主要功能：
Blob 是Caffe作為數據傳輸的媒介，無論是網絡權重參數，還是輸入數據，都是轉化為Blob數據結構來存儲，網絡，求解器等都是直接與此結構打交道的。
其直觀的可以把它看成一個有4緯的結構體（包含數據和梯度），而實際上，它們只是一維的指針而已，其4維結構通過shape屬性得以計算出來（根據C語言的數據順序）。
其成員變量有：
protected: shared_ptr<SyncedMemory> data_;// 存放數據 shared_ptr<SyncedMemory> diff_;//存放梯度 vector<int> shape_; //存放形狀 int count_; //數據個數 int capacity_; //數據容量- 1
成員函數，見的最多的有：
const Dtype* cpu_data() const; //cpu使用的數據 void set_cpu_data(Dtype* data);//用數據塊的值來blob里面的data。 const Dtype* gpu_data() const;//返回不可更改的指針，下同 const Dtype* cpu_diff() const; const Dtype* gpu_diff() const; Dtype* mutable_cpu_data();//返回可更改的指針，下同 Dtype* mutable_gpu_data(); Dtype* mutable_cpu_diff(); Dtype* mutable_gpu_diff();- 1
總之，帶mutable_開頭的意味着可以對返回的指針內容進行更改，而不帶mutable_開頭的返回const 指針，不能對其指針的內容進行修改，
int offset(const int n, const int c = 0, const int h = 0,const int w = 0) const // 通過n,c,h,w 4個參數來計算一維向量的偏移量。 Dtype data_at(const int n, const int c, const int h,const int w) const//通過n,c,h,w 4個參數來來獲取該向量位置上的值。 Dtype diff_at(const int n, const int c, const int h,const int w) const//同上 inline const shared_ptr<SyncedMemory>& data() const { CHECK(data_); return data_;//返回數據，不能修改 } inline const shared_ptr<SyncedMemory>& diff() const { CHECK(diff_); return diff_;//返回梯度，不能修改 } Reshape(...)//reshape 有多種多態的實現，可以是四個數字，長度為四的vector，其它blob等。 if (count_ > capacity_) { capacity_ = count_; data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype))); diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype))); }//當空間不夠的時候，需要擴大容量，reset。 - 1
源代碼：
#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; 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); 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; for (int i = 0; i < shape_.size(); ++i) { stream << shape_[i] << " "; } stream << "(" << count_ << ")"; 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(); } 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); 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. */ 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. */ 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); } 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); } 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 */ void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false, bool reshape = false); 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_; }