Caffe源码阅读笔记(1):Blob

blob是caffe基础的数据结构，是用来保存学习到的参数以及网络传输过程中产生数据的类，数据的交换和存储都依赖于blob。

blob具有CPU和GPU之间同步的能力,它是4维的数组(Num, Channels, Height, Width)。

设Blob数据维度为 number N x channel K x height H x width W，Blob是row-major保存的，因此在(n, k, h, w)位置的值物理位置为((n * K + k) * H + h) * W + w，其中Number/N是batch size。

行主序和列主序 Row Major and Column Major

向量写为[1x3]矩阵形式：V=|xyz| , 被称为行主序(Row Major)。

向量写为[3x1]矩阵形式:V=∣∣∣∣xyz∣∣∣∣ , 被称为列主序(Column Major)。

先来看看头文件

caffe/include/caffe/blob.hpp

Caffe类中成员变量名都带有后缀“_”,这样就容易区分临时变量和类成员变量。

#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;

namespace caffe {

/**
* @brief A wrapper around SyncedMemory holders serving as the basic
*        computational unit through which Layer%s, Net%s, and Solver%s
*        interact.
*      封装了SyncedMemory类，作为基本的计算单元使用于Layer，Net，Solver等
* TODO(dox): more thorough description.
*/
template <typename Dtype>
class Blob {
public:
//构造函数
Blob()
: data_(), diff_(), count_(0), capacity_(0) {}

/// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.
//显式构造函数
explicit Blob(const int num, const int channels, const int height,
const int width);
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);
/**
* @brief Change the dimensions of the blob, allocating new memory if
*        necessary.
*        改变blob当前的尺寸，必要时从新分配内存
* 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);
//得到Blob形状字符串用于打印log
inline string shape_string() const {
ostringstream stream;
for (int i = 0; i < shape_.size(); ++i) {
stream << shape_[i] << " ";
}
stream << "(" << count_ << ")";
return stream.str();
}
//返回Blob形状
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.
*/
//返回第index维度的尺寸
inline int shape(int index) const {
return shape_[CanonicalAxisIndex(index)];
}
//返回维度的数目
inline int num_axes() const { return shape_.size(); }
//返回Blob中元素的总数
inline int count() const { return count_; }

/**
* @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.
*/
//返回从start_axis到end_axis的元素总数
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());
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.
*/
//返回从start_axis开始的元素总数
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.
*/
//转换坐标轴索引，将[-N，N]转换为[0,N),负索引表示从后往前访问，-1表示最后一个元素，-2表示第N-2个元素，以此类推
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;
}
//获取某一维的尺寸,Blob是一个4维数组，维度从低到高分别为：num，channels，height，weight
/// @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 width: use shape(3) instead.
inline int width() const { return LegacyShape(3); }
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);
}
//计算偏移量
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
*/
//拷贝Blob
void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false,
bool reshape = false);
//存取器（getter/setter）
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)];
}

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)];
}

inline const shared_ptr<SyncedMemory>& data() const {
CHECK(data_);
return data_;
}

inline const shared_ptr<SyncedMemory>& diff() const {
CHECK(diff_);
return diff_;
}

const Dtype* cpu_data() const;
void set_cpu_data(Dtype* data);
const int* gpu_shape() const;
const Dtype* gpu_data() const;
const Dtype* cpu_diff() const;
const Dtype* gpu_diff() const;
Dtype* mutable_cpu_data();
Dtype* mutable_gpu_data();
Dtype* mutable_cpu_diff();
Dtype* mutable_gpu_diff();
void Update();
//反序列化，从BlobProto中恢复一个Blob对象
void FromProto(const BlobProto& proto, bool reshape = true);
//序列化函数，将内存中Blob对象保存到BlobProto中
void ToProto(BlobProto* proto, bool write_diff = false) const;

/// @brief Compute the sum of absolute values (L1 norm) of the data.
//计算data的L1范数
Dtype asum_data() const;
/// @brief Compute the sum of absolute values (L1 norm) of the diff.
//计算diff的L1范数
Dtype asum_diff() const;
/// @brief Compute the sum of squares (L2 norm squared) of the data.
//计算data的L2范数
Dtype sumsq_data() const;
/// @brief Compute the sum of squares (L2 norm squared) of the diff.
//计算diff的L2范数
Dtype sumsq_diff() const;

/// @brief Scale the blob data by a constant factor.
//data乘以一个标量
void scale_data(Dtype scale_factor);
/// @brief Scale the blob diff by a constant factor.
//diff乘以一个标量
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.
*/
//共享另一个Blob的data
void ShareData(const Blob& other);
/**
* @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.
*/
//共享另一个Blob的diff
void ShareDiff(const Blob& other);

bool ShapeEquals(const BlobProto& other);

protected:
shared_ptr<SyncedMemory> data_;   //存放指向data的指针
shared_ptr<SyncedMemory> diff_;   //存放指向diff的指针
shared_ptr<SyncedMemory> shape_data_;
vector<int> shape_;   //形状信息
int count_;   //存放有效元素数目信息
int capacity_;    //存放Blob容器的容量信息

DISABLE_COPY_AND_ASSIGN(Blob); //禁用拷贝构造函数，赋值运算符重载
};  // class Blob

}  // namespace caffe

#endif  // CAFFE_BLOB_HPP_

[参考]：

caffe官方文档

《二十一天实战caffe》

Caffe的三级结构(Blobs,Layers,Nets)

caffe 中 BLOB的实现