blob to the net.template <typename Dtype>void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id, const int top_id, set<string>* available_blobs, map<string, int>* blob_name_to_idx) { shared_ptr<LayerParameter> layer_param( new LayerParameter(param.layer(layer_id))); //param.layer(layer_id),第layer_id层的layer参数 const string& blob_name = (layer_param->top_size() > top_id) ? layer_param->top(top_id) : "(automatic)"; // Check if we are doing in-place computation if (blob_name_to_idx && layer_param->bottom_size() > top_id && blob_name == layer_param->bottom(top_id)) { // In-place computation LOG_IF(INFO, Caffe::root_solver()) << layer_param->name() << " -> " << blob_name << " (in-place)"; top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get()); top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]); } else if (blob_name_to_idx && blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) { // If we are not doing in-place computation but have duplicated blobs, // raise an error. LOG(FATAL) << "Top blob '" << blob_name << "' produced by multiple sources."; } else { // Normal output. if (Caffe::root_solver()) { LOG(INFO) << layer_param->name() << " -> " << blob_name; //这里layer_param->name()指的是层的名字,blob_name指的是top或bottom的名字 } shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>()); //构造函数 new一个bolb_pointer const int blob_id = blobs_.size(); blobs_.push_back(blob_pointer); //blobs_是一个向量,值为vector of length 0, capacity 0 //在其尾部插入blob_pointer值为vector of length 1, capacity 1 = {{px = //0x6af420, pn = {pi_ = 0x6af480}}} //感觉一开始的blibs_就是一个向量,里面储存的是可以0指向blob的的只能指针,然后将指向 //blob_pointer的指针赋给了它 blob_names_.push_back(blob_name); blob_need_backward_.push_back(false); if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; } //*blob_name_to_idx= std::map with 1 elements = {["data"] = 0}/*blob_name_to_idx是一个局部变量,其实它是在当前layer的top blob 和下一层的bottom blob间起着一个桥梁作用。 blob_name_to_idx中元素的pair是从网络最开始一层一层搭建的过程中压入map的,其中的name和id都是不重复的。name是关键字——不重复是map数据结构的必然要求,id也是不重复的——0,1,2... blob_name_to_idx和blobs_一样,在"Normal output"的情形下,每次遍历到一个top blob的时候都会更新 参考 http://www.itdaan.com/blog/2016/03/26/726330.html*/ /// top_vecs stores the vectors containing the output for each layer //vector<vector<Blob<Dtype>*> > top_vecs_; //vector<vector<int> > top_id_vecs_; top_id_vecs_[layer_id].push_back(blob_id); top_vecs_[layer_id].push_back(blob_pointer.get()); } if (available_blobs) { available_blobs->insert(blob_name); }}/*总结:AppendTop主要干了以下几件事:1.new了bolb类的指针;2.将blob的指针,名字等压入blobs;3.更新map类型的blob_name_to_idx以及set类型的available_blobs;现在只是一个初始化过程,还没有进行 数据的处理,现在只是搭框架。*/附3:AppendParam函数 template <typename Dtype> void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id, const int param_id) { const LayerParameter& layer_param = layers_[layer_id]->layer_param();//模板类Layer的layer_param方法,返回Layerparameter类型成员 const int param_size = layer_param.param_size(); string param_name = (param_size > param_id) ? layer_param.param(param_id).name() : ""; if (param_name.size()) { param_display_names_.push_back(param_name);//vector<string> param_display_names_ 这里param_name获取的是PaParamSpec类型中的name成员,如果有name且非空,就把name压入该向量,否则就压入param_id } else { ostringstream param_display_name; param_display_name << param_id; param_display_names_.push_back(param_display_name.str()); } //Append 参数blob 每一次循环,net_param_id和param_id_vecs_都会更新 const int net_param_id = params_.size();//vector<shared_ptr<Blob<Dtype> > > params_--->The parameters in the network,整个网络的参数的id,!!!不管这个参数有没有non-emty name,是否参与share!!! params_.push_back(layers_[layer_id]->blobs()[param_id]);//将当前layer当前"参数blob"压入params_ --->vector<shared_ptr<Blob<Dtype> > > params_ param_id_vecs_[layer_id].push_back(net_param_id);//将整个网络的参数按层的形式来存储,存储的元素可以理解为params_这个向量的下标值(类型为整型) param_layer_indices_.push_back(make_pair(layer_id, param_id));//param_layer_indices_是向量,其元素为当layer_id 与当前param_id 组成的pair.vector<pair<int, int> > param_layer_indices_ //获取每个param_id所对应的Paramspec类型成员,如果param_id >= param_size 则返回default_param_spec。注意param_size <= num_param_blobs ParamSpec default_param_spec; const ParamSpec* param_spec = (layer_param.param_size() > param_id) ? &layer_param.param(param_id) : &default_param_spec; if (!param_size || !param_name.size() || (param_name.size() && param_names_index_.find(param_name) == param_names_index_.end())) { // This layer "owns" this parameter blob -- it is either anonymous // (i.e., not given a param_name) or explicitly given a name that we // haven't already seen. // 相反,如果param_name不为空,而且能够在param_names_index_中找到,说明这个parameter已经存在于之前的某个或者某些网络层里,说明这个parameter是共享于多个layer // 在caffe.proto的message ParamSpec里关于name的注释——>To share a parameter between two layers, give it a (non-empty) name, 可见,如果一个parameter是共享与多个网络层,那么它会有一个非空的name param_owners_.push_back(-1);//vector<int> param_owners_ 是一个存储parameter "onwer"的一个向量 ——> -1 表示当前Layer就是该parameter的"owner" //添加param_name if (param_name.size()) { //map<string, int> param_names_index_是整个网络的参数non-empty name与index的映射。 //注意,这个name是ParamSpec 类型中的name,而且,""To share a parameter between two layers, give it a (non-empty) name"",所以说这个map中存储的pair是<会被share的parameter_name, 其对应index> param_names_index_[param_name] = net_param_id;//map<string, int> param_names_index_ 。虽然每一次循环,net_param_id都会更新,但是net_param_id只有当param_name.size()>0时才会被压入向量param_names_index_ } //添加learnable_param const int learnable_param_id = learnable_params_.size();//vector<Blob<Dtype>*> learnable_params_ learnable_params_.push_back(params_[net_param_id].get());//压入learnable parameter ---> 在模板类layer中,定义了一个blobs_成员,其存储的就是learnable parameter。随后压入learnable_param_id learnable_param_ids_.push_back(learnable_param_id);//vector<int> learnable_param_ids_ has_params_lr_.push_back(param_spec->has_lr_mult());//vector<bool> has_params_lr_ has_params_decay_.push_back(param_spec->has_decay_mult()); params_lr_.push_back(param_spec->lr_mult());//vector<float> params_lr_ params_weight_decay_.push_back(param_spec->decay_mult()); } else { // Named param blob with name we've seen before: share params const int owner_net_param_id = param_names_index_[param_name];//因为"To share a parameter between two layers, give it a (non-empty) name",所以这句代码就是获取shared parameter的"owner" net_param_id param_owners_.push_back(owner_net_param_id);//vector<int> param_owners_ const pair<int, int>& owner_index = param_layer_indices_[owner_net_param_id];//只获取了那些shared的parameter,即具有non-empty name的parameter的pair<layer_id, param_id> const int owner_layer_id = owner_index.first; const int owner_param_id = owner_index.second; LOG_IF(INFO, Caffe::root_solver()) << "Sharing parameters '" << param_name << "' owned by " << "layer '" << layer_names_[owner_layer_id] << "', param " << "index " << owner_param_id; Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();//获取当前层的当前参数Blob Blob<Dtype>* owner_blob = layers_[owner_layer_id]->blobs()[owner_param_id].get();//获取owner layer的对应的参数blob const int param_size = layer_param.param_size(); if (param_size > param_id && (layer_param.param(param_id).share_mode() == ParamSpec_DimCheckMode_PERMISSIVE)) { // Permissive dimension checking -- only check counts are the same. CHECK_EQ(this_blob->count(), owner_blob->count()) << "Cannot share param '" << param_name << "' owned by layer '" << layer_names_[owner_layer_id] << "' with layer '" << layer_names_[layer_id] << "'; count mismatch. Owner layer param " << "shape is " << owner_blob->shape_string() << "; sharing layer " << "shape is " << this_blob->shape_string(); } else { // Strict dimension checking -- all dims must be the same. CHECK(this_blob->shape() == owner_blob->shape()) << "Cannot share param '" << param_name << "' owned by layer '" << layer_names_[owner_layer_id] << "' with layer '" << layer_names_[layer_id] << "'; shape mismatch. Owner layer param " << "shape is " << owner_blob->shape_string() << "; sharing layer " << "expects shape " << this_blob->shape_string(); } //获取owner layer的learnable_param_id,并且压入当前layer的向量learnable_param_ids_。 //而且在这里也没有把参数blob压入learnable_params_向量(只是将id压入learnable_param_ids_),从而避免当前layer与sharing layer之间关于shared parameter blob 的重复 const int learnable_param_id = learnable_param_ids_[owner_net_param_id];//vector<int> learnable_param_ids_ ; vector<float> params_lr_; learnable_param_ids_.push_back(learnable_param_id); if (param_spec->has_lr_mult()) { if (has_params_lr_[learnable_param_id]) { CHECK_EQ(param_spec->lr_mult(), params_lr_[learnable_param_id]) << "Shared param '" << param_name << "' has mismatched lr_mult."; } else { has_params_lr_[learnable_param_id] = true; params_lr_[learnable_param_id] = param_spec->lr_mult(); } } if (param_spec->has_decay_mult()) { if (has_params_decay_[learnable_param_id]) { CHECK_EQ(param_spec->decay_mult(), params_weight_decay_[learnable_param_id]) << "Shared param '" << param_name << "' has mismatched decay_mult."; } else { has_params_decay_[learnable_param_id] = true; params_weight_decay_[learnable_param_id] = param_spec->decay_mult(); } } } } ps:借鉴的这个网址http://blog.csdn.net/iamzhangzhuping/article/details/50537240附4:I0509 15:23:17.999642 6356 layer_factory.hpp:77] Creating layer mnist[New Thread 0x7ffff0bc6700 (LWP 6357)]I0509 15:23:18.007805 6356 net.cpp:91] Creating Layer mnistI0509 15:23:18.007853 6357 db_lmdb.cpp:38] Opened lmdb examples/mnist/mnist_train_lmdbI0509 15:23:18.007879 6356 net.cpp:399] mnist -> dataI0509 15:23:18.008003 6356 net.cpp:399] mnist -> labelI0509 15:23:18.008141 6356 data_layer.cpp:41] output data size: 64,1,28,28I0509 15:23:18.008430 6356 base_data_layer.cpp:69] Initializing prefetch[New Thread 0x7effebfff700 (LWP 6358)]I0509 15:23:18.009194 6356 base_data_layer.cpp:72] Prefetch initialized.I0509 15:23:18.009217 6356 net.cpp:141] Setting up mnistI0509 15:23:18.009263 6356 net.cpp:148] Top shape: 64 1 28 28 (50176)I0509 15:23:18.009282 6356 net.cpp:148] Top shape: 64 (64)I0509 15:23:18.009294 6356 net.cpp:156] Memory required for data: 200960I0509 15:23:18.009320 6356 layer_factory.hpp:77] Creating layer conv1I0509 15:23:18.009393 6356 net.cpp:91] Creating Layer conv1I0509 15:23:18.009428 6356 net.cpp:425] conv1 <- dataI0509 15:23:18.009490 6356 net.cpp:399] conv1 -> conv1I0509 15:23:18.009726 6356 net.cpp:141] Setting up conv1I0509 15:23:18.009752 6356 net.cpp:148] Top shape: 64 20 24 24 (737280)I0509 15:23:18.009764 6356 net.cpp:156] Memory required for data: 3150080I0509 15:23:18.009879 6356 layer_factory.hpp:77] Creating layer pool1I0509 15:23:18.009918 6356 net.cpp:91] Creating Layer pool1I0509 15:23:18.009935 6356 net.cpp:425] pool1 <- conv1I0509 15:23:18.009965 6356 net.cpp:399] pool1 -> pool1I0509 15:23:18.010017 6356 net.cpp:141] Setting up pool1I0509 15:23:18.010040 6356 net.cpp:148] Top shape: 64 20 12 12 (184320)I0509 15:23:18.010063 6356 net.cpp:156] Memory required for data: 3887360I0509 15:23:18.010081 6356 layer_factory.hpp:77] Creating layer conv2I0509 15:23:18.010113 6356 net.cpp:91] Creating Layer conv2I0509 15:23:18.010128 6356 net.cpp:425] conv2 <- pool1I0509 15:23:18.010161 6356 net.cpp:399] conv2 -> conv2I0509 15:23:18.010467 6358 data_layer.cpp:102] Prefetch batch: 1 ms.I0509 15:23:18.010498 6358 data_layer.cpp:103] Read time: 0.112 ms.I0509 15:23:18.010507 6358 data_layer.cpp:104] Transform time: 0.714 ms.I0509 15:23:18.011415 6358 data_layer.cpp:102] Prefetch batch: 0 ms.I0509 15:23:18.011430 6358 data_layer.cpp:103] Read time: 0.076 ms.I0509 15:23:18.011437 6358 data_layer.cpp:104] Transform time: 0.565 ms.I0509 15:23:18.011806 6356 net.cpp:141] Setting up conv2I0509 15:23:18.011836 6356 net.cpp:148] Top shape: 64 50 8 8 (204800)I0509 15:23:18.011848 6356 net.cpp:156] Memory required for data: 4706560I0509 15:23:18.011881 6356 layer_factory.hpp:77] Creating layer pool2I0509 15:23:18.011915 6356 net.cpp:91] Creating Layer pool2I0509 15:23:18.011934 6356 net.cpp:425] pool2 <- conv2I0509 15:23:18.011976 6356 net.cpp:399] pool2 -> pool2I0509 15:23:18.012018 6356 net.cpp:141] Setting up pool2I0509 15:23:18.012035 6356 net.cpp:148] Top shape: 64 50 4 4 (51200)I0509 15:23:18.012043 6356 net.cpp:156] Memory required for data: 4911360I0509 15:23:18.012054 6356 layer_factory.hpp:77] Creating layer ip1I0509 15:23:18.012079 6356 net.cpp:91] Creating Layer ip1I0509 15:23:18.012122 6356 net.cpp:425] ip1 <- pool2I0509 15:23:18.012156 6356 net.cpp:399] ip1 -> ip1I0509 15:23:18.012449 6358 data_layer.cpp:102] Prefetch batch: 0 ms.I0509 15:23:18.012465 6358 data_layer.cpp:103] Read time: 0.099 ms.I0509 15:23:18.012475 6358 data_layer.cpp:104] Transform time: 0.595 ms.I0509 15:23:18.035526 6356 net.cpp:141] Setting up ip1I0509 15:23:18.035575 6356 net.cpp:148] Top shape: 64 500 (32000)I0509 15:23:18.035583 6356 net.cpp:156] Memory required for data: 5039360I0509 15:23:18.035614 6356 layer_factory.hpp:77] Creating layer relu1I0509 15:23:18.035656 6356 net.cpp:91] Creating Layer relu1I0509 15:23:18.035681 6356 net.cpp:425] relu1 <- ip1I0509 15:23:18.035698 6356 net.cpp:386] relu1 -> ip1 (in-place)I0509 15:23:18.035717 6356 net.cpp:141] Setting up relu1I0509 15:23:18.035727 6356 net.cpp:148] Top shape: 64 500 (32000)I0509 15:23:18.035732 6356 net.cpp:156] Memory required for data: 5167360I0509 15:23:18.035739 6356 layer_factory.hpp:77] Creating layer ip2I0509 15:23:18.035755 6356 net.cpp:91] Creating Layer ip2I0509 15:23:18.035764 6356 net.cpp:425] ip2 <- ip1I0509 15:23:18.035806 6356 net.cpp:399] ip2 -> ip2I0509 15:23:18.036211 6356 net.cpp:141] Setting up ip2I0509 15:23:18.036257 6356 net.cpp:148] Top shape: 64 10 (640)I0509 15:23:18.036262 6356 net.cpp:156] Memory required for data: 5169920I0509 15:23:18.036274 6356 layer_factory.hpp:77] Creating layer lossI0509 15:23:18.036298 6356 net.cpp:91] Creating Layer lossI0509 15:23:18.036308 6356 net.cpp:425] loss <- ip2I0509 15:23:18.036320 6356 net.cpp:425] loss <- labelI0509 15:23:18.036336 6356 net.cpp:399] loss -> lossI0509 15:23:18.036363 6356 layer_factory.hpp:77] Creating layer lossI0509 15:23:18.036408 6356 net.cpp:141] Setting up lossI0509 15:23:18.036420 6356 net.cpp:148] Top shape: (1)I0509 15:23:18.036427 6356 net.cpp:151] with loss weight 1I0509 15:23:18.036437 6356 net.cpp:156] Memory required for data: 5169924
0 0
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