【深度学习】【caffe实用工具4】笔记26 windows下使用Caffe中的源代码进行【训练】和【预测】

/**************************************************************************************************************************
文件说明:
        【1】windows下使用源代码进行【训练】和【预测】
		【2】在caffe例程和Ubuntu下，我们一般都是通过脚本(shell/bat)调用已经生成好的caffe.exe文件，然后给这个文件通过主函数中
		     的argv[1]......argv[n]传入参数进行训练，但是，我感觉这样总是对一个程序和一个项目的把控能力不强，我更愿意在源代码
			 中进行改动，然后运行源代码
运行环境:
        Windows+cuda7.5+cuDnnv5.0+ OpenCv+vs2013
时间地点:
        陕西师范大学 文津楼 2017 8.9
作    者:
        九 月
***************************************************************************************************************************/
/**************************************************************************************************************************
训练阶段的具体操作:
(一)准备自己的图片数据库
     （1）训练图片和测试图片的来源:
	      链接：http://pan.baidu.com/s/1cpRduQ 密码：z98o
		  这是一个小型的图像数据库，这个图片数据库中有500张图片；其中训练样本400张；测试样本100张；总工分为5类：bus,dinosaur,
		  elephant,rose,horse
		  图片的尺寸为size==384*256
		  图片的格式为JPEG格式
	  (2)在E:\caffeInstall2013\caffe-master\data建立自己的数据文件夹myself
	      在myself文件夹下建立train文件夹和val文件夹。
		  train文件夹存放训练图像(训练集)，val文件夹存放测试图像(测试集)
	  (3)编写train.txt和val.txt文本文件
(二)将图片数据转化为LMDB数据格式
       使用caffe【使用工具】中的convert_imagenet.cpp
(三)计算图像的均值文件(减均值操作)
       使用caffe【使用工具】中的compute_image_mean.cpp
(四)创建网络模型，编写超参数配置文件
       需要注意的一点是，需要将网络模型和超参数配置文件中的所有路径换成【绝对路径】
(五)开始训练
总结:
     训练过程需要:
	    （1）LMDB类型的数据
		 (2)均值文件
		 (3)网络模型(结构参数)
		 (4)训练超参数文件(训练超参数)
***************************************************************************************************************************/
#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,     "E://caffeInstall2013//caffe-master//examples//mnist//lenet_solver.prototxt",
	                         "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
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 <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";
  return caffe::SolverAction::NONE;
}

// 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.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);
  }

  if (gpus.size() > 1) 
  {
    caffe::P2PSync<float> sync(solver, NULL, solver->param());
    sync.Run(gpus);
  } 
  else 
  {
    LOG(INFO) << "Starting Optimization";
    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);
  //std::system("e:");
  //std::system("cd \caffeInstall2013\caffe-master");
  argc = 2;
  argv[1] = "train";
  //std::system("pause");
  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");
  }
}