【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
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");
}
}