在caffe根目录下输入如下命令:
./build/tools/caffe.bin, 得到如下显示
usage:caffe<command><args>
# 这个是告诉你使用格式, caffe 后接上 一个command命令,后面再接其他参数
commands: #你能选择的命令有一下这么几种
train #训练或者微调一个模型
test #对一个模型打分
device—query #显示GPU诊断信息
time #评估模型执行时间
Flags form tools/caffe.cpp #其他一些参数的总览
-gpu (可选;给定时运行GPU模式,用’ , ’分隔开不同的gpu,
‘-gpu all’表示运行在所有可用的gpu设备上,此时有效训练批量大小就是gpu设备数乘以batch_size)
-iterations (循环迭代次数,默认为50)
-level (可选;定义网络水平,也是NetState中的一个,但我也还不清楚这个的作用)
-model (指定模型定义文本文件名,xxx.prototxt)
-phase (可选;网络是处于TEST还是TRAIN阶段,当你使用command中time命令时,再指定phase就可以选择计算TEST或者TRAIN的耗时)
-sighup_effect (可选;当收到SIGHUP信号时要采取的动作,可选项:snapshot、stop、none,默认为snapshot,即打印快照)
-sigint_effect (可选;当收到当收到SIGINT信号时要采取的动作,可选项同上,默认stop)
-snapshot (可选,恢复训练时指定上次中止的快照,就是比如训练到一般按Ctrl+C终止训练(Linux中这个Ctrl+C不是copy,而是终止当前操作),就会得到一个solverstate 文件,下次恢复训练时就可以指定这个)
-solver ( 指定sovler.prototxt文件,在train的时候需要这个参数)
-stage (可选;也是NetState中的一个,但我也还不清楚这个的作用)
-weights ( 指定用于微调的预训练权值,也即 训练后得到的**.caffemodel文件,不可与snapshot同时出现)
注:这个文件的内容有些多,我也只是选择性的阅读并注释了部分。
#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.
// ##从指定的caffemodel中向训练、预测网络载入训练过的权值。
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.
// ##将用户在命令行上指定的信号效果转换为相应的枚举
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;
}
//======================================== 训练/微调 模型 ===========================================//
// Train / Finetune a model.
int train() {
CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train."; //检查用户是否传入solver文件
CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size()) //检查参数里面--weights和--snapshot有没有同时出现
<< "Give a snapshot to resume training or weights to finetune " //因为--weights是在从头启动训练的时候需要的参数,表示对模型的finetune,
"but not both."; //而--snapshot表示的是继续训练模型, 之前暂停了模型训练,现在继续训练
vector<string> stages = get_stages_from_flags();
caffe::SolverParameter solver_param; //###获取并解析用户定义的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.
//##这一段代码对于gpu的选择很关键,我们已经了解到,可以在输入命令行的时候配置gpu信息,也可以在solver.prototxt中定义GPU信息
//##此时先看命令行中是否设置了gpu,如果没有,再按照solver.prototxt中的描述来,
//##如果solver.prototxt中只是选用了gpu而没有指定几号,就默认0号
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);
}
}
// ##下面这几行在核验gpu检测结果,如果没有gpu信息,那么则使用cpu训练,否则,就开始一些GPU训练的初始化工作
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());
// ##在这里查询了一下用户有没有定义snapshot参数和weights参数,因为如果定义了这两个参数,代表用户可能会希望从之前的
中断训练处继续训练或者借用其他模型初始化网络,caffe在对两个参数相关的内容进行处理时都要用到solver指针
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);
}
// ##如果有不止一块gpu参与训练,那么将开启多gpu训练模式
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()接口正式开始优化网络
}
LOG(INFO) << "Optimization Done.";
return 0;
}
RegisterBrewFunction(train);
//====================================测试 模型===========================================//
// Test: score a model.
int test() { //##需要输入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
// ##设置设备的id和模式,如果没有设置GPU就会默认采用CPU来test,这一点我上篇博文有提到
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. ##实例化此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);
//===================================== 计时:评测模型执行时间 =========================================//
// 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 ##指定设备id和mode,如果没有设置gpu,就会默认采用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. ##实例化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.
// ##做一次干净的前向、反向流程,保证完成存储区分配
LOG(INFO) << "Performing Forward";
// Note that for the speed benchmark, we will assume that the network does
// not take any input blobs.
// ##速度测试,假定网络不需要任何输入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函数 ======================================================//
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");
}
}