因为想梳理data_layer的过程。整理一半发现有几个很重要的头文件就是题目列出的这几个：

追本溯源，先从根基開始学起。这里面都是些什么鬼呢？

common类

命名空间的使用：google、cv、caffe{boost、std}。

然后在项目中就能够任意使用google、opencv、c++的标准库、以及c++高级库boost。

caffe採用单例模式封装boost的智能指针（caffe的灵魂）、std一些标准的使用方法、重要的初始化内容（随机数生成器的内容以及google的gflags和glog的初始化）。

提供一个统一的接口。方便移植和开发。为毛使用随机数？我也不是非常清楚，知乎的一个解释：

随机数在caffe中是很重要的，最重要的应用是权值的初始化，如高斯、xavier等。初始化的好坏直接影响终于的训练结果，其它的应用如训练图像的随机crop和mirror、dropout层的神经元的选择。RNG类是对Boost以及STL中随机数函数的封装，以方便使用。至于想每次产生同样的随机数，仅仅要设定固定的种子就可以。见caffe.proto中random_seed的定义：

    // If non-negative, the seed with which the Solver will initialize the Caffe

    // random number generator -- useful for reproducible results. Otherwise,

    // (and by default) initialize using a seed derived from the system clock.

    optional int64 random_seed = 20 [default = -1];

头文件：

#ifndef CAFFE_COMMON_HPP_#define CAFFE_COMMON_HPP_#include 
     
      #include 
      
       #include 
       
        #include 
        
         #include 
         
          #include 
          
            // NOLINT(readability/streams)#include 
           
             // NOLINT(readability/streams)#include 
            
           
          
         
        
       
      
     

Generator是实际产生随机数的。 */ namespace caffe { // We will use the boost shared_ptr instead of the new C++11 one mainly // because cuda does not work (at least now) well with C++11 features. using boost::shared_ptr; // Common functions and classes from std that caffe often uses. using std::fstream; using std::ios; //using std::isnan;//vc++的编译器不支持这两个函数 //using std::isinf; using std::iterator; using std::make_pair; using std::map; using std::ostringstream; using std::pair; using std::set; using std::string; using std::stringstream; using std::vector; // A global initialization function that you should call in your main function. // Currently it initializes google flags and google logging. void GlobalInit(int* pargc, char*** pargv); // A singleton class to hold common caffe stuff, such as the handler that // caffe is going to use for cublas, curand, etc. class Caffe { public: ~Caffe(); // Thread local context for Caffe. Moved to common.cpp instead of // including boost/thread.hpp to avoid a boost/NVCC issues (#1009, #1010) // on OSX. Also fails on Linux with CUDA 7.0.18. //Get函数利用Boost的局部线程存储功能实现 static Caffe& Get(); //Brew就是CPU，GPU的枚举类型，这个名字是不是来自Homebrew？？？Mac的软件包管理器，我猜的。

。。。 enum Brew { CPU, GPU }; // This random number generator facade hides boost and CUDA rng // implementation from one another (for cross-platform compatibility). class RNG { public: RNG();//利用系统的熵池或者时间来初始化RNG内部的generator_ explicit RNG(unsigned int seed); explicit RNG(const RNG&); RNG& operator=(const RNG&); void* generator(); private: class Generator; shared_ptr<Generator> generator_; }; // Getters for boost rng, curand, and cublas handles inline static RNG& rng_stream() { if (!Get().random_generator_) { Get().random_generator_.reset(new RNG()); } return *(Get().random_generator_); } #ifndef CPU_ONLY// GPU inline static cublasHandle_t cublas_handle() { return Get().cublas_handle_; }// cublas的句柄 inline static curandGenerator_t curand_generator() {//curandGenerator句柄 return Get().curand_generator_; } #endif //以下这一块就是设置CPU和GPU以及训练的时候线程并行数目吧 // Returns the mode: running on CPU or GPU. inline static Brew mode() { return Get().mode_; } // The setters for the variables // Sets the mode. It is recommended that you don't change the mode halfway // into the program since that may cause allocation of pinned memory being // freed in a non-pinned way, which may cause problems - I haven't verified // it personally but better to note it here in the header file. inline static void set_mode(Brew mode) { Get().mode_ = mode; } // Sets the random seed of both boost and curand static void set_random_seed(const unsigned int seed); // Sets the device. Since we have cublas and curand stuff, set device also // requires us to reset those values. static void SetDevice(const int device_id); // Prints the current GPU status. static void DeviceQuery(); // Parallel training info inline static int solver_count() { return Get().solver_count_; } inline static void set_solver_count(int val) { Get().solver_count_ = val; } inline static bool root_solver() { return Get().root_solver_; } inline static void set_root_solver(bool val) { Get().root_solver_ = val; } protected: #ifndef CPU_ONLY cublasHandle_t cublas_handle_;// cublas的句柄 curandGenerator_t curand_generator_;// curandGenerator句柄 #endif shared_ptr<RNG> random_generator_; Brew mode_; int solver_count_; bool root_solver_; private: // The private constructor to avoid duplicate instantiation. //避免实例化 Caffe(); // 禁止caffe这个类被复制构造函数和赋值进行构造 DISABLE_COPY_AND_ASSIGN(Caffe); }; } // namespace caffe #endif // CAFFE_COMMON_HPP_

cpp文件：

#include 
     
      #include 
      
       #include 
       
        #include 
        
         #include 
         
          #include "caffe/common.hpp"#include "caffe/util/rng.hpp"namespace caffe {// Make sure each thread can have different values.// boost::thread_specific_ptr是线程局部存储机制// 一開始的值是NULLstatic boost::thread_specific_ptr
          
            thread_instance_;Caffe& Caffe::Get() { if (!thread_instance_.get()) {// 假设当前线程没有caffe实例 thread_instance_.reset(new Caffe());// 则新建一个caffe的实例并返回 } return *(thread_instance_.get());}// random seeding// linux下的熵池下获取随机数的种子int64_t cluster_seedgen(void) { int64_t s, seed, pid; FILE* f = fopen("/dev/urandom", "rb"); if (f && fread(&seed, 1, sizeof(seed), f) == sizeof(seed)) { fclose(f); return seed; } LOG(INFO) << "System entropy source not available, " "using fallback algorithm to generate seed instead."; if (f) fclose(f); // 採用传统的基于时间来生成随机数种子 pid = getpid(); s = time(NULL); seed = std::abs(((s * 181) * ((pid - 83) * 359)) % 104729); return seed;}// 初始化gflags和glogvoid GlobalInit(int* pargc, char*** pargv) { // Google flags. ::gflags::ParseCommandLineFlags(pargc, pargv, true); // Google logging. ::google::InitGoogleLogging(*(pargv)[0]); // Provide a backtrace on segfault. ::google::InstallFailureSignalHandler();}#ifdef CPU_ONLY // CPU-only Caffe.Caffe::Caffe() : random_generator_(), mode_(Caffe::CPU),// shared_ptr
           
             random_generator_; Brew mode_; solver_count_(1), root_solver_(true) { }// int solver_count_; bool root_solver_;Caffe::~Caffe() { }// 手动设定随机数生成器的种子void Caffe::set_random_seed(const unsigned int seed) { // RNG seed Get().random_generator_.reset(new RNG(seed));
            }void Caffe::SetDevice(const int device_id) { NO_GPU;}void Caffe::DeviceQuery() { NO_GPU;}// 定义RNG内部的Generator类class Caffe::RNG::Generator { public: Generator() : rng_(new caffe::rng_t(cluster_seedgen())) {}// linux下的熵池生成随机数种子，注意typedef boost::mt19937 rng_t;这个在utils/rng.hpp头文件中面 explicit Generator(unsigned int seed) : rng_(new caffe::rng_t(seed)) {}// 採用给定的种子初始化 caffe::rng_t* rng() { return rng_.get(); }// 属性 private: shared_ptr
            
              rng_;// 内部变量};// 实现RNG内部的构造函数Caffe::RNG::RNG() : generator_(new Generator()) { }Caffe::RNG::RNG(unsigned int seed) : generator_(new Generator(seed)) { }// 实现RNG内部的运算符重载Caffe::RNG& Caffe::RNG::operator=(const RNG& other) { generator_ = other.generator_; return *this;}void* Caffe::RNG::generator() { return static_cast
             
              (generator_->rng());}#else // Normal GPU + CPU Caffe.// 构造函数，初始化cublas和curand库的句柄Caffe::Caffe() : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(), mode_(Caffe::CPU), solver_count_(1), root_solver_(true) { // Try to create a cublas handler, and report an error if failed (but we will // keep the program running as one might just want to run CPU code). // 初始化cublas并获得句柄 if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) { LOG(ERROR) << "Cannot create Cublas handle. Cublas won't be available."; } // Try to create a curand handler. if (curandCreateGenerator(&curand_generator_, CURAND_RNG_PSEUDO_DEFAULT) != CURAND_STATUS_SUCCESS || curandSetPseudoRandomGeneratorSeed(curand_generator_, cluster_seedgen()) != CURAND_STATUS_SUCCESS) { LOG(ERROR) << "Cannot create Curand generator. Curand won't be available."; }}Caffe::~Caffe() { // 销毁句柄 if (cublas_handle_) CUBLAS_CHECK(cublasDestroy(cublas_handle_)); if (curand_generator_) { CURAND_CHECK(curandDestroyGenerator(curand_generator_)); }}// 初始化CUDA的随机数种子以及cpu的随机数种子void Caffe::set_random_seed(const unsigned int seed) { // Curand seed static bool g_curand_availability_logged = false;// 推断是否log了curand的可用性。假设没有则log一次，log之后则再也不log。用的是静态变量 if (Get().curand_generator_) { // CURAND_CHECK见/utils/device_alternate.hpp中的宏定义 CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(curand_generator(), seed)); CURAND_CHECK(curandSetGeneratorOffset(curand_generator(), 0)); } else { if (!g_curand_availability_logged) { LOG(ERROR) << "Curand not available. Skipping setting the curand seed."; g_curand_availability_logged = true; } } // RNG seed // CPU code Get().random_generator_.reset(new RNG(seed));}// 设置GPU设备并初始化句柄以及随机数种子void Caffe::SetDevice(const int device_id) { int current_device; CUDA_CHECK(cudaGetDevice(¤t_device));// 获取当前设备id if (current_device == device_id) { return; } // The call to cudaSetDevice must come before any calls to Get, which // may perform initialization using the GPU. // 在Get之前必须先运行cudasetDevice函数 CUDA_CHECK(cudaSetDevice(device_id)); // 清理曾经的句柄 if (Get().cublas_handle_) CUBLAS_CHECK(cublasDestroy(Get().cublas_handle_)); if (Get().curand_generator_) { CURAND_CHECK(curandDestroyGenerator(Get().curand_generator_)); } // 创建新句柄 CUBLAS_CHECK(cublasCreate(&Get().cublas_handle_)); CURAND_CHECK(curandCreateGenerator(&Get().curand_generator_, CURAND_RNG_PSEUDO_DEFAULT)); // 设置随机数种子 CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(Get().curand_generator_, cluster_seedgen()));}// 获取设备信息void Caffe::DeviceQuery() { cudaDeviceProp prop; int device; if (cudaSuccess != cudaGetDevice(&device)) { printf("No cuda device present.\n"); return; } // #define CUDA_CHECK(condition) \ /* Code block avoids redefinition of cudaError_t error */ \ //do { \ // cudaError_t error = condition; \ // CHECK_EQ(error, cudaSuccess) << " " << cudaGetErrorString(error); \ //} while (0) CUDA_CHECK(cudaGetDeviceProperties(&prop, device)); LOG(INFO) << "Device id: " << device; LOG(INFO) << "Major revision number: " << prop.major; LOG(INFO) << "Minor revision number: " << prop.minor; LOG(INFO) << "Name: " << prop.name; LOG(INFO) << "Total global memory: " << prop.totalGlobalMem; LOG(INFO) << "Total shared memory per block: " << prop.sharedMemPerBlock; LOG(INFO) << "Total registers per block: " << prop.regsPerBlock; LOG(INFO) << "Warp size: " << prop.warpSize; LOG(INFO) << "Maximum memory pitch: " << prop.memPitch; LOG(INFO) << "Maximum threads per block: " << prop.maxThreadsPerBlock; LOG(INFO) << "Maximum dimension of block: " << prop.maxThreadsDim[0] << ", " << prop.maxThreadsDim[1] << ", " << prop.maxThreadsDim[2]; LOG(INFO) << "Maximum dimension of grid: " << prop.maxGridSize[0] << ", " << prop.maxGridSize[1] << ", " << prop.maxGridSize[2]; LOG(INFO) << "Clock rate: " << prop.clockRate; LOG(INFO) << "Total constant memory: " << prop.totalConstMem; LOG(INFO) << "Texture alignment: " << prop.textureAlignment; LOG(INFO) << "Concurrent copy and execution: " << (prop.deviceOverlap ? "Yes" : "No"); LOG(INFO) << "Number of multiprocessors: " << prop.multiProcessorCount; LOG(INFO) << "Kernel execution timeout: " << (prop.kernelExecTimeoutEnabled ? "Yes" : "No"); return;}class Caffe::RNG::Generator { public: Generator() : rng_(new caffe::rng_t(cluster_seedgen())) {} explicit Generator(unsigned int seed) : rng_(new caffe::rng_t(seed)) {} caffe::rng_t* rng() { return rng_.get(); } private: shared_ptr
              
                rng_;};Caffe::RNG::RNG() : generator_(new Generator()) { }Caffe::RNG::RNG(unsigned int seed) : generator_(new Generator(seed)) { }Caffe::RNG& Caffe::RNG::operator=(const RNG& other) { generator_.reset(other.generator_.get()); return *this;}void* Caffe::RNG::generator() { return static_cast
               
                (generator_->rng());}// cublas的geterrorstringconst char* cublasGetErrorString(cublasStatus_t error) { switch (error) { case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS"; case CUBLAS_STATUS_NOT_INITIALIZED: return "CUBLAS_STATUS_NOT_INITIALIZED"; case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED"; case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE"; case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH"; case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR"; case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED"; case CUBLAS_STATUS_INTERNAL_ERROR: return "CUBLAS_STATUS_INTERNAL_ERROR";#if CUDA_VERSION >= 6000 case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED";#endif#if CUDA_VERSION >= 6050 case CUBLAS_STATUS_LICENSE_ERROR: return "CUBLAS_STATUS_LICENSE_ERROR";#endif } return "Unknown cublas status";}// curand的getlasterrorstringconst char* curandGetErrorString(curandStatus_t error) { switch (error) { case CURAND_STATUS_SUCCESS: return "CURAND_STATUS_SUCCESS"; case CURAND_STATUS_VERSION_MISMATCH: return "CURAND_STATUS_VERSION_MISMATCH"; case CURAND_STATUS_NOT_INITIALIZED: return "CURAND_STATUS_NOT_INITIALIZED"; case CURAND_STATUS_ALLOCATION_FAILED: return "CURAND_STATUS_ALLOCATION_FAILED"; case CURAND_STATUS_TYPE_ERROR: return "CURAND_STATUS_TYPE_ERROR"; case CURAND_STATUS_OUT_OF_RANGE: return "CURAND_STATUS_OUT_OF_RANGE"; case CURAND_STATUS_LENGTH_NOT_MULTIPLE: return "CURAND_STATUS_LENGTH_NOT_MULTIPLE"; case CURAND_STATUS_DOUBLE_PRECISION_REQUIRED: return "CURAND_STATUS_DOUBLE_PRECISION_REQUIRED"; case CURAND_STATUS_LAUNCH_FAILURE: return "CURAND_STATUS_LAUNCH_FAILURE"; case CURAND_STATUS_PREEXISTING_FAILURE: return "CURAND_STATUS_PREEXISTING_FAILURE"; case CURAND_STATUS_INITIALIZATION_FAILED: return "CURAND_STATUS_INITIALIZATION_FAILED"; case CURAND_STATUS_ARCH_MISMATCH: return "CURAND_STATUS_ARCH_MISMATCH"; case CURAND_STATUS_INTERNAL_ERROR: return "CURAND_STATUS_INTERNAL_ERROR"; } return "Unknown curand status";}#endif // CPU_ONLY} // namespace caffe