#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;
/*下面利用glog来实现caffe命令的一些参数，比较方便，可以在终端中运行caffe可以看到这些参数help信息*/
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)(); //上面命令参数的处理函数原型。
typedef std::map<caffe::string, BrewFunction> BrewMap;
BrewMap g_brew_map; //存放命令参数和处理函数的map
/*注册参数和处理函数的宏定义，这里为啥比较麻烦的利用创建一个类来实现注册后面会有解释。
宏的参数就是命令参数名称，处理函数也叫同一个名称*/
#define RegisterBrewFunction(func) \
namespace { \
class __Registerer_##func { \
public: /* NOLINT */ \
__Registerer_##func() { \
g_brew_map[#func] = &func; \ //命令参数和对应function填入map， #func是把func转换为字符串
} \
}; \
__Registerer_##func g_registerer_##func; \
}
static BrewFunction GetBrewFunction(const caffe::string& name) { //通过name来获得处理函数
if (g_brew_map.count(name)) { // count会返回name为key的元素的数量，对于map来说，不能重复，有的话返回1,没有返回0
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)); //获得gpu的信息
#else
NO_GPU; //#define NO_GPU LOG(FATAL) << "Cannot use GPU in CPU-only Caffe: check mode."
#endif
for (int i = 0; i < count; ++i) {
gpus->push_back(i);
}
} else if (FLAGS_gpu.size()) {
vector<string> strings;
/*下面这个boost提供的一个字符串split函数后面会使用多次，还是挺方便的，
会将一个字符串按照传入的分割符来切成多个string。*/
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，看看是train还是test
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, 也是利用了split函数， 返回一个vector
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.
/*定义device_query命令的处理函数，我们可以看到处理函数也叫device_query。*/
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;
}
/*前面我们看到注册这个宏的时候为什么要麻烦的写一个类，然后在类的构造函数中来注册，
就是为了这里可以在外部调用这个宏，而不是必须放在函数的内部。这个宏展开的时候会
定义一个class，然后创建一个class的对象，这时会自动调用该class的构造函数，在构造
函数中将处理函数set到map中。*/
RegisterBrewFunction(device_query); //注册device_query命令和处理函数。
// Translate the signal effect the user specified on the command-line to the
// corresponding enumeration.
caffe::SolverAction::Enum GetRequestedAction( //user可以通过命令来控制caffe运行的过程
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() {
//检测有没有指定solver文件，solver是控制train流程的，比如学习率，test_net数量，步长等一系列参数
/*比如利用example中的lenet网络训练的时候，命令行为“
caffe train --solver=examples/mnist/lenet_solver.prototxt 大家可以找到这个文件打开看看*/
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;
/*从命令中传入的solver.prototxt文件中得到solver_param. 都利用protobuf工具来获得。*/
caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param);
solver_param.mutable_train_state()->set_level(FLAGS_level);//设置train state里面的level参数
for (int i = 0; i < stages.size(); i++) {
solver_param.mutable_train_state()->add_stage(stages[i]); //设置train state里面的stage参数。
}
// If the gpus flag is not provided, allow the mode and device to be set
// in the solver prototxt.
/*如果命令行中没有指定gpu，则使用solver.prototxt中指定的gpu*/
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);
}
}
/*获取本机器gpu的信息*/
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());
}
/*这里是设置signal的处理函数，在caffe运行时，我们可以在终端利用ctrl-c等给caffe.bin发送signal，
caffe.bin接受到signal后来做相应的处理，比如snapshot等。*/
caffe::SignalHandler signal_handler(
GetRequestedAction(FLAGS_sigint_effect),
GetRequestedAction(FLAGS_sighup_effect));
if (FLAGS_snapshot.size()) {//如果从命令中传入了snapshot文件，则将权重清空，下面从snapshot文件中载入权重。
solver_param.clear_weights();
} else if (FLAGS_weights.size()) {
solver_param.clear_weights();
solver_param.add_weights(FLAGS_weights);
}
//创建Solver对象，并赋值给智能指针
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());
}
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(); //这是运行的主要函数，前面都是准备资源。
}
LOG(INFO) << "Optimization Done.";
return 0;
}
RegisterBrewFunction(train); //将train函数注册进command map中。
// 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.";
//关于level和stage的问题可以参考 https://blog.csdn.net/qq_28660035/article/details/80306772
vector<string> stages = get_stages_from_flags(); //从命令中获取stage
// Set device id and mode 设置cpu mode还是gpu 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. 创建一个net对象，
Net<float> caffe_net(FLAGS_model, caffe::TEST, FLAGS_level, &stages);
caffe_net.CopyTrainedLayersFrom(FLAGS_weights); //传入训练好的权重
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;
//一次前向计算返回的结果， iter_loss为损失值。result为每一层的输出blob，
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) {
//这两个vector是并存的，一个记录score，一个记录blob的index。每个blobs含有多个score data。
//这样就能知道test_score里面的每个score隶属于哪个blob。
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");
}
}