Caffe学习系列（17）： blob
对于blob.h文件。
先看成员变量。定义了6个保护的成员变量，包括前、后向传播的数据，新、旧形状数据（？），
数据个数及容量。
再看成员函数。包括构造函数（4个参数），reshape（改变blob形状），以及很多inline函数。
#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() : 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);//构造函数，explicit防止隐式转换 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); //输出数据维度 inline string shape_string() const { ostringstream stream; for (int i = 0; i < shape_.size(); ++i) { stream << shape_[i] << " "; } stream << "(" << count_ << ")";//数据个数 return stream.str(); } 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. */ inline int shape(int index) const { return shape_[CanonicalAxisIndex(index)]; } inline int num_axes() const { return shape_.size(); }//返回维度 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. */ inline int count(int start_axis, int end_axis) const { CHECK_LE(start_axis, end_axis);//判断维度的索引是否在范围内 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);//数据的所有维度相乘，即数据的个数 } return count; } /** * @brief Compute the volume of a slice spanning from a particular first * axis to the final axis. *给定的维度到最后的维度之间包含的数据 * @param start_axis The first axis to include in the slice. */ 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). *支持负数索引，相当于从后往前， * @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. */ 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; } /// @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 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); 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; } //同上，参数不同 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 */ //复制blob，如果diff为false的话，则复制data，否则复制diff //reshape为true，改变blob的形状 void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false, bool reshape = false); //获取内存下的数据（forward采用） 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)]; } //获取内存中diff数据（反向传播采用） 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)]; } //同步内存shared_ptr inline const shared_ptr<SyncedMemory>& data() const { CHECK(data_); return data_; } inline const shared_ptr<SyncedMemory>& diff() const { CHECK(diff_); return diff_; } //属性 const Dtype* cpu_data() const; void set_cpu_data(Dtype* data); const int* gpu_shape() const; const Dtype* gpu_data() const; void set_gpu_data(Dtype* data); const Dtype* cpu_diff() const; const Dtype* gpu_diff() const; Dtype* mutable_cpu_data(); Dtype* mutable_gpu_data(); Dtype* mutable_cpu_diff(); Dtype* mutable_gpu_diff(); //计算 void Update(); //从protobuf序列化文件中读取blob对象 void FromProto(const BlobProto& proto, bool reshape = true); //将对象序列化为protobuf中 void ToProto(BlobProto* proto, bool write_diff = false) const; //计算绝对值 /// @brief Compute the sum of absolute values (L1 norm) of the data. Dtype asum_data() const; /// @brief Compute the sum of absolute values (L1 norm) of the diff. Dtype asum_diff() const; //计算平方和 /// @brief Compute the sum of squares (L2 norm squared) of the data. 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. 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. *将别的blob的data和diff指针给这个blob，实现数据的共享 同时注意到的是，这个操作会引起这个blob里面的syncedmemory被释放， 因为shared_ptr被用=重置的时候，会调用其析构器？ 在前向传递中，对简单的复制比较有用 * 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); //判断形状是否相等 bool ShapeEquals(const BlobProto& other); protected: shared_ptr<SyncedMemory> data_;//前向传播的数据， shared_ptr<SyncedMemory> diff_;//反向传播的数据 shared_ptr<SyncedMemory> shape_data_;//旧的形状数据 vector<int> shape_;//新的形状数据 int count_;//数据的个数 int capacity_;//容量 DISABLE_COPY_AND_ASSIGN(Blob); }; // class Blob } // namespace caffe #endif // CAFFE_BLOB_HPP_
对于blob.cpp文件，主要关注几个函数的实现。
Reshape函数：将shape_和shape_data_置为新的blob大小，同时统计数据的个数，并为data和diff开辟空间。
#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>//老方法调用新方法 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);//是否小于规定的最大BLOB的维度(35维) count_ = 1; shape_.resize(shape.size());//将旧的数据大小置为新的数据大小 if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) { //shape_和shape_data_的区别就在于后者分配了空间（有什么用呢？） shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int))); } int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data()); for (int i = 0; i < shape.size(); ++i) { CHECK_GE(shape[i], 0);//检查数据是否合法 if (count_ != 0) { CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX"; } count_ *= shape[i];//数据个数 shape_[i] = shape[i];//复制shape到新的和旧的形状数据 shape_data[i] = shape[i]; } if (count_ > capacity_) {//如果超过了容量，重新分配内存 capacity_ = count_; data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype))); diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype))); } } template <typename Dtype> void Blob<Dtype>::Reshape(const BlobShape& shape) { CHECK_LE(shape.dim_size(), kMaxBlobAxes); vector<int> shape_vec(shape.dim_size()); for (int i = 0; i < shape.dim_size(); ++i) { shape_vec[i] = shape.dim(i); } Reshape(shape_vec); } template <typename Dtype> void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) { Reshape(other.shape()); } 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); } template <typename Dtype> Blob<Dtype>::Blob(const vector<int>& shape) // capacity_ must be initialized before calling Reshape : capacity_(0) { Reshape(shape); } template <typename Dtype> const int* Blob<Dtype>::gpu_shape() const { CHECK(shape_data_); return (const int*)shape_data_->gpu_data(); } //得到data template <typename Dtype> const Dtype* Blob<Dtype>::cpu_data() const { CHECK(data_); return (const Dtype*)data_->cpu_data(); } //设置data 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); } template <typename Dtype> const Dtype* Blob<Dtype>::gpu_data() const { CHECK(data_); return (const Dtype*)data_->gpu_data(); } 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); } template <typename Dtype> const Dtype* Blob<Dtype>::cpu_diff() const { CHECK(diff_); return (const Dtype*)diff_->cpu_data(); } 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()); } //关键字mutable，变量被其修饰时，即使函数为const也能修改之 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()); } //复制blob template <typename Dtype> void Blob<Dtype>::ShareData(const Blob& other) { CHECK_EQ(count_, other.count()); data_ = other.data(); } 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; } //更新 根据data_的head来更新，更新为data=-1*diff+data 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: // 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 // 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; } 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data的L1范数 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; } template <> unsigned int Blob<unsigned int>::sumsq_data() const { NOT_IMPLEMENTED; return 0; } template <> int Blob<int>::sumsq_data() const { NOT_IMPLEMENTED; return 0; } //L2范数 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: data = cpu_data(); sumsq = caffe_cpu_dot(count_, data, data); break; case SyncedMemory::HEAD_AT_GPU: case SyncedMemory::SYNCED: #ifndef CPU_ONLY data = gpu_data(); caffe_gpu_dot(count_, data, data, &sumsq); #else NO_GPU; #endif break; case SyncedMemory::UNINITIALIZED: return 0; default: LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head(); } return sumsq; } template <> unsigned int Blob<unsigned int>::sumsq_diff() const { NOT_IMPLEMENTED; return 0; } template <> int Blob<int>::sumsq_diff() const { NOT_IMPLEMENTED; return 0; } template <typename Dtype> Dtype Blob<Dtype>::sumsq_diff() const { Dtype sumsq; const Dtype* diff; if (!diff_) { return 0; } switch (diff_->head()) { case SyncedMemory::HEAD_AT_CPU: diff = cpu_diff(); sumsq = caffe_cpu_dot(count_, diff, diff); break; case SyncedMemory::HEAD_AT_GPU: case SyncedMemory::SYNCED: #ifndef CPU_ONLY diff = gpu_diff(); caffe_gpu_dot(count_, diff, diff, &sumsq); break; #else NO_GPU; #endif case SyncedMemory::UNINITIALIZED: return 0; default: LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head(); } return sumsq; } template <> void Blob<unsigned int>::scale_data(unsigned int scale_factor) { NOT_IMPLEMENTED; } template <> void Blob<int>::scale_data(int scale_factor) { NOT_IMPLEMENTED; } //将data部分乘一个因子 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(); } } //两个blob的shape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()) {//判断是否是旧的blob（为何能判断？） // 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(); } //不是则复制判断 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; } //复制diff和data 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."; } } // template <typename Dtype> void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) { if (reshape) { vector<int> shape; if (proto.has_num() || proto.has_channels() || //如果是旧的blob则直接转为新的blob中的数据 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 { CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)"; } // copy data复制data和diff Dtype* data_vec = mutable_cpu_data();//获取当前数据的互斥指针 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(); 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); } } } template <> void Blob<double>::ToProto(BlobProto* proto, bool write_diff) const { proto->clear_shape(); for (int i = 0; i < shape_.size(); ++i) { proto->mutable_shape()->add_dim(shape_[i]); } proto->clear_double_data(); proto->clear_double_diff(); const double* data_vec = cpu_data(); for (int i = 0; i < count_; ++i) { proto->add_double_data(data_vec[i]); } if (write_diff) { const double* diff_vec = cpu_diff(); for (int i = 0; i < count_; ++i) { proto->add_double_diff(diff_vec[i]); } } } 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
Blob：4个维度 n x c x h x w；
bottom[0] 、bottom[1]代表该层有几个输入。
bottom[0]->count(): 输入中，元素的总维数（个数）
bottom[0]->nums(): 输入中，块（block）的个数，该参数还对应batch_size,即同时输入了几张图片
c：是卷积核（filter）的个数，每个卷积核产生一个通道的输出；在输入层，c直接就是图像的通道数；
还有一个变量，dim;：每个块的维度（元素个数）
形象化：
| xxxxx | xxxxx | xxxxx | xxxxx | xxxxx | xxxxx | xxxxx | xxxxx |
上图，nums = 8， dim = 5， count = 5*8 =40；
参考：http://blog.csdn.net/qq_14975217/article/details/51524042
http://blog.csdn.net/xizero00/article/details/50886829
http://www.cnblogs.com/louyihang-loves-baiyan/p/5149628.html
关于常见的BLAS函数，参考：http://www.cnblogs.com/huashiyiqike/p/3886670.html
关于protobuf，参考：https://www.ibm.com/developerworks/cn/linux/l-cn-gpb/
