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由于想梳理data_layer的过程，整理一半发现有几个非常重要的头文件就是题目列出的这几个：
追本溯源，先从根基开始学起。这里面都是些什么鬼呢？
common类
命名空间的使用：google、cv、caffe{boost、std}。然后在项目中就可以随意使用google、opencv、c++的标准库、以及c++高级库boost。caffe采用单例模式封装boost的智能指针（caffe的灵魂）、std一些标准的用法、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？我也不是很清楚，知乎的一个解释：
随机数在caffe中是非常重要的，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最终的训练结果，其他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。至于想每次产生相同的随机数，只要设定固定的种子即可，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.
// 检测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另一个类
// 禁止某个类通过赋值来初始化另一个类
#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
//下面这一块就是设置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
// 一开始的值是NULL
static boost::thread_specific_ptr<Caffe> thread_instance_;
Caffe& Caffe::Get() {
if (!thread_instance_.get()) {// 如果当前线程没有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);
// 采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头文件里面
explicit Generator(unsigned int seed) : rng_(new caffe::rng_t(seed)) {}// 采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;// 判断是否log了curand的可用性，如果没有则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之前必须先执行cudasetDevice函数
CUDA_CHECK(cudaSetDevice(device_id));
// 清理以前的句柄
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
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原文地址：http://blog.csdn.net/langb2014/article/details/50998340
