五、caffe 之gflags&glogs解析

为了弄懂是caffe/tool/caffe.cpp的源文件代码，首先提供一下原代码；

#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(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)();typedef std::map<caffe::string, BrewFunction> BrewMap;BrewMap g_brew_map;#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)) {    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 devicesstatic 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 flagscaffe::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 flagsvector<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.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.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.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(FLAGS_solver.size(), 0) << "Need a solver definition to train.";  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();  caffe::SolverParameter solver_param;  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.  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;  get_gpus(&gpus);  if (gpus.size() == 0) {    LOG(INFO) << "Use CPU.";    Caffe::set_mode(Caffe::CPU);  } else {    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(&device_prop, gpus[i]);      LOG(INFO) << "GPU " << gpus[i] << ": " << device_prop.name;    }#endif    solver_param.set_device_id(gpus[0]);    Caffe::SetDevice(gpus[0]);    Caffe::set_mode(Caffe::GPU);    Caffe::set_solver_count(gpus.size());  }  caffe::SignalHandler signal_handler(        GetRequestedAction(FLAGS_sigint_effect),        GetRequestedAction(FLAGS_sighup_effect));  shared_ptr<caffe::Solver<float> >      solver(caffe::SolverRegistry<float>::CreateSolver(solver_param));  solver->SetActionFunction(signal_handler.GetActionFunction());  if (FLAGS_snapshot.size()) {    LOG(INFO) << "Resuming from " << FLAGS_snapshot;    solver->Restore(FLAGS_snapshot.c_str());  } else if (FLAGS_weights.size()) {    CopyLayers(solver.get(), FLAGS_weights);  }  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();  }  LOG(INFO) << "Optimization Done.";  return 0;}RegisterBrewFunction(train);// Test: score a model.int test() {  CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to score.";  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);  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.  Net<float> caffe_net(FLAGS_model, caffe::TEST, FLAGS_level, &stages);  caffe_net.CopyTrainedLayersFrom(FLAGS_weights);  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) {    float iter_loss;    const vector<Blob<float>*>& result =        caffe_net.Forward(&iter_loss);    loss += iter_loss;    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]];        LOG(INFO) << "Batch " << i << ", " << output_name << " = " << score;      }    }  }  loss /= FLAGS_iterations;  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);// 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);  }  // 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);  LOG(INFO) << "Initial loss: " << initial_loss;  LOG(INFO) << "Performing Backward";  caffe_net.Backward();  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();    }    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();    }    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();  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;  // Set version  gflags::SetVersionString(AS_STRING(CAFFE_VERSION));  // 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");  // Run tool or show usage.  caffe::GlobalInit(&argc, &argv);  if (argc == 2) {#ifdef WITH_PYTHON_LAYER    try {#endif      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");  }}

还需要了解一下基本的gflags的基本知识。
- gflags头文件

#include <gflags/gflags.h>

• 初始化所有参数

google::ParseCommandLineFlags(&argc, &argv, flags);

argc和argv就是 main 的入口参数，因为这个函数会改变他们的值，所以都是以指针传入。

第三个参数被称为 flags。如果它是true,ParseCommandLineFlags会从argv中移除标识和它们的参数，相应减少argc的值。然后 argv 只保留命令行参数。

相反， flags是false,ParseCommandLineFlags会保留argc不变，但将会重新调整它们的顺序，使得标识再前面。

• gflags 暂时支持如下参数的类型：

DEFINE_bool: booleanDEFINE_int32: 32-bit integerDEFINE_int64: 64-bit integerDEFINE_uint64: unsigned 64-bit integerDEFINE_double: doubleDEFINE_string: C++ string

提供一个简单的列子

#include <iostream>#include <gflags/gflags.h>#include<map>DEFINE_bool(isvip, false, "If Is VIP");DEFINE_string(ip, "127.0.0.1", "connect ip");DECLARE_int32(port);DEFINE_int32(port, 80, "listen port");int main(int argc, char** argv){   google::ParseCommandLineFlags(&argc, &argv, true);  std::cout<<"ip:"<<FLAGS_ip<<std::endl;  std::cout<<"port:"<<FLAGS_port<<std::endl;std::cout<<"ip:"<<FLAGS_ip.size()<<std::endl;  if (FLAGS_isvip)  {      std::cout<<"isvip:"<<FLAGS_isvip<<std::endl;  }  google::ShutDownCommandLineFlags();  return 0;}

ubuntu@ubuntu:~$ g++ d.cpp -o test -lgflags -lpthread ubuntu@ubuntu:~$ ./test ip:127.0.0.1port:80ip:9ubuntu@ubuntu:~$  ./test -ip=123.23.31.41 -port=43ip:123.23.31.41port:43ip:12

基本的语法会使用即可。再看下面这个例子。

#include <iostream>#include <gflags/gflags.h>/** *  定义命令行参数变量 *  默认的主机地址为 127.0.0.1，变量解释为 'the server host' *  默认的端口为 12306，变量解释为 'the server port' */DEFINE_string(host, "127.0.0.1", "the server host");DEFINE_int32(port, 12306, "the server port");int main(int argc, char** argv) {    // 解析命令行参数，一般都放在 main 函数中开始位置    gflags::ParseCommandLineFlags(&argc, &argv, true);    // 访问参数变量，加上 FLAGS_    std::cout << "The server host is: " << FLAGS_host        << ", the server port is: " << FLAGS_port << std::endl;    return 0;}

google::ParseCommandLineFlags(&argc, &argv, true);
三个参数的作用：

如果设为true，argv中只保留argv[0]，argc会被设置为1。

如果为false，则argv和argc会被保留，注意函数会调整argv中的顺序。

ubuntu@ubuntu:~$ g++ test.cpp -o test -lgflags -lpthread ubuntu@ubuntu:~$ ./test The server host is: 127.0.0.1, the server port is: 12306ubuntu@ubuntu:~$ ./test -host 10.123.78.90 -port 8008The server host is: 10.123.78.90, the server port is: 8008

在学习一下基本的glog 的知识

#include <glog/logging.h>int main(int argc,char* argv[]){    google::InitGoogleLogging(argv[0]);    LOG(INFO) << "Hello, GOOGLE!";  // INFO 级别的日志    LOG(ERROR) << "ERROR, GOOGLE!";  // ERROR 级别的日志，直接输出到控制台上（c++ 会介绍stdout 和 stderr 区别）    VLOG(10) << "VLOG INFO 10";// 自定义信息    return 0;}

ubuntu@ubuntu:~$ g++ d.cpp -o test -lgflags -lpthread -lglog ubuntu@ubuntu:~$ ./test E0620 20:04:27.647083 10570 d.cpp:8] ERROR, GOOGLE!

如果想把信息都输入到控制台上；
logtostderr (bool, default=false) //是否将所有日志输出到 stderr，而非文件
alsologtostderr(bool,default=false)　　//是否同时将日志输出到文件和stderr

#include <glog/logging.h>int main(int argc,char* argv[]){ FLAGS_alsologtostderr = 1;//添加这条语句（c++ 会介绍stdout 和 stderr 区别） 会是log(INFO)的信息也输出到控制台上    google::InitGoogleLogging(argv[0]);    LOG(INFO) << "Hello, GOOGLE!";  // INFO 级别的日志    LOG(ERROR) << "ERROR, GOOGLE!";  // ERROR 级别的日志，直接输出到控制台上（c++ 会介绍stdout 和 stderr 区别）    VLOG(10) << "VLOG INFO 10";// 自定义信息    DLOG(INFO) << "Found cookies";//只有在调试状态下有用    return 0;}

ubuntu@ubuntu:~$ g++ d.cpp -o test -lgflags -lpthread -lglog ubuntu@ubuntu:~$ ./test I0620 20:02:56.162489 10557 d.cpp:7] Hello, GOOGLE!E0620 20:02:56.162962 10557 d.cpp:8] ERROR, GOOGLE!ubuntu@ubuntu:~$ 

源代码中其他一些函数的含义：

version信息：使用google::SetVersionString设定，使用google::VersionString访问
help信息：使用google::SetUsageMessage设定，使用google::ProgramUsage访问
注意：google::SetUsageMessage和google::SetVersionString必须在google::ParseCommandLineFlags之前执行

 caffe::GlobalInit(&argc, &argv); 

原函数体在caffe/src/caffe/common.cpp中，

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();}

这里实现了命令行的解析和初始化 glog