Caffe源码：blob 分析

目录

• 目录
• 简单介绍
• 源代码分析
  • Reshape 函数
  • Blob 构造函数
  • data_数据操作函数
  • 反向传播导数diff_ 操作函数
  • ShareData 函数
  • Updata 函数
  • asum_data 函数
  • asum_diff 函数
  • sumsq_data 函数
  • sumsq_diff函数
  • scale_data 函数
  • scale_diff函数
  • ShapeEquals函数
  • CopyFrom 函数
  • FromProto 函数
  • ToProto 函数

    简单介绍

    Blob 在caffe源码 blob.hpp中是一个模板类。 
    protected 的成员变量有：data_ , diff_ , shape_ , count_ , capacity_ ，其中data_ 和 diff_ 是共享SyncedMemory 类（在syncedmem的源码中定义）的智能指针，shape_是int型的vector，count_ 和capacity_ 是整型变量。 
    其成员函数主要有：Reshape 、ReshapeLike、SharedData、 Updata 等等。 
    blob.hpp 包含了caffe.pb.h ，说明caffe protobuf 会向blob 传递参数

    源代码分析

  • 1.Reshape 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); }                                       //该函数将num,channels,height,width传递给vector 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    count_ *= shape[i];    shape_[i] = shape[i];  }  if (count_ > capacity_) {    capacity_ = count_;      data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));    diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));  }                                    //由于count_超过了当前capacity_ 因此需要重新分配内存空间}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}template <typename Dtype>void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {  Reshape(other.shape());}                                 //用已知的Blob的shape来对shape_ 进行reshape

    
    

    2.Blob 构造函数：

    //用构造函数的重载的方法定义2个构造函数，以便提供不同的初始化的方法。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num，channels，height， width 初始化template <typename Dtype>Blob<Dtype>::Blob(const vector<int>& shape)  // capacity_ must be initialized before calling Reshape  : capacity_(0) {  Reshape(shape);}//用shape 初始化

    3.data_数据操作函数：

    template <typename Dtype>const Dtype* Blob<Dtype>::cpu_data() const {  CHECK(data_);  return (const Dtype*)data_->cpu_data();} //返回cpu 中的数据template <typename Dtype>void Blob<Dtype>::set_cpu_data(Dtype* data) {  CHECK(data);  data_->set_cpu_data(data);}// 清空cpu 数据template <typename Dtype>const Dtype* Blob<Dtype>::gpu_data() const {  CHECK(data_);  return (const Dtype*)data_->gpu_data();}//返回gpu 中的数据

    
    

    4.反向传播导数diff_ 操作函数：

    template <typename Dtype>const Dtype* Blob<Dtype>::cpu_diff() const {  CHECK(diff_);  return (const Dtype*)diff_->cpu_data();}//返回cpu 中的数据template <typename Dtype>const Dtype* Blob<Dtype>::gpu_diff() const {  CHECK(diff_);  return (const Dtype*)diff_->gpu_data();}//返回gpu 中的数据

    
    

    5.ShareData 函数：

    template <typename Dtype>void Blob<Dtype>::ShareData(const Blob& other) {  CHECK_EQ(count_, other.count());  data_ = other.data();}//当前的blob 的data_ 指向已知blob的数据template <typename Dtype>void Blob<Dtype>::ShareDiff(const Blob& other) {  CHECK_EQ(count_, other.count());  diff_ = other.diff();}//当前的blob 的diff_ 指向已知blob的反向传播导数

    6.Updata 函数：

    该函数用于参数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    caffe_axpy<Dtype>(count_, Dtype(-1),        static_cast<const Dtype*>(diff_->cpu_data()),        static_cast<Dtype*>(data_->mutable_cpu_data()));  //data_-diff_    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    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.";  }}

7.asum_data 函数：

template <typename Dtype>Dtype Blob<Dtype>::asum_data() const {  if (!data_) { return 0; }  switch (data_->head()) {  case SyncedMemory::HEAD_AT_CPU:         //数据在cpu上    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;}  // 返回data_ 中所有 element 的绝对值之和

8.asum_diff 函数：

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;}  // 返回diff_ 中所有 element 的绝对值之和

9.sumsq_data 函数：

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: //数据在cpu上    data = cpu_data();    sumsq = caffe_cpu_dot(count_, data, data);  //sumsq = sum(data[i]^2)    break;  case SyncedMemory::HEAD_AT_GPU:  case SyncedMemory::SYNCED:#ifndef CPU_ONLY    data = gpu_data();    //数据在gpu上    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;}//返回 data_ 中所有 element 的平方和

10.sumsq_diff函数：

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;}//返回 diff_ 中所有 element 的平方和

11.scale_data 函数:

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();  }}// 给data乘以scale_factor

12.scale_diff函数

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();  }}// 给diff乘以scale_factor

13.ShapeEquals函数：

template <typename Dtype>bool Blob<Dtype>::ShapeEquals(const BlobProto& other) {//BlobProto 是定义在caffe.proto 中的一个message，其字段有 data， diff， shape， num， channels， height， width  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true

14.CopyFrom 函数：

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source 拷贝数据 ， copy_diff控制是拷贝diff还是data

15.FromProto 函数：

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).      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  }}

16.ToProto 函数：

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>;}  //

本文转自：http://blog.csdn.net/seven_first/article/details/47398613#1reshape-函数