caffe源码解读之net

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    #include <algorithm>      #include <map>      #include <set>      #include <string>      #include <utility>      #include <vector>            #include "caffe/common.hpp"      #include "caffe/layer.hpp"      #include "caffe/net.hpp"      #include "caffe/proto/caffe.pb.h"      #include "caffe/util/insert_splits.hpp"      #include "caffe/util/io.hpp"      #include "caffe/util/math_functions.hpp"      #include "caffe/util/upgrade_proto.hpp"            #include "caffe/util/channel.hpp"      #include "caffe/util/mpi_functions.hpp"            #include "caffe/test/test_caffe_main.hpp"      #include "caffe/vision_layers.hpp"            namespace caffe {      /*     功能：调用Init函数初始化网络     输入：NetParameter& param     输出：无     */      template <typename Dtype>      Net<Dtype>::Net(const NetParameter& param) {        Init(param);      }      /*     功能：调用Init函数初始化网络     输入：string& param_file     输出：无     */      template <typename Dtype>      Net<Dtype>::Net(const string& param_file, Phase phase) {        NetParameter param;        ReadNetParamsFromTextFileOrDie(param_file, ¶m);        param.mutable_state()->set_phase(phase);        Init(param);      }      /*     功能：初始化网络     输入：NetParameter& in_param     输出：无     步骤：     <1> 调用InsertSplits()函数从in_param读入新网络到param     <2> 定义name_，blob_name_to_idx，available_blobs，num_layers     <3> param.input_size()返回输入层blob的个数;         param.input(i)表示第i个blob的名字;         param.layers_size()返回网络的层数。     <4> 对每一个输入层的blob：         产生一块和当前blob一样大的空间 e.g. imput_dim=[12 55 66 39 20 24 48 64]表示第一个blob的四个维数为 12 55 66 39，第二个为 20 24 48 64 接着blob_pointer指向这块空间         blob_pointer压到blobs_中 vector<shared_ptr<Blob<Dtype>>> blobs_         blob_name压到blob_names_中 vector<string> blob_names_         param.force_backward()压到blob_need_backward_中vector<bool> blob_need_backward_         i 压到 net_input_blob_indices_中 net_input_blob_indices_ -> vector         blob_pointer.get() 压到 net_input_blobs_中         注意与blobs_的区别         vector<shared_ptr<Blob<Dtype>>> blobs_         vector<Blob<Dtype>*> net_input_blobs_         shared_ptr类型的参数调用.get()则得到Blob*类型         map<string, int> blob_name_to_idx         初始化为输入层的每个blob的名字 set<string> available_blobs         计算所需内存 memory_used += blob_pointer->count()          <5> 存每一层的输入blob指针 vector<vector<Blob<Dtype>*> > bottom_vecs_         存每一层输入(bottom)的id vector<vector<int> > bottom_id_vecs_         存每一层输出(top)的blob vector<vector<Blob<Dtype>*> > top_vecs_         用网络的层数param.layers_size()去初始化上面四个变量         vector<vector<int> > top_id_vecs_     <6> 对第i层（很大的一个for循环）：         param.layers(i)返回的是关于第当前层的参数：         layer_param = param.layers(i)         把当前层的参数转换为shared_ptr<Layer<Dtype>>，并压入到layers_中         把当前层的名字压入到layer_names_：vector<string> layer_names_         判断当前层是否需要反馈 need_backward = param.force_backward()              下面开始产生当前层：分为处理bottom的blob和top的blob两个步骤         对第j个bottom的blob：             layer_param.bottom_size()存的是当前层的输入blob数量             layer_param.bottom(j)存的是第j个输入blob的名字             读取当前blob的id，其中blob_name_to_idx在输入层初始化过了             blob_name_to_idx[blob_name] = i             输出当前blob的名字             存入第j个输入blob的指针bottom_vecs_[i].push_back(blobs_[blob_id].get())             存入第j个输入blob的id bottom_id_vecs_[i].push_back(blob_id)             更新need_backward             从available_blobs中删除第j个blob的名字              对第j个top的blob：             layer_param.top_size()存的是当前层的输出blob数量             layer_param.top(j)存的是第j个输出blob的名字             判断是否进行同址计算             输出当前blob的名字             定义一块新的blob空间，用blob_pointer指向这块空间             把这个指针存入到blobs_中             把blob_name、force_backward、idx存入对应的容器中             向available_blobs插入当前blob的名字             top_vecs_[i]对于第i层，插入当前blob的指针             top_id_vecs_[i]对于第i层，插入当前blob的id         输出当前层位于top的blob的信息         计算所需内存         判断当前层i是否需要backward          <7> 所有名字在available_blobs中的blob为当前层的输出blob，存入net_output_blobs_中     <8> 建立每个blob的name和index的对应关系map：blob_names_index_     <9> 建立每个层的name和index的对应关系map：layer_names_index_     <10> 调用GetLearningRateAndWeightDecay函数     */      template <typename Dtype>      void Net<Dtype>::Init(const NetParameter& in_param) {        // Set phase from the state.        phase_ = in_param.state().phase();        // Filter layers based on their include/exclude rules and        // the current NetState.        NetParameter filtered_param;        FilterNet(in_param, &filtered_param);        LOG(INFO) << "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);        // Basically, build all the layers and set up their connections.        name_ = param.name();        map<string, int> blob_name_to_idx;//blob_name_to_idx是一个map,其关键字是不重复的        set<string> available_blobs;//available_blobs是一个set,其关键字是不重复的        CHECK(param.input_dim_size() == 0 || param.input_shape_size() == 0)            << "Must specify either input_shape OR deprecated input_dim, not both.";        if (param.input_dim_size() > 0) {          // Deprecated 4D dimensions.          CHECK_EQ(param.input_size() * 4, param.input_dim_size())              << "Incorrect input blob dimension specifications.";        } else {          CHECK_EQ(param.input_size(), param.input_shape_size())              << "Exactly one input_shape must be specified per input.";        }        memory_used_ = 0;        // set the input blobs        for (int input_id = 0; input_id < param.input_size(); ++input_id) {          const int layer_id = -1;  // inputs have fake layer ID -1          AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);        }        DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);        // For each layer, set up its input and output        bottom_vecs_.resize(param.layer_size());        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());              for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {          // Inherit phase from net if unset.          if (!param.layer(layer_id).has_phase()) {            param.mutable_layer(layer_id)->set_phase(phase_);//实参phase_是网络的phase,为模板类layer设置shape_属性            }          // Setup BN params implicitly.          if (param.layer(layer_id).type() == "BN") {            LayerParameter* layer_param = param.mutable_layer(layer_id);            if (layer_param->param_size() > 2) {              LOG(FATAL) << "Layer " << layer_param->name()                         << " must have no more than two specified params";            }            while (layer_param->param_size() < 4) {              ParamSpec* param = layer_param->add_param();              if (layer_param->param_size() <= 2) {                param->set_lr_mult(1);                param->set_decay_mult(0);              } else {                param->set_lr_mult(0);                param->set_decay_mult(0);              }            }          }          // Setup layer.          const LayerParameter& layer_param = param.layer(layer_id);       //检查LayerParameter类型propagate_down成员的个数师傅达标           if (layer_param.propagate_down_size() > 0) {            CHECK_EQ(layer_param.propagate_down_size(),                layer_param.bottom_size())                << "propagate_down param must be specified "                << "either 0 or bottom_size times ";          }          layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));          layer_names_.push_back(layer_param.name());          LOG(INFO) << "Creating Layer " << layer_param.name();          bool need_backward = false;                // Figure out this layer's input and output          #ifdef USE_MPI          vector<bool> source_layer_need_sync;          for (int bottom_id = 0; bottom_id < layer_param.bottom_size();               ++bottom_id) {                  const int blob_id = AppendBottom(param, layer_id, bottom_id,                                             &available_blobs, &blob_name_to_idx);            int src_layer_id = top_layer_indices_[blob_id].first;            if (src_layer_id>=0) source_layer_need_sync.push_back(layers_[src_layer_id]->need_sync());            if (source_layer_need_sync.size()>0){              CHECK_EQ(source_layer_need_sync.back(), source_layer_need_sync[0])                <<" blob "<<layer_param.bottom(0)                <<" and blob "<< layer_param.bottom(bottom_id)                <<" are from layers with different paralle mode. This is not supported.";            }            // If a blob needs backward, this layer should provide it.      /*blob_need_backward_,整个网络中，所有非参数blob，是否需要backward。注意，这里所说的所有非参数blob其实指的是AppendTop函数中遍历的所有top blob,并不是每一层的top+bottom,因为这一层的top就是下一层的bottom,网络是一层一层堆起来的。      */            need_backward |= blob_need_backward_[blob_id];          }                if (layers_[layer_id]->is_gathering()){            layers_[layer_id]->set_need_sync(false);          } else {            if(layers_[layer_id]->is_scattering()){              layers_[layer_id]->set_need_sync(true);            } else {              if ((source_layer_need_sync.size() > 0)) {                layers_[layer_id]->set_need_sync(source_layer_need_sync[0]);                LOG(INFO) << "This layer is inheriting previous layer's sync mode: " << source_layer_need_sync[0];              }            }          }          #else          for (int bottom_id = 0; bottom_id < layer_param.bottom_size();               ++bottom_id) {            const int blob_id = AppendBottom(param, layer_id, bottom_id,                                             &available_blobs, &blob_name_to_idx);            // If a blob needs backward, this layer should provide it.            need_backward |= blob_need_backward_[blob_id];          }          #endif                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);          }          // 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();          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.          LOG(INFO) << "Setting up " << layer_names_[layer_id];      //每次循环，都会更新向量blob_loss_weights            layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);          for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {      //blob_loss_weights_,每次遍历一个layer的时候，都会resize blob_loss_weights_, 然后调用模板类layer的loss函数返回loss_weight             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));            }      //top_id_vecs_中存储的最基本元素是blob_id ——> 每一个新的blob都会赋予其一个blob_id，但是这个blob_id可能是会有重复的             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(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();            if (layer->loss(top_id)) {              LOG(INFO) << "    with loss weight " << layer->loss(top_id);            }            memory_used_ += top_vecs_[layer_id][top_id]->count();          }          DLOG(INFO) << "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;//need backward 则为真。            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);      //设定一个Layer的parameter blob 是否需要计算diff backward--->set_param_propagate_down是模板类Layer的方法。            }          for (int param_id = 0; param_id < num_param_blobs; ++param_id) {       //添加parameter blob,如果当前layer没有parameter blob(num_param_blobs==0),比如RELU，那么就不进入循环，不添加parameter blob         //AppendParam只是执行为当前layer添加parameter blob的相关工作，并不会修改与backward的相关属性              AppendParam(param, layer_id, param_id);          }          // Finally, set the backward flag          layer_need_backward_.push_back(need_backward);      //在上述的AppendTop函数中，在遍历当前层的每一个top blob的时候都会将一个false（默认值）压入向量blob_need_backward_。在下面的代码中，如果这个layer need backward，则会更新blob_need_backward_          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;                    //special treatment for "Gather" layer              //This layer should be transparent to bp inferring.              if (strcmp(layers_[layer_id]->type(), "Gather")==0){                blob_need_backward_[top_id_vecs_[layer_id][top_id]]                    = blob_need_backward_[bottom_id_vecs_[layer_id][top_id]];              }            }          }        }        // 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 backward        // computation for the entire layer      // 需要注意的是，上述代码中关于backward设置的部分，是按照前向的顺序设置的，而下面的代码是按后向顺序修正前向设置的结果。        // 一个layer是否需要backward computation，主要依据两个方面：(1)该layer的top blob 是否参与loss的计算；(2):该layer的bottom blob 是否需要backward computation，比如Data层一般就不需要backward computation          set<string> blobs_under_loss;        set<string> blobs_skip_backp;        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。            for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {            //blob_names_整个网络中，所有非参数blob的name            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())) {              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;//只是跳出当前if语句           }          // 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_,整个网络所有网络层的bottom blob是否需要backward              bottom_need_backward_[layer_id][bottom_id] = false;            }          }          if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }          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();//修正前向设置的结果               ++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);//为blobs_under_loss添加新元素            } 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);//为blobs_skip_backp添加新元素            }          }        }        //Handle force_backward if needed.Netparameter类型的force_backward方法          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(INFO) << "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]);        }        for (size_t blob_id = 0; blob_id < blob_names_.size(); ++blob_id) {          blob_names_index_[blob_names_[blob_id]] = blob_id;      //第一次使用向量blob_names_index_,逐一添加元素，是一个map          }        for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) {          layer_names_index_[layer_names_[layer_id]] = layer_id;      //第一次使用向量layer_names_index_，逐一添加元素，是一个map          }        GetLearningRateAndWeightDecay();        debug_info_ = param.debug_info();        LOG(INFO) << "Network initialization done.";        LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);      }      //FilterNet()给定当前phase/level/stage，移除指定层       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;//如果不包含include，只要meet一个include_size(idx)即可            }          }          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;//如果包含include，只要符合一个include_size(idx)即可            }          }          if (layer_included) {            param_filtered->add_layer()->CopyFrom(layer_param);          }        }      }      //StateMeetsRule()中net的state是否满足NetStaterule        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(INFO) << "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(INFO) << "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(INFO) << "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(INFO) << "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(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i)                      << "' specified by a rule in layer " << layer_name;            return false;          }        }        return true;      }            // Helper for Net::Init: add a new input or top blob to the net.  (Inputs have      // layer_id == -1, tops have layer_id >= 0.)      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((layer_id >= 0) ?          (new LayerParameter(param.layer(layer_id))) : NULL);        const string& blob_name = layer_param ?            (layer_param->top_size() > top_id ?                layer_param->top(top_id) : "(automatic)") : param.input(top_id);        // Check if we are doing in-place computation        if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id &&            blob_name == layer_param->bottom(top_id)) {          // In-place computation          LOG(INFO) << 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) << "Duplicate blobs produced by multiple sources.";        } else {          // Normal output.          if (layer_param) {            LOG(INFO) << layer_param->name() << " -> " << blob_name;          } else {            LOG(INFO) << "Input " << top_id << " -> " << blob_name;          }          shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());      //blobs只是存储中间结果；每次遍历到一个top blob都会更新blob_id            const int blob_id = blobs_.size();          blobs_.push_back(blob_pointer);          blob_names_.push_back(blob_name);          blob_need_backward_.push_back(false);          top_layer_indices_.push_back(make_pair(layer_id, blob_id));      /*     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       */      //添加新元素-->map可以通过下标访问符为（关联）容器添加新元素           if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }          if (layer_id == -1) {            // Set the (explicitly specified) dimensions of the input blob.            if (param.input_dim_size() > 0) {              blob_pointer->Reshape(param.input_dim(top_id * 4),                                    param.input_dim(top_id * 4 + 1),                                    param.input_dim(top_id * 4 + 2),                                    param.input_dim(top_id * 4 + 3));            } else {              blob_pointer->Reshape(param.input_shape(top_id));            }            net_input_blob_indices_.push_back(blob_id);      //当layer_id==-1时，即当前层为输入层的时候，会向net_input_blob_indices_里添加新元素,即add new input blob             net_input_blobs_.push_back(blob_pointer.get());          } else {            top_id_vecs_[layer_id].push_back(blob_id);      //当layer_id ！=-1时，即当前层不是输入层的时候，会向net_input_blob_indices_里添加新元素,即add new top blob             top_vecs_[layer_id].push_back(blob_pointer.get());          }              }        if (available_blobs) { available_blobs->insert(blob_name); }      }            // 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 blob input " << blob_name                     << " (at index " << bottom_id << ") to layer " << layer_id;        }      //blob_name_to_idx是一个map,其关键字是不重复的。blob_name_to_idx在输入层初始化      //过了-->*blob_name_to_idx)[blob_name] = blob_id        const int blob_id = (*blob_name_to_idx)[blob_name];        LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name;      //存储整个网络所有网络层的bottom blob指针,实际上存储的是前一层的top，因为网络是一层一层堆起来的        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 propagate_down = true;        // Check if the backpropagation on bottom_id should be skipped        if (layer_param.propagate_down_size() > 0)          propagate_down = layer_param.propagate_down(bottom_id);        const bool need_backward = blob_need_backward_[blob_id] &&                                propagate_down;//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;      }            template <typename Dtype>      void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,                                   const int param_id) {      //模板类Layer的layer_param方法，返回Layerparameter类型成员        const LayerParameter& layer_param = layers_[layer_id]->layer_param();        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()) {      //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            param_display_names_.push_back(param_name);        } else {          ostringstream param_display_name;          param_display_name << param_id;          param_display_names_.push_back(param_display_name.str());        }      //params_,整个网络的参数blob。 不管这个参数有没有non-emty name，是否参与share!!!          const int net_param_id = params_.size(); //Append 参数blob 每一次循环，net_param_id和param_id_vecs_都会更新          params_.push_back(layers_[layer_id]->blobs()[param_id]);      //param_id_vecs_,存储的基本元素是net_param_id，每遍历一个参数blob,net_param_id和param_id_vecs_都会更新        param_id_vecs_[layer_id].push_back(net_param_id);      //param_layer_indices_其元素为当layer_id 与当前param_id 组成的pair.vector<pair<int, int> > param_layer_indices_        param_layer_indices_.push_back(make_pair(layer_id, param_id));        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.      /*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" ,     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);      //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_      */          }        } else {          // Named param blob with name we've seen before: share params      //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            const int owner_net_param_id = param_names_index_[param_name];          param_owners_.push_back(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 pair<int, int>& owner_index =              param_layer_indices_[owner_net_param_id];          const int owner_layer_id = owner_index.first;          const int owner_param_id = owner_index.second;          LOG(INFO) << "Sharing parameters '" << param_name << "' owned by "                    << "layer '" << layer_names_[owner_layer_id] << "', param "                    << "index " << owner_param_id;      //获取当前层的当前参数Blob            Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();      //获取owner layer的对应的参数blob          Blob<Dtype>* owner_blob =              layers_[owner_layer_id]->blobs()[owner_param_id].get();          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())                << "Shared parameter blobs must have the same count.";          } else {            // Strict dimension checking -- all dims must be the same.            CHECK(this_blob->shape() == owner_blob->shape());          }          layers_[layer_id]->blobs()[param_id]->ShareData(              *layers_[owner_layer_id]->blobs()[owner_param_id]);        }      }      /*     功能：收集学习速率和权重衰减，即更新params_、params_lr_和params_weight_decay_     输入：无     输出：无     步骤：对每一层     1. 把当前层的所有blob存入params_中     params_// The parameters in the network     2. 如果有lr, 则把当前层的所有blob的lr存入params_lr_中; 否则, lr默认为1     3. 如果有 weight_decay，则把当前层的所有 blob 的 weight_decay存入 params_weight_decay_ 中     4. 否则，weight_decay 默认为1     */      template <typename Dtype>      void Net<Dtype>::GetLearningRateAndWeightDecay() {        LOG(INFO) << "Collecting Learning Rate and Weight Decay.";        ParamSpec default_param_spec;        for (int i = 0; i < layers_.size(); ++i) {          vector<shared_ptr<Blob<Dtype> > >& layer_blobs = layers_[i]->blobs();          for (int j = 0; j < layer_blobs.size(); ++j) {            const ParamSpec* param_spec =                (layers_[i]->layer_param().param_size() > j) ?                &layers_[i]->layer_param().param(j) : &default_param_spec;            params_lr_.push_back(param_spec->lr_mult());            params_weight_decay_.push_back(param_spec->decay_mult());          }        }      }            template <typename Dtype>      Dtype Net<Dtype>::ForwardFromTo(int start, int end) {        CHECK_GE(start, 0);        CHECK_LT(end, layers_.size());        Dtype loss = 0;        if (debug_info_) {          for (int i = 0; i < net_input_blobs_.size(); ++i) {            InputDebugInfo(i);          }        }        for (int i = start; i <= end; ++i) {          // LOG(ERROR) << "Forwarding " << layer_names_[i];          Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);          loss += layer_loss;          if (debug_info_) { ForwardDebugInfo(i); }        }            #ifdef USE_CUDNN        if (Caffe::mode() == Caffe::GPU)          CuDNNConvolutionLayer<Dtype>::RuntimeOptimize(1000);      #endif        return loss;      }            template <typename Dtype>      Dtype Net<Dtype>::ForwardFrom(int start) {        return ForwardFromTo(start, layers_.size() - 1);      }            template <typename Dtype>      Dtype Net<Dtype>::ForwardTo(int end) {        return ForwardFromTo(0, end);      }      /*     功能：前馈预先填满，即预先进行一次前馈     输入：Dtype* loss     输出：net_output_blobs_，前馈后的输出层blob：vector     */      template <typename Dtype>      const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {        if (loss != NULL) {          *loss = ForwardFromTo(0, layers_.size() - 1);        } else {          ForwardFromTo(0, layers_.size() - 1);        }        return net_output_blobs_;      }      /*     功能：把网络输入层的blob读到net_input_blobs_，然后进行前馈，计算出loss     输入：整个网络输入层的blob     输出：整个网络输出层的blob     */      template <typename Dtype>      const vector<Blob<Dtype>*>& Net<Dtype>::Forward(          const vector<Blob<Dtype>*> & bottom, Dtype* loss) {        // Copy bottom to internal bottom        for (int i = 0; i < bottom.size(); ++i) {          net_input_blobs_[i]->CopyFrom(*bottom[i]);        }        return ForwardPrefilled(loss);      }      /*     功能：Forward的重载，只是输入层的blob以string的格式传入     */      template <typename Dtype>      string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {        BlobProtoVector blob_proto_vec;        if (net_input_blobs_.size()) {          blob_proto_vec.ParseFromString(input_blob_protos);          CHECK_EQ(blob_proto_vec.blobs_size(), net_input_blobs_.size())              << "Incorrect input size.";          for (int i = 0; i < blob_proto_vec.blobs_size(); ++i) {            net_input_blobs_[i]->FromProto(blob_proto_vec.blobs(i));          }        }        ForwardPrefilled(loss);        blob_proto_vec.Clear();        for (int i = 0; i < net_output_blobs_.size(); ++i) {          net_output_blobs_[i]->ToProto(blob_proto_vec.add_blobs());        }        string output;        blob_proto_vec.SerializeToString(&output);        return output;      }            template <typename Dtype>      void Net<Dtype>::BackwardFromTo(int start, int end) {        CHECK_GE(end, 0);        CHECK_LT(start, layers_.size());              for (int i = start; i >= end; --i) {          if (layer_need_backward_[i]) {            layers_[i]->Backward(                top_vecs_[i], bottom_need_backward_[i], bottom_vecs_[i]);            if (debug_info_) { BackwardDebugInfo(i); }            #ifdef USE_MPI            if ((Caffe::parallel_mode() == Caffe::MPI) && (Caffe::remaining_sub_iter() == 0)) {              for (int n = 0; n < param_layer_indices_.size(); ++n) {                bool ready_for_sync = false;                      //decide whether we need to sync the gradient of this blob                if ((param_layer_indices_[n].first == i)) {                  if (param_owners_[n] == -1) {                    ready_for_sync = true;                  } else {                    // this blob is a shared one, we need to make sure no more gradients will be                    // accumulated to it before transmission                    int owner_id = param_owners_[n];                    ready_for_sync = true;                    for (int m = n - 1; m >= 0; --m) {                      if ((param_owners_[m] == owner_id) && (param_layer_indices_[m].first >= end)) {                        // there are still layers holding this shared blob,                        // not secure the do the transmission                        ready_for_sync = false;                        break;                      }                    }                  }                }                //sync gradient                if (ready_for_sync && layers_[i]->need_sync())                  caffe_iallreduce(                      this->params_[n]->mutable_cpu_diff(),                      this->params_[n]->count()                  );              }            }      #endif //USE_MPI                }        }      }            template <typename Dtype>      void Net<Dtype>::InputDebugInfo(const int input_id) {        const Blob<Dtype>& blob = *net_input_blobs_[input_id];        const string& blob_name = blob_names_[net_input_blob_indices_[input_id]];        const Dtype data_abs_val_mean = blob.asum_data() / blob.count();        LOG(INFO) << "    [Forward] "           << "Input " << blob_name << " data: " << data_abs_val_mean;      }            template <typename Dtype>      void Net<Dtype>::ForwardDebugInfo(const int layer_id) {        for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {          const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];          const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];          const Dtype data_abs_val_mean = blob.asum_data() / blob.count();          LOG(INFO) << "    [Forward] "             << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name             << " data: " << data_abs_val_mean;        }        for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();             ++param_id) {          const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];          const int net_param_id = param_id_vecs_[layer_id][param_id];          const string& blob_name = param_display_names_[net_param_id];          const Dtype data_abs_val_mean = blob.asum_data() / blob.count();          LOG(INFO) << "    [Forward] "             << "Layer " << layer_names_[layer_id] << ", param blob " << blob_name             << " data: " << data_abs_val_mean;        }      }            template <typename Dtype>      void Net<Dtype>::BackwardDebugInfo(const int layer_id) {        const vector<Blob<Dtype>*>& bottom_vec = bottom_vecs_[layer_id];        for (int bottom_id = 0; bottom_id < bottom_vec.size(); ++bottom_id) {          if (!bottom_need_backward_[layer_id][bottom_id]) { continue; }          const Blob<Dtype>& blob = *bottom_vec[bottom_id];          const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];          const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();          LOG(INFO) << "    [Backward] "              << "Layer " << layer_names_[layer_id] << ", bottom blob " << blob_name              << " diff: " << diff_abs_val_mean;        }        for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();             ++param_id) {          if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }          const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];          const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();          LOG(INFO) << "    [Backward] "              << "Layer " << layer_names_[layer_id] << ", param blob " << param_id              << " diff: " << diff_abs_val_mean;        }      }            template <typename Dtype>      void Net<Dtype>::UpdateDebugInfo(const int param_id) {        const Blob<Dtype>& blob = *params_[param_id];        const int param_owner = param_owners_[param_id];        const string& layer_name = layer_names_[param_layer_indices_[param_id].first];        const string& param_display_name = param_display_names_[param_id];        const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();        if (param_owner < 0) {          const Dtype data_abs_val_mean = blob.asum_data() / blob.count();          LOG(INFO) << "    [Update] Layer " << layer_name              << ", param " << param_display_name              << " data: " << data_abs_val_mean << "; diff: " << diff_abs_val_mean;        } else {          const string& owner_layer_name =              layer_names_[param_layer_indices_[param_owner].first];          LOG(INFO) << "    [Update] Layer " << layer_name              << ", param blob " << param_display_name              << " (owned by layer " << owner_layer_name << ", "              << "param " << param_display_names_[param_owners_[param_id]] << ")"              << " diff: " << diff_abs_val_mean;        }      }      /*     功能：从Other网络复制某些层     步骤：对Other网络的第i层（源层）：     1. 定义一个Layer的指针指向第i层     2. 读取第i层（源层）的名字     3. 找通过名字来找目标层如果没找到，即target_layer_id == layer_names_.size()则忽略Other的第i层，即Other的第i层不需要share给网络     4. 如果找到了，即other的第i层需要share给网络，则把目标层的所有blob读到target_blobs中         1判断目标层和源层的blob数量是否相等         2判断每个blob大小是否相等         3调用ShareData函数把源层的blob赋给目标层的blob          */      template <typename Dtype>      void Net<Dtype>::ShareTrainedLayersWith(const Net* other) {        int num_source_layers = other->layers().size();        for (int i = 0; i < num_source_layers; ++i) {          Layer<Dtype>* source_layer = other->layers()[i].get();          const string& source_layer_name = other->layer_names()[i];          int target_layer_id = 0;          while (target_layer_id != layer_names_.size() &&              layer_names_[target_layer_id] != source_layer_name) {            ++target_layer_id;          }          if (target_layer_id == layer_names_.size()) {            DLOG(INFO) << "Ignoring source layer " << source_layer_name;            continue;          }          DLOG(INFO) << "Copying source layer " << source_layer_name;          vector<shared_ptr<Blob<Dtype> > >& target_blobs =              layers_[target_layer_id]->blobs();          CHECK_EQ(target_blobs.size(), source_layer->blobs().size())              << "Incompatible number of blobs for layer " << source_layer_name;          for (int j = 0; j < target_blobs.size(); ++j) {            Blob<Dtype>* source_blob = source_layer->blobs()[j].get();            CHECK(target_blobs[j]->shape() == source_blob->shape());            target_blobs[j]->ShareData(*source_blob);          }        }      }            template <typename Dtype>      void Net<Dtype>::BackwardFrom(int start) {        BackwardFromTo(start, 0);      }            template <typename Dtype>      void Net<Dtype>::BackwardTo(int end) {        BackwardFromTo(layers_.size() - 1, end);      }      /*     功能：对整个网络进行反向传播     */      template <typename Dtype>      void Net<Dtype>::Backward() {        BackwardFromTo(layers_.size() - 1, 0);        if (debug_info_) {          Dtype asum_data = 0, asum_diff = 0, sumsq_data = 0, sumsq_diff = 0;          for (int i = 0; i < params_.size(); ++i) {            if (param_owners_[i] >= 0) { continue; }            asum_data += params_[i]->asum_data();            asum_diff += params_[i]->asum_diff();            sumsq_data += params_[i]->sumsq_data();            sumsq_diff += params_[i]->sumsq_diff();          }          const Dtype l2norm_data = std::sqrt(sumsq_data);          const Dtype l2norm_diff = std::sqrt(sumsq_diff);          LOG(ERROR) << "    [Backward] All net params (data, diff): "              << "L1 norm = (" << asum_data << ", " << asum_diff << "); "              << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";        }      }            template <typename Dtype>      void Net<Dtype>::Reshape() {        for (int i = 0; i < layers_.size(); ++i) {          layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);        }            #ifdef USE_CUDNN        if (Caffe::mode() == Caffe::GPU)          CuDNNConvolutionLayer<Dtype>::RuntimeOptimize(1000);      #endif      }      /*     功能：和ShareTrainedLayersWith一样     步骤：不同的是调用FromProto函数把源层的blob赋给目标层的blob     */      template <typename Dtype>      void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {        int num_source_layers = param.layer_size();        for (int i = 0; i < num_source_layers; ++i) {          const LayerParameter& source_layer = param.layer(i);          const string& source_layer_name = source_layer.name();          int target_layer_id = 0;          while (target_layer_id != layer_names_.size() &&              layer_names_[target_layer_id] != source_layer_name) {            ++target_layer_id;          }          if (target_layer_id == layer_names_.size()) {            DLOG(INFO) << "Ignoring source layer " << source_layer_name;            continue;          }          DLOG(INFO) << "Copying source layer " << source_layer_name;          vector<shared_ptr<Blob<Dtype> > >& target_blobs =              layers_[target_layer_id]->blobs();          CHECK_EQ(target_blobs.size(), source_layer.blobs_size())              << "Incompatible number of blobs for layer " << source_layer_name;          for (int j = 0; j < target_blobs.size(); ++j) {            const bool kReshape = false;            target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);          }        }      }      /*     功能：从文件中读入NetParameter param，然后调用CopyTrainedLayersFrom()     */      template <typename Dtype>      void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {        NetParameter param;        ReadNetParamsFromBinaryFileOrDie(trained_filename, ¶m);        CopyTrainedLayersFrom(param);      }      /*     功能：把网络的参数存入prototxt中     步骤：     1. 设置网络的名字：param->set_name(name_)     2. 加入输入层blob的名字     3. 对于第i层：         1加入bottom的blob的名字         2加入top的blob的名字         3写到proto中          */      template <typename Dtype>      void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {        param->Clear();        param->set_name(name_);        // Add bottom and top        for (int i = 0; i < net_input_blob_indices_.size(); ++i) {          param->add_input(blob_names_[net_input_blob_indices_[i]]);        }        DLOG(INFO) << "Serializing " << layers_.size() << " layers";        for (int i = 0; i < layers_.size(); ++i) {          LayerParameter* layer_param = param->add_layer();      //bottom_id_vecs_存储整个网络所有网络层的bottom blob的ID          for (int j = 0; j < bottom_id_vecs_[i].size(); ++j) {            layer_param->add_bottom(blob_names_[bottom_id_vecs_[i][j]]);          }          for (int j = 0; j < top_id_vecs_[i].size(); ++j) {            layer_param->add_top(blob_names_[top_id_vecs_[i][j]]);          }          layers_[i]->ToProto(layer_param, write_diff);        }      }      /*     功能：更新params_中blob的值     */      template <typename Dtype>      void Net<Dtype>::Update() {        // First, accumulate the diffs of any shared parameters into their owner's        // diff. (Assumes that the learning rate, weight decay, etc. have already been        // accounted for in the current diff.)        for (int i = 0; i < params_.size(); ++i) {          if (param_owners_[i] < 0) { continue; }          if (debug_info_) { UpdateDebugInfo(i); }          const int count = params_[i]->count();          const Dtype* this_diff;          Dtype* owner_diff;          switch (Caffe::mode()) {          case Caffe::CPU:            this_diff = params_[i]->cpu_diff();            owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();            caffe_add(count, this_diff, owner_diff, owner_diff);            break;          case Caffe::GPU:      #ifndef CPU_ONLY            this_diff = params_[i]->gpu_diff();            owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();            caffe_gpu_add(count, this_diff, owner_diff, owner_diff);      #else            NO_GPU;      #endif            break;          default:            LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();          }        }        // Now, update the owned parameters.        for (int i = 0; i < params_.size(); ++i) {          if (param_owners_[i] >= 0) { continue; }          if (debug_info_) { UpdateDebugInfo(i); }          params_[i]->Update();        }      }      /*     功能：判断是否存在名字为blob_name的blob     */      template <typename Dtype>      bool Net<Dtype>::has_blob(const string& blob_name) const {        return blob_names_index_.find(blob_name) != blob_names_index_.end();      }      /*     功能：给一个blob的名字，返回这个blob的指针     */      template <typename Dtype>      const shared_ptr<Blob<Dtype> > Net<Dtype>::blob_by_name(          const string& blob_name) const {        shared_ptr<Blob<Dtype> > blob_ptr;        if (has_blob(blob_name)) {          blob_ptr = blobs_[blob_names_index_.find(blob_name)->second];        } else {          blob_ptr.reset((Blob<Dtype>*)(NULL));          LOG(WARNING) << "Unknown blob name " << blob_name;        }        return blob_ptr;      }      /*     功能：判断是否存在名字为layer_name的layer     */      template <typename Dtype>      bool Net<Dtype>::has_layer(const string& layer_name) const {        return layer_names_index_.find(layer_name) != layer_names_index_.end();      }            /*     功能：给一个layer的名字，返回这个layer的指针     */      template <typename Dtype>      const shared_ptr<Layer<Dtype> > Net<Dtype>::layer_by_name(          const string& layer_name) const {        shared_ptr<Layer<Dtype> > layer_ptr;        if (has_layer(layer_name)) {          layer_ptr = layers_[layer_names_index_.find(layer_name)->second];        } else {          layer_ptr.reset((Layer<Dtype>*)(NULL));          LOG(WARNING) << "Unknown layer name " << layer_name;        }        return layer_ptr;      }            INSTANTIATE_CLASS(Net);            }  // namespace caffe