Caffe net.hpp net.cpp学习

主要定义了一个模板类net


首先看一下数据成员：

/// @brief The network name
  string name_;
/// @brief The phase: TRAIN or TEST
  Phase phase_;


/// @brief Individual layers in the net
  vector<shared_ptr<Layer<Dtype> > > layers_;
  vector<string> layer_names_;
  map<string, int> layer_names_index_;
  vector<bool> layer_need_backward_;

/// @brief the blobs storing intermediate results between the layer. 
  vector<shared_ptr<Blob<Dtype> > > blobs_;//blobs_存储的是中间结果，是针对整个网络中所有非参数blob而设计的一个变量。我觉得params_存储的也是中间结果
  vector<string> blob_names_;//整个网络中，所有非参数blob的name。
  map<string, int> blob_names_index_;
  vector<bool> 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。

/// bottom_vecs stores the vectors containing the input for each layer.
/// They don't actually host the blobs (blobs_ does), so we simply store
/// pointers.
  vector<vector<Blob<Dtype>*> > bottom_vecs_;//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
  vector<vector<int> > bottom_id_vecs_;//存储整个网络所有网络层的bottom blob的ID
  vector<vector<bool> > bottom_need_backward_;//整个网络所有网络层的bottom blob是否需要backward

/// top_vecs stores the vectors containing the output for each layer
  vector<vector<Blob<Dtype>*> > top_vecs_;//存储整个网络所有网络层的top blob指针.
  vector<vector<int> > top_id_vecs_;//存储整个网络所有网络层的top blob的ID.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,top_vecs_则与之对应，但是这个blob_id可能是会有重复的（因为in-place）

/// Vector of weight in the loss (or objective) function of each net blob,
/// indexed by blob_id.
  vector<Dtype> 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
  vector<vector<int> > 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
  vector<int> param_owners_;//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"
  vector<string> param_display_names_;
  vector<pair<int, int> > 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_
  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。

/// blob indices for the input and the output of the net整个网络的输入输出blob以及ID
  vector<int> net_input_blob_indices_;
  vector<int> net_output_blob_indices_;
  vector<Blob<Dtype>*> net_input_blobs_;
  vector<Blob<Dtype>*> net_output_blobs_;

/// The parameters in the network.
  vector<shared_ptr<Blob<Dtype> > > 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!!!
  vector<Blob<Dtype>*> learnable_params_;//
  /**
   * The mapping from params_ -> learnable_params_: we have
   * learnable_param_ids_.size() == params_.size(),
   * and learnable_params_[learnable_param_ids_[i]] == params_[i].get()
   * if and only if params_[i] is an "owner"; otherwise, params_[i] is a sharer
   * and learnable_params_[learnable_param_ids_[i]] gives its owner.
   */
  vector<int> learnable_param_ids_;

/// the learning rate multipliers for learnable_params_
  vector<float> params_lr_;
  vector<bool> has_params_lr_;

/// the weight decay multipliers for learnable_params_
  vector<float> params_weight_decay_;
  vector<bool> has_params_decay_;

/// The bytes of memory used by this net
  size_t memory_used_;

/// Whether to compute and display debug info for the net.
  bool debug_info_;

/// The root net that actually holds the shared layers in data parallelism
  const Net* const root_net_;

先看一下AppendTop AppendBottom AppendParam:

================================AppendTop函数================================================
// 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_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()) {
      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>());
    const int blob_id = blobs_.size();//blobs只是存储中间结果；每次遍历到一个top blob都会更新blob_id
    blobs_.push_back(blob_pointer);
    blob_names_.push_back(blob_name);
    blob_need_backward_.push_back(false);
    //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
    if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }//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 (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);
      net_input_blobs_.push_back(blob_pointer.get());//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
    } else {
      top_id_vecs_[layer_id].push_back(blob_id);
      top_vecs_[layer_id].push_back(blob_pointer.get());//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
    }
  }
  if (available_blobs) { available_blobs->insert(blob_name); }
}

================================AppendBottom函数================================================
// 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];//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
  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 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;
}

================================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();
      }
    }
  }
}

然后看一下Init函数

====================================Init函数================================================
template <typename Dtype>
void Net<Dtype>::Init(const NetParameter& in_param) {
  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();
  // Filter layers based on their include/exclude rules and
  // the current NetState.
  NetParameter filtered_param;
  FilterNet(in_param, &filtered_param);
  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);
  // 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);//available_blobs存储blob name.blob_name_to_idx是如何初始化的？？？？？
  }
  // 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) {
    // 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_);//实参phase_是网络的phase,为模板类layer设置shape_属性
    }
    // 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 ";
    }
    //Creating Layer
    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);//调用的是模板类Layer的SetShared方法
    } else {
      layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
    }
    layer_names_.push_back(layer_param.name());//为layer_names_添加新元素
    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) {
      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];//在遍历所有的bottom_id的过程中，只要有一次使得need_backward为真，则这个for循环结束后，need_backward也为真
    }
    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);//在AppendTop函数中，会为向量blob_need_backward_添加新元素
    }
    // 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.
    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]);
    }
    LOG_IF(INFO, Caffe::root_solver())
        << "Setting up " << layer_names_[layer_id];
    //每次循环，都会更新向量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));
      }
      //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_IF(INFO, Caffe::root_solver())
          << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
      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;//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
    // 在这里初始化向量layer_need_backward_
    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;
      }
    }
  }
  // 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
  // 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) {
      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_[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();//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_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]);
  }
  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
  }
  ShareWeights();
  debug_info_ = param.debug_info();
  LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";
}

以上几个函数最为主要。

然后看开其他的一些函数：

================================FilterNet函数========================================
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


ForwardPrefilled()用于前馈预先填满，即预先进行一次前馈。


Forward()把网络输入层的blob读到net_input_blobs_，然后进行前馈，计算出loss。Forward的重载，只是输入层的blob以string的格式传入。


Backward()对整个网络进行反向传播。


Reshape()用于改变每层的尺寸，比如输出的feature map的size


Update()更新params_中blob的值。


ShareTrainedLayersWith(Net* other)从Other网络复制某些层 。


CopyTrainedLayersFrom()调用FromProto函数把源层的blob赋给目标层的blob。


ToProto()把网络的参数存入prototxt中。


params_lr()和params_weight_decay()学习速率和权重衰减;


blob_by_name()判断是否存在名字为blob_name的blob；