Caffe学习 四 框架概览
1. Caffe核心代码
• blob[.cpp .h] 基本的数据结构Blob类
• common[.cpp .h] 定义Caffe类
• internal_thread[.cpp .h] 使用boost::thread线程库
• net[.cpp .h] 网络结构类Net
• solver[.cpp .h] 优化方法类Solver
• data_transformer[.cpp .h] 输入数据的基本操作类DataTransformer
• syncedmem[.cpp .h] 分配内存和释放内存类CaffeMallocHost，用于同步GPU，CPU数据
• layer[.cpp .h] 层类Layer
• layers/ 此文件夹下面的代码全部至少继承了类Layer, 从layer_factory中注册继承
2. Caffe三级结构（Blobs,Layers,Nets）
• Blob：用于数据的保存、交换和操作，Caffe基础存储结构
• Layer：用于模型和计算的基础
• Net：整合连接Layers
Caffe 通过 SyncedMemory 和 Blob 封装了底层数据，为 Caffe 框架上的其他组件提供最基础的数据抽象，后面的 Layer 参数，Net 参数以及 Solver 的参数等都是 Blob 数据，所以理解 Blob 抽象和管理数据的实现方式有助于后续 Caffe 源码的阅读，也是阅读 Caffe 源码的第一步。
代码注释参考luoyetx's blog的Caffe 源码阅读 Blob。
3.Blob
include\caffe\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; //lob 原本在 Caffe 中被表示为一个 4 维数组 (num x channel x height x width)，现在可以表示多维数组，最高维数由宏 kMaxBlobAxes 确定 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禁止单参数构造函数的隐式转换 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); //Reshape函数将num,channels,height,width传递给vector shape_ /* Blob作为一个最基础的类，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操作中来分配空间。 改变Blob大小时，内存将被重新分配。 */ /** * @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); //根据shape来初始化shape_和shape_data_，以及为blob分配空间 inline string shape_string() const { ostringstream stream; for (int i = 0; i < shape_.size(); ++i) { stream << shape_[i] << " "; } stream << "(" << count_ << ")"; return stream.str(); } //iniline节省调用开销 //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. */ inline int shape(int index) const { return shape_[CanonicalAxisIndex(index)]; } //获取index维的大小 inline int num_axes() const { return shape_.size(); } //获取维的个数 inline int count() const { return count_; } //获取当前data的大小 /** * @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. */ //统计Blob某一片（slice）的容量（volume） ////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()); //比较大小或者是否相等，谷歌的一个日志库GLOG 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 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. */ //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。 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; } //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 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， //偏差是根据对应的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 */ //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;//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(); 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 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。 */ 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 ，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://智能指针 shared_ptr<SyncedMemory> data_;//用于正向传播的数据 shared_ptr<SyncedMemory> diff_;//diff_存储偏差 shared_ptr<SyncedMemory> shape_data_;//存储Blob的形状 vector<int> shape_;//存储Blob的形状 int count_;//元素个数，个数*通道数*高度*宽度 int capacity_;//当前元素个数 DISABLE_COPY_AND_ASSIGN(Blob); }; // class Blob } // namespace caffe #endif // CAFFE_BLOB_HPP_
4.Layer & Net
Layer与Theano卷积神经网络中定义的Layer接近。
接受Blob的输入，进行正向和反向传播。
以ConvolutionLayer为例
include\caffe\layers\conv_layer.hpp (更多细节在base_conv_layer.hpp中）。
protected: virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); virtual void Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom); virtual void Backward_gpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom); virtual inline bool reverse_dimensions() { return false; } virtual void compute_output_shape();
Net就是之前随笔中的网络参数和自定义网络。
