【caffe Blob】caffe中与Blob相关的代码注释、使用举例

首先，Blob使用的小例子（通过运行结果即可知道相关功能）：

#include <vector>
#include <caffe/blob.hpp>
#include <caffe/util/io.hpp>//磁盘读写
#include <iostream>

using namespace std;
using namespace caffe;

int  main()
{
    Blob<float> a;
    cout<<"Size: "<<a.shape_string()<<endl;
    a.Reshape(1,2,3,4);
    cout<<"Size: "<<a.shape_string()<<endl;
    a.Reshape(1,1,1,4);
    cout<<"Size: "<<a.shape_string()<<endl;
    
    float* p=a.mutable_cpu_data();
    float* q=a.mutable_cpu_diff();
    for(int i=0;i<a.count();i++)
    {
        p[i]=i;
        q[i]=a.count()-1-i;
    }
    cout<<"L1: "<<a.asum_data()<<endl;
    cout<<"L2: "<<a.sumsq_data()<<endl;
    //a.Update();

    //磁盘读写
    BlobProto bp;
    a.ToProto(&bp,true);//a序列化，连带diff（默认不带）
    WriteProtoToBinaryFile(bp,"a.blob");
    BlobProto bp2;
    ReadProtoFromBinaryFileOrDie("a.blob",&bp2);
    Blob<float> b;
    b.FromProto(bp2,true);//从序列化对象中克隆b（连同形状）
    b.Update();
    cout<<"L1: "<<b.asum_data()<<endl;
    cout<<"L2: "<<b.sumsq_data()<<endl;
    
    return 0;
}

 编译：

export LD_LIBRARY_PATH=./build/lib/:$LD_LIBRARY_PATH

g++ -o app ./bambootry/try.cpp -I ./include/ -D CPU_ONLY 
-I ./.build_release/src/ 
-L ./.build_release/lib/ -lcaffe -lglog -lboost_system

运行结果：

进入正题，代码注释

 src/caffe/proto/caffe.proto中Blob部分

// Specifies the shape (dimensions) of a Blob.表示Blob每个维度的大小
message BlobShape {
  repeated int64 dim = 1 [packed = true];//packed表示这些值在内存中紧密排布没有空洞
}
//该结构描述Blob在磁盘中序列化的形态
message BlobProto {
  optional BlobShape shape = 7; //可选，包括一个BlobShape对象
  repeated float data = 5 [packed = true]; //包括若干浮点元素，存储数据或权重，元素数目由shape或（num，channels，height，weight）决定
  repeated float diff = 6 [packed = true]; //包括若干浮点元素，用于存储增量信息，维度与data数组一致
  repeated double double_data = 8 [packed = true]; //data 类型double
  repeated double double_diff = 9 [packed = true]; //diff 类型double

  // 4D dimensions -- deprecated.  Use "shape" instead.可选，维度信息。新版本推荐使用shape
  optional int32 num = 1 [default = 0];
  optional int32 channels = 2 [default = 0];
  optional int32 height = 3 [default = 0];
  optional int32 width = 4 [default = 0];
}

include/caffe/blob.hpp （其中使用了SyncedMemory类，具体有关该类的内容在其他文件中） 作为基本计算单元服务 Layer Net Solver等

#ifndef CAFFE_BLOB_HPP_
#define CAFFE_BLOB_HPP_

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

#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h" //由protoc生成的头文件，声明了BlobProto、BlobShape等遵循caffe.proto协议的数据结构
#include "caffe/syncedmem.hpp" //CPU和GPU共享内存类，用于数据同步

const int kMaxBlobAxes = 32; //Blob最大维数

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) {} //默认构造函数

  /// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.
  explicit Blob(const int num, const int channels, const int height,
      const int width); //显式构造函数，避免隐式数据类型转换
  explicit Blob(const vector<int>& shape); 

  /// @brief Deprecated; use <code>Reshape(const vector<int>& shape)</code>.
  // 变形函数，根据输入参数重新设置当前Blob形状，必要时重新分配内存
  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.
   *
   * 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，见Caffe运行log
  //例如： Top Shape: 100 1 28 28 (78400)
  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.
   */
   //返回某一维度尺寸
  inline int shape(int index) const {
    return shape_[CanonicalAxisIndex(index)];
  }
  //返回维度数目
  inline int num_axes() const { return shape_.size(); }
  //返回Blob中元素总数
  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Blob中某几个维度子集的元素总数
  inline int count(int start_axis, int end_axis) const {
    CHECK_LE(start_axis, end_axis);//保证start_sxis<=end_axis
    CHECK_GE(start_axis, 0);//保证start_sxis>=0
    CHECK_GE(end_axis, 0);//保证end_axis>=0
    CHECK_LE(start_axis, num_axes());//保证start_sxis小于总维度数目
    CHECK_LE(end_axis, num_axes());//保证end_axis小于总维度数目
    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.
   */
   //计算从某一个维度开始的元素总数
  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
  inline int CanonicalAxisIndex(int axis_index) const {
    CHECK_GE(axis_index, -num_axes())//保证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())//保证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();//负索引号表示从后向前索引，例如-1为最后一个，即正索引号的N-1
    }
    return axis_index;
  }

  /// @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); }
  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);
  }
  //offset函数用于计算偏移量
  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到当前Blob
  void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false,
      bool reshape = false);
  //下面为一系列存取器
  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;//只读访问cpu data
  void set_cpu_data(Dtype* data);//设置cpu data
  const int* gpu_shape() const;
  const Dtype* gpu_data() const;//只读访问gpu data
  void set_gpu_data(Dtype* data);//设置gpu data
  const Dtype* cpu_diff() const;//只读访问cpu diff
  const Dtype* gpu_diff() const;//只读访问gpu diff
  Dtype* mutable_cpu_data();//读写访问
  Dtype* mutable_gpu_data();//读写访问
  Dtype* mutable_cpu_diff();//读写访问
  Dtype* mutable_gpu_diff();//读写访问
  void Update();//Blob更新运算
  void FromProto(const BlobProto& proto, bool reshape = true);//反序列化函数，从BlobProto中恢复一个Blob对象
  void ToProto(BlobProto* proto, bool write_diff = false) const;//序列化函数，将内存中的Blob对象保存到BlobProto中

  /// @brief Compute the sum of absolute values (L1 norm) of the data.
  Dtype asum_data() const;//计算data的L1范数，即绝对值求和
  /// @brief Compute the sum of absolute values (L1 norm) of the diff.
  Dtype asum_diff() const;//计算diff的L1范数，即绝对值求和
  /// @brief Compute the sum of squares (L2 norm squared) of the data.
  Dtype sumsq_data() const;//计算data的平方和，用于L2范数
  /// @brief Compute the sum of squares (L2 norm squared) of the diff.
  Dtype sumsq_diff() const;//计算diff的平方和，用于L2范数

  /// @brief Scale the blob data by a constant factor.
  void scale_data(Dtype scale_factor);//data乘一个标量
  /// @brief Scale the blob diff by a constant factor.
  void scale_diff(Dtype scale_factor);//diff乘一个标量

  /**
   * @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);
  /**
   * @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);//共享另一个Blob的diff_

  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_;
  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);//禁止拷贝构造函数、赋值运算符重载
};  // class Blob

}  // namespace caffe

#endif  // CAFFE_BLOB_HPP_

 include/caffe/syncedmem.hpp

#ifndef CAFFE_SYNCEDMEM_HPP_
#define CAFFE_SYNCEDMEM_HPP_

#include <cstdlib>

#ifdef USE_MKL
  #include "mkl.h"
#endif

#include "caffe/common.hpp"

namespace caffe {

// If CUDA is available and in GPU mode, host memory will be allocated pinned,
// using cudaMallocHost. It avoids dynamic pinning for transfers (DMA).
// The improvement in performance seems negligible in the single GPU case,
// but might be more significant for parallel training. Most importantly,
// it improved stability for large models on many GPUs.
// 如果是GPU模式且CUDA使能，则主机内存会以页锁定内存方式分配（使用cudaMallocHost（）函数）
// 对单GPU提升并不明显，对多GPU提升非常明显
inline void CaffeMallocHost(void** ptr, size_t size, bool* use_cuda) {
#ifndef CPU_ONLY
  if (Caffe::mode() == Caffe::GPU) {
    CUDA_CHECK(cudaMallocHost(ptr, size));
    *use_cuda = true;
    return;
  }
#endif
#ifdef USE_MKL
  *ptr = mkl_malloc(size ? size:1, 64);
#else
  *ptr = malloc(size);
#endif
  *use_cuda = false;
  CHECK(*ptr) << "host allocation of size " << size << " failed";
}
// 与CaffeMallocHost对应
inline void CaffeFreeHost(void* ptr, bool use_cuda) {
#ifndef CPU_ONLY
  if (use_cuda) {
    CUDA_CHECK(cudaFreeHost(ptr));
    return;
  }
#endif
#ifdef USE_MKL
  mkl_free(ptr);
#else
  free(ptr);
#endif
}


/**
 * @brief Manages memory allocation and synchronization between the host (CPU)
 *        and device (GPU).
 *
 * TODO(dox): more thorough description.
 */
 // 该类负责存储分配以及主机设备间同步
class SyncedMemory {
 public:
  SyncedMemory();
  explicit SyncedMemory(size_t size);
  ~SyncedMemory();
  const void* cpu_data();       //只读
  void set_cpu_data(void* data);//设置
  const void* gpu_data();       //只读
  void set_gpu_data(void* data);//设置
  void* mutable_cpu_data();     //读写
  void* mutable_gpu_data();     //读写
  //状态机变量，4种状态：未初始化 CPU数据有效 GPU数据有效 已同步
  enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED }；
  SyncedHead head() { return head_; }//获取当前状态机变量值
  size_t size() { return size_; }    //获取当前存储空间尺寸

#ifndef CPU_ONLY
  void async_gpu_push(const cudaStream_t& stream);
#endif

 private:
  void check_device();

  void to_cpu();  //数据同步到CPU
  void to_gpu();  //数据同步到GPU
  void* cpu_ptr_; //位于CPU的数据指针
  void* gpu_ptr_; //位于GPU的数据指针
  size_t size_;   //存储空间大小
  SyncedHead head_;  //状态机变量
  bool own_cpu_data_;//标志是否拥有CPU数据权（否，即从别的对象共享）
  bool cpu_malloc_use_cuda_;
  bool own_gpu_data_;//标志是否拥有GPU数据权
  int device_;       

  DISABLE_COPY_AND_ASSIGN(SyncedMemory);
};  // class SyncedMemory

}  // namespace caffe

#endif  // CAFFE_SYNCEDMEM_HPP_

 src/caffe/blob.cpp

#include <climits>
#include <vector>

#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/syncedmem.hpp"
#include "caffe/util/math_functions.hpp"

namespace caffe {
//变维函数，将（num,channels,height,width）转为vector<int>并调用重载函数
template <typename Dtype>
void Blob<Dtype>::Reshape(const int num, const int channels, const int height,
    const int width) {
  vector<int> shape(4);
  shape[0] = num;
  shape[1] = channels;
  shape[2] = height;
  shape[3] = width;
  Reshape(shape);
}
//真正的变维函数
template <typename Dtype>
void Blob<Dtype>::Reshape(const vector<int>& shape) {
  CHECK_LE(shape.size(), kMaxBlobAxes);//保证vector维度小于kMaxBlobAxes
  count_ = 1;//用于计算元素总数=num*channels*height*width
  shape_.resize(shape.size());//成员变量维度重置
  if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) {
    shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int)));
  }
  int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
  for (int i = 0; i < shape.size(); ++i) {
    CHECK_GE(shape[i], 0);
    if (count_ != 0) {//保证count不溢出
      CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
    }
    count_ *= shape[i];
    shape_[i] = shape[i];//成员变量赋值
    shape_data[i] = shape[i];
  }
  if (count_ > capacity_) {//如果新的count_大于当前分配的空间容量
    capacity_ = count_;    //扩容，重新分配data_和diff_空间
    data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
    diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
  }
}

template <typename Dtype>
void Blob<Dtype>::Reshape(const BlobShape& shape) {
  CHECK_LE(shape.dim_size(), kMaxBlobAxes);
  vector<int> shape_vec(shape.dim_size());
  for (int i = 0; i < shape.dim_size(); ++i) {
    shape_vec[i] = shape.dim(i);
  }
  Reshape(shape_vec);
}

template <typename Dtype>
void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {
  Reshape(other.shape());
}
//构造函数
template <typename Dtype>
Blob<Dtype>::Blob(const int num, const int channels, const int height,
    const int width)
  // capacity_ must be initialized before calling Reshape
  : capacity_(0) {
  //调用Reshape之前必须初始化capacity，否则会导致不可预期的后果
  Reshape(num, channels, height, width);
}

template <typename Dtype>
Blob<Dtype>::Blob(const vector<int>& shape)
  // capacity_ must be initialized before calling Reshape
  : capacity_(0) {
  Reshape(shape);
}

template <typename Dtype>
const int* Blob<Dtype>::gpu_shape() const {
  CHECK(shape_data_);
  return (const int*)shape_data_->gpu_data();
}
//只读获得cpu data指针
template <typename Dtype>
const Dtype* Blob<Dtype>::cpu_data() const {
  CHECK(data_);//保证data_不为空
  return (const Dtype*)data_->cpu_data();
}
//修改cpu data指针
template <typename Dtype>
void Blob<Dtype>::set_cpu_data(Dtype* data) {
  CHECK(data);
  // Make sure CPU and GPU sizes remain equal
  size_t size = count_ * sizeof(Dtype);
  if (data_->size() != size) {
    data_.reset(new SyncedMemory(size));
    diff_.reset(new SyncedMemory(size));
  }
  data_->set_cpu_data(data);//设置成员变量值为传入参数值
}
//只读获得gpu data指针
template <typename Dtype>
const Dtype* Blob<Dtype>::gpu_data() const {
  CHECK(data_);//保证不为空
  return (const Dtype*)data_->gpu_data();
}
//修改gpu data指针
template <typename Dtype>
void Blob<Dtype>::set_gpu_data(Dtype* data) {
  CHECK(data);
  // Make sure CPU and GPU sizes remain equal
  size_t size = count_ * sizeof(Dtype);
  if (data_->size() != size) {
    data_.reset(new SyncedMemory(size));
    diff_.reset(new SyncedMemory(size));
  }
  data_->set_gpu_data(data);
}
//只读获得cpu diff指针
template <typename Dtype>
const Dtype* Blob<Dtype>::cpu_diff() const {
  CHECK(diff_);
  return (const Dtype*)diff_->cpu_data();
}
//只读获得gpu diff指针
template <typename Dtype>
const Dtype* Blob<Dtype>::gpu_diff() const {
  CHECK(diff_);
  return (const Dtype*)diff_->gpu_data();
}
//读写访问cpu_data
template <typename Dtype>
Dtype* Blob<Dtype>::mutable_cpu_data() {
  CHECK(data_);
  return static_cast<Dtype*>(data_->mutable_cpu_data());
}
//读写访问gpu_data
template <typename Dtype>
Dtype* Blob<Dtype>::mutable_gpu_data() {
  CHECK(data_);
  return static_cast<Dtype*>(data_->mutable_gpu_data());
}
//读写访问cpu_diff
template <typename Dtype>
Dtype* Blob<Dtype>::mutable_cpu_diff() {
  CHECK(diff_);
  return static_cast<Dtype*>(diff_->mutable_cpu_data());
}
//读写访问gpu_diff
template <typename Dtype>
Dtype* Blob<Dtype>::mutable_gpu_diff() {
  CHECK(diff_);
  return static_cast<Dtype*>(diff_->mutable_gpu_data());
}
//共享另一个Blob的data指针
template <typename Dtype>
void Blob<Dtype>::ShareData(const Blob& other) {
  CHECK_EQ(count_, other.count());
  data_ = other.data();
}
//共享另一个Blob的diff指针
template <typename Dtype>
void Blob<Dtype>::ShareDiff(const Blob& other) {
  CHECK_EQ(count_, other.count());
  diff_ = other.diff();
}

// The "update" method is used for parameter blobs in a Net, which are stored
// as Blob<float> or Blob<double> -- hence we do not define it for
// Blob<int> or Blob<unsigned int>.
//更新函数用于网络参数Blob的更新。其中int和unsigned int类型的处理并未实现
//实现的类型为Blob<float>和Blob<double>
template <> void Blob<unsigned int>::Update() { NOT_IMPLEMENTED; }
template <> void Blob<int>::Update() { NOT_IMPLEMENTED; }

template <typename Dtype>
void Blob<Dtype>::Update() {
  // We will perform update based on where the data is located.
  //data在哪儿就在哪儿更新
  switch (data_->head()) {
  case SyncedMemory::HEAD_AT_CPU:
    // perform computation on CPU
    //执行CPU运算，data[i]=data[i]-diff[i]，i=0~count-1
    caffe_axpy<Dtype>(count_, Dtype(-1),
        static_cast<const Dtype*>(diff_->cpu_data()),
        static_cast<Dtype*>(data_->mutable_cpu_data()));
    break;
  case SyncedMemory::HEAD_AT_GPU://data位于GPU或CPU/GPU已同步
  case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
    // perform computation on GPU
    //执行GPU运算,data[i]=data[i]-diff[i]，i=0~count-1
    caffe_gpu_axpy<Dtype>(count_, Dtype(-1),
        static_cast<const Dtype*>(diff_->gpu_data()),
        static_cast<Dtype*>(data_->mutable_gpu_data()));
#else
    NO_GPU;//编译时如果打开CPU_ONLY，则GPU禁用
#endif
    break;
  default:
    LOG(FATAL) << "Syncedmem not initialized.";
  }
}

template <> unsigned int Blob<unsigned int>::asum_data() const {
  NOT_IMPLEMENTED;
  return 0;
}

template <> int Blob<int>::asum_data() const {
  NOT_IMPLEMENTED;
  return 0;
}
//计算L1范数，即绝对值和
template <typename Dtype>
Dtype Blob<Dtype>::asum_data() const {
  if (!data_) { return 0; }
  switch (data_->head()) {
  case SyncedMemory::HEAD_AT_CPU:
    return caffe_cpu_asum(count_, cpu_data());//执行CPU上的asum计算
  case SyncedMemory::HEAD_AT_GPU:
  case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
  {
    Dtype asum;
    caffe_gpu_asum(count_, gpu_data(), &asum);//执行GPU上的asum计算
    return asum;
  }
#else
    NO_GPU;
#endif
  case SyncedMemory::UNINITIALIZED:
    return 0;
  default:
    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
  }
  return 0;
}

template <> unsigned int Blob<unsigned int>::asum_diff() const {
  NOT_IMPLEMENTED;
  return 0;
}

template <> int Blob<int>::asum_diff() const {
  NOT_IMPLEMENTED;
  return 0;
}

template <typename Dtype>
Dtype Blob<Dtype>::asum_diff() const {
  if (!diff_) { return 0; }
  switch (diff_->head()) {
  case SyncedMemory::HEAD_AT_CPU:
    return caffe_cpu_asum(count_, cpu_diff());
  case SyncedMemory::HEAD_AT_GPU:
  case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
  {
    Dtype asum;
    caffe_gpu_asum(count_, gpu_diff(), &asum);
    return asum;
  }
#else
    NO_GPU;
#endif
  case SyncedMemory::UNINITIALIZED:
    return 0;
  default:
    LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
  }
  return 0;
}

template <> unsigned int Blob<unsigned int>::sumsq_data() const {
  NOT_IMPLEMENTED;
  return 0;
}

template <> int Blob<int>::sumsq_data() const {
  NOT_IMPLEMENTED;
  return 0;
}
//用于L2范数，平方和
template <typename Dtype>
Dtype Blob<Dtype>::sumsq_data() const {
  Dtype sumsq;
  const Dtype* data;
  if (!data_) { return 0; }
  switch (data_->head()) {
  case SyncedMemory::HEAD_AT_CPU:
    data = cpu_data();
    sumsq = caffe_cpu_dot(count_, data, data);
    break;
  case SyncedMemory::HEAD_AT_GPU:
  case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
    data = gpu_data();
    caffe_gpu_dot(count_, data, data, &sumsq);
#else
    NO_GPU;
#endif
    break;
  case SyncedMemory::UNINITIALIZED:
    return 0;
  default:
    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
  }
  return sumsq;
}

template <> unsigned int Blob<unsigned int>::sumsq_diff() const {
  NOT_IMPLEMENTED;
  return 0;
}

template <> int Blob<int>::sumsq_diff() const {
  NOT_IMPLEMENTED;
  return 0;
}

template <typename Dtype>
Dtype Blob<Dtype>::sumsq_diff() const {
  Dtype sumsq;
  const Dtype* diff;
  if (!diff_) { return 0; }
  switch (diff_->head()) {
  case SyncedMemory::HEAD_AT_CPU:
    diff = cpu_diff();
    sumsq = caffe_cpu_dot(count_, diff, diff);
    break;
  case SyncedMemory::HEAD_AT_GPU:
  case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
    diff = gpu_diff();
    caffe_gpu_dot(count_, diff, diff, &sumsq);
    break;
#else
    NO_GPU;
#endif
  case SyncedMemory::UNINITIALIZED:
    return 0;
  default:
    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
  }
  return sumsq;
}

template <> void Blob<unsigned int>::scale_data(unsigned int scale_factor) {
  NOT_IMPLEMENTED;
}

template <> void Blob<int>::scale_data(int scale_factor) {
  NOT_IMPLEMENTED;
}
//对data_进行幅度缩放
template <typename Dtype>
void Blob<Dtype>::scale_data(Dtype scale_factor) {
  Dtype* data;
  if (!data_) { return; }
  switch (data_->head()) {
  case SyncedMemory::HEAD_AT_CPU:
    data = mutable_cpu_data();
    caffe_scal(count_, scale_factor, data);
    return;
  case SyncedMemory::HEAD_AT_GPU:
  case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
    data = mutable_gpu_data();
    caffe_gpu_scal(count_, scale_factor, data);
    return;
#else
    NO_GPU;
#endif
  case SyncedMemory::UNINITIALIZED:
    return;
  default:
    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
  }
}

template <> void Blob<unsigned int>::scale_diff(unsigned int scale_factor) {
  NOT_IMPLEMENTED;
}

template <> void Blob<int>::scale_diff(int scale_factor) {
  NOT_IMPLEMENTED;
}

template <typename Dtype>
void Blob<Dtype>::scale_diff(Dtype scale_factor) {
  Dtype* diff;
  if (!diff_) { return; }
  switch (diff_->head()) {
  case SyncedMemory::HEAD_AT_CPU:
    diff = mutable_cpu_diff();
    caffe_scal(count_, scale_factor, diff);
    return;
  case SyncedMemory::HEAD_AT_GPU:
  case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
    diff = mutable_gpu_diff();
    caffe_gpu_scal(count_, scale_factor, diff);
    return;
#else
    NO_GPU;
#endif
  case SyncedMemory::UNINITIALIZED:
    return;
  default:
    LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
  }
}
//判断形状是否相同
template <typename Dtype>
bool Blob<Dtype>::ShapeEquals(const BlobProto& other) {
  if (other.has_num() || other.has_channels() ||
      other.has_height() || other.has_width()) {
    // Using deprecated 4D Blob dimensions --每个维度对比
    // shape is (num, channels, height, width).
    // Note: we do not use the normal Blob::num(), Blob::channels(), etc.
    // methods as these index from the beginning of the blob shape, where legacy
    // parameter blobs were indexed from the end of the blob shape (e.g., bias
    // Blob shape (1 x 1 x 1 x N), IP layer weight Blob shape (1 x 1 x M x N)).
    return shape_.size() <= 4 &&
           LegacyShape(-4) == other.num() &&
           LegacyShape(-3) == other.channels() &&
           LegacyShape(-2) == other.height() &&
           LegacyShape(-1) == other.width();
  }
  //直接对比
  vector<int> other_shape(other.shape().dim_size());
  for (int i = 0; i < other.shape().dim_size(); ++i) {
    other_shape[i] = other.shape().dim(i);
  }
  return shape_ == other_shape;
}
//从另一个Blob拷贝data（可选diff），必要时进行变维
template <typename Dtype>
void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
  if (source.count() != count_ || source.shape() != shape_) {
    if (reshape) {
      ReshapeLike(source);
    } else {
      LOG(FATAL) << "Trying to copy blobs of different sizes.";
    }
  }
  switch (Caffe::mode()) {
  case Caffe::GPU:
    if (copy_diff) {
      caffe_copy(count_, source.gpu_diff(),
          static_cast<Dtype*>(diff_->mutable_gpu_data()));
    } else {
      caffe_copy(count_, source.gpu_data(),
          static_cast<Dtype*>(data_->mutable_gpu_data()));
    }
    break;
  case Caffe::CPU:
    if (copy_diff) {
      caffe_copy(count_, source.cpu_diff(),
          static_cast<Dtype*>(diff_->mutable_cpu_data()));
    } else {
      caffe_copy(count_, source.cpu_data(),
          static_cast<Dtype*>(data_->mutable_cpu_data()));
    }
    break;
  default:
    LOG(FATAL) << "Unknown caffe mode.";
  }
}
//从BlobProto中加载一个Blob
template <typename Dtype>
void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
  if (reshape) {//获取维度信息
    vector<int> shape;
    if (proto.has_num() || proto.has_channels() ||
        proto.has_height() || proto.has_width()) {
      // Using deprecated 4D Blob dimensions --
      // shape is (num, channels, height, width).
      shape.resize(4);
      shape[0] = proto.num();
      shape[1] = proto.channels();
      shape[2] = proto.height();
      shape[3] = proto.width();
    } else {
      shape.resize(proto.shape().dim_size());
      for (int i = 0; i < proto.shape().dim_size(); ++i) {
        shape[i] = proto.shape().dim(i);
      }
    }
    Reshape(shape);//变维
  } else {
    CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)";
  }
  // copy data 加载数据
  Dtype* data_vec = mutable_cpu_data();
  if (proto.double_data_size() > 0) {//若之前保存的是double类型的data
    CHECK_EQ(count_, proto.double_data_size());
    for (int i = 0; i < count_; ++i) {
      data_vec[i] = proto.double_data(i);//加载double data
    }
  } else {
    CHECK_EQ(count_, proto.data_size());
    for (int i = 0; i < count_; ++i) {
      data_vec[i] = proto.data(i);//否则加载float data
    }
  }
  if (proto.double_diff_size() > 0) {//若之前保存的是double类型的diff
    CHECK_EQ(count_, proto.double_diff_size());
    Dtype* diff_vec = mutable_cpu_diff();
    for (int i = 0; i < count_; ++i) {
      diff_vec[i] = proto.double_diff(i);//加载double diff
    }
  } else if (proto.diff_size() > 0) {
    CHECK_EQ(count_, proto.diff_size());
    Dtype* diff_vec = mutable_cpu_diff();
    for (int i = 0; i < count_; ++i) {
      diff_vec[i] = proto.diff(i);//否则加载float diff
    }
  }
}

template <>
void Blob<double>::ToProto(BlobProto* proto, bool write_diff) const {
  proto->clear_shape();
  for (int i = 0; i < shape_.size(); ++i) {
    proto->mutable_shape()->add_dim(shape_[i]);
  }
  proto->clear_double_data();
  proto->clear_double_diff();
  const double* data_vec = cpu_data();
  for (int i = 0; i < count_; ++i) {
    proto->add_double_data(data_vec[i]);
  }
  if (write_diff) {
    const double* diff_vec = cpu_diff();
    for (int i = 0; i < count_; ++i) {
      proto->add_double_diff(diff_vec[i]);
    }
  }
}
//将Blob中的data（可选diff）导出到BlobProto结构体，便于存储到磁盘文件中
template <>
void Blob<float>::ToProto(BlobProto* proto, bool write_diff) const {
  proto->clear_shape();//重置proto维度，保证与Blob相同
  for (int i = 0; i < shape_.size(); ++i) {
    proto->mutable_shape()->add_dim(shape_[i]);
  }
  proto->clear_data();//清楚data
  proto->clear_diff();//清楚diff
  const float* data_vec = cpu_data();//将data导出到proto
  for (int i = 0; i < count_; ++i) {
    proto->add_data(data_vec[i]);
  }
  if (write_diff) {//如果要求导出diff
    const float* diff_vec = cpu_diff();//导出diff到proto
    for (int i = 0; i < count_; ++i) {
      proto->add_diff(diff_vec[i]);
    }
  }
}

INSTANTIATE_CLASS(Blob);//实例化Blob类模板（float,double ）
template class Blob<int>;
template class Blob<unsigned int>;

}  // namespace caffe

初学者，慢慢看，待更新……

摘抄参考赵永科《深度学习 21天实战caffe》