#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(,,,);

    cout<<"Size: "<<a.shape_string()<<endl;

    a.Reshape(,,,);

    cout<<"Size: "<<a.shape_string()<<endl;

    float* p=a.mutable_cpu_data();

    float* q=a.mutable_cpu_diff();

    for(int i=;i<a.count();i++)

    {

        p[i]=i;

        q[i]=a.count()--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 ;

}

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

// Specifies the shape (dimensions) of a Blob.表示Blob每个维度的大小

message BlobShape {

  repeated int64 dim =  [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 = ; //可选，包括一个BlobShape对象

  repeated float data =  [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 =  [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 =  [packed = true]; //data 类型double

  repeated double double_diff =  [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 =  [default = ];

  optional int32 channels =  [default = ];

  optional int32 height =  [default = ];

  optional int32 width =  [default = ];

}

 #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 = ; //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_(), capacity_() {} //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 = ; 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, );//保证start_sxis>=0

     CHECK_GE(end_axis, );//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 = ;

     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 < ) {

       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(); }

   /// @brief Deprecated legacy shape accessor channels: use shape(1) instead.

   inline int channels() const { return LegacyShape(); }

   /// @brief Deprecated legacy shape accessor height: use shape(2) instead.

   inline int height() const { return LegacyShape(); }

   /// @brief Deprecated legacy shape accessor width: use shape(3) instead.

   inline int width() const { return LegacyShape(); }

   inline int LegacyShape(int index) const {

     CHECK_LE(num_axes(), )

         << "Cannot use legacy accessors on Blobs with > 4 axes.";

     CHECK_LT(index, );

     CHECK_GE(index, -);

     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 ;

     }

     return shape(index);

   }

   //offset函数用于计算偏移量

   inline int offset(const int n, const int c = , const int h = ,

       const int w = ) const {

     CHECK_GE(n, );

     CHECK_LE(n, num());

     CHECK_GE(channels(), );

     CHECK_LE(c, channels());

     CHECK_GE(height(), );

     CHECK_LE(h, height());

     CHECK_GE(width(), );

     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 = ;

     for (int i = ; i < num_axes(); ++i) {

       offset *= shape(i);

       if (indices.size() > i) {

         CHECK_GE(indices[i], );

         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_

 #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:, );

 #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_

 #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();

   shape[] = num;

   shape[] = channels;

   shape[] = height;

   shape[] = 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_ = ;//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 = ; i < shape.size(); ++i) {

     CHECK_GE(shape[i], );

     if (count_ != ) {//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 = ; 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_() {

   //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_() {

   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(-),

         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(-),

         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 ;

 }

 template <> int Blob<int>::asum_data() const {

   NOT_IMPLEMENTED;

   return ;

 }

 //计算L1范数，即绝对值和

 template <typename Dtype>

 Dtype Blob<Dtype>::asum_data() const {

   if (!data_) { return ; }

   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 ;

   default:

     LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();

   }

   return ;

 }

 template <> unsigned int Blob<unsigned int>::asum_diff() const {

   NOT_IMPLEMENTED;

   return ;

 }

 template <> int Blob<int>::asum_diff() const {

   NOT_IMPLEMENTED;

   return ;

 }

 template <typename Dtype>

 Dtype Blob<Dtype>::asum_diff() const {

   if (!diff_) { return ; }

   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 ;

   default:

     LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();

   }

   return ;

 }

 template <> unsigned int Blob<unsigned int>::sumsq_data() const {

   NOT_IMPLEMENTED;

   return ;

 }

 template <> int Blob<int>::sumsq_data() const {

   NOT_IMPLEMENTED;

   return ;

 }

 //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 ; }

   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 ;

   default:

     LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();

   }

   return sumsq;

 }

 template <> unsigned int Blob<unsigned int>::sumsq_diff() const {

   NOT_IMPLEMENTED;

   return ;

 }

 template <> int Blob<int>::sumsq_diff() const {

   NOT_IMPLEMENTED;

   return ;

 }

 template <typename Dtype>

 Dtype Blob<Dtype>::sumsq_diff() const {

   Dtype sumsq;

   const Dtype* diff;

   if (!diff_) { return ; }

   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 ;

   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() <=  &&

            LegacyShape(-) == other.num() &&

            LegacyShape(-) == other.channels() &&

            LegacyShape(-) == other.height() &&

            LegacyShape(-) == other.width();

   }

   //直接对比

   vector<int> other_shape(other.shape().dim_size());

   for (int i = ; 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();

       shape[] = proto.num();

       shape[] = proto.channels();

       shape[] = proto.height();

       shape[] = proto.width();

     } else {

       shape.resize(proto.shape().dim_size());

       for (int i = ; 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() > ) {//若之前保存的是double类型的data

     CHECK_EQ(count_, proto.double_data_size());

     for (int i = ; i < count_; ++i) {

       data_vec[i] = proto.double_data(i);//加载double data

     }

   } else {

     CHECK_EQ(count_, proto.data_size());

     for (int i = ; i < count_; ++i) {

       data_vec[i] = proto.data(i);//否则加载float data

     }

   }

   if (proto.double_diff_size() > ) {//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 = ; i < count_; ++i) {

       diff_vec[i] = proto.double_diff(i);//加载double diff

     }

   } else if (proto.diff_size() > ) {

     CHECK_EQ(count_, proto.diff_size());

     Dtype* diff_vec = mutable_cpu_diff();

     for (int i = ; 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 = ; 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 = ; i < count_; ++i) {

     proto->add_double_data(data_vec[i]);

   }

   if (write_diff) {

     const double* diff_vec = cpu_diff();

     for (int i = ; 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 = ; 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 = ; 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 = ; 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