梳理caffe代码blob(三)

贯穿整个caffe的就是数据blob：

#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"#include "caffe/util/math_functions.hpp"const int kMaxBlobAxes = INT_MAX;//blob最大维数目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()//默认构造函数       : data_(), diff_(), count_(0), capacity_(0) {}  /// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.  //explicit关键字的作用是禁止单参数构造函数的隐式转换  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>./*Reshape函数将num,channels,height,width传递给vector shape_ */  void Reshape(const int num, const int channels, const int height,      const int width); /** *Blob作为一个最基础的类，其中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adjust the dimensions of a top blob。同时在改变Blob大小时， *内存将会被重新分配如果内存大小不够了，并且额外的内存将不会被释放。对input的blob进行reshape, *如果立马调用Net::Backward是会出错的，因为reshape之后，要么Net::forward或者Net::Reshape就会 *被调用来将新的input shape 传播到高层 */  //根据shape来初始化shape_和shape_data_，以及为data_ 和diff_ 分配空间。   void Reshape(const vector<int>& shape);  void Reshape(const BlobShape& shape);  void ReshapeLike(const Blob& other);  //iniline主要是将代码进行复制，扩充，会使代码总量上升，好处就是可以节省调用的开销,以string形式获取shape_，用于打印blob的log  inline string shape_string() const {    ostringstream stream;    for (int i = 0; i < shape_.size(); ++i) {      stream << shape_[i] << " ";    }    stream << "(" << count_ << ")";    return stream.str();  }//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.   *///获取index维的大小，返回某一维的尺寸  inline int shape(int index) const {    return shape_[CanonicalAxisIndex(index)];  }//获取维的个数  inline int num_axes() const { return shape_.size(); }//获取当前data的大小  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.   *//*多个count()函数，主要还是为了统计Blob的容量（volume），或者是某一片（slice），从某个axis到具体某个axis的shape乘积。*///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());    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 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，转换坐标轴索引[-N，N]为[0，N]  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;  }  //Blob中的4个基本变量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 width: use shape(3) instead.  inline int width() const { return LegacyShape(3); }//data_维数不大于4时才能使用，功能同shape()类似。  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，  //偏移量是根据对应的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   */  //按值拷贝blob到当前blob。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;//只读获取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()，mutable是可读写访问  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，就是合并data和diff  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。从proto中恢复一个blob对象*/  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 ，将blob序列化为proto，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://data_指针，指针类型是shared_ptr，属于boost库的一个智能指针，这一部分主要用来申请内存存储data，data主要是正向传播的时候用的  shared_ptr<SyncedMemory> data_;//diff_主要用来存储偏差，update data  shared_ptr<SyncedMemory> diff_;//shape_存储Blob的形状  vector<int> shape_;//count_表示Blob中的元素个数，也就是个数*通道数*高度*宽度  int count_;//capacity表示当前的元素个数，因为Blob可能会reshape  int capacity_;  DISABLE_COPY_AND_ASSIGN(Blob);//禁止拷贝和赋值运算};  // class Blob}  // namespace caffe#endif  // CAFFE_BLOB_HPP_

顺便将实现部分也贴出来，方便对照：

#include <climits>#include <vector>#include "caffe/blob.hpp"#include "caffe/common.hpp"#include "caffe/syncedmem.hpp"#include "caffe/util/math_functions.hpp"namespace caffe {template <typename Dtype>//该函数将num,channels,height,width传递给vector shape_ 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);}template <typename Dtype>void Blob<Dtype>::Reshape(const vector<int>& shape) {  CHECK_LE(shape.size(), kMaxBlobAxes);  count_ = 1;  shape_.resize(shape.size());//重新定义vector shape_ 的size  for (int i = 0; i < shape.size(); ++i) {    CHECK_GE(shape[i], 0);//确保shape 每个元素为正数    CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";    count_ *= shape[i];    shape_[i] = shape[i];  }  //由于count_超过了当前capacity_ 因此需要重新分配内存空间  if (count_ > capacity_) {    capacity_ = count_;    data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));    diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));  }}template <typename Dtype>// BlobShape 在caffe.proto 中定义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);//dim 包含num，channels，height， width  }  Reshape(shape_vec);//用protobuf传递来dim 对shape_ 进行reshape}//用已知的Blob的shape来对shape_ 进行reshapetemplate <typename Dtype>void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {  Reshape(other.shape());}//用num，channels，height， width 初始化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) {  Reshape(num, channels, height, width);}//用shape 初始化template <typename Dtype>Blob<Dtype>::Blob(const vector<int>& shape)  // capacity_ must be initialized before calling Reshape  : capacity_(0) {  Reshape(shape);}//返回cpu 中的数据template <typename Dtype>const Dtype* Blob<Dtype>::cpu_data() const {  CHECK(data_);  return (const Dtype*)data_->cpu_data();}// 清空cpu 数据template <typename Dtype>void Blob<Dtype>::set_cpu_data(Dtype* data) {  CHECK(data);  data_->set_cpu_data(data);}//返回gpu 中的数据template <typename Dtype>const Dtype* Blob<Dtype>::gpu_data() const {  CHECK(data_);  return (const Dtype*)data_->gpu_data();}//反向传播导数diff_ 操作函数,返回cpu 中的数据template <typename Dtype>const Dtype* Blob<Dtype>::cpu_diff() const {  CHECK(diff_);  return (const Dtype*)diff_->cpu_data();}//返回gpu 中的数据template <typename Dtype>const Dtype* Blob<Dtype>::gpu_diff() const {  CHECK(diff_);  return (const Dtype*)diff_->gpu_data();}template <typename Dtype>Dtype* Blob<Dtype>::mutable_cpu_data() {  CHECK(data_);  return static_cast<Dtype*>(data_->mutable_cpu_data());}template <typename Dtype>Dtype* Blob<Dtype>::mutable_gpu_data() {  CHECK(data_);  return static_cast<Dtype*>(data_->mutable_gpu_data());}template <typename Dtype>Dtype* Blob<Dtype>::mutable_cpu_diff() {  CHECK(diff_);  return static_cast<Dtype*>(diff_->mutable_cpu_data());}template <typename Dtype>Dtype* Blob<Dtype>::mutable_gpu_diff() {  CHECK(diff_);  return static_cast<Dtype*>(diff_->mutable_gpu_data());}//当前的blob 的data_ 指向已知blob的数据template <typename Dtype>void Blob<Dtype>::ShareData(const Blob& other) {  CHECK_EQ(count_, other.count());  data_ = other.data();}//当前的blob 的diff_ 指向已知blob的反向传播导数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>.template <> void Blob<unsigned int>::Update() { NOT_IMPLEMENTED; }template <> void Blob<int>::Update() { NOT_IMPLEMENTED; }//Updata函数用于参数blob的更新（weight，bias 等减去对应的导数）template <typename Dtype>void Blob<Dtype>::Update() {  // We will perform update based on where the data is located.  switch (data_->head()) {  case SyncedMemory::HEAD_AT_CPU://数据在cpu上，则在cpu上进行计算    // perform computation on CPU    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:  case SyncedMemory::SYNCED:#ifndef CPU_ONLY//如果没有定义CPU_ONLY，且数据在gpu上，则在gpu上进行计算    // perform computation on GPU    caffe_gpu_axpy<Dtype>(count_, Dtype(-1),        static_cast<const Dtype*>(diff_->gpu_data()),        static_cast<Dtype*>(data_->mutable_gpu_data()));#else    NO_GPU;#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;}//返回data_ 中所有 element 的绝对值之和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());  case SyncedMemory::HEAD_AT_GPU:  case SyncedMemory::SYNCED:#ifndef CPU_ONLY  {    Dtype asum;    caffe_gpu_asum(count_, gpu_data(), &asum);    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;}//返回diff_ 中所有 element 的绝对值之和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;}//返回 data_ 中所有 element 的平方和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;}//返回 diff_ 中所有 element 的平方和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;}// 给data乘以scale_factortemplate <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;}// 给diff乘以scale_factortemplate <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();  }}//BlobProto 是定义在caffe.proto 中的一个message，其字段有 data,diff,shape,num,channels,height,widthtemplate <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 --    // 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();  }  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;}//检查当前的blob和已知的 other 的 shape 是否相同，相同返回truetemplate <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source 拷贝数据,copy_diff控制是拷贝diff还是datatemplate <typename Dtype>void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {  if (reshape) {    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);  } else {//如果不做reshape要求当前的blob的shape和proto传入的shape相同    CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)";  }  // copy data  Dtype* data_vec = mutable_cpu_data();  for (int i = 0; i < count_; ++i) {    data_vec[i] = proto.data(i);  }//将proto传入的data拷贝到cpu数据  if (proto.diff_size() > 0) {    Dtype* diff_vec = mutable_cpu_diff();    for (int i = 0; i < count_; ++i) {      diff_vec[i] = proto.diff(i);    }//将proto传入的diff 拷贝到cpu数据  }}template <typename Dtype>void Blob<Dtype>::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_data();  proto->clear_diff();  const Dtype* data_vec = cpu_data();  for (int i = 0; i < count_; ++i) {    proto->add_data(data_vec[i]);//将data写入proto  }  if (write_diff) {    const Dtype* diff_vec = cpu_diff();    for (int i = 0; i < count_; ++i) {      proto->add_diff(diff_vec[i]);//将diff写入proto    }  }}INSTANTIATE_CLASS(Blob);template class Blob<int>;template class Blob<unsigned int>;}  // namespace caffe