Layer是構(gòu)成Net的基石瘤礁，如同Blob是構(gòu)成Layer的基石一樣，我們首先來看一下caffe.proto中定義的LayerParameter到底是怎么一回事：

message LayerParameter {
  optional string name = 1; // the layer name
  optional string type = 2; // the layer type
  repeated string bottom = 3; // the name of each bottom blob
  repeated string top = 4; // the name of each top blob

  // The train / test phase for computation.
  optional Phase phase = 10;

  // The amount of weight to assign each top blob in the objective.
  // Each layer assigns a default value, usually of either 0 or 1,
  // to each top blob.
  repeated float loss_weight = 5; 

  // Specifies training parameters (multipliers on global learning constants,
  // and the name and other settings used for weight sharing).
  repeated ParamSpec param = 6; 

  // The blobs containing the numeric parameters of the layer.
  repeated BlobProto blobs = 7; 

  // Specifies whether to backpropagate to each bottom. If unspecified,
  // Caffe will automatically infer whether each input needs backpropagation
  // to compute parameter gradients. If set to true for some inputs,
  // backpropagation to those inputs is forced; if set false for some inputs,
  // backpropagation to those inputs is skipped.
  //
  // The size must be either 0 or equal to the number of bottoms.
  repeated bool propagate_down = 11;

  // Rules controlling whether and when a layer is included in the network,
  // based on the current NetState.  You may specify a non-zero number of rules
  // to include OR exclude, but not both.  If no include or exclude rules are
  // specified, the layer is always included.  If the current NetState meets
  // ANY (i.e., one or more) of the specified rules, the layer is
  // included/excluded.
  repeated NetStateRule include = 8; 
  repeated NetStateRule exclude = 9; 

  // Parameters for data pre-processing.
  optional TransformationParameter transform_param = 100; 

  // Parameters shared by loss layers.
  optional LossParameter loss_param = 101; 

  // Layer type-specific parameters.
  //
  // Note: certain layers may have more than one computational engine
  // for their implementation. These layers include an Engine type and
  // engine parameter for selecting the implementation.
  // The default for the engine is set by the ENGINE switch at compile-time.
  optional AccuracyParameter accuracy_param = 102; 
  optional ArgMaxParameter argmax_param = 103; 
  optional BatchNormParameter batch_norm_param = 139;
......
}

其中這個phase當(dāng)然是控制它在TEST和TRAIN模式下層不同的表現(xiàn)的參數(shù)坎背，在某種狀態(tài)下可能要將其排除掉；loss_weight是用來控制top blob在整個loss的計算中所占的權(quán)重的寄雀，一般情況下對于loss層設(shè)置為1得滤，其他層設(shè)為0即可；ParamSpec param定制一個訓(xùn)練參數(shù)盒犹，它是在全局化訓(xùn)練參數(shù)的基礎(chǔ)上再乘以一個乘子懂更，我們看下這個類的具體情況：

// Specifies training parameters (multipliers on global learning constants,
// and the name and other settings used for weight sharing).
message ParamSpec {
  // The names of the parameter blobs -- useful for sharing parameters among
  // layers, but never required otherwise.  To share a parameter between two
  // layers, give it a (non-empty) name.
  optional string name = 1;

  // Whether to require shared weights to have the same shape, or just the same
  // count -- defaults to STRICT if unspecified.
  optional DimCheckMode share_mode = 2;
  enum DimCheckMode {
    // STRICT (default) requires that num, channels, height, width each match.
    STRICT = 0;
    // PERMISSIVE requires only the count (num*channels*height*width) to match.
    PERMISSIVE = 1;
  }

  // The multiplier on the global learning rate for this parameter.
  optional float lr_mult = 3 [default = 1.0];

  // The multiplier on the global weight decay for this parameter.
  optional float decay_mult = 4 [default = 1.0];
}

其實這個類中的東西就這幾個參數(shù)，學(xué)習(xí)速率乘子急膀，全局衰減乘子還有就是如果想要共享這個blob的參數(shù)沮协，需要給它定義一個名字。那么回到之前的LayerParameter中的定義卓嫂，可以發(fā)現(xiàn)repeated BlobProto blobs就是我們這個層的參數(shù)blob皂股，比如在普通的CNN網(wǎng)絡(luò)中，它可能包含了weight blob和bias blob兩個命黔，其他的網(wǎng)絡(luò)可能包含了更多，這是由網(wǎng)絡(luò)的結(jié)構(gòu)決定的就斤。propagate_down這個參數(shù)呢就是限制是否我們要往down blob傳遞梯度的一個bool數(shù)組悍募，它要么全是0，要么它的大小要同down blob的數(shù)量一致洋机。還有兩個參數(shù)要控制這個層是包含在Net內(nèi)還是不包含坠宴，它決定于當(dāng)前的NetState，我們來看NetState和NetStateRule這兩個類到底包含了哪些東西：

message NetState {
  optional Phase phase = 1 [default = TEST];
  optional int32 level = 2 [default = 0];
  repeated string stage = 3;
}

message NetStateRule {
  // Set phase to require the NetState have a particular phase (TRAIN or TEST)
  // to meet this rule.
  optional Phase phase = 1;

  // Set the minimum and/or maximum levels in which the layer should be used.
  // Leave undefined to meet the rule regardless of level.
  optional int32 min_level = 2;
  optional int32 max_level = 3;

  // Customizable sets of stages to include or exclude.
  // The net must have ALL of the specified stages and NONE of the specified
  // "not_stage"s to meet the rule.
  // (Use multiple NetStateRules to specify conjunctions of stages.)
  repeated string stage = 4;
  repeated string not_stage = 5;
}

這個里面寫得不是非常詳細(xì)绷旗，我們可以再后面再來慢慢分析喜鼓，不過你也可以推測出來副砍，這基本上是設(shè)置一些規(guī)則，到時候可以讓這個層到底是在這個Net中還是不在里面庄岖。下面還有兩個主要的參數(shù)TransformationParameter和LossParameter我們后面再仔細(xì)分享豁翎，反正就這么幾個比較特別的參數(shù)，然后就是各個層特有的參數(shù)了隅忿，這都是在proto文件中用optional關(guān)鍵詞限定的心剥。
到此為止，我們先來看看Layer.hpp里面包含了什么東西吧：

#ifndef CAFFE_LAYER_H_
#define CAFFE_LAYER_H_

#include <algorithm>
#include <string>
#include <vector>

#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/layer_factory.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/math_functions.hpp"

/**
 Forward declare boost::thread instead of including boost/thread.hpp
 to avoid a boost/NVCC issues (#1009, #1010) on OSX.
 */
namespace boost { class mutex; }

namespace caffe {

/**
 * @brief An interface for the units of computation which can be composed into a
 *        Net.
 *
 * Layer%s must implement a Forward function, in which they take their input
 * (bottom) Blob%s (if any) and compute their output Blob%s (if any).
 * They may also implement a Backward function, in which they compute the error
 * gradients with respect to their input Blob%s, given the error gradients with
 * their output Blob%s.
 */
template <typename Dtype>
class Layer {
 public:
  /**
   * You should not implement your own constructor. Any set up code should go
   * to SetUp(), where the dimensions of the bottom blobs are provided to the
   * layer.
   */
  explicit Layer(const LayerParameter& param)
    : layer_param_(param), is_shared_(false) {
      // Set phase and copy blobs (if there are any).
      phase_ = param.phase();
      if (layer_param_.blobs_size() > 0) {
        blobs_.resize(layer_param_.blobs_size());
        for (int i = 0; i < layer_param_.blobs_size(); ++i) {
          blobs_[i].reset(new Blob<Dtype>());
          blobs_[i]->FromProto(layer_param_.blobs(i));
        }
      }
    }

這個顯式的構(gòu)造函數(shù)直接將參數(shù)復(fù)制到layer_param_這個保護成員中背桐，同時因為phase_這個成員比較常用优烧，所以就單獨進行了賦值；另外呢最重要的是將blobs中的參數(shù)呢全部復(fù)制到自己定義的Blob對象中去了链峭，這樣就比較方便自己的操作畦娄，因為畢竟BlobProto功能太弱了，在復(fù)制的過程中使用了Blob對象帶的FromProto方法弊仪，非常簡單明了熙卡。

  virtual ~Layer() {}

  /**
   * @brief Implements common layer setup functionality.
   *
   * @param bottom the preshaped input blobs
   * @param top
   *     the allocated but unshaped output blobs, to be shaped by Reshape
   *
   * Checks that the number of bottom and top blobs is correct.
   * Calls LayerSetUp to do special layer setup for individual layer types,
   * followed by Reshape to set up sizes of top blobs and internal buffers.
   * Sets up the loss weight multiplier blobs for any non-zero loss weights.
   * This method may not be overridden.
   */
  void SetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
    InitMutex();
    CheckBlobCounts(bottom, top);
    LayerSetUp(bottom, top);
    Reshape(bottom, top);
    SetLossWeights(top);
  }

這個SetUp函數(shù)呢是每一層的配置函數(shù)，InitMutex函數(shù)是在多任務(wù)的時候進行鎖定的撼短；CheckBlobCounts是檢查各個blob是否滿足要求的檢查函數(shù)再膳，而LayerSetUp則是每個層自己的專門的配置函數(shù)，需要在繼承的時候去實現(xiàn)的虛函數(shù)曲横；Reshape也是喂柒，最后說下這個SetLossWeights這個函數(shù)我之前沒見過，而且它計算loss的方式居然是用weights點積data禾嫉，然后求和灾杰，不知道為什么要這樣。熙参。

  /**
   * @brief Does layer-specific setup: your layer should implement this function
   *        as well as Reshape.
   *
   * @param bottom
   *     the preshaped input blobs, whose data fields store the input data for
   *     this layer
   * @param top
   *     the allocated but unshaped output blobs
   *
   * This method should do one-time layer specific setup. This includes reading
   * and processing relevent parameters from the <code>layer_param_</code>.
   * Setting up the shapes of top blobs and internal buffers should be done in
   * <code>Reshape</code>, which will be called before the forward pass to
   * adjust the top blob sizes.
   */
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {}

看到?jīng)]艳吠，這個函數(shù)主要是讀取處理這個特殊層的參數(shù)的，比如說conv_layer啊孽椰，就要讀取stride昭娩，kernel_size之類的參數(shù)，這都是特殊層才有的參數(shù)黍匾。

  /**
   * @brief Whether a layer should be shared by multiple nets during data
   *        parallelism. By default, all layers except for data layers should
   *        not be shared. data layers should be shared to ensure each worker
   *        solver access data sequentially during data parallelism.
   */
  virtual inline bool ShareInParallel() const { return false; }

  /** @brief Return whether this layer is actually shared by other nets.
   *         If ShareInParallel() is true and using more than one GPU and the
   *         net has TRAIN phase, then this function is expected return true.
   */
  inline bool IsShared() const { return is_shared_; }

  /** @brief Set whether this layer is actually shared by other nets
   *         If ShareInParallel() is true and using more than one GPU and the
   *         net has TRAIN phase, then is_shared should be set true.
   */
  inline void SetShared(bool is_shared) {
    CHECK(ShareInParallel() || !is_shared)
        << type() << "Layer does not support sharing.";
    is_shared_ = is_shared;
  }

這幾個函數(shù)是處理層的數(shù)據(jù)共享問題的栏渺，現(xiàn)在還用不到；

  /**
   * @brief Adjust the shapes of top blobs and internal buffers to accommodate
   *        the shapes of the bottom blobs.
   *
   * @param bottom the input blobs, with the requested input shapes
   * @param top the top blobs, which should be reshaped as needed
   *
   * This method should reshape top blobs as needed according to the shapes
   * of the bottom (input) blobs, as well as reshaping any internal buffers
   * and making any other necessary adjustments so that the layer can
   * accommodate the bottom blobs.
   */
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) = 0;

根據(jù)bottom blob的配置調(diào)整top blob的配置锐涯，同時要設(shè)置一些內(nèi)部緩存的形狀磕诊。。。

  /**
   * @brief Given the bottom blobs, compute the top blobs and the loss.
   *
   * @param bottom
   *     the input blobs, whose data fields store the input data for this layer
   * @param top
   *     the preshaped output blobs, whose data fields will store this layers'
   *     outputs
   * \return The total loss from the layer.
   *
   * The Forward wrapper calls the relevant device wrapper function
   * (Forward_cpu or Forward_gpu) to compute the top blob values given the
   * bottom blobs.  If the layer has any non-zero loss_weights, the wrapper
   * then computes and returns the loss.
   *
   * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
   */
  inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);

這個就是正向傳播的函數(shù)

  /**
   * @brief Given the top blob error gradients, compute the bottom blob error
   *        gradients.
   *
   * @param top
   *     the output blobs, whose diff fields store the gradient of the error
   *     with respect to themselves
   * @param propagate_down
   *     a vector with equal length to bottom, with each index indicating
   *     whether to propagate the error gradients down to the bottom blob at
   *     the corresponding index
   * @param bottom
   *     the input blobs, whose diff fields will store the gradient of the error
   *     with respect to themselves after Backward is run
   *
   * The Backward wrapper calls the relevant device wrapper function
   * (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the
   * top blob diffs.
   *
   * Your layer should implement Backward_cpu and (optionally) Backward_gpu.
   */
  inline void Backward(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down,
      const vector<Blob<Dtype>*>& bottom);

同樣霎终，反向傳播函數(shù)滞磺；注意top blob的梯度放在了它的diff數(shù)據(jù)體中；

  /**
   * @brief Returns the vector of learnable parameter blobs.
   */
  vector<shared_ptr<Blob<Dtype> > >& blobs() {
    return blobs_;
  }

  /**
   * @brief Returns the layer parameter.
   */
  const LayerParameter& layer_param() const { return layer_param_; }

  /**
   * @brief Writes the layer parameter to a protocol buffer
   */
  virtual void ToProto(LayerParameter* param, bool write_diff = false);

  /**
   * @brief Returns the scalar loss associated with a top blob at a given index.
   */
  inline Dtype loss(const int top_index) const {
    return (loss_.size() > top_index) ? loss_[top_index] : Dtype(0);
  }

  /**
   * @brief Sets the loss associated with a top blob at a given index.
   */
  inline void set_loss(const int top_index, const Dtype value) {
    if (loss_.size() <= top_index) {
      loss_.resize(top_index + 1, Dtype(0));
    }
    loss_[top_index] = value;
  }

感覺這個地方是不是有BUG莱褒，击困，，實現(xiàn)有問題吧保礼，怎么能全部resize到0沛励，然后賦值呢，炮障，那這樣豈不是只有最后一個有值目派，其它前面的都是0？胁赢？企蹭？？mark以下先

  /**
   * @brief Returns the layer type.
   */
  virtual inline const char* type() const { return ""; }

上面幾個函數(shù)都是很輕量的智末，理解一下就好了

  /**
   * @brief Returns the exact number of bottom blobs required by the layer,
   *        or -1 if no exact number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some exact number of bottom blobs.
   */
  virtual inline int ExactNumBottomBlobs() const { return -1; }
  /**
   * @brief Returns the minimum number of bottom blobs required by the layer,
   *        or -1 if no minimum number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some minimum number of bottom blobs.
   */
  virtual inline int MinBottomBlobs() const { return -1; }
  /**
   * @brief Returns the maximum number of bottom blobs required by the layer,
   *        or -1 if no maximum number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some maximum number of bottom blobs.
   */
  virtual inline int MaxBottomBlobs() const { return -1; }
  /**
   * @brief Returns the exact number of top blobs required by the layer,
   *        or -1 if no exact number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some exact number of top blobs.
   */
  virtual inline int ExactNumTopBlobs() const { return -1; }
  /**
   * @brief Returns the minimum number of top blobs required by the layer,
   *        or -1 if no minimum number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some minimum number of top blobs.
   */
  virtual inline int MinTopBlobs() const { return -1; }
  /**
   * @brief Returns the maximum number of top blobs required by the layer,
   *        or -1 if no maximum number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some maximum number of top blobs.
   */
  virtual inline int MaxTopBlobs() const { return -1; }
  /**
   * @brief Returns true if the layer requires an equal number of bottom and
   *        top blobs.
   *
   * This method should be overridden to return true if your layer expects an
   * equal number of bottom and top blobs.
   */
  virtual inline bool EqualNumBottomTopBlobs() const { return false; }

這一堆函數(shù)都是一些top blob和bottom blob的一些限制函數(shù)谅摄，用來檢查這個層的參數(shù)是否設(shè)置正確的，這些都是虛函數(shù)系馆，可以在特定層中自定義送漠；

  /**
   * @brief Return whether "anonymous" top blobs are created automatically
   *        by the layer.
   *
   * If this method returns true, Net::Init will create enough "anonymous" top
   * blobs to fulfill the requirement specified by ExactNumTopBlobs() or
   * MinTopBlobs().
   */
  virtual inline bool AutoTopBlobs() const { return false; }

如果這個返回為true，則在搭建Net時可能會根據(jù)限制條件構(gòu)建一些沒有命名的blob以滿足要求由蘑；這能增強程序的健壯性闽寡，但是使得可理解性下降了；

  /**
   * @brief Return whether to allow force_backward for a given bottom blob
   *        index.
   *
   * If AllowForceBackward(i) == false, we will ignore the force_backward
   * setting and backpropagate to blob i only if it needs gradient information
   * (as is done when force_backward == false).
   */
  virtual inline bool AllowForceBackward(const int bottom_index) const {
    return true;
  }

  /**
   * @brief Specifies whether the layer should compute gradients w.r.t. a
   *        parameter at a particular index given by param_id.
   *
   * You can safely ignore false values and always compute gradients
   * for all parameters, but possibly with wasteful computation.
   */
  inline bool param_propagate_down(const int param_id) {
    return (param_propagate_down_.size() > param_id) ?
        param_propagate_down_[param_id] : false;
  }
  /**
   * @brief Sets whether the layer should compute gradients w.r.t. a
   *        parameter at a particular index given by param_id.
   */
  inline void set_param_propagate_down(const int param_id, const bool value) {
    if (param_propagate_down_.size() <= param_id) {
      param_propagate_down_.resize(param_id + 1, true);
    }
    param_propagate_down_[param_id] = value;
  }

這三個函數(shù)都是與反向傳播有關(guān)系的尼酿；

 protected:
  /** The protobuf that stores the layer parameters */
  LayerParameter layer_param_;
  /** The phase: TRAIN or TEST */
  Phase phase_;
  /** The vector that stores the learnable parameters as a set of blobs. */
  vector<shared_ptr<Blob<Dtype> > > blobs_;
  /** Vector indicating whether to compute the diff of each param blob. */
  vector<bool> param_propagate_down_;

  /** The vector that indicates whether each top blob has a non-zero weight in
   *  the objective function. */
  vector<Dtype> loss_;

保護成員中包括了基本的參數(shù)成員爷狈；

  /** @brief Using the CPU device, compute the layer output. */
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) = 0;
  /**
   * @brief Using the GPU device, compute the layer output.
   *        Fall back to Forward_cpu() if unavailable.
   */
  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
    // LOG(WARNING) << "Using CPU code as backup.";
    return Forward_cpu(bottom, top);
  }

  /**
   * @brief Using the CPU device, compute the gradients for any parameters and
   *        for the bottom blobs if propagate_down is true.
   */
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down,
      const vector<Blob<Dtype>*>& bottom) = 0;
  /**
   * @brief Using the GPU device, compute the gradients for any parameters and
   *        for the bottom blobs if propagate_down is true.
   *        Fall back to Backward_cpu() if unavailable.
   */
  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down,
      const vector<Blob<Dtype>*>& bottom) {
    // LOG(WARNING) << "Using CPU code as backup.";
    Backward_cpu(top, propagate_down, bottom);
  }

這四個函數(shù)就是我們在繼承中要實現(xiàn)的，其中cpu版本是要強制實現(xiàn)的裳擎，gpu版本可以選擇性實現(xiàn)涎永；

  /**
   * Called by the parent Layer's SetUp to check that the number of bottom
   * and top Blobs provided as input match the expected numbers specified by
   * the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.
   */
  virtual void CheckBlobCounts(const vector<Blob<Dtype>*>& bottom,
                               const vector<Blob<Dtype>*>& top) {
    if (ExactNumBottomBlobs() >= 0) {
      CHECK_EQ(ExactNumBottomBlobs(), bottom.size())
          << type() << " Layer takes " << ExactNumBottomBlobs()
          << " bottom blob(s) as input.";
    }
    if (MinBottomBlobs() >= 0) {
      CHECK_LE(MinBottomBlobs(), bottom.size())
          << type() << " Layer takes at least " << MinBottomBlobs()
          << " bottom blob(s) as input.";
    }
    if (MaxBottomBlobs() >= 0) {
      CHECK_GE(MaxBottomBlobs(), bottom.size())
          << type() << " Layer takes at most " << MaxBottomBlobs()
          << " bottom blob(s) as input.";
    }
    if (ExactNumTopBlobs() >= 0) {
      CHECK_EQ(ExactNumTopBlobs(), top.size())
          << type() << " Layer produces " << ExactNumTopBlobs()
          << " top blob(s) as output.";
    }
    if (MinTopBlobs() >= 0) {
      CHECK_LE(MinTopBlobs(), top.size())
          << type() << " Layer produces at least " << MinTopBlobs()
          << " top blob(s) as output.";
    }
    if (MaxTopBlobs() >= 0) {
      CHECK_GE(MaxTopBlobs(), top.size())
          << type() << " Layer produces at most " << MaxTopBlobs()
          << " top blob(s) as output.";
    }
    if (EqualNumBottomTopBlobs()) {
      CHECK_EQ(bottom.size(), top.size())
          << type() << " Layer produces one top blob as output for each "
          << "bottom blob input.";
    }
  }

檢查函數(shù)；

  /**
   * Called by SetUp to initialize the weights associated with any top blobs in
   * the loss function. Store non-zero loss weights in the diff blob.
   */
  inline void SetLossWeights(const vector<Blob<Dtype>*>& top) {
    const int num_loss_weights = layer_param_.loss_weight_size();
    if (num_loss_weights) {
      CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be "
          "unspecified or specified once per top blob.";
      for (int top_id = 0; top_id < top.size(); ++top_id) {
        const Dtype loss_weight = layer_param_.loss_weight(top_id);
        if (loss_weight == Dtype(0)) { continue; }
        this->set_loss(top_id, loss_weight);
        const int count = top[top_id]->count();
        Dtype* loss_multiplier = top[top_id]->mutable_cpu_diff();
        caffe_set(count, loss_weight, loss_multiplier);
      }
    }
  }

將loss_weight這個參數(shù)存儲在top blob的diff數(shù)據(jù)體中鹿响；

 private:
  /** Whether this layer is actually shared by other nets*/
  bool is_shared_;

  /** The mutex for sequential forward if this layer is shared */
  shared_ptr<boost::mutex> forward_mutex_;

  /** Initialize forward_mutex_ */
  void InitMutex();
  /** Lock forward_mutex_ if this layer is shared */
  void Lock();
  /** Unlock forward_mutex_ if this layer is shared */
  void Unlock();

  DISABLE_COPY_AND_ASSIGN(Layer);
};  // class Layer

這些私有成員其實是與進程有關(guān)的東西羡微，用到了boost中的lock之類的；

// Forward and backward wrappers. You should implement the cpu and
// gpu specific implementations instead, and should not change these
// functions.
template <typename Dtype>
inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  // Lock during forward to ensure sequential forward
  Lock();
  Dtype loss = 0;
  Reshape(bottom, top);
  switch (Caffe::mode()) {
  case Caffe::CPU:
    Forward_cpu(bottom, top);
    for (int top_id = 0; top_id < top.size(); ++top_id) {
      if (!this->loss(top_id)) { continue; }
      const int count = top[top_id]->count();
      const Dtype* data = top[top_id]->cpu_data();
      const Dtype* loss_weights = top[top_id]->cpu_diff();
      loss += caffe_cpu_dot(count, data, loss_weights);
    }
    break;
  case Caffe::GPU:
    Forward_gpu(bottom, top);
#ifndef CPU_ONLY
    for (int top_id = 0; top_id < top.size(); ++top_id) {
      if (!this->loss(top_id)) { continue; }
      const int count = top[top_id]->count();
      const Dtype* data = top[top_id]->gpu_data();
      const Dtype* loss_weights = top[top_id]->gpu_diff();
      Dtype blob_loss = 0;
      caffe_gpu_dot(count, data, loss_weights, &blob_loss);
      loss += blob_loss;
    }
#endif
    break;
  default:
    LOG(FATAL) << "Unknown caffe mode.";
  }
  Unlock();
  return loss;
}

這個函數(shù)是基類的函數(shù)惶我，也是我們會一直使用的拷淘，這其實就是一個wrapper，我們在具體層的開發(fā)時需要設(shè)計里面的Forward_cpu指孤，Backward_cpu等函數(shù)，，注意里面開始就加鎖Lock恃轩，最后解鎖Unlock结洼；另外還做了Reshape，我的想法是這應(yīng)該是在SetUp中已經(jīng)做過了啊叉跛，怎么又做一遍呢松忍。。另外如果Caffe::GPU和CPU_ONLY同時存在筷厘，是不是就不要計算loss了鸣峭，，尷尬啊

template <typename Dtype>
inline void Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down,
    const vector<Blob<Dtype>*>& bottom) {
  switch (Caffe::mode()) {
  case Caffe::CPU:
    Backward_cpu(top, propagate_down, bottom);
    break;
  case Caffe::GPU:
    Backward_gpu(top, propagate_down, bottom);
    break;
  default:
    LOG(FATAL) << "Unknown caffe mode.";
  }
}

// Serialize LayerParameter to protocol buffer
template <typename Dtype>
void Layer<Dtype>::ToProto(LayerParameter* param, bool write_diff) {
  param->Clear();
  param->CopyFrom(layer_param_);
  param->clear_blobs();
  for (int i = 0; i < blobs_.size(); ++i) {
    blobs_[i]->ToProto(param->add_blobs(), write_diff);
  }
}

這個寫到LayerParameter *里面這個函數(shù)其實就是一個io函數(shù)酥艳，只不過我們在進行了一系列操作后摊溶，需要將blob_中的東西寫入到param中的blobs去，這樣才算成功充石；

}  // namespace caffe

#endif  // CAFFE_LAYER_H_

最后我們來看看這個基類的cpp文件莫换，其實因為是基類，所以它的cpp文件相當(dāng)簡單骤铃；就是關(guān)于進程的加鎖解鎖以及具體化layer模板拉岁；

#include <boost/thread.hpp>
#include "caffe/layer.hpp"

namespace caffe {

template <typename Dtype>
void Layer<Dtype>::InitMutex() {
  forward_mutex_.reset(new boost::mutex());
}

template <typename Dtype>
void Layer<Dtype>::Lock() {
  if (IsShared()) {
    forward_mutex_->lock();
  }
}

template <typename Dtype>
void Layer<Dtype>::Unlock() {
  if (IsShared()) {
    forward_mutex_->unlock();
  }
}

INSTANTIATE_CLASS(Layer);

}  // namespace caffe