1、怎麼用這個命令

在caffe根目錄下輸入如下命令：

 ./build/tools/caffe.bin, 得到如下顯示

usage：caffe<command><args>

# 這個是告訴你使用格式， caffe 後接上 一個command命令，後面再接其他引數

commands： #你能選擇的命令有一下這麼幾種

train #訓練或者微調一個模型

test  #對一個模型打分

device—query #顯示GPU診斷資訊

time #評估模型執行時間

Flags form tools/caffe.cpp   #其他一些引數的總覽

-gpu         

                     ‘-gpu all’表示執行在所有可用的gpu裝置上，此時有效訓練批量大小就是gpu裝置數乘以batch_size）

-iterations (迴圈迭代次數，預設為50)

-level          (可選；定義網路水平，也是NetState中的一個，但我也還不清楚這個的作用)

-model        (指定模型定義文字檔名，xxx.prototxt)

-phase        (可選；網路是處於TEST還是TRAIN階段，當你使用command中time命令時，再指定phase就可以選擇計算TEST或者TRAIN的耗時)

-sighup_effect (可選；當收到SIGHUP訊號時要採取的動作，可選項：snapshot、stop、none，預設為snapshot，即列印快照)

-sigint_effect (可選；當收到當收到SIGINT訊號時要採取的動作，可選項同上，預設stop)

-snapshot （可選，恢復訓練時指定上次中止的快照，就是比如訓練到一般按Ctrl+C終止訓練（Linux中這個Ctrl+C不是copy，而是終止當前操作），就會得到一個solverstate                        檔案，下次恢復訓練時就可以指定這個）

-solver       ( 指定sovler.prototxt檔案，在train的時候需要這個引數)

-stage        (可選；也是NetState中的一個，但我也還不清楚這個的作用)

-weights    ( 指定用於微調的預訓練權值，也即 訓練後得到的**.caffemodel檔案，不可與snapshot同時出現)

2、caffe.cpp原始檔

注：這個檔案的內容有些多，我也只是選擇性的閱讀並註釋了部分。

#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 devices #解析GPU id，或者使用所有可用的GPU
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
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的命令格式
//     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.
// ##從指定的caffemodel中向訓練、預測網路載入訓練過的權值。
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.
// ##將使用者在命令列上指定的訊號效果轉換為相應的列舉
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";
  return caffe::SolverAction::NONE;
}

//======================================== 訓練/微調 模型 ===========================================//
// Train / Finetune a model.
int train() {
  CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train."; //檢查使用者是否傳入solver檔案
  CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size())                    //檢查引數裡面--weights和--snapshot有沒有同時出現
      << "Give a snapshot to resume training or weights to finetune "       //因為--weights是在從頭啟動訓練的時候需要的引數,表示對模型的finetune，
      "but not both.";                                                      //而--snapshot表示的是繼續訓練模型, 之前暫停了模型訓練，現在繼續訓練
  vector<string> stages = get_stages_from_flags();

  caffe::SolverParameter solver_param;  //###獲取並解析使用者定義的solver.prototxt
  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.
  //##這一段程式碼對於gpu的選擇很關鍵，我們已經瞭解到，可以在輸入命令列的時候配置gpu資訊,也可以在solver.prototxt中定義GPU資訊
  //##此時先看命令列中是否設定了gpu，如果沒有，再按照solver.prototxt中的描述來，
  //##如果solver.prototxt中只是選用了gpu而沒有指定幾號，就預設0號
  if (FLAGS_gpu.size() == 0
      && 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);
      }
  }

// ##下面這幾行在核驗gpu檢測結果，如果沒有gpu資訊，那麼則使用cpu訓練，否則，就開始一些GPU訓練的初始化工作
  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());

// ##在這裡查詢了一下使用者有沒有定義snapshot引數和weights引數，因為如果定義了這兩個引數，代表使用者可能會希望從之前的 
     中斷訓練處繼續訓練或者借用其他模型初始化網路，caffe在對兩個引數相關的內容進行處理時都要用到solver指標 
  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);
  }
// ##如果有不止一塊gpu參與訓練，那麼將開啟多gpu訓練模式
  if (gpus.size() > 1) {
    caffe::P2PSync<float> sync(solver, NULL, solver->param());
    sync.Run(gpus);
  } else {
    LOG(INFO) << "Starting Optimization";
    solver->Solve();     //使用Solve()介面正式開始優化網路
  }
  LOG(INFO) << "Optimization Done.";
  return 0;
}
RegisterBrewFunction(train);

//====================================測試 模型===========================================//
// Test: score a model.
int test() { //##需要輸入model
  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  
  // ##設定裝置的id和模式,如果沒有設定GPU就會預設採用CPU來test，這一點我上篇博文有提到
  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. ##例項化此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);

//===================================== 計時：評測模型執行時間 =========================================//
// 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  ##指定裝置id和mode，如果沒有設定gpu，就會預設採用cpu
  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. ##例項化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.
  // ##做一次乾淨的前向、反向流程，保證完成儲存區分配
  LOG(INFO) << "Performing Forward";
  // Note that for the speed benchmark, we will assume that the network does
  // not take any input blobs.
  // ##速度測試，假定網路不需要任何輸入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);

//================================================ main函式 ======================================================//
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");
  }
}