Caffe-HRT/tools/caffe.cpp at master · cnsuhao/Caffe-HRT · GitHub

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#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

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";

}

// 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.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);

}

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);

}

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();

}

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);

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");

}

Caffe-HRT/tools/caffe.cpp at master · cnsuhao/Caffe-HRT · GitHub

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