600 lines
27 KiB
C++
600 lines
27 KiB
C++
// Copyright (C) 2018-2020 Intel Corporation
|
||
// SPDX-License-Identifier: Apache-2.0
|
||
//
|
||
|
||
#include <algorithm>
|
||
#include <chrono>
|
||
#include <memory>
|
||
#include <map>
|
||
#include <string>
|
||
#include <vector>
|
||
#include <utility>
|
||
|
||
#include <inference_engine.hpp>
|
||
#include <vpu/vpu_plugin_config.hpp>
|
||
#include <cldnn/cldnn_config.hpp>
|
||
#include <samples/common.hpp>
|
||
#include <samples/slog.hpp>
|
||
#include <samples/args_helper.hpp>
|
||
|
||
#include "benchmark_app.hpp"
|
||
#include "infer_request_wrap.hpp"
|
||
#include "progress_bar.hpp"
|
||
#include "statistics_report.hpp"
|
||
#include "inputs_filling.hpp"
|
||
#include "utils.hpp"
|
||
|
||
#ifdef __linux__
|
||
#include <sys/time.h>
|
||
#include <sys/resource.h>
|
||
#endif
|
||
|
||
using namespace InferenceEngine;
|
||
|
||
static const size_t progressBarDefaultTotalCount = 1000;
|
||
|
||
uint64_t getDurationInMilliseconds(uint32_t duration) {
|
||
return duration * 1000LL;
|
||
}
|
||
|
||
uint64_t getDurationInNanoseconds(uint32_t duration) {
|
||
return duration * 1000000000LL;
|
||
}
|
||
|
||
bool ParseAndCheckCommandLine(int argc, char *argv[]) {
|
||
// ---------------------------Parsing and validating input arguments--------------------------------------
|
||
slog::info << "Parsing input parameters" << slog::endl;
|
||
gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
|
||
if (FLAGS_help || FLAGS_h) {
|
||
showUsage();
|
||
showAvailableDevices();
|
||
return false;
|
||
}
|
||
|
||
if (FLAGS_m.empty()) {
|
||
throw std::logic_error("Model is required but not set. Please set -m option.");
|
||
}
|
||
|
||
if (FLAGS_api != "async" && FLAGS_api != "sync") {
|
||
throw std::logic_error("Incorrect API. Please set -api option to `sync` or `async` value.");
|
||
}
|
||
|
||
if (!FLAGS_report_type.empty() &&
|
||
FLAGS_report_type != noCntReport && FLAGS_report_type != averageCntReport && FLAGS_report_type != detailedCntReport) {
|
||
std::string err = "only " + std::string(noCntReport) + "/" + std::string(averageCntReport) + "/" + std::string(detailedCntReport) +
|
||
" report types are supported (invalid -report_type option value)";
|
||
throw std::logic_error(err);
|
||
}
|
||
|
||
if ((FLAGS_report_type == averageCntReport) && ((FLAGS_d.find("MULTI") != std::string::npos))) {
|
||
throw std::logic_error("only " + std::string(detailedCntReport) + " report type is supported for MULTI device");
|
||
}
|
||
|
||
return true;
|
||
}
|
||
|
||
static void next_step(const std::string additional_info = "") {
|
||
static size_t step_id = 0;
|
||
static const std::map<size_t, std::string> step_names = {
|
||
{ 1, "Parsing and validating input arguments" },
|
||
{ 2, "Loading Inference Engine" },
|
||
{ 3, "Setting device configuration" },
|
||
{ 4, "Reading the Intermediate Representation network" },
|
||
{ 5, "Resizing network to match image sizes and given batch" },
|
||
{ 6, "Configuring input of the model" },
|
||
{ 7, "Loading the model to the device" },
|
||
{ 8, "Setting optimal runtime parameters" },
|
||
{ 9, "Creating infer requests and filling input blobs with images" },
|
||
{ 10, "Measuring performance" },
|
||
{ 11, "Dumping statistics report" }
|
||
};
|
||
|
||
step_id++;
|
||
if (step_names.count(step_id) == 0)
|
||
THROW_IE_EXCEPTION << "Step ID " << step_id << " is out of total steps number " << step_names.size();
|
||
|
||
std::cout << "[Step " << step_id << "/" << step_names.size() << "] " << step_names.at(step_id)
|
||
<< (additional_info.empty() ? "" : " (" + additional_info + ")") << std::endl;
|
||
}
|
||
|
||
template <typename T>
|
||
T getMedianValue(const std::vector<T> &vec) {
|
||
std::vector<T> sortedVec(vec);
|
||
std::sort(sortedVec.begin(), sortedVec.end());
|
||
return (sortedVec.size() % 2 != 0) ?
|
||
sortedVec[sortedVec.size() / 2ULL] :
|
||
(sortedVec[sortedVec.size() / 2ULL] + sortedVec[sortedVec.size() / 2ULL - 1ULL]) / static_cast<T>(2.0);
|
||
}
|
||
|
||
/**
|
||
* @brief The entry point of the benchmark application
|
||
*/
|
||
int main(int argc, char *argv[]) {
|
||
std::shared_ptr<StatisticsReport> statistics;
|
||
try {
|
||
ExecutableNetwork exeNetwork;
|
||
|
||
// ----------------- 1. Parsing and validating input arguments -------------------------------------------------
|
||
next_step();
|
||
|
||
if (!ParseAndCheckCommandLine(argc, argv)) {
|
||
return 0;
|
||
}
|
||
|
||
bool isNetworkCompiled = fileExt(FLAGS_m) == "blob";
|
||
if (isNetworkCompiled) {
|
||
slog::info << "Network is compiled" << slog::endl;
|
||
}
|
||
|
||
if (!FLAGS_report_type.empty()) {
|
||
std::vector<gflags::CommandLineFlagInfo> flags;
|
||
StatisticsReport::Parameters command_line_arguments;
|
||
gflags::GetAllFlags(&flags);
|
||
|
||
for (auto &flag : flags) {
|
||
if (!flag.is_default) {
|
||
command_line_arguments.push_back({ flag.name, flag.current_value });
|
||
}
|
||
}
|
||
statistics = std::make_shared<StatisticsReport>(StatisticsReport::Config{FLAGS_report_type, FLAGS_report_folder});
|
||
statistics->addParameters(StatisticsReport::Category::COMMAND_LINE_PARAMETERS, command_line_arguments);
|
||
}
|
||
|
||
/** This vector stores paths to the processed images **/
|
||
std::vector<std::string> inputFiles;
|
||
parseInputFilesArguments(inputFiles);
|
||
|
||
if (FLAGS_nstreams.empty()) {
|
||
slog::warn << "-nstreams default value is determined automatically for a device. "
|
||
"Although the automatic selection usually provides a reasonable performance,"
|
||
"but it still may be non-optimal for some cases, for more information look at README." << slog::endl<< slog::endl;
|
||
}
|
||
|
||
// ----------------- 2. Loading the Inference Engine -----------------------------------------------------------
|
||
next_step();
|
||
|
||
// Get optimal runtime parameters for device
|
||
std::string device_name = FLAGS_d;
|
||
|
||
Core ie;
|
||
|
||
if (FLAGS_d.find("CPU") != std::string::npos && !FLAGS_l.empty()) {
|
||
// CPU (MKLDNN) extensions is loaded as a shared library and passed as a pointer to base extension
|
||
const auto extension_ptr = InferenceEngine::make_so_pointer<InferenceEngine::IExtension>(FLAGS_l);
|
||
ie.AddExtension(extension_ptr, "CPU");
|
||
slog::info << "CPU (MKLDNN) extensions is loaded " << FLAGS_l << slog::endl;
|
||
}
|
||
|
||
if ((FLAGS_d.find("GPU") != std::string::npos) && !FLAGS_c.empty()) {
|
||
// Load clDNN Extensions
|
||
ie.SetConfig({ {CONFIG_KEY(CONFIG_FILE), FLAGS_c} });
|
||
slog::info << "GPU extensions is loaded " << FLAGS_c << slog::endl;
|
||
}
|
||
|
||
slog::info << "InferenceEngine: " << GetInferenceEngineVersion() << slog::endl;
|
||
slog::info << "Device info: " << slog::endl;
|
||
std::cout << ie.GetVersions(device_name) << std::endl;
|
||
|
||
// ----------------- 3. Setting device configuration -----------------------------------------------------------
|
||
next_step();
|
||
|
||
bool perf_counts = (FLAGS_report_type == detailedCntReport ||
|
||
FLAGS_report_type == averageCntReport ||
|
||
FLAGS_pc ||
|
||
!FLAGS_exec_graph_path.empty());
|
||
|
||
auto devices = parseDevices(device_name);
|
||
std::map<std::string, uint32_t> device_nstreams = parseValuePerDevice(devices, FLAGS_nstreams);
|
||
for (auto& device : devices) {
|
||
if (device == "CPU") { // CPU supports few special performance-oriented keys
|
||
// limit threading for CPU portion of inference
|
||
if (FLAGS_nthreads != 0)
|
||
ie.SetConfig({{ CONFIG_KEY(CPU_THREADS_NUM), std::to_string(FLAGS_nthreads) }}, device);
|
||
|
||
if ((device_name.find("MULTI") != std::string::npos) &&
|
||
(device_name.find("GPU") != std::string::npos)) {
|
||
ie.SetConfig({{ CONFIG_KEY(CPU_BIND_THREAD), CONFIG_VALUE(NO) }}, device);
|
||
} else {
|
||
// pin threads for CPU portion of inference
|
||
ie.SetConfig({{ CONFIG_KEY(CPU_BIND_THREAD), FLAGS_pin }}, device);
|
||
}
|
||
|
||
// for CPU execution, more throughput-oriented execution via streams
|
||
if (FLAGS_api == "async")
|
||
ie.SetConfig({{ CONFIG_KEY(CPU_THROUGHPUT_STREAMS),
|
||
(device_nstreams.count(device) > 0 ? std::to_string(device_nstreams.at(device)) :
|
||
"CPU_THROUGHPUT_AUTO") }}, device);
|
||
device_nstreams[device] = std::stoi(ie.GetConfig(device, CONFIG_KEY(CPU_THROUGHPUT_STREAMS)).as<std::string>());
|
||
} else if (device == ("GPU")) {
|
||
if (FLAGS_api == "async")
|
||
ie.SetConfig({{ CONFIG_KEY(GPU_THROUGHPUT_STREAMS),
|
||
(device_nstreams.count(device) > 0 ? std::to_string(device_nstreams.at(device)) :
|
||
"GPU_THROUGHPUT_AUTO") }}, device);
|
||
device_nstreams[device] = std::stoi(ie.GetConfig(device, CONFIG_KEY(GPU_THROUGHPUT_STREAMS)).as<std::string>());
|
||
|
||
if ((device_name.find("MULTI") != std::string::npos) &&
|
||
(device_name.find("CPU") != std::string::npos)) {
|
||
// multi-device execution with the CPU + GPU performs best with GPU trottling hint,
|
||
// which releases another CPU thread (that is otherwise used by the GPU driver for active polling)
|
||
ie.SetConfig({{ CLDNN_CONFIG_KEY(PLUGIN_THROTTLE), "1" }}, "GPU");
|
||
}
|
||
} else if (device == "MYRIAD") {
|
||
ie.SetConfig({{ CONFIG_KEY(LOG_LEVEL), CONFIG_VALUE(LOG_WARNING) }}, device);
|
||
}
|
||
}
|
||
|
||
auto double_to_string = [] (const double number) {
|
||
std::stringstream ss;
|
||
ss << std::fixed << std::setprecision(2) << number;
|
||
return ss.str();
|
||
};
|
||
auto get_total_ms_time = [] (Time::time_point& startTime) {
|
||
return std::chrono::duration_cast<ns>(Time::now() - startTime).count() * 0.000001;
|
||
};
|
||
|
||
|
||
size_t batchSize = FLAGS_b;
|
||
Precision precision = Precision::UNSPECIFIED;
|
||
std::string topology_name = "";
|
||
if (!isNetworkCompiled) {
|
||
// ----------------- 4. Reading the Intermediate Representation network ----------------------------------------
|
||
next_step();
|
||
|
||
slog::info << "Loading network files" << slog::endl;
|
||
|
||
auto startTime = Time::now();
|
||
CNNNetwork cnnNetwork = ie.ReadNetwork(FLAGS_m);
|
||
auto duration_ms = double_to_string(get_total_ms_time(startTime));
|
||
slog::info << "Read network took " << duration_ms << " ms" << slog::endl;
|
||
if (statistics)
|
||
statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{
|
||
{"read network time (ms)", duration_ms}
|
||
});
|
||
|
||
const InputsDataMap inputInfo(cnnNetwork.getInputsInfo());
|
||
if (inputInfo.empty()) {
|
||
throw std::logic_error("no inputs info is provided");
|
||
}
|
||
|
||
// ----------------- 5. Resizing network to match image sizes and given batch ----------------------------------
|
||
next_step();
|
||
|
||
if (FLAGS_b != 0) {
|
||
ICNNNetwork::InputShapes shapes = cnnNetwork.getInputShapes();
|
||
bool reshape = false;
|
||
for (const InputsDataMap::value_type& item : inputInfo) {
|
||
auto layout = item.second->getTensorDesc().getLayout();
|
||
|
||
int batchIndex = -1;
|
||
if ((layout == Layout::NCHW) || (layout == Layout::NCDHW) ||
|
||
(layout == Layout::NHWC) || (layout == Layout::NDHWC) ||
|
||
(layout == Layout::NC)) {
|
||
batchIndex = 0;
|
||
} else if (layout == CN) {
|
||
batchIndex = 1;
|
||
}
|
||
if ((batchIndex != -1) && (shapes[item.first][batchIndex] != FLAGS_b)) {
|
||
shapes[item.first][batchIndex] = FLAGS_b;
|
||
reshape = true;
|
||
}
|
||
}
|
||
if (reshape) {
|
||
slog::info << "Resizing network to batch = " << FLAGS_b << slog::endl;
|
||
cnnNetwork.reshape(shapes);
|
||
}
|
||
}
|
||
|
||
batchSize = cnnNetwork.getBatchSize();
|
||
precision = cnnNetwork.getPrecision();
|
||
topology_name = cnnNetwork.getName();
|
||
slog::info << (FLAGS_b != 0 ? "Network batch size was changed to: " : "Network batch size: ") << batchSize <<
|
||
", precision: " << precision << slog::endl;
|
||
|
||
// ----------------- 6. Configuring input ----------------------------------------------------------------------
|
||
next_step();
|
||
|
||
for (auto& item : inputInfo) {
|
||
if (isImage(item.second)) {
|
||
/** Set the precision of input data provided by the user, should be called before load of the network to the device **/
|
||
item.second->setPrecision(Precision::U8);
|
||
}
|
||
}
|
||
// ----------------- 7. Loading the model to the device --------------------------------------------------------
|
||
next_step();
|
||
|
||
std::map<std::string, std::string> config = {{ CONFIG_KEY(PERF_COUNT), perf_counts ? CONFIG_VALUE(YES) :
|
||
CONFIG_VALUE(NO) }};
|
||
startTime = Time::now();
|
||
exeNetwork = ie.LoadNetwork(cnnNetwork, device_name, config);
|
||
duration_ms = double_to_string(get_total_ms_time(startTime));
|
||
slog::info << "Load network took " << duration_ms << " ms" << slog::endl;
|
||
if (statistics)
|
||
statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{
|
||
{"load network time (ms)", duration_ms}
|
||
});
|
||
} else {
|
||
next_step();
|
||
slog::info << "Skipping the step for compiled network" << slog::endl;
|
||
next_step();
|
||
slog::info << "Skipping the step for compiled network" << slog::endl;
|
||
next_step();
|
||
slog::info << "Skipping the step for compiled network" << slog::endl;
|
||
// ----------------- 7. Loading the model to the device --------------------------------------------------------
|
||
next_step();
|
||
auto startTime = Time::now();
|
||
exeNetwork = ie.ImportNetwork(FLAGS_m, device_name, {});
|
||
auto duration_ms = double_to_string(get_total_ms_time(startTime));
|
||
slog::info << "Import network took " << duration_ms << " ms" << slog::endl;
|
||
if (statistics)
|
||
statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{
|
||
{"import network time (ms)", duration_ms}
|
||
});
|
||
if (batchSize == 0) {
|
||
batchSize = 1;
|
||
}
|
||
}
|
||
// ----------------- 8. Setting optimal runtime parameters -----------------------------------------------------
|
||
next_step();
|
||
|
||
// Number of requests
|
||
uint32_t nireq = FLAGS_nireq;
|
||
if (nireq == 0) {
|
||
std::string key = METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS);
|
||
try {
|
||
nireq = exeNetwork.GetMetric(key).as<unsigned int>();
|
||
} catch (const details::InferenceEngineException& ex) {
|
||
THROW_IE_EXCEPTION
|
||
<< "Every device used with the benchmark_app should "
|
||
<< "support OPTIMAL_NUMBER_OF_INFER_REQUESTS ExecutableNetwork metric. "
|
||
<< "Failed to query the metric for the " << device_name << " with error:" << ex.what();
|
||
}
|
||
}
|
||
|
||
// Iteration limit
|
||
uint32_t niter = FLAGS_niter;
|
||
if ((niter > 0) && (FLAGS_api == "async")) {
|
||
niter = ((niter + nireq - 1)/nireq)*nireq;
|
||
if (FLAGS_niter != niter) {
|
||
slog::warn << "Number of iterations was aligned by request number from "
|
||
<< FLAGS_niter << " to " << niter << " using number of requests " << nireq << slog::endl;
|
||
}
|
||
}
|
||
|
||
// Time limit
|
||
uint32_t duration_seconds = 0;
|
||
if (FLAGS_t != 0) {
|
||
// time limit
|
||
duration_seconds = FLAGS_t;
|
||
} else if (FLAGS_niter == 0) {
|
||
// default time limit
|
||
duration_seconds = deviceDefaultDeviceDurationInSeconds(device_name);
|
||
}
|
||
uint64_t duration_nanoseconds = getDurationInNanoseconds(duration_seconds);
|
||
|
||
if (statistics) {
|
||
statistics->addParameters(StatisticsReport::Category::RUNTIME_CONFIG,
|
||
{
|
||
{"topology", topology_name},
|
||
{"target device", device_name},
|
||
{"API", FLAGS_api},
|
||
{"precision", std::string(precision.name())},
|
||
{"batch size", std::to_string(batchSize)},
|
||
{"number of iterations", std::to_string(niter)},
|
||
{"number of parallel infer requests", std::to_string(nireq)},
|
||
{"duration (ms)", std::to_string(getDurationInMilliseconds(duration_seconds))},
|
||
});
|
||
for (auto& nstreams : device_nstreams) {
|
||
std::stringstream ss;
|
||
ss << "number of " << nstreams.first << " streams";
|
||
statistics->addParameters(StatisticsReport::Category::RUNTIME_CONFIG,
|
||
{
|
||
{ss.str(), std::to_string(nstreams.second)},
|
||
});
|
||
}
|
||
}
|
||
|
||
// ----------------- 9. Creating infer requests and filling input blobs ----------------------------------------
|
||
next_step();
|
||
|
||
InferRequestsQueue inferRequestsQueue(exeNetwork, nireq);
|
||
const InferenceEngine::ConstInputsDataMap info(exeNetwork.GetInputsInfo());
|
||
fillBlobs(inputFiles, batchSize, info, inferRequestsQueue.requests);
|
||
|
||
// ----------------- 10. Measuring performance ------------------------------------------------------------------
|
||
size_t progressCnt = 0;
|
||
size_t progressBarTotalCount = progressBarDefaultTotalCount;
|
||
size_t iteration = 0;
|
||
|
||
std::stringstream ss;
|
||
ss << "Start inference " << FLAGS_api << "ronously";
|
||
if (FLAGS_api == "async") {
|
||
if (!ss.str().empty()) {
|
||
ss << ", ";
|
||
}
|
||
ss << nireq << " inference requests";
|
||
std::stringstream device_ss;
|
||
for (auto& nstreams : device_nstreams) {
|
||
if (!device_ss.str().empty()) {
|
||
device_ss << ", ";
|
||
}
|
||
device_ss << nstreams.second << " streams for " << nstreams.first;
|
||
}
|
||
if (!device_ss.str().empty()) {
|
||
ss << " using " << device_ss.str();
|
||
}
|
||
}
|
||
ss << ", limits: ";
|
||
if (duration_seconds > 0) {
|
||
ss << getDurationInMilliseconds(duration_seconds) << " ms duration";
|
||
}
|
||
if (niter != 0) {
|
||
if (duration_seconds == 0) {
|
||
progressBarTotalCount = niter;
|
||
}
|
||
if (duration_seconds > 0) {
|
||
ss << ", ";
|
||
}
|
||
ss << niter << " iterations";
|
||
}
|
||
next_step(ss.str());
|
||
|
||
// warming up - out of scope
|
||
auto inferRequest = inferRequestsQueue.getIdleRequest();
|
||
if (!inferRequest) {
|
||
THROW_IE_EXCEPTION << "No idle Infer Requests!";
|
||
}
|
||
|
||
if (FLAGS_api == "sync") {
|
||
inferRequest->infer();
|
||
} else {
|
||
inferRequest->startAsync();
|
||
}
|
||
inferRequestsQueue.waitAll();
|
||
inferRequestsQueue.resetTimes();
|
||
|
||
auto startTime = Time::now();
|
||
auto execTime = std::chrono::duration_cast<ns>(Time::now() - startTime).count();
|
||
|
||
/** Start inference & calculate performance **/
|
||
/** to align number if iterations to guarantee that last infer requests are executed in the same conditions **/
|
||
ProgressBar progressBar(progressBarTotalCount, FLAGS_stream_output, FLAGS_progress);
|
||
|
||
while ((niter != 0LL && iteration < niter) ||
|
||
(duration_nanoseconds != 0LL && (uint64_t)execTime < duration_nanoseconds) ||
|
||
(FLAGS_api == "async" && iteration % nireq != 0)) {
|
||
inferRequest = inferRequestsQueue.getIdleRequest();
|
||
if (!inferRequest) {
|
||
THROW_IE_EXCEPTION << "No idle Infer Requests!";
|
||
}
|
||
|
||
if (FLAGS_api == "sync") {
|
||
inferRequest->infer();
|
||
} else {
|
||
// As the inference request is currently idle, the wait() adds no additional overhead (and should return immediately).
|
||
// The primary reason for calling the method is exception checking/re-throwing.
|
||
// Callback, that governs the actual execution can handle errors as well,
|
||
// but as it uses just error codes it has no details like ‘what()’ method of `std::exception`
|
||
// So, rechecking for any exceptions here.
|
||
inferRequest->wait();
|
||
inferRequest->startAsync();
|
||
}
|
||
iteration++;
|
||
|
||
execTime = std::chrono::duration_cast<ns>(Time::now() - startTime).count();
|
||
|
||
if (niter > 0) {
|
||
progressBar.addProgress(1);
|
||
} else {
|
||
// calculate how many progress intervals are covered by current iteration.
|
||
// depends on the current iteration time and time of each progress interval.
|
||
// Previously covered progress intervals must be skipped.
|
||
auto progressIntervalTime = duration_nanoseconds / progressBarTotalCount;
|
||
size_t newProgress = execTime / progressIntervalTime - progressCnt;
|
||
progressBar.addProgress(newProgress);
|
||
progressCnt += newProgress;
|
||
}
|
||
}
|
||
|
||
// wait the latest inference executions
|
||
inferRequestsQueue.waitAll();
|
||
|
||
double latency = getMedianValue<double>(inferRequestsQueue.getLatencies());
|
||
double totalDuration = inferRequestsQueue.getDurationInMilliseconds();
|
||
double fps = (FLAGS_api == "sync") ? batchSize * 1000.0 / latency :
|
||
batchSize * 1000.0 * iteration / totalDuration;
|
||
|
||
if (statistics) {
|
||
statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{
|
||
{"total execution time (ms)", double_to_string(totalDuration)},
|
||
{"total number of iterations", std::to_string(iteration)},
|
||
});
|
||
if (device_name.find("MULTI") == std::string::npos) {
|
||
statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{
|
||
{"latency (ms)", double_to_string(latency)},
|
||
});
|
||
}
|
||
statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{
|
||
{"throughput", double_to_string(fps)}
|
||
});
|
||
}
|
||
|
||
progressBar.finish();
|
||
|
||
// ----------------- 11. Dumping statistics report -------------------------------------------------------------
|
||
next_step();
|
||
|
||
if (!FLAGS_exec_graph_path.empty()) {
|
||
try {
|
||
CNNNetwork execGraphInfo = exeNetwork.GetExecGraphInfo();
|
||
execGraphInfo.serialize(FLAGS_exec_graph_path);
|
||
slog::info << "executable graph is stored to " << FLAGS_exec_graph_path << slog::endl;
|
||
} catch (const std::exception & ex) {
|
||
slog::err << "Can't get executable graph: " << ex.what() << slog::endl;
|
||
}
|
||
}
|
||
|
||
if (perf_counts) {
|
||
std::vector<std::map<std::string, InferenceEngine::InferenceEngineProfileInfo>> perfCounts;
|
||
for (size_t ireq = 0; ireq < nireq; ireq++) {
|
||
auto reqPerfCounts = inferRequestsQueue.requests[ireq]->getPerformanceCounts();
|
||
if (FLAGS_pc) {
|
||
slog::info << "Pefrormance counts for " << ireq << "-th infer request:" << slog::endl;
|
||
printPerformanceCounts(reqPerfCounts, std::cout, getFullDeviceName(ie, FLAGS_d), false);
|
||
}
|
||
perfCounts.push_back(reqPerfCounts);
|
||
}
|
||
if (statistics) {
|
||
statistics->dumpPerformanceCounters(perfCounts);
|
||
}
|
||
}
|
||
|
||
if (statistics)
|
||
statistics->dump();
|
||
|
||
std::cout << "Count: " << iteration << " iterations" << std::endl;
|
||
std::cout << "Duration: " << double_to_string(totalDuration) << " ms" << std::endl;
|
||
if (device_name.find("MULTI") == std::string::npos)
|
||
std::cout << "Latency: " << double_to_string(latency) << " ms" << std::endl;
|
||
std::cout << "Throughput: " << double_to_string(fps) << " FPS" << std::endl;
|
||
} catch (const std::exception& ex) {
|
||
slog::err << ex.what() << slog::endl;
|
||
|
||
if (statistics) {
|
||
statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{
|
||
{"error", ex.what()},
|
||
});
|
||
statistics->dump();
|
||
}
|
||
|
||
return 3;
|
||
}
|
||
|
||
#ifdef __linux__
|
||
std::ifstream status("/proc/self/status");
|
||
std::string line;
|
||
while (std::getline(status, line)) {
|
||
std::string title;
|
||
std::istringstream iss(line);
|
||
iss >> title;
|
||
if (title == "VmPeak:") {
|
||
size_t val;
|
||
iss >> val;
|
||
std::cout << "Peak Virtual Memory (VmPeak) Size, kBytes: " << val << std::endl;
|
||
} else if (title == "VmHWM:") {
|
||
size_t val;
|
||
iss >> val;
|
||
std::cout << "Peak Resident Memory (VmHWM) Size, kBytes: " << val << std::endl;
|
||
}
|
||
}
|
||
#endif
|
||
|
||
return 0;
|
||
}
|