caffe源码阅读之（一）：caffe.cpp

转自：https://blog.csdn.net/jiongnima/article/details/55053059

caffe.cpp文件完成对网络模型以及模型配置参数的读入和提取，提供了网络模型训练的入口函数train和对模型的测试入口函数test。文件中使用了很多gflags和glog指令，gflags是google的一个开源的处理命令行参数的库，glog是一个有效的日志记录工具。

使用caffe训练神经网络的基本过程：

　　准备数据集->配置网络结构->配置网络训练参数->对网络进行训练->使用模型接口文件调用与使用训练完成的深度神经网络

每次启动脚本文件或者从命令行输入命令开始训练深度神经网络时，总是从caffe.cpp这个文件开始对命令进行解析并执行，因此，caffe.cpp训练网络的入口，

caff.cpp文件所在的路径为：./tools/caffe.cpp

#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;

/**********************************************************************
DEFINE_string/int32 是gflags（google的一个开源的处理命令行参数的库）中的宏定义
作用是获取命令行中传入的配置的参数值，它的调用格式是（name，val，txt），其中name
是命令行中的配置项，val是配置项的值，txt是描述信息，如果命令行中没有配置变量的值，
则使用DEFINE中的默认值
**********************************************************************/
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.
/*声明一个BrewFunction函数指针类型，可以用它来定义一个函数指针
在main函数中通过这个函数指针调用相应的train或test函数
*/
typedef int (*BrewFunction)(); /*typedef 声明函数指针的方式比接直接声明要简洁*/
typedef std::map<caffe::string, BrewFunction> BrewMap;
BrewMap g_brew_map;


/*在C++宏定义中， \的作用是把内容分行显示；   #的作用是把其后的内容转换成字符串
 * 下面的#define RegisterBrewFunction(func)宏定义的作用是将参数func转化为字符串，
 * 并存储在g_brew_map这个容器中，而func对应了四种值：train/test/time/device_query，
 * 这四个函数标志了四个功能的入口， 留意下方对应的那四个函数的尾部就可得知。*/
#define RegisterBrewFunction(func) \
namespace { \
class __Registerer_##func { \
 public: /* NOLINT */ \
  __Registerer_##func() { \
    g_brew_map[#func] = &func; \
  } \
}; \
__Registerer_##func g_registerer_##func; \
}

static BrewFunction GetBrewFunction(const caffe::string& name) {
  if (g_brew_map.count(name)) { /**判断传入的name是不是train，test，device_query或time中的一种*/
    return g_brew_map[name];
  } else {
    LOG(ERROR) << "Available caffe actions:";/**对于其他传入值，打印出错误信息---不含有对应的操作*/
    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
//获取可用的gpu设备
static void get_gpus(vector<int>* gpus) {
  if (FLAGS_gpu == "all") {
    int count = 0;
#ifndef CPU_ONLY
    CUDA_CHECK(cudaGetDeviceCount(&count));
#else
    NO_GPU;
#endif
    for (int i = 0; i < count; ++i) {
      gpus->push_back(i);
    }
  } else if (FLAGS_gpu.size()) {
    vector<string> strings;
    boost::split(strings, FLAGS_gpu, boost::is_any_of(","));
    for (int i = 0; i < strings.size(); ++i) {
      gpus->push_back(boost::lexical_cast<int>(strings[i]));
    }
  } else {
    CHECK_EQ(gpus->size(), 0);
  }
}

// Parse phase from flags
//返回flags阶段，是TRAIN还是TEST，用于进一步分析
caffe::Phase get_phase_from_flags(caffe::Phase default_value) {
  if (FLAGS_phase == "")
    return default_value;
  if (FLAGS_phase == "TRAIN")
    return caffe::TRAIN;
  if (FLAGS_phase == "TEST")
    return caffe::TEST;
  LOG(FATAL) << "phase must be \"TRAIN\" or \"TEST\"";
  return caffe::TRAIN;  // Avoid warning
}

// Parse stages from flags
vector<string> get_stages_from_flags() {
  vector<string> stages;
  boost::split(stages, FLAGS_stage, boost::is_any_of(","));
  return stages;
}

// caffe commands to call by
//     caffe <command> <args>
//
// To add a command, define a function "int command()" and register it with
// RegisterBrewFunction(action);

// Device Query: show diagnostic information for a GPU device.
//查询GPU设备信息

int device_query() {
  LOG(INFO) << "Querying GPUs " << FLAGS_gpu;
  vector<int> gpus;
  get_gpus(&gpus);
  for (int i = 0; i < gpus.size(); ++i) {
    caffe::Caffe::SetDevice(gpus[i]);
    caffe::Caffe::DeviceQuery();
  }
  return 0;
}
RegisterBrewFunction(device_query);

// Load the weights from the specified caffemodel(s) into the train and
// test nets.
// 赋值Layers
void CopyLayers(caffe::Solver<float>* solver, const std::string& model_list) {
  std::vector<std::string> model_names;
  boost::split(model_names, model_list, boost::is_any_of(","));
  for (int i = 0; i < model_names.size(); ++i) {
    LOG(INFO) << "Finetuning from " << model_names[i];
    solver->net()->CopyTrainedLayersFrom(model_names[i]);
    for (int j = 0; j < solver->test_nets().size(); ++j) {
      solver->test_nets()[j]->CopyTrainedLayersFrom(model_names[i]);
    }
  }

// Translate the signal effect the user specified on the command-line to the
// corresponding enumeration.
//将交互端传来的string类型的标志转换成枚举类型的变量
caffe::SolverAction::Enum GetRequestedAction(
    const std::string& flag_value) {
  if (flag_value == "stop") {
    return caffe::SolverAction::STOP;
  }
  if (flag_value == "snapshot") {
    return caffe::SolverAction::SNAPSHOT;
  }
  if (flag_value == "none") {
    return caffe::SolverAction::NONE;
  }
  LOG(FATAL) << "Invalid signal effect \""<< flag_value << "\" was specified";
}

// Train / Finetune a model.
int train() {
  /**CHECK_GT是一个宏定义，用来检查传入的参数中“--solver=”后边是否为空，若为空，
  则报错并输出错误信息，但此处并没有对文件路径合法性检查*/
  CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train.";
  /*检查命令行中输入的 --snapshot和--weights信息，这两者可以不设置或只设置其一*/
  CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size())
      << "Give a snapshot to resume training or weights to finetune "
      "but not both.";
  vector<string> stages = get_stages_from_flags();

  /********************************************************************
    实例化SolverParameter类，该类报错了solver参数和网络参数的优化规则，SolverParameter是
    通过Google ProtocolBuffer自动生成的一个类， 在SharedCtor函数里对网络的各个参数进行了初始化设置
   ********************************************************************/
  caffe::SolverParameter solver_param;
  /**从传入的slover文件读入网络的各个参数并传给solver_param对象*/
  caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param);

  solver_param.mutable_train_state()->set_level(FLAGS_level);
  for (int i = 0; i < stages.size(); i++) {
    solver_param.mutable_train_state()->add_stage(stages[i]);
  }

  // If the gpus flag is not provided, allow the mode and device to be set
  // in the solver prototxt.
  /*根据命令参数 -gpu或者solver.prototxt中提供的GPU配置信息设置GPU*/
  if (FLAGS_gpu.size() == 0
      && solver_param.has_solver_mode()
      && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
      if (solver_param.has_device_id()) {
          FLAGS_gpu = "" +
              boost::lexical_cast<string>(solver_param.device_id());
      } else {  // Set default GPU if unspecified
          FLAGS_gpu = "" + boost::lexical_cast<string>(0);
      }
  }

  vector<int> gpus;//GPU编号
  get_gpus(&gpus); //获取可用的GPU
  if (gpus.size() == 0) { //如果没有可用的GPU，则设置运行模式为GPU
    LOG(INFO) << "Use CPU.";
    Caffe::set_mode(Caffe::CPU);
  } else {
    //若存在多个GPU，则使用多个GPU一起工作
    ostringstream s;
    for (int i = 0; i < gpus.size(); ++i) {
      s << (i ? ", " : "") << gpus[i];
    }
    LOG(INFO) << "Using GPUs " << s.str();
#ifndef CPU_ONLY
    cudaDeviceProp device_prop;
    for (int i = 0; i < gpus.size(); ++i) {
      /*cudaGetDeviceProperties函数用于获取设备的信息*/
      cudaGetDeviceProperties(&device_prop, gpus[i]);
      /*输出设备的型号（名称）*/
      LOG(INFO) << "GPU " << gpus[i] << ": " << device_prop.name;
    }
#endif
    //GPU设置
    solver_param.set_device_id(gpus[0]);
    Caffe::SetDevice(gpus[0]);
    Caffe::set_mode(Caffe::GPU);
    Caffe::set_solver_count(gpus.size());
  }

    /******************************************************************
    括号内的两个函数，从最右边的开始执行，将 “stop”“snapshot”“none”
    转换成标准信号，即解析。
    函数执行结果是将SIGINT_action对应STOP， SIGHUP_action对应SNAPSHOT
    ******************************************************************/
  caffe::SignalHandler signal_handler(
        GetRequestedAction(FLAGS_sigint_effect),
        GetRequestedAction(FLAGS_sighup_effect));

  if (FLAGS_snapshot.size()) {
    solver_param.clear_weights();
  } else if (FLAGS_weights.size()) {
    solver_param.clear_weights();
    solver_param.add_weights(FLAGS_weights);
  }
  /*通过智能指针创建solver指针，指向caffe::Solver对象*/
  shared_ptr<caffe::Solver<float> >
      solver(caffe::SolverRegistry<float>::CreateSolver(solver_param));

  solver->SetActionFunction(signal_handler.GetActionFunction());
  /*******************************************************************************
    判断用户是否定义了snapshot或者weights这两个参数中的一个，如果定义了则需要通过
    Solver提供的接口从snapshot或者weights文件中去读取已经训练好的网络的参数，继续训练
  ********************************************************************************/
  if (FLAGS_snapshot.size()) {
    //打印信息，从上一个中断的训练结果文件继续训练
    LOG(INFO) << "Resuming from " << FLAGS_snapshot;
    //读入sanpshot文件内容
    solver->Restore(FLAGS_snapshot.c_str());
  }
//如果有不止一个GPU，用线程并行优化网络
  LOG(INFO) << "Starting Optimization";
  if (gpus.size() > 1) {
#ifdef USE_NCCL
    caffe::NCCL<float> nccl(solver);
    nccl.Run(gpus, FLAGS_snapshot.size() > 0 ? FLAGS_snapshot.c_str() : NULL);
#else
    LOG(FATAL) << "Multi-GPU execution not available - rebuild with USE_NCCL";
#endif
  } else {
    solver->Solve();//执行网络模型优化，跳到solver文件执行
  }
  LOG(INFO) << "Optimization Done.";
  return 0;
}
RegisterBrewFunction(train);

//test时间对网络模型的测试和评估
// Test: score a model.
int test() {
  //判断命令行参数里是否传入网络模型
  CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to score.";
  //判断命令行参数里是否传入了训练好的模型参数文件，后缀为caffemodel
  CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
  vector<string> stages = get_stages_from_flags();

  // Set device id and mode
  vector<int> gpus;
  get_gpus(&gpus);//查询GPU设备信息，无可用则设置为运行模式为CPU
  if (gpus.size() != 0) {
    LOG(INFO) << "Use GPU with device ID " << gpus[0];
#ifndef CPU_ONLY
    cudaDeviceProp device_prop;
    cudaGetDeviceProperties(&device_prop, gpus[0]);
    LOG(INFO) << "GPU device name: " << device_prop.name;
#endif
    Caffe::SetDevice(gpus[0]);
    Caffe::set_mode(Caffe::GPU);
  } else {
    LOG(INFO) << "Use CPU.";
    Caffe::set_mode(Caffe::CPU);
  }
  // Instantiate the caffe net.
  // 创建一个网络模型对象
  Net<float> caffe_net(FLAGS_model, caffe::TEST, FLAGS_level, &stages);
  caffe_net.CopyTrainedLayersFrom(FLAGS_weights);//加载训练好的模型文件
  LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";

  vector<int> test_score_output_id;
  vector<float> test_score;
  float loss = 0;
  for (int i = 0; i < FLAGS_iterations; ++i) {//FLAGS_iterations是配置的测试迭代次数
    float iter_loss;
    const vector<Blob<float>*>& result =
        caffe_net.Forward(&iter_loss); //把测试数据流在网络中做前向传播
    loss += iter_loss; //累加每次的损失
    int idx = 0;
    for (int j = 0; j < result.size(); ++j) {
      const float* result_vec = result[j]->cpu_data();
      for (int k = 0; k < result[j]->count(); ++k, ++idx) {
        const float score = result_vec[k];
        if (i == 0) {
          test_score.push_back(score);
          test_score_output_id.push_back(j);
        } else {
          test_score[idx] += score;
        }
        const std::string& output_name = caffe_net.blob_names()[
            caffe_net.output_blob_indices()[j]];
        //输出迭代的accuracy与loss信息
        LOG(INFO) << "Batch " << i << ", " << output_name << " = " << score;
      }
    }
  }
  loss /= FLAGS_iterations; // 计算平均损失并打印
  LOG(INFO) << "Loss: " << loss;
  for (int i = 0; i < test_score.size(); ++i) {
    const std::string& output_name = caffe_net.blob_names()[
        caffe_net.output_blob_indices()[test_score_output_id[i]]];
    const float loss_weight = caffe_net.blob_loss_weights()[
        caffe_net.output_blob_indices()[test_score_output_id[i]]];
    std::ostringstream loss_msg_stream;
    const float mean_score = test_score[i] / FLAGS_iterations;
    if (loss_weight) {
      loss_msg_stream << " (* " << loss_weight
                      << " = " << loss_weight * mean_score << " loss)";
    }
    LOG(INFO) << output_name << " = " << mean_score << loss_msg_stream.str();
  }

  return 0;
}
RegisterBrewFunction(test);


//test函数用于测试网络模型的执行时间
// Time: benchmark the execution time of a model.
int time() {
  CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
  caffe::Phase phase = get_phase_from_flags(caffe::TRAIN);
  vector<string> stages = get_stages_from_flags();

  // Set device id and mode
  vector<int> gpus;
  get_gpus(&gpus);
  if (gpus.size() != 0) {
    LOG(INFO) << "Use GPU with device ID " << gpus[0];
    Caffe::SetDevice(gpus[0]);
    Caffe::set_mode(Caffe::GPU);
  } else {
    LOG(INFO) << "Use CPU.";
    Caffe::set_mode(Caffe::CPU);
  }
  //创建网络模型对象
  // Instantiate the caffe net.
  Net<float> caffe_net(FLAGS_model, phase, FLAGS_level, &stages);

  // Do a clean forward and backward pass, so that memory allocation are done
  // and future iterations will be more stable.
  LOG(INFO) << "Performing Forward";
  // Note that for the speed benchmark, we will assume that the network does
  // not take any input blobs.
  float initial_loss;
  caffe_net.Forward(&initial_loss); //执行一遍前向传播
  LOG(INFO) << "Initial loss: " << initial_loss;
  LOG(INFO) << "Performing Backward";
  caffe_net.Backward();//执行反向传播

  const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
  const vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
  const vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
  const vector<vector<bool> >& bottom_need_backward =
      caffe_net.bottom_need_backward();
  LOG(INFO) << "*** Benchmark begins ***";
  LOG(INFO) << "Testing for " << FLAGS_iterations << " iterations.";
  Timer total_timer;
  total_timer.Start();
  Timer forward_timer;
  Timer backward_timer;
  Timer timer;
  std::vector<double> forward_time_per_layer(layers.size(), 0.0);
  std::vector<double> backward_time_per_layer(layers.size(), 0.0);
  double forward_time = 0.0;
  double backward_time = 0.0;
  for (int j = 0; j < FLAGS_iterations; ++j) {
    Timer iter_timer;
    iter_timer.Start();
    forward_timer.Start();
    for (int i = 0; i < layers.size(); ++i) {
      timer.Start();
      layers[i]->Forward(bottom_vecs[i], top_vecs[i]);
      forward_time_per_layer[i] += timer.MicroSeconds();//累加前向传播中每层网络耗时
    }
    forward_time += forward_timer.MicroSeconds();
    backward_timer.Start();
    for (int i = layers.size() - 1; i >= 0; --i) {
      timer.Start();
      layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
                          bottom_vecs[i]);
      backward_time_per_layer[i] += timer.MicroSeconds();//累加反向传播中每次网络耗时
    }
    backward_time += backward_timer.MicroSeconds();
    LOG(INFO) << "Iteration: " << j + 1 << " forward-backward time: "
      << iter_timer.MilliSeconds() << " ms.";
  }
  LOG(INFO) << "Average time per layer: ";
  for (int i = 0; i < layers.size(); ++i) {
    const caffe::string& layername = layers[i]->layer_param().name();
    LOG(INFO) << std::setfill(' ') << std::setw(10) << layername <<
      "\tforward: " << forward_time_per_layer[i] / 1000 /
      FLAGS_iterations << " ms.";
    LOG(INFO) << std::setfill(' ') << std::setw(10) << layername  <<
      "\tbackward: " << backward_time_per_layer[i] / 1000 /
      FLAGS_iterations << " ms.";
  }
  total_timer.Stop();
  //打印总的前向传播和反向传播平均耗时以及训练流程平均耗时
  LOG(INFO) << "Average Forward pass: " << forward_time / 1000 /
    FLAGS_iterations << " ms.";
  LOG(INFO) << "Average Backward pass: " << backward_time / 1000 /
    FLAGS_iterations << " ms.";
  LOG(INFO) << "Average Forward-Backward: " << total_timer.MilliSeconds() /
    FLAGS_iterations << " ms.";
  LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() << " ms.";
  LOG(INFO) << "*** Benchmark ends ***";
  return 0;
}
RegisterBrewFunction(time);

int main(int argc, char** argv) {
  // Print output to stderr (while still logging).
  FLAGS_alsologtostderr = 1; //输出打印信息
  /************************************************************************
    AS_STRING(CAFFE_VERSION)是一个宏定义，把形参字符串化，相当于”CAFFE_VERSION“
    SetVersinoString函数用于设置版本号，目前应该是一个保留接口，并没有实际用处，这里
    传入的是“CAFFE_VERSION”,也不是具体的版本号
    *************************************************************************/
  // Set version
  gflags::SetVersionString(AS_STRING(CAFFE_VERSION));
  /***************************************************************************
    程序使用信息，说明程序的简单使用方式，如果输入的参数不符号要求，会在之后调用
    函数ShowUsageWithFlagsRestric输出这些信息，gflags是google的一个开源的处理命令行
    参数的库
    ***************************************************************************/
  // Usage message.
  gflags::SetUsageMessage("command line brew\n"
      "usage: caffe <command> <args>\n\n"
      "commands:\n"
      "  train           train or finetune a model\n"
      "  test            score a model\n"
      "  device_query    show GPU diagnostic information\n"
      "  time            benchmark model execution time");
  /****************************************************************************
    初始化flags和logging， argv[0]是本程序exe的完整路径，函数内部会把argc的值减去1，
    由3变成了2（2是实际输入的变量个数）
    ****************************************************************************/
  // Run tool or show usage.
  caffe::GlobalInit(&argc, &argv);
  if (argc == 2) {
#ifdef WITH_PYTHON_LAYER
    try {
#endif
    //根据argv[1]（这里是train）的配置，返回train或其他3个函数的指针并执行对应函数
    //一共可以有4个不同的参数，其他3个分别是 train，device_query和time
      return GetBrewFunction(caffe::string(argv[1]))();
#ifdef WITH_PYTHON_LAYER
    } catch (bp::error_already_set) {
      PyErr_Print();
      return 1;
    }
#endif
  } else {
    gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe");
  }
}