caffe tool分析之----caffe.cpp

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于 2019-08-03 10:19:36 发布

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CC 4.0 BY-SA版权

分类专栏： caffe

本文链接：https://blog.youkuaiyun.com/langzijing/article/details/98311791

#ifdef WITH_PYTHON_LAYER
#include "boost/python.hpp"
namespace bp = boost::python;
#endif

#include <gflags/gflags.h>
#include <glog/logging.h>

#include <cstring>
#include <map>
#include <string>
#include <vector>

#include "boost/algorithm/string.hpp"
#include "caffe/caffe.hpp"
#include "caffe/util/signal_handler.h"

using caffe::Blob;
using caffe::Caffe;
using caffe::Net;
using caffe::Layer;
using caffe::Solver;
using caffe::shared_ptr;
using caffe::string;
using caffe::Timer;
using caffe::vector;
using std::ostringstream;
/*下面利用glog来实现caffe命令的一些参数，比较方便，可以在终端中运行caffe可以看到这些参数help信息*/
DEFINE_string(gpu, "",
    "Optional; run in GPU mode on given device IDs separated by ','."
    "Use '-gpu all' to run on all available GPUs. The effective training "
    "batch size is multiplied by the number of devices.");
DEFINE_string(solver, "",
    "The solver definition protocol buffer text file.");
DEFINE_string(model, "",
    "The model definition protocol buffer text file.");
DEFINE_string(phase, "",
    "Optional; network phase (TRAIN or TEST). Only used for 'time'.");
DEFINE_int32(level, 0,
    "Optional; network level.");
DEFINE_string(stage, "",
    "Optional; network stages (not to be confused with phase), "
    "separated by ','.");
DEFINE_string(snapshot, "",
    "Optional; the snapshot solver state to resume training.");
DEFINE_string(weights, "",
    "Optional; the pretrained weights to initialize finetuning, "
    "separated by ','. Cannot be set simultaneously with snapshot.");
DEFINE_int32(iterations, 50,
    "The number of iterations to run.");
DEFINE_string(sigint_effect, "stop",
             "Optional; action to take when a SIGINT signal is received: "
              "snapshot, stop or none.");
DEFINE_string(sighup_effect, "snapshot",
             "Optional; action to take when a SIGHUP signal is received: "
             "snapshot, stop or none.");

// A simple registry for caffe commands.
typedef int (*BrewFunction)(); //上面命令参数的处理函数原型。
typedef std::map<caffe::string, BrewFunction> BrewMap;
BrewMap g_brew_map; //存放命令参数和处理函数的map

/*注册参数和处理函数的宏定义，这里为啥比较麻烦的利用创建一个类来实现注册后面会有解释。
宏的参数就是命令参数名称，处理函数也叫同一个名称*/
#define RegisterBrewFunction(func) \  
namespace { \
class __Registerer_##func { \
 public: /* NOLINT */ \
  __Registerer_##func() { \
    g_brew_map[#func] = &func; \ //命令参数和对应function填入map， #func是把func转换为字符串
  } \
}; \
__Registerer_##func g_registerer_##func; \
}

static BrewFunction GetBrewFunction(const caffe::string& name) { //通过name来获得处理函数
  if (g_brew_map.count(name)) { // count会返回name为key的元素的数量，对于map来说，不能重复，有的话返回1,没有返回0
    return g_brew_map[name];
  } else {
    LOG(ERROR) << "Available caffe actions:";
    for (BrewMap::iterator it = g_brew_map.begin();
         it != g_brew_map.end(); ++it) {
      LOG(ERROR) << "\t" << it->first;
    }
    LOG(FATAL) << "Unknown action: " << name;
    return NULL;  // not reachable, just to suppress old compiler warnings.
  }
}

// Parse GPU ids or use all available devices
static void get_gpus(vector<int>* gpus) {
  if (FLAGS_gpu == "all") {
    int count = 0;
#ifndef CPU_ONLY
    CUDA_CHECK(cudaGetDeviceCount(&count)); //获得gpu的信息
#else
    NO_GPU; //#define NO_GPU LOG(FATAL) << "Cannot use GPU in CPU-only Caffe: check mode."
#endif
    for (int i = 0; i < count; ++i) {
      gpus->push_back(i);
    }
  } else if (FLAGS_gpu.size()) {