Caffe学习 四 框架概览

1. Caffe核心代码

• blob[.cpp .h] 基本的数据结构Blob类 
• common[.cpp .h] 定义Caffe类 
• internal_thread[.cpp .h] 使用boost::thread线程库 
• net[.cpp .h] 网络结构类Net 
• solver[.cpp .h] 优化方法类Solver 
• data_transformer[.cpp .h] 输入数据的基本操作类DataTransformer 
• syncedmem[.cpp .h] 分配内存和释放内存类CaffeMallocHost，用于同步GPU，CPU数据 
• layer[.cpp .h] 层类Layer 
• layers/ 此文件夹下面的代码全部至少继承了类Layer, 从layer_factory中注册继承

2. Caffe三级结构（Blobs,Layers,Nets）

• Blob：用于数据的保存、交换和操作，Caffe基础存储结构 
• Layer：用于模型和计算的基础 
• Net：整合连接Layers

Caffe 通过 SyncedMemory 和 Blob 封装了底层数据，为 Caffe 框架上的其他组件提供最基础的数据抽象，后面的 Layer 参数，Net 参数以及 Solver 的参数等都是 Blob 数据，所以理解 Blob 抽象和管理数据的实现方式有助于后续 Caffe 源码的阅读，也是阅读 Caffe 源码的第一步。

代码注释参考luoyetx's blog的Caffe 源码阅读 Blob。

3.Blob

includecaffelob.hpp

#ifndef CAFFE_BLOB_HPP_
#define CAFFE_BLOB_HPP_

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

#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/syncedmem.hpp"

const int kMaxBlobAxes = 32;
//lob 原本在 Caffe 中被表示为一个 4 维数组 (num x channel x height x width)，现在可以表示多维数组，最高维数由宏 kMaxBlobAxes 确定

namespace caffe {

/**
 * @brief A wrapper around SyncedMemory holders serving as the basic
 *        computational unit through which Layer%s, Net%s, and Solver%s
 *        interact.
 *
 * TODO(dox): more thorough description.
 */
template <typename Dtype>
class Blob {
 public:
  Blob()
       : data_(), diff_(), count_(0), capacity_(0) {}


  /// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.
  explicit Blob(const int num, const int channels, const int height,
      const int width);
  //explicit禁止单参数构造函数的隐式转换
  explicit Blob(const vector<int>& shape);

  /// @brief Deprecated; use <code>Reshape(const vector<int>& shape)</code>.
  void Reshape(const int num, const int channels, const int height,
      const int width);
      //Reshape函数将num,channels,height,width传递给vector shape_

  /*
  Blob作为一个最基础的类，构造函数开辟一个内存空间来存储数据，
  Reshape函数在Layer中的reshape或者forward操作中来分配空间。
  改变Blob大小时，内存将被重新分配。
  */
  /**
   * @brief Change the dimensions of the blob, allocating new memory if
   *        necessary.
   *
   * This function can be called both to create an initial allocation
   * of memory, and to adjust the dimensions of a top blob during Layer::Reshape
   * or Layer::Forward. When changing the size of blob, memory will only be
   * reallocated if sufficient memory does not already exist, and excess memory
   * will never be freed.
   *
   * Note that reshaping an input blob and immediately calling Net::Backward is
   * an error; either Net::Forward or Net::Reshape need to be called to
   * propagate the new input shape to higher layers.
   */
  void Reshape(const vector<int>& shape);
  void Reshape(const BlobShape& shape);
  void ReshapeLike(const Blob& other);
  //根据shape来初始化shape_和shape_data_，以及为blob分配空间
  inline string shape_string() const {
    ostringstream stream;
    for (int i = 0; i < shape_.size(); ++i) {
      stream << shape_[i] << " ";
    }
    stream << "(" << count_ << ")";
    return stream.str();
  }
  //iniline节省调用开销
  //获取shape_
  inline const vector<int>& shape() const { return shape_; }
  /**
   * @brief Returns the dimension of the index-th axis (or the negative index-th
   *        axis from the end, if index is negative).
   *
   * @param index the axis index, which may be negative as it will be
   *        "canonicalized" using CanonicalAxisIndex.
   *        Dies on out of range index.
   */
  inline int shape(int index) const {
    return shape_[CanonicalAxisIndex(index)];
  }
  //获取index维的大小
  inline int num_axes() const { return shape_.size(); }
  //获取维的个数
  inline int count() const { return count_; }
  //获取当前data的大小
  /**
   * @brief Compute the volume of a slice; i.e., the product of dimensions
   *        among a range of axes.
   *
   * @param start_axis The first axis to include in the slice.
   *
   * @param end_axis The first axis to exclude from the slice.
   */
   //统计Blob某一片（slice）的容量（volume）
   ////获取某几维数据的大小
  inline int count(int start_axis, int end_axis) const {
    CHECK_LE(start_axis, end_axis);
    CHECK_GE(start_axis, 0);
    CHECK_GE(end_axis, 0);
    CHECK_LE(start_axis, num_axes());
    CHECK_LE(end_axis, num_axes());
    //比较大小或者是否相等，谷歌的一个日志库GLOG
    int count = 1;
    for (int i = start_axis; i < end_axis; ++i) {
      count *= shape(i);
    }
    return count;
  }
  /**
   * @brief Compute the volume of a slice spanning from a particular first
   *        axis to the final axis.
   *
   * @param start_axis The first axis to include in the slice.
   */
   //获取某一维到结束数据的大小
  inline int count(int start_axis) const {
    return count(start_axis, num_axes());
  }

  /**
   * @brief Returns the 'canonical' version of a (usually) user-specified axis,
   *        allowing for negative indexing (e.g., -1 for the last axis).
   *
   * @param axis_index the axis index.
   *        If 0 <= index < num_axes(), return index.
   *        If -num_axes <= index <= -1, return (num_axes() - (-index)),
   *        e.g., the last axis index (num_axes() - 1) if index == -1,
   *        the second to last if index == -2, etc.
   *        Dies on out of range index.
   */
   //Blob的Index是可以从负坐标开始读的,标准化索引,主要是对参数索引进行标准化,以满足要求。
  inline int CanonicalAxisIndex(int axis_index) const {
    CHECK_GE(axis_index, -num_axes())
        << "axis " << axis_index << " out of range for " << num_axes()
        << "-D Blob with shape " << shape_string();
    CHECK_LT(axis_index, num_axes())
        << "axis " << axis_index << " out of range for " << num_axes()
        << "-D Blob with shape " << shape_string();
    if (axis_index < 0) {
      return axis_index + num_axes();
    }
    return axis_index;
  }

  //num,channel,height,width可以直接通过shape(0),shape(1),shape(2),shape(3)来访问
  /// @brief Deprecated legacy shape accessor num: use shape(0) instead.
  inline int num() const { return LegacyShape(0); }
  /// @brief Deprecated legacy shape accessor channels: use shape(1) instead.
  inline int channels() const { return LegacyShape(1); }
  /// @brief Deprecated legacy shape accessor height: use shape(2) instead.
  inline int height() const { return LegacyShape(2); }
  /// @brief Deprecated legacy shape accessor  use shape(3) instead.
  inline int width() const { return LegacyShape(3); }
  ////data_维数不大于4时才能使用
  inline int LegacyShape(int index) const {
    CHECK_LE(num_axes(), 4)
        << "Cannot use legacy accessors on Blobs with > 4 axes.";
    CHECK_LT(index, 4);
    CHECK_GE(index, -4);
    if (index >= num_axes() || index < -num_axes()) {
      // Axis is out of range, but still in [0, 3] (or [-4, -1] for reverse
      // indexing) -- this special case simulates the one-padding used to fill
      // extraneous axes of legacy blobs.
      return 1;
    }
    return shape(index);
  }
  //计算offset,offset计算的方式也支持两种方式，一种直接指定n,c,h,w或者放到一个vector中进行计算，
  //偏差是根据对应的n,c,h,w，返回的offset是((n*channels()+c)*height()+h)*width()+w
  inline int offset(const int n, const int c = 0, const int h = 0,
      const int w = 0) const {
    CHECK_GE(n, 0);
    CHECK_LE(n, num());
    CHECK_GE(channels(), 0);
    CHECK_LE(c, channels());
    CHECK_GE(height(), 0);
    CHECK_LE(h, height());
    CHECK_GE(width(), 0);
    CHECK_LE(w, width());
    return ((n * channels() + c) * height() + h) * width() + w;
  }

  inline int offset(const vector<int>& indices) const {
    CHECK_LE(indices.size(), num_axes());
    int offset = 0;
    for (int i = 0; i < num_axes(); ++i) {
      offset *= shape(i);
      if (indices.size() > i) {
        CHECK_GE(indices[i], 0);
        CHECK_LT(indices[i], shape(i));
        offset += indices[i];
      }
    }
    return offset;
  }
  /**
   * @brief Copy from a source Blob.
   *
   * @param source the Blob to copy from
   * @param copy_diff if false, copy the data; if true, copy the diff
   * @param reshape if false, require this Blob to be pre-shaped to the shape
   *        of other (and die otherwise); if true, Reshape this Blob to other's
   *        shape if necessary
   */
  //一个blob中copy数据 ，通过开关控制是否copy_diff,如果是False则copy data。reshape控制是否需要reshape
  void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false,
      bool reshape = false);
/*这一部分函数主要通过给定的位置访问数据，根据位置计算与数据起始
  的偏差offset，在通过cpu_data*指针获得地址
*/
//获取某位置的data_数据
  inline Dtype data_at(const int n, const int c, const int h,
      const int w) const {
    return cpu_data()[offset(n, c, h, w)];
  }
//获取某位置的diff_数据
  inline Dtype diff_at(const int n, const int c, const int h,
      const int w) const {
    return cpu_diff()[offset(n, c, h, w)];
  }

  inline Dtype data_at(const vector<int>& index) const {
    return cpu_data()[offset(index)];
  }

  inline Dtype diff_at(const vector<int>& index) const {
    return cpu_diff()[offset(index)];
  }
//获取data_
  inline const shared_ptr<SyncedMemory>& data() const {
    CHECK(data_);
    return data_;
  }
//获取diff_
  inline const shared_ptr<SyncedMemory>& diff() const {
    CHECK(diff_);
    return diff_;
  }
  //这里有data和diff两类数据，而这个diff就是我们所熟知的偏差，前者主要存储
  //前向传递的数据，而后者存储的是反向传播中的梯度
  const Dtype* cpu_data() const;//获取data_ cpu指针
  void set_cpu_data(Dtype* data);//设置data_的cpu指针，只是修改了指针
  const Dtype* gpu_data() const;//获取data_的gpu指针
  const Dtype* cpu_diff() const;//获取diff_的cpu指针
  const Dtype* gpu_diff() const;//获取diff_的gpu指针
  Dtype* mutable_cpu_data();//见SyncedMemory的mutable_cpu_data();
  Dtype* mutable_gpu_data();//见SyncedMemory的mutable_gpu_data();
  Dtype* mutable_cpu_diff();//见SyncedMemory的mutable_cpu_data();
  Dtype* mutable_gpu_diff();//见SyncedMemory的mutable_gpu_data();
  //更新data_的数据,减去diff_的数据
  void Update();
/*
其中用到math_functions.hpp中的函数caffe_axpy(),该函数封装了cblas_saxpy，实现的是Y=alpha*X+Y。
由此，知该函数的功能是data_=(data_-diff_)。另外，该函数只实现了对double和float型数据，
对于unsigned int和int由于该函数主要是在Net中被调用，只有Blob<float>和Blob<double>型式，
因此没有定义unsigned int和int。
*/
  void FromProto(const BlobProto& proto, bool reshape = true);
/*
由BlobProto对Blob进行赋值操作。reshape代表是否允许修改shape_的大小。
需要注意的是再这里有double和float两种类型的数据 ，在代码中可以看到具体的体现
*/
  void ToProto(BlobProto* proto, bool write_diff = false) const;

  /// @brief Compute the sum of absolute values (L1 norm) of the data.
/*
功能：计算L1范数
说明：其中用到了math_function.hpp中的函数caffe_cpu_asum()和caffe_gpu_asum，实现的功能是对向量X求其每个元素绝对值的和，不同的是X分别在cpu和gpu中。
*/
  Dtype asum_data() const;
  /// @brief Compute the sum of absolute values (L1 norm) of the diff.
  Dtype asum_diff() const;
  /// @brief Compute the sum of squares (L2 norm squared) of the data.
/*
功能：计算L2范数。
说明：用到了math_function.hpp中的caffe_cpu_dot(),caffe_cpu_strided_dot(),caffe_gpu_dot(), caffe_gpu_strided_dot()。具体就是就向量X的平方和。
*/
  Dtype sumsq_data() const;
  /// @brief Compute the sum of squares (L2 norm squared) of the diff.
  Dtype sumsq_diff() const;

  /// @brief Scale the blob data by a constant factor.
/*
功能：正规化data_。
说明：用到math_function.hpp中的caffe_scal()和caffe_gpu_scal()函数，就是对向量X乘上一个因子。
*/
  void scale_data(Dtype scale_factor);
  /// @brief Scale the blob diff by a constant factor.
  void scale_diff(Dtype scale_factor);

  /**
   * @brief Set the data_ shared_ptr to point to the SyncedMemory holding the
   *        data_ of Blob other -- useful in Layer%s which simply perform a copy
   *        in their Forward pass.
   *
   * This deallocates the SyncedMemory holding this Blob's data_, as
   * shared_ptr calls its destructor when reset with the "=" operator.
   */
  void ShareData(const Blob& other);//本Blob共享other的data_
  /**
   * @brief Set the diff_ shared_ptr to point to the SyncedMemory holding the
   *        diff_ of Blob other -- useful in Layer%s which simply perform a copy
   *        in their Forward pass.
   *
   * This deallocates the SyncedMemory holding this Blob's diff_, as
   * shared_ptr calls its destructor when reset with the "=" operator.
   */
  void ShareDiff(const Blob& other);//本Blob共享other的diff_

  bool ShapeEquals(const BlobProto& other);//判断other与本Blob形状是否相同。

 protected://智能指针
  shared_ptr<SyncedMemory> data_;//用于正向传播的数据
  shared_ptr<SyncedMemory> diff_;//diff_存储偏差
  shared_ptr<SyncedMemory> shape_data_;//存储Blob的形状
  vector<int> shape_;//存储Blob的形状
  int count_;//元素个数，个数*通道数*高度*宽度
  int capacity_;//当前元素个数

  DISABLE_COPY_AND_ASSIGN(Blob);
};  // class Blob

}  // namespace caffe

#endif  // CAFFE_BLOB_HPP_

4.Layer & Net

Layer与Theano卷积神经网络中定义的Layer接近。

接受Blob的输入，进行正向和反向传播。

以ConvolutionLayer为例

includecaffelayersconv_layer.hpp (更多细节在base_conv_layer.hpp中）。

 protected:
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
  virtual inline bool reverse_dimensions() { return false; }
  virtual void compute_output_shape();

Net就是之前随笔中的网络参数和自定义网络。