梳理caffe代码common(八)

女爷i 2022-03-30 13:14 232阅读 0赞

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 <boost/shared_ptr.hpp>
    #include <gflags/gflags.h>
    #include <glog/logging.h>
    
    #include <climits>
    #include <cmath>
    #include <fstream>  // NOLINT(readability/streams)
    #include <iostream>  // NOLINT(readability/streams)
    #include <map>
    #include <set>
    #include <sstream>
    #include <string>
    #include <utility>  // pair
    #include <vector>
    
    #include "caffe/util/device_alternate.hpp"
    
    // Convert macro to string
    // 将宏转换为字符串
    #define STRINGIFY(m) #m
    #define AS_STRING(m) STRINGIFY(m)
    
    // gflags 2.1 issue: namespace google was changed to gflags without warning.
    // Luckily we will be able to use GFLAGS_GFLAGS_H_ to detect if it is version
    // 2.1. If yes, we will add a temporary solution to redirect the namespace.
    // TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
    // remove the following hack.
    // 检測gflags2.1
    #ifndef GFLAGS_GFLAGS_H_
    namespace gflags = google;
    #endif  // GFLAGS_GFLAGS_H_
    
    // Disable the copy and assignment operator for a class.
    // 禁止某个类通过构造函数直接初始化还有一个类
    // 禁止某个类通过赋值来初始化还有一个类
    #define DISABLE_COPY_AND_ASSIGN(classname) \
    private:\
      classname(const classname&);\
      classname& operator=(const classname&)
    
    // Instantiate a class with float and double specifications.
    #define INSTANTIATE_CLASS(classname) \
      char gInstantiationGuard##classname; \
      template class classname<float>; \
      template class classname<double>
    
    // 初始化GPU的前向传播函数
    #define INSTANTIATE_LAYER_GPU_FORWARD(classname) \
      template void classname<float>::Forward_gpu( \
          const std::vector<Blob<float>*>& bottom, \
          const std::vector<Blob<float>*>& top); \
      template void classname<double>::Forward_gpu( \
          const std::vector<Blob<double>*>& bottom, \
          const std::vector<Blob<double>*>& top);
    
    // 初始化GPU的反向传播函数
    #define INSTANTIATE_LAYER_GPU_BACKWARD(classname) \
      template void classname<float>::Backward_gpu( \
          const std::vector<Blob<float>*>& top, \
          const std::vector<bool>& propagate_down, \
          const std::vector<Blob<float>*>& bottom); \
      template void classname<double>::Backward_gpu( \
          const std::vector<Blob<double>*>& top, \
          const std::vector<bool>& propagate_down, \
          const std::vector<Blob<double>*>& bottom)
    
    // 初始化GPU的前向反向传播函数
    #define INSTANTIATE_LAYER_GPU_FUNCS(classname) \
      INSTANTIATE_LAYER_GPU_FORWARD(classname); \
      INSTANTIATE_LAYER_GPU_BACKWARD(classname)
    
    // A simple macro to mark codes that are not implemented, so that when the code
    // is executed we will see a fatal log.
    // NOT_IMPLEMENTED实际上调用的LOG(FATAL) << "Not Implemented Yet"
    #define NOT_IMPLEMENTED LOG(FATAL) << "Not Implemented Yet"
    
    // See PR #1236
    namespace cv { class Mat; }
    /*
    Caffe类里面有个RNG。RNG这个类里面还有个Generator类在RNG里面会用到Caffe里面的Get()函数来获取一个新的Caffe类的实例。然后RNG里面用到了Generator。

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 <boost/thread.hpp>
    #include <glog/logging.h>
    #include <cmath>
    #include <cstdio>
    #include <ctime>
    
    #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NULL
    static boost::thread_specific_ptr<Caffe> 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glog
    void 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<RNG> 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));
    <span style="font-family:Microsoft YaHei;">}</span>
    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<caffe::rng_t> 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<void*>(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<caffe::rng_t> 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<void*>(generator_->rng());
    }
    // cublas的geterrorstring
    const 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getlasterrorstring
    const 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