caffe源码 之 Blob类

本文主要解析caffe框架中源码文件/src/caffe/blob.cpp，该文件主要实现caffe的数据存储与传递。

caffe中Blob类主要用来表示网络中的数据，包括训练数据，网络各层自身的参数(包括权值、偏置以及它们的梯度)，网络之间传递的数据都是通过 Blob 来实现的，同时 Blob 数据也支持在 CPU 与 GPU 上存储，能够在两者之间做同步。

下面是我看源码时，搜集的注释，以及对源码的理解

Blob.hpp：：：：：：：：：：：：：：：：

#ifndef CAFFE_BLOB_HPP_#define CAFFE_BLOB_HPP_#include <algorithm>#include <string>#include <vector>#include "caffe/common.hpp"#include "caffe/proto/caffe.pb.h"#include "caffe/syncedmem.hpp"const int kMaxBlobAxes = 32;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() //构造函数       : data_(), diff_(), count_(0), capacity_(0) {}  /// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.  explicit Blob(const int num, const int channels, const int height,      const int width);  explicit 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.   *   * 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);  inline string shape_string() const {    ostringstream stream;    //输出数据的维度,以空格分隔，最后输出一维维度(total)     for (int i = 0; i < shape_.size(); ++i) {      stream << shape_[i] << " ";    }    stream << "(" << count_ << ")";    return stream.str();  }  //返回blob的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.   */   //获取第index维的大小，返回某一维的尺寸  inline int shape(int index) const {    return shape_[CanonicalAxisIndex(index)];  }  //返回数据维度就维的个数  inline int num_axes() const { return shape_.size(); }  //返回数据的所有维度的乘积,即数据的个数    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.   */   // 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.   */   // 给定的维度到最后的维度之间包含的数据个数    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.   */   // 支持负数维度索引，负数表示从后往前，返回的是正确的维度索引（相当于将负数索引进行的转换）    // 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 {    // 判断是否在范围内[-numaxes, numaxes]      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;  }  /// @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); }  // 检查blob的维度个数是不是小于4，Blob中的4个维num,channel,height,width可以直接通过shape(0),shape(1),shape(2),shape(3)来访问  // 返回的是每维数据的大小,等同于shape()函数的功能s  inline int LegacyShape(int index) const {    CHECK_LE(num_axes(), 4)        << "Cannot use legacy accessors on Blobs with > 4 axes.";    CHECK_LT(index, 4);   // 检查维度索引是不是小于4      CHECK_GE(index, -4);  // 检查维度索引是不是大于-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);  }  // 计算一维线性偏移量    inline int offset(const int n, const int c = 0, const int h = 0,      const int w = 0) const {    CHECK_GE(n, 0);        /*判断输入参数是否超过阈值*/    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;  }  // 计算一维线性偏移量,只不过参数用的是vector<int>   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   * 从给定的blob进行复制，如果copy_diff=true则新的blob复制的是diff,   * 如果reshape=true则改变新blob的形状    */  void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false,      bool reshape = false);  // 获取在内存下的数据(前向传播所用的数据)    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)];  }  // 获取在内存下的后向传播的数据   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)];  }  // 获取cpu内存中offset指定位置的前向传播数据  inline Dtype data_at(const vector<int>& index) const {    return cpu_data()[offset(index)];  }  // 获取cpu内存中offset指定位置的返向传播数据  inline Dtype diff_at(const vector<int>& index) const {    return cpu_diff()[offset(index)];  }  // 返回前向传播数据地址(前向传播数据一般为图像本身数据)  inline const shared_ptr<SyncedMemory>& data() const {    CHECK(data_);    return data_;  }  // 返回后向传播数据地址(后向传播数据一般为图像数据导数)  inline const shared_ptr<SyncedMemory>& diff() const {    CHECK(diff_);    return diff_;  }  //内存数据的地址返回，数据清空等操作,详见.cpp  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;  // 一些内存同步与处理的函数见SycedMem.cpp中具体定义  Dtype* mutable_cpu_data();  Dtype* mutable_gpu_data();  Dtype* mutable_cpu_diff();  Dtype* mutable_gpu_diff();  // 数据更新，blob里面的data部分减去diff部分  void Update();  // 从protobuf序列化文件读取blob对象    void FromProto(const BlobProto& proto, bool reshape = true);  // 将对象序列化为protobuf文件   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.  // 归一化data数据  void scale_data(Dtype scale_factor);  /// @brief Scale the blob diff by a constant factor.  // 归一化diff数据  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.   */   // 与other共享data数据,把other的data数据指针传给本blob  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.   */   // 与other共享diff数据,把other的diff数据指针传给本blob  void ShareDiff(const Blob& other);  // 判断本blob与other形状是否相等    bool ShapeEquals(const BlobProto& other); protected:/*shared_ptr属于boost库的智能指针*/    // 前向传播的数据    shared_ptr<SyncedMemory> data_;    // diff是反向传播的数据即偏差    shared_ptr<SyncedMemory> diff_;    // 旧的存储Blob的形状  shared_ptr<SyncedMemory> shape_data_;    // 新的存储Blob的形状  vector<int> shape_;    //数据的个数,也就是个数*通道数*高度*宽度  (实际数据的大小)  int count_;    //元素个数  (内存最大能存储数据的大小)  int capacity_;  DISABLE_COPY_AND_ASSIGN(Blob);};  // class Blob}  // namespace caffe#endif  // CAFFE_BLOB_HPP_

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 {template <typename Dtype>  /*老的reshape方法，调用下面的新reshape*/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> /*新的reshape及其具体实现*/void Blob<Dtype>::Reshape(const vector<int>& shape) {  CHECK_LE(shape.size(), kMaxBlobAxes); //是否小于规定的最大BLOB的维度(32维)    count_ = 1;  shape_.resize(shape.size()); //首先将大小设置为vector<int> shape_; 即新的形状数据的大小    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) {    // 检查形状数据是否合法      CHECK_GE(shape[i], 0);    if (count_ != 0) {      CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";    }    // 计算数据个数      count_ *= shape[i];    // 复制shape到新的和旧的形状数据      shape_[i] = shape[i];    shape_data[i] = shape[i];  }  // 判断是否大于存储的容量    if (count_ > capacity_) {    capacity_ = count_;    // 重新分配内存      data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));    diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));  }}// 所谓的reshape实际上就仅仅是复制了shape的数据而已  template <typename Dtype>void Blob<Dtype>::Reshape(const BlobShape& shape) {  CHECK_LE(shape.dim_size(), kMaxBlobAxes);// 维度是否小于32    vector<int> shape_vec(shape.dim_size());  // 复制形状数据    for (int i = 0; i < shape.dim_size(); ++i) {    shape_vec[i] = shape.dim(i);  }  // 调用新的reshape函数    Reshape(shape_vec);}/*依照其他blob来修改当前blob的形状*/template <typename Dtype>void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {  Reshape(other.shape());}/*blob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) {  Reshape(num, channels, height, width);  //先初始化容量为0，然后用reshape来分配内存了}/*blob构造函数*/template <typename Dtype>Blob<Dtype>::Blob(const vector<int>& shape)  // capacity_ must be initialized before calling Reshape  : capacity_(0) {  Reshape(shape);}/*返回gpu中blob对象中数据的内存地址*/template <typename Dtype> const int* Blob<Dtype>::gpu_shape() const {  CHECK(shape_data_);  return (const int*)shape_data_->gpu_data();}/*返回cpu中blob对象中数据的内存地址*/template <typename Dtype>const Dtype* Blob<Dtype>::cpu_data() const {  CHECK(data_);  return (const Dtype*)data_->cpu_data();}/*调用SyncedMemory的set_cpu_data函数来设置cpu的数据的内存地址,并清空数据*/template <typename Dtype>void Blob<Dtype>::set_cpu_data(Dtype* data) {  CHECK(data);  data_->set_cpu_data(data);}/*返回gpu中blob对象中数据的内存地址*/template <typename Dtype>const Dtype* Blob<Dtype>::gpu_data() const {  CHECK(data_);  return (const Dtype*)data_->gpu_data();}/*返回cpu中blob对象中数据的导数的内存地址*/template <typename Dtype>const Dtype* Blob<Dtype>::cpu_diff() const {  CHECK(diff_);  return (const Dtype*)diff_->cpu_data();}/*返回gpu中blob对象中数据的导数的内存地址*/template <typename Dtype>const Dtype* Blob<Dtype>::gpu_diff() const {  CHECK(diff_);  return (const Dtype*)diff_->gpu_data();}//调用SyncedMemory.cpp中的mutable_cpu_data()template <typename Dtype>Dtype* Blob<Dtype>::mutable_cpu_data() {  CHECK(data_);  return static_cast<Dtype*>(data_->mutable_cpu_data());}//调用SyncedMemory.cpp中的mutable_gpu_data()template <typename Dtype>Dtype* Blob<Dtype>::mutable_gpu_data() {  CHECK(data_);  return static_cast<Dtype*>(data_->mutable_gpu_data());}//调用SyncedMemory.cpp中的mutable_cpu_data()template <typename Dtype>Dtype* Blob<Dtype>::mutable_cpu_diff() {  CHECK(diff_);  return static_cast<Dtype*>(diff_->mutable_cpu_data());}//调用SyncedMemory.cpp中的mutable_gpu_data()template <typename Dtype>Dtype* Blob<Dtype>::mutable_gpu_diff() {  CHECK(diff_);  return static_cast<Dtype*>(diff_->mutable_gpu_data());}// 当前blob数据的指针指向其他blob的数据,以实现共享datatemplate <typename Dtype>void Blob<Dtype>::ShareData(const Blob& other) {  CHECK_EQ(count_, other.count());  data_ = other.data();}// 当前blob数据的指针指向其他blob的数据,以实现共享difftemplate <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; }// Update是计算data=-1 * diff + data  // 更新data_的数据，合并data与difftemplate <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:    // perform computation on CPU    // axpby即alpha * x plus beta *y 这个含义,blas的函数命名真是见名知意    // caffe_axpy计算的是Y=alpha * X + Y ，其中alpha=-1了这里    // 存储的时候用到了mutable_cpu_data，防止其他线程访问      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    // 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;}// 计算data的L1范数  // 调用math_function.hpp中的函数caffe_cpu_asum()和caffe_gpu_asum// 实现求cpu_data或者gpu_data中每个元素绝对值的和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;}// 计算diff的L1范数 // 调用math_function.hpp中的函数caffe_cpu_asum()和caffe_gpu_asum// 实现求cpu_diff或者gpu_diff中每个元素绝对值的和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;}// 计算sum of square of data(L2范数)  // 调用math_function.hpp中的中的函数caffe_cpu_dot(),caffe_cpu_strided_dot(),caffe_gpu_dot(), caffe_gpu_strided_dot()// 实现求cpu_data或者gpu_data中每个元素绝对值的平方的和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;}// 计算sum of square of diff(L2范数)  // 调用math_function.hpp中的中的函数caffe_cpu_dot(),caffe_cpu_strided_dot(),caffe_gpu_dot(), caffe_gpu_strided_dot()// 实现求cpu_diff或者gpu_diff中每个元素绝对值的平方的和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;}// 将data部分乘以一个因子scale_factor  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;}// 将diff部分乘以一个因子sacle_factor  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();  }}//两个blob的形状是否一样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 --    // 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();  }  // 如果不是旧的blob则直接判断    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;}// 从别的blob进行复制  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);//复制shape数据     } else {      LOG(FATAL) << "Trying to copy blobs of different sizes.";    }  }  switch (Caffe::mode()) {  case Caffe::GPU:    // GPU复制diff      if (copy_diff) {      // 这都用 template <> void caffe_copy<float>(const int N, const float* X, float* Y) { cblas_scopy(N, X, 1, Y, 1); }        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;    // CPU复制diff    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.";  }}// 从定义在caffe.proto 中的一个message来复制数据template <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).      // 如果是旧的blob直接转换为新的blob中的shape数据        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);// 复制shape数据到当前blob    } else {    CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)";  }  // copy data  Dtype* data_vec = mutable_cpu_data();// 获取当前的blob在内存上的数据指针，该指针是互斥的   if (proto.double_data_size() > 0) {    CHECK_EQ(count_, proto.double_data_size());    for (int i = 0; i < count_; ++i) {      data_vec[i] = proto.double_data(i);    }  } else {    CHECK_EQ(count_, proto.data_size());    for (int i = 0; i < count_; ++i) {      data_vec[i] = proto.data(i);    }  }  if (proto.double_diff_size() > 0) {    CHECK_EQ(count_, proto.double_diff_size());    Dtype* diff_vec = mutable_cpu_diff();// 获取当前的diff在内存上的数据指针，该指针是互斥的    for (int i = 0; i < count_; ++i) {      diff_vec[i] = proto.double_diff(i);    }  } 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);    }  }}//将数据写到prototemplate <>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]);//将data写入proto    }  if (write_diff) {    const double* diff_vec = cpu_diff();    for (int i = 0; i < count_; ++i) {      proto->add_double_diff(diff_vec[i]);//将diff写入proto      }  }}template <>void Blob<float>::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 float* data_vec = cpu_data();  for (int i = 0; i < count_; ++i) {    proto->add_data(data_vec[i]);  }  if (write_diff) {    const float* diff_vec = cpu_diff();    for (int i = 0; i < count_; ++i) {      proto->add_diff(diff_vec[i]);    }  }}INSTANTIATE_CLASS(Blob);template class Blob<int>;template class Blob<unsigned int>;}  // namespace caffe