d72e39d8f3
* fix benchmark filed to pass on MULTI device when enable num_streams. Signed-off-by: Wang, Yang <yang4.wang@intel.com> * Update. Signed-off-by: Wang, Yang <yang4.wang@intel.com> * Remove part of test cases because MULTI plguin will not check if the unspported property is valid now. Signed-off-by: Wang, Yang <yang4.wang@intel.com> * Remove some incorrect config for MULTI test case since MULTI will pass through those unrecognized config without any exception. Signed-off-by: Wang, Yang <yang4.wang@intel.com> * Support MULTI to set nstreams to multi target devices by using ov::device:properties. Signed-off-by: Wang, Yang <yang4.wang@intel.com> * Support AUTO to set nstreams with multi target devices bu using ov:device::properties. Signed-off-by: Wang, Yang <yang4.wang@intel.com> * Update. Signed-off-by: Wang, Yang <yang4.wang@intel.com> * Update. Signed-off-by: Wang, Yang <yang4.wang@intel.com> * Update format. Signed-off-by: Wang, Yang <yang4.wang@intel.com> Signed-off-by: Wang, Yang <yang4.wang@intel.com> Co-authored-by: Chen Peter <peter.chen@intel.com>
1230 lines
58 KiB
C++
1230 lines
58 KiB
C++
// Copyright (C) 2018-2022 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <algorithm>
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#include <chrono>
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#include <map>
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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// clang-format off
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#include "openvino/openvino.hpp"
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#include "openvino/pass/serialize.hpp"
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#include "gna/gna_config.hpp"
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#include "gpu/gpu_config.hpp"
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#include "samples/args_helper.hpp"
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#include "samples/common.hpp"
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#include "samples/slog.hpp"
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#include "benchmark_app.hpp"
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#include "infer_request_wrap.hpp"
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#include "inputs_filling.hpp"
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#include "progress_bar.hpp"
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#include "remote_tensors_filling.hpp"
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#include "statistics_report.hpp"
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#include "utils.hpp"
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// clang-format on
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static const size_t progressBarDefaultTotalCount = 1000;
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bool parse_and_check_command_line(int argc, char* argv[]) {
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// ---------------------------Parsing and validating input
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// arguments--------------------------------------
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slog::info << "Parsing input parameters" << slog::endl;
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gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
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if (FLAGS_help || FLAGS_h) {
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show_usage();
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showAvailableDevices();
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return false;
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}
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if (FLAGS_m.empty()) {
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show_usage();
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throw std::logic_error("Model is required but not set. Please set -m option.");
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}
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if (FLAGS_latency_percentile > 100 || FLAGS_latency_percentile < 1) {
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show_usage();
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throw std::logic_error("The percentile value is incorrect. The applicable values range is [1, 100].");
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}
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if (FLAGS_api != "async" && FLAGS_api != "sync") {
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throw std::logic_error("Incorrect API. Please set -api option to `sync` or `async` value.");
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}
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if (!FLAGS_hint.empty() && FLAGS_hint != "throughput" && FLAGS_hint != "tput" && FLAGS_hint != "latency" &&
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FLAGS_hint != "cumulative_throughput" && FLAGS_hint != "ctput" && FLAGS_hint != "none") {
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throw std::logic_error("Incorrect performance hint. Please set -hint option to"
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"`throughput`(tput), `latency', 'cumulative_throughput'(ctput) value or 'none'.");
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}
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if (FLAGS_hint != "none" && (FLAGS_nstreams != "" || FLAGS_nthreads != 0 || FLAGS_pin != "")) {
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throw std::logic_error("-nstreams, -nthreads and -pin options are fine tune options. To use them you "
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"should explicitely set -hint option to none. This is not OpenVINO limitation "
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"(those options can be used in OpenVINO together), but a benchmark_app UI rule.");
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}
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if (!FLAGS_report_type.empty() && FLAGS_report_type != noCntReport && FLAGS_report_type != averageCntReport &&
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FLAGS_report_type != detailedCntReport) {
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std::string err = "only " + std::string(noCntReport) + "/" + std::string(averageCntReport) + "/" +
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std::string(detailedCntReport) +
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" report types are supported (invalid -report_type option value)";
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throw std::logic_error(err);
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}
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if ((FLAGS_report_type == averageCntReport) && ((FLAGS_d.find("MULTI") != std::string::npos))) {
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throw std::logic_error("only " + std::string(detailedCntReport) + " report type is supported for MULTI device");
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}
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bool isNetworkCompiled = fileExt(FLAGS_m) == "blob";
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bool isPrecisionSet = !(FLAGS_ip.empty() && FLAGS_op.empty() && FLAGS_iop.empty());
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if (isNetworkCompiled && isPrecisionSet) {
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std::string err = std::string("Cannot set precision for a compiled network. ") +
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std::string("Please re-compile your network with required precision "
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"using compile_tool");
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throw std::logic_error(err);
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}
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return true;
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}
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static void next_step(const std::string additional_info = "") {
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static size_t step_id = 0;
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static const std::map<size_t, std::string> step_names = {
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{1, "Parsing and validating input arguments"},
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{2, "Loading OpenVINO Runtime"},
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{3, "Setting device configuration"},
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{4, "Reading network files"},
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{5, "Resizing network to match image sizes and given batch"},
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{6, "Configuring input of the model"},
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{7, "Loading the model to the device"},
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{8, "Setting optimal runtime parameters"},
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{9, "Creating infer requests and preparing input blobs with data"},
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{10, "Measuring performance"},
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{11, "Dumping statistics report"}};
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step_id++;
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OPENVINO_ASSERT(step_names.count(step_id) != 0,
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"Step ID ",
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step_id,
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" is out of total steps number ",
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step_names.size());
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std::cout << "[Step " << step_id << "/" << step_names.size() << "] " << step_names.at(step_id)
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<< (additional_info.empty() ? "" : " (" + additional_info + ")") << std::endl;
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}
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ov::hint::PerformanceMode get_performance_hint(const std::string& device, const ov::Core& core) {
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ov::hint::PerformanceMode ov_perf_hint = ov::hint::PerformanceMode::UNDEFINED;
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auto supported_properties = core.get_property(device, ov::supported_properties);
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if (std::find(supported_properties.begin(), supported_properties.end(), ov::hint::performance_mode) !=
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supported_properties.end()) {
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if (FLAGS_hint != "") {
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if (FLAGS_hint == "throughput" || FLAGS_hint == "tput") {
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slog::warn << "Device(" << device << ") performance hint is set to THROUGHPUT" << slog::endl;
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ov_perf_hint = ov::hint::PerformanceMode::THROUGHPUT;
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} else if (FLAGS_hint == "latency") {
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slog::warn << "Device(" << device << ") performance hint is set to LATENCY" << slog::endl;
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ov_perf_hint = ov::hint::PerformanceMode::LATENCY;
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} else if (FLAGS_hint == "cumulative_throughput" || FLAGS_hint == "ctput") {
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slog::warn << "Device(" << device << ") performance hint is set to CUMULATIVE_THROUGHPUT" << slog::endl;
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ov_perf_hint = ov::hint::PerformanceMode::CUMULATIVE_THROUGHPUT;
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} else if (FLAGS_hint == "none") {
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slog::warn << "No device(" << device << ") performance hint is set" << slog::endl;
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ov_perf_hint = ov::hint::PerformanceMode::UNDEFINED;
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}
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} else {
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ov_perf_hint =
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FLAGS_api == "sync" ? ov::hint::PerformanceMode::LATENCY : ov::hint::PerformanceMode::THROUGHPUT;
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slog::warn << "Performance hint was not explicitly specified in command line. "
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"Device("
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<< device << ") performance hint will be set to " << ov_perf_hint << "." << slog::endl;
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}
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} else {
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if (FLAGS_hint != "") {
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slog::warn << "Device(" << device << ") does not support performance hint property(-hint)." << slog::endl;
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}
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}
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return ov_perf_hint;
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}
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/**
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* @brief The entry point of the benchmark application
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*/
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int main(int argc, char* argv[]) {
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std::shared_ptr<StatisticsReport> statistics;
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try {
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ov::CompiledModel compiledModel;
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// ----------------- 1. Parsing and validating input arguments
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// -------------------------------------------------
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next_step();
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if (!parse_and_check_command_line(argc, argv)) {
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return 0;
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}
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bool isNetworkCompiled = fileExt(FLAGS_m) == "blob";
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if (isNetworkCompiled) {
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slog::info << "Network is compiled" << slog::endl;
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}
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std::vector<gflags::CommandLineFlagInfo> flags;
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StatisticsReport::Parameters command_line_arguments;
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gflags::GetAllFlags(&flags);
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for (auto& flag : flags) {
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if (!flag.is_default) {
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command_line_arguments.emplace_back(flag.name, flag.name, flag.current_value);
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}
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}
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if (!FLAGS_report_type.empty()) {
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statistics = FLAGS_json_stats ? std::make_shared<StatisticsReportJSON>(
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StatisticsReport::Config{FLAGS_report_type, FLAGS_report_folder})
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: std::make_shared<StatisticsReport>(
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StatisticsReport::Config{FLAGS_report_type, FLAGS_report_folder});
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statistics->add_parameters(StatisticsReport::Category::COMMAND_LINE_PARAMETERS, command_line_arguments);
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}
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auto isFlagSetInCommandLine = [&command_line_arguments](const std::string& name) {
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return (std::find_if(command_line_arguments.begin(),
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command_line_arguments.end(),
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[name](const StatisticsVariant& p) {
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return p.json_name == name;
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}) != command_line_arguments.end());
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};
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std::string device_name = FLAGS_d;
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// Parse devices
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auto devices = parse_devices(device_name);
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// Parse nstreams per device
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std::map<std::string, std::string> device_nstreams = parse_value_per_device(devices, FLAGS_nstreams);
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std::map<std::string, std::string> device_infer_precision =
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parse_value_per_device(devices, FLAGS_infer_precision);
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// Load device config file if specified
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std::map<std::string, ov::AnyMap> config;
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if (!FLAGS_load_config.empty()) {
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load_config(FLAGS_load_config, config);
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}
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/** This vector stores paths to the processed images with input names**/
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auto inputFiles = parse_input_arguments(gflags::GetArgvs());
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// ----------------- 2. Loading the OpenVINO Runtime
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// -----------------------------------------------------------
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next_step();
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ov::Core core;
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if (!FLAGS_extensions.empty()) {
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// Extensions are loaded as a shared library
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core.add_extension(FLAGS_extensions);
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slog::info << "Extensions are loaded: " << FLAGS_extensions << slog::endl;
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}
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// Load clDNN Extensions
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if ((FLAGS_d.find("GPU") != std::string::npos) && !FLAGS_c.empty()) {
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// Override config if command line parameter is specified
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if (!config.count("GPU"))
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config["GPU"] = {};
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config["GPU"][CONFIG_KEY(CONFIG_FILE)] = FLAGS_c;
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}
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if (config.count("GPU") && config.at("GPU").count(CONFIG_KEY(CONFIG_FILE))) {
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auto ext = config.at("GPU").at(CONFIG_KEY(CONFIG_FILE)).as<std::string>();
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core.set_property("GPU", {{CONFIG_KEY(CONFIG_FILE), ext}});
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slog::info << "GPU extensions are loaded: " << ext << slog::endl;
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}
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slog::info << "OpenVINO: " << ov::get_openvino_version() << slog::endl;
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slog::info << "Device info: " << slog::endl;
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slog::info << core.get_versions(device_name) << slog::endl;
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// ----------------- 3. Setting device configuration
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// -----------------------------------------------------------
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next_step();
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auto getDeviceTypeFromName = [](std::string device) -> std::string {
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return device.substr(0, device.find_first_of(".("));
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};
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// Set default values from dumped config
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std::set<std::string> default_devices;
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for (auto& device : devices) {
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auto default_config = config.find(getDeviceTypeFromName(device));
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if (default_config != config.end()) {
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if (!config.count(device)) {
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config[device] = default_config->second;
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default_devices.emplace(default_config->first);
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}
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}
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}
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for (auto& device : default_devices) {
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config.erase(device);
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}
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bool perf_counts = false;
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// check if using the virtual device
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auto if_auto = std::find(devices.begin(), devices.end(), "AUTO") != devices.end();
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auto if_multi = std::find(devices.begin(), devices.end(), "MULTI") != devices.end();
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// Remove the hardware devices if AUTO/MULTI appears in the devices list.
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if (if_auto || if_multi) {
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devices.clear();
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std::string virtual_device;
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if (if_auto) {
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virtual_device = "AUTO";
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devices.push_back("AUTO");
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}
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if (if_multi) {
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virtual_device = "MULTI";
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devices.push_back("MULTI");
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}
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parse_value_for_virtual_device(virtual_device, device_nstreams);
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parse_value_for_virtual_device(virtual_device, device_infer_precision);
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}
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// Update config per device according to command line parameters
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for (auto& device : devices) {
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auto& device_config = config[device];
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// high-level performance modes
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auto ov_perf_hint = get_performance_hint(device, core);
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if (ov_perf_hint != ov::hint::PerformanceMode::UNDEFINED) {
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device_config.emplace(ov::hint::performance_mode(ov_perf_hint));
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if (FLAGS_nireq != 0)
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device_config.emplace(ov::hint::num_requests(FLAGS_nireq));
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}
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// Set performance counter
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if (isFlagSetInCommandLine("pc")) {
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// set to user defined value
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device_config.emplace(ov::enable_profiling(FLAGS_pc));
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} else if (device_config.count(ov::enable_profiling.name()) &&
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(device_config.at(ov::enable_profiling.name()).as<bool>())) {
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slog::warn << "Performance counters for " << device
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<< " device is turned on. To print results use -pc option." << slog::endl;
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} else if (FLAGS_report_type == detailedCntReport || FLAGS_report_type == averageCntReport) {
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slog::warn << "Turn on performance counters for " << device << " device since report type is "
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<< FLAGS_report_type << "." << slog::endl;
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device_config.emplace(ov::enable_profiling(true));
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} else if (!FLAGS_exec_graph_path.empty()) {
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slog::warn << "Turn on performance counters for " << device << " device due to execution graph dumping."
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<< slog::endl;
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device_config.emplace(ov::enable_profiling(true));
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} else {
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// set to default value
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device_config.emplace(ov::enable_profiling(FLAGS_pc));
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}
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perf_counts = (device_config.at(ov::enable_profiling.name()).as<bool>()) ? true : perf_counts;
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auto supported_properties = core.get_property(device, ov::supported_properties);
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auto supported = [&](const std::string& key) {
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return std::find(std::begin(supported_properties), std::end(supported_properties), key) !=
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std::end(supported_properties);
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};
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// the rest are individual per-device settings (overriding the values set with perf modes)
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auto setThroughputStreams = [&]() {
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std::string key = getDeviceTypeFromName(device) + "_THROUGHPUT_STREAMS";
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auto it_device_nstreams = device_nstreams.find(device);
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if (it_device_nstreams != device_nstreams.end()) {
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// set to user defined value
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if (supported(key)) {
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device_config[key] = it_device_nstreams->second;
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} else if (supported(ov::num_streams.name())) {
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// Use API 2.0 key for streams
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key = ov::num_streams.name();
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device_config[key] = it_device_nstreams->second;
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} else if (device == "MULTI" || device == "AUTO") {
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// check if the element contains the hardware device property
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auto value_vec = split(it_device_nstreams->second, ' ');
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if (value_vec.size() == 1) {
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key = ov::num_streams.name();
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device_config[key] = it_device_nstreams->second;
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} else {
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// set device nstreams properties in the AUTO/MULTI plugin
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std::stringstream strm(it_device_nstreams->second);
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std::map<std::string, std::string> devices_property;
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ov::util::Read<std::map<std::string, std::string>>{}(strm, devices_property);
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for (auto it : devices_property) {
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device_config.insert(
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ov::device::properties(it.first, ov::num_streams(std::stoi(it.second))));
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}
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}
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} else {
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throw std::logic_error("Device " + device + " doesn't support config key '" + key + "' " +
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"and '" + ov::num_streams.name() + "'!" +
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"Please specify -nstreams for correct devices in format "
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"<dev1>:<nstreams1>,<dev2>:<nstreams2>" +
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" or via configuration file.");
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}
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} else if (ov_perf_hint == ov::hint::PerformanceMode::UNDEFINED && !device_config.count(key) &&
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(FLAGS_api == "async")) {
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slog::warn << "-nstreams default value is determined automatically for " << device
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<< " device. "
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"Although the automatic selection usually provides a "
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"reasonable performance, "
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"but it still may be non-optimal for some cases, for more "
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"information look at README."
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<< slog::endl;
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if (std::string::npos == device.find("MYRIAD")) { // MYRIAD sets the default number of
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// streams implicitly (without _AUTO)
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if (supported(key)) {
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device_config[key] = std::string(getDeviceTypeFromName(device) + "_THROUGHPUT_AUTO");
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} else if (supported(ov::num_streams.name())) {
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// Use API 2.0 key for streams
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key = ov::num_streams.name();
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device_config[key] = ov::streams::AUTO;
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}
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}
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}
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auto it_streams = device_config.find(ov::num_streams.name());
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if (it_streams != device_config.end())
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device_nstreams[device] = it_streams->second.as<std::string>();
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};
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auto set_infer_precision = [&] {
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auto it_device_infer_precision = device_infer_precision.find(device);
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if (it_device_infer_precision != device_infer_precision.end()) {
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// set to user defined value
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if (!supported(ov::hint::inference_precision.name())) {
|
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throw std::logic_error("Device " + device + " doesn't support config key '" +
|
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ov::hint::inference_precision.name() + "'! " +
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"Please specify -infer_precision for correct devices in format "
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"<dev1>:<infer_precision1>,<dev2>:<infer_precision2>" +
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" or via configuration file.");
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}
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device_config.emplace(ov::hint::inference_precision(it_device_infer_precision->second));
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}
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};
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auto fix_pin_option = [](const std::string& str) -> std::string {
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if (str == "NO")
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return "NONE";
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else if (str == "YES")
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return "CORE";
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else
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return str;
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};
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if (supported(ov::inference_num_threads.name()) && isFlagSetInCommandLine("nthreads")) {
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device_config.emplace(ov::inference_num_threads(FLAGS_nthreads));
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}
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if (supported(ov::affinity.name()) && isFlagSetInCommandLine("pin")) {
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device_config.emplace(ov::affinity(fix_pin_option(FLAGS_pin)));
|
||
}
|
||
|
||
if (device.find("CPU") != std::string::npos || device.find("GPU") != std::string::npos) {
|
||
// CPU supports few special performance-oriented keys
|
||
// for CPU and GPU execution, more throughput-oriented execution via streams
|
||
setThroughputStreams();
|
||
set_infer_precision();
|
||
} else if (device.find("MYRIAD") != std::string::npos) {
|
||
device_config.emplace(ov::log::level(ov::log::Level::WARNING));
|
||
setThroughputStreams();
|
||
} else if (device.find("GNA") != std::string::npos) {
|
||
set_infer_precision();
|
||
} else if (device.find("AUTO") != std::string::npos) {
|
||
setThroughputStreams();
|
||
set_infer_precision();
|
||
device_nstreams.erase(device);
|
||
} else if (device.find("MULTI") != std::string::npos) {
|
||
setThroughputStreams();
|
||
set_infer_precision();
|
||
if ((device_name.find("GPU") != std::string::npos) && (device_name.find("CPU") != std::string::npos)) {
|
||
slog::warn << "GPU throttling is turned on. Multi-device execution with "
|
||
"the CPU + GPU performs best with GPU throttling hint, "
|
||
<< "which releases another CPU thread (that is otherwise "
|
||
"used by the GPU driver for active polling)."
|
||
<< slog::endl;
|
||
|
||
device_config.insert(ov::device::properties("GPU", {{GPU_CONFIG_KEY(PLUGIN_THROTTLE), 1}}));
|
||
// limit threading for CPU portion of inference
|
||
if (!isFlagSetInCommandLine("pin")) {
|
||
auto it_affinity = device_config.find(ov::affinity.name());
|
||
if (it_affinity != device_config.end()) {
|
||
slog::warn << "Turn off threads pinning for " << device
|
||
<< " device since multi-scenario with GPU device is used." << slog::endl;
|
||
it_affinity->second = ov::Affinity::NONE;
|
||
}
|
||
}
|
||
}
|
||
device_nstreams.erase(device);
|
||
}
|
||
}
|
||
|
||
for (auto&& item : config) {
|
||
core.set_property(item.first, item.second);
|
||
}
|
||
|
||
size_t batchSize = FLAGS_b;
|
||
ov::element::Type type = ov::element::undefined;
|
||
std::string topology_name = "";
|
||
std::vector<benchmark_app::InputsInfo> app_inputs_info;
|
||
std::string output_name;
|
||
|
||
// Takes priority over config from file
|
||
if (!FLAGS_cache_dir.empty()) {
|
||
core.set_property(ov::cache_dir(FLAGS_cache_dir));
|
||
}
|
||
|
||
// If set batch size, disable the auto batching
|
||
if (FLAGS_b > 0) {
|
||
core.set_property(ov::hint::allow_auto_batching(false));
|
||
}
|
||
|
||
bool isDynamicNetwork = false;
|
||
|
||
if (FLAGS_load_from_file && !isNetworkCompiled) {
|
||
next_step();
|
||
slog::info << "Skipping the step for loading network from file" << slog::endl;
|
||
next_step();
|
||
slog::info << "Skipping the step for loading network from file" << slog::endl;
|
||
next_step();
|
||
slog::info << "Skipping the step for loading network from file" << slog::endl;
|
||
auto startTime = Time::now();
|
||
compiledModel = core.compile_model(FLAGS_m, device_name);
|
||
auto duration_ms = get_duration_ms_till_now(startTime);
|
||
slog::info << "Load network took " << double_to_string(duration_ms) << " ms" << slog::endl;
|
||
slog::info << "Original network I/O parameters:" << slog::endl;
|
||
printInputAndOutputsInfoShort(compiledModel);
|
||
|
||
if (statistics)
|
||
statistics->add_parameters(
|
||
StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{StatisticsVariant("load network time (ms)", "load_network_time", duration_ms)});
|
||
|
||
convert_io_names_in_map(inputFiles, compiledModel.inputs());
|
||
app_inputs_info = get_inputs_info(FLAGS_shape,
|
||
FLAGS_layout,
|
||
batchSize,
|
||
FLAGS_data_shape,
|
||
inputFiles,
|
||
FLAGS_iscale,
|
||
FLAGS_imean,
|
||
compiledModel.inputs());
|
||
if (batchSize == 0) {
|
||
batchSize = 1;
|
||
}
|
||
|
||
} else if (!isNetworkCompiled) {
|
||
// ----------------- 4. Reading the Intermediate Representation network
|
||
// ----------------------------------------
|
||
next_step();
|
||
|
||
slog::info << "Loading network files" << slog::endl;
|
||
|
||
auto startTime = Time::now();
|
||
auto model = core.read_model(FLAGS_m);
|
||
auto duration_ms = get_duration_ms_till_now(startTime);
|
||
slog::info << "Read network took " << double_to_string(duration_ms) << " ms" << slog::endl;
|
||
slog::info << "Original network I/O parameters:" << slog::endl;
|
||
printInputAndOutputsInfoShort(*model);
|
||
|
||
if (statistics)
|
||
statistics->add_parameters(
|
||
StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{StatisticsVariant("read network time (ms)", "read_network_time", duration_ms)});
|
||
|
||
const auto& inputInfo = std::const_pointer_cast<const ov::Model>(model)->inputs();
|
||
if (inputInfo.empty()) {
|
||
throw std::logic_error("no inputs info is provided");
|
||
}
|
||
|
||
// ----------------- 5. Resizing network to match image sizes and given
|
||
// batch ----------------------------------
|
||
for (auto& item : model->inputs()) {
|
||
if (item.get_tensor().get_names().empty()) {
|
||
item.get_tensor_ptr()->set_names(
|
||
std::unordered_set<std::string>{item.get_node_shared_ptr()->get_name()});
|
||
}
|
||
}
|
||
next_step();
|
||
convert_io_names_in_map(inputFiles, std::const_pointer_cast<const ov::Model>(model)->inputs());
|
||
// Parse input shapes if specified
|
||
bool reshape = false;
|
||
app_inputs_info = get_inputs_info(FLAGS_shape,
|
||
FLAGS_layout,
|
||
FLAGS_b,
|
||
FLAGS_data_shape,
|
||
inputFiles,
|
||
FLAGS_iscale,
|
||
FLAGS_imean,
|
||
inputInfo,
|
||
reshape);
|
||
if (reshape) {
|
||
benchmark_app::PartialShapes shapes = {};
|
||
for (auto& item : app_inputs_info[0])
|
||
shapes[item.first] = item.second.partialShape;
|
||
slog::info << "Reshaping network: " << get_shapes_string(shapes) << slog::endl;
|
||
startTime = Time::now();
|
||
model->reshape(shapes);
|
||
duration_ms = get_duration_ms_till_now(startTime);
|
||
slog::info << "Reshape network took " << double_to_string(duration_ms) << " ms" << slog::endl;
|
||
if (statistics)
|
||
statistics->add_parameters(
|
||
StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{StatisticsVariant("reshape network time (ms)", "reshape_network_time", duration_ms)});
|
||
}
|
||
|
||
// ----------------- 6. Configuring inputs and outputs
|
||
// ----------------------------------------------------------------------
|
||
next_step();
|
||
auto preproc = ov::preprocess::PrePostProcessor(model);
|
||
|
||
std::map<std::string, std::string> user_precisions_map;
|
||
if (!FLAGS_iop.empty()) {
|
||
user_precisions_map = parseArgMap(FLAGS_iop);
|
||
convert_io_names_in_map(user_precisions_map,
|
||
std::const_pointer_cast<const ov::Model>(model)->inputs(),
|
||
std::const_pointer_cast<const ov::Model>(model)->outputs());
|
||
}
|
||
|
||
const auto input_precision = FLAGS_ip.empty() ? ov::element::undefined : getPrecision2(FLAGS_ip);
|
||
const auto output_precision = FLAGS_op.empty() ? ov::element::undefined : getPrecision2(FLAGS_op);
|
||
|
||
const auto& inputs = model->inputs();
|
||
for (int i = 0; i < inputs.size(); i++) {
|
||
const auto& item = inputs[i];
|
||
auto iop_precision = ov::element::undefined;
|
||
auto type_to_set = ov::element::undefined;
|
||
std::string name;
|
||
try {
|
||
// Some tensors might have no names, get_any_name will throw exception in that case.
|
||
// -iop option will not work for those tensors.
|
||
name = item.get_any_name();
|
||
iop_precision = getPrecision2(user_precisions_map.at(item.get_any_name()));
|
||
} catch (...) {
|
||
}
|
||
|
||
if (iop_precision != ov::element::undefined) {
|
||
type_to_set = iop_precision;
|
||
} else if (input_precision != ov::element::undefined) {
|
||
type_to_set = input_precision;
|
||
} else if (!name.empty() && app_inputs_info[0].at(name).is_image()) {
|
||
// image input, set U8
|
||
type_to_set = ov::element::u8;
|
||
}
|
||
|
||
auto& in = preproc.input(item.get_any_name());
|
||
if (type_to_set != ov::element::undefined) {
|
||
in.tensor().set_element_type(type_to_set);
|
||
|
||
if (!name.empty()) {
|
||
for (auto& info : app_inputs_info) {
|
||
info.at(name).type = type_to_set;
|
||
}
|
||
}
|
||
}
|
||
// Explicitly set inputs layout.
|
||
if (!name.empty() && !app_inputs_info[0].at(name).layout.empty()) {
|
||
in.model().set_layout(app_inputs_info[0].at(name).layout);
|
||
}
|
||
}
|
||
|
||
const auto& outs = model->outputs();
|
||
for (int i = 0; i < outs.size(); i++) {
|
||
const auto& item = outs[i];
|
||
auto iop_precision = ov::element::undefined;
|
||
try {
|
||
// Some tensors might have no names, get_any_name will throw exception in that case.
|
||
// -iop option will not work for those tensors.
|
||
iop_precision = getPrecision2(user_precisions_map.at(item.get_any_name()));
|
||
} catch (...) {
|
||
}
|
||
|
||
if (iop_precision != ov::element::undefined) {
|
||
preproc.output(i).tensor().set_element_type(iop_precision);
|
||
} else if (output_precision != ov::element::undefined) {
|
||
preproc.output(i).tensor().set_element_type(output_precision);
|
||
}
|
||
}
|
||
|
||
model = preproc.build();
|
||
|
||
// Check if network has dynamic shapes
|
||
auto input_info = app_inputs_info[0];
|
||
isDynamicNetwork = std::any_of(input_info.begin(),
|
||
input_info.end(),
|
||
[](const std::pair<std::string, benchmark_app::InputInfo>& i) {
|
||
return i.second.partialShape.is_dynamic();
|
||
});
|
||
|
||
topology_name = model->get_friendly_name();
|
||
|
||
// Calculate batch size according to provided layout and shapes (static case)
|
||
if (!isDynamicNetwork && app_inputs_info.size()) {
|
||
batchSize = get_batch_size(app_inputs_info.front());
|
||
|
||
slog::info << "Network batch size: " << batchSize << slog::endl;
|
||
} else if (batchSize == 0) {
|
||
batchSize = 1;
|
||
}
|
||
|
||
printInputAndOutputsInfoShort(*model);
|
||
// ----------------- 7. Loading the model to the device
|
||
// --------------------------------------------------------
|
||
next_step();
|
||
startTime = Time::now();
|
||
compiledModel = core.compile_model(model, device_name);
|
||
duration_ms = get_duration_ms_till_now(startTime);
|
||
slog::info << "Load network took " << double_to_string(duration_ms) << " ms" << slog::endl;
|
||
if (statistics)
|
||
statistics->add_parameters(
|
||
StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{StatisticsVariant("load network time (ms)", "load_network_time", 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();
|
||
|
||
std::ifstream modelStream(FLAGS_m, std::ios_base::binary | std::ios_base::in);
|
||
if (!modelStream.is_open()) {
|
||
throw std::runtime_error("Cannot open model file " + FLAGS_m);
|
||
}
|
||
compiledModel = core.import_model(modelStream, device_name, {});
|
||
modelStream.close();
|
||
|
||
auto duration_ms = get_duration_ms_till_now(startTime);
|
||
slog::info << "Import network took " << double_to_string(duration_ms) << " ms" << slog::endl;
|
||
slog::info << "Original network I/O paramteters:" << slog::endl;
|
||
printInputAndOutputsInfoShort(compiledModel);
|
||
|
||
if (statistics)
|
||
statistics->add_parameters(
|
||
StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{StatisticsVariant("import network time (ms)", "import_network_time", duration_ms)});
|
||
|
||
convert_io_names_in_map(inputFiles, compiledModel.inputs());
|
||
app_inputs_info = get_inputs_info(FLAGS_shape,
|
||
FLAGS_layout,
|
||
FLAGS_b,
|
||
FLAGS_data_shape,
|
||
inputFiles,
|
||
FLAGS_iscale,
|
||
FLAGS_imean,
|
||
compiledModel.inputs());
|
||
if (batchSize == 0) {
|
||
batchSize = 1;
|
||
}
|
||
}
|
||
|
||
if (isDynamicNetwork && FLAGS_api == "sync") {
|
||
throw std::logic_error("Benchmarking of the model with dynamic shapes is available for async API only."
|
||
"Please use -api async -nstreams 1 -nireq 1 to emulate sync behavior");
|
||
}
|
||
|
||
// Defining of benchmark mode
|
||
// for static models inference only mode is used as default one
|
||
bool inferenceOnly = FLAGS_inference_only;
|
||
if (isDynamicNetwork) {
|
||
if (isFlagSetInCommandLine("inference_only") && inferenceOnly && app_inputs_info.size() != 1) {
|
||
throw std::logic_error(
|
||
"Dynamic models with different input data shapes must be benchmarked only in full mode.");
|
||
}
|
||
inferenceOnly = isFlagSetInCommandLine("inference_only") && inferenceOnly && app_inputs_info.size() == 1;
|
||
}
|
||
|
||
// ----------------- 8. Querying optimal runtime parameters
|
||
// -----------------------------------------------------
|
||
next_step();
|
||
// output of the actual settings that the device selected
|
||
for (const auto& device : devices) {
|
||
auto supported_properties = compiledModel.get_property(ov::supported_properties);
|
||
slog::info << "Device: " << device << slog::endl;
|
||
for (const auto& cfg : supported_properties) {
|
||
try {
|
||
if (cfg == ov::supported_properties)
|
||
continue;
|
||
|
||
auto prop = compiledModel.get_property(cfg);
|
||
slog::info << " { " << cfg << " , " << prop.as<std::string>() << " }" << slog::endl;
|
||
} catch (const ov::Exception&) {
|
||
}
|
||
}
|
||
}
|
||
|
||
// Update number of streams
|
||
for (auto&& ds : device_nstreams) {
|
||
try {
|
||
const std::string key = getDeviceTypeFromName(ds.first) + "_THROUGHPUT_STREAMS";
|
||
device_nstreams[ds.first] = core.get_property(ds.first, key).as<std::string>();
|
||
} catch (const ov::Exception&) {
|
||
device_nstreams[ds.first] = core.get_property(ds.first, ov::num_streams.name()).as<std::string>();
|
||
}
|
||
}
|
||
|
||
// Number of requests
|
||
uint32_t nireq = FLAGS_nireq;
|
||
if (nireq == 0) {
|
||
if (FLAGS_api == "sync") {
|
||
nireq = 1;
|
||
} else {
|
||
try {
|
||
nireq = compiledModel.get_property(ov::optimal_number_of_infer_requests);
|
||
} catch (const std::exception& ex) {
|
||
throw ov::Exception("Every device used with the benchmark_app should support " +
|
||
std::string(ov::optimal_number_of_infer_requests.name()) +
|
||
" Failed to query the metric for the " + device_name +
|
||
" with error:" + ex.what());
|
||
}
|
||
}
|
||
}
|
||
|
||
// Iteration limit
|
||
uint32_t niter = FLAGS_niter;
|
||
size_t shape_groups_num = app_inputs_info.size();
|
||
if ((niter > 0) && (FLAGS_api == "async")) {
|
||
if (shape_groups_num > nireq) {
|
||
niter = ((niter + shape_groups_num - 1) / shape_groups_num) * shape_groups_num;
|
||
if (FLAGS_niter != niter) {
|
||
slog::warn << "Number of iterations was aligned by data shape groups number from " << FLAGS_niter
|
||
<< " to " << niter << " using number of possible input shapes " << shape_groups_num
|
||
<< slog::endl;
|
||
}
|
||
} else {
|
||
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 = device_default_device_duration_in_seconds(device_name);
|
||
}
|
||
uint64_t duration_nanoseconds = get_duration_in_nanoseconds(duration_seconds);
|
||
|
||
if (statistics) {
|
||
statistics->add_parameters(
|
||
StatisticsReport::Category::RUNTIME_CONFIG,
|
||
StatisticsReport::Parameters(
|
||
{StatisticsVariant("benchmark mode", "benchmark_mode", inferenceOnly ? "inference only" : "full"),
|
||
StatisticsVariant("topology", "topology", topology_name),
|
||
StatisticsVariant("target device", "target_device", device_name),
|
||
StatisticsVariant("API", "api", FLAGS_api),
|
||
StatisticsVariant("precision", "precision", type.get_type_name()),
|
||
StatisticsVariant("batch size", "batch_size", batchSize),
|
||
StatisticsVariant("number of iterations", "iterations_num", niter),
|
||
StatisticsVariant("number of parallel infer requests", "nireq", nireq),
|
||
StatisticsVariant("duration (ms)", "duration", get_duration_in_milliseconds(duration_seconds))}));
|
||
for (auto& nstreams : device_nstreams) {
|
||
std::stringstream ss;
|
||
ss << "number of " << nstreams.first << " streams";
|
||
|
||
std::string dev_name = nstreams.first;
|
||
std::transform(dev_name.begin(), dev_name.end(), dev_name.begin(), [](unsigned char c) {
|
||
return c == ' ' ? '_' : std::tolower(c);
|
||
});
|
||
|
||
statistics->add_parameters(StatisticsReport::Category::RUNTIME_CONFIG,
|
||
{StatisticsVariant(ss.str(), dev_name + "_streams_num", nstreams.second)});
|
||
}
|
||
}
|
||
|
||
// ----------------- 9. Creating infer requests and filling input blobs
|
||
// ----------------------------------------
|
||
next_step();
|
||
|
||
InferRequestsQueue inferRequestsQueue(compiledModel, nireq, app_inputs_info.size(), FLAGS_pcseq);
|
||
|
||
bool inputHasName = false;
|
||
if (inputFiles.size() > 0) {
|
||
inputHasName = inputFiles.begin()->first != "";
|
||
}
|
||
bool newInputType = isDynamicNetwork || inputHasName;
|
||
// create vector to store remote input blobs buffer
|
||
std::vector<::gpu::BufferType> clInputsBuffer;
|
||
bool useGpuMem = false;
|
||
|
||
std::map<std::string, ov::TensorVector> inputsData;
|
||
if (isFlagSetInCommandLine("use_device_mem")) {
|
||
if (device_name.find("GPU") == 0) {
|
||
inputsData = ::gpu::get_remote_input_tensors(inputFiles,
|
||
app_inputs_info,
|
||
compiledModel,
|
||
clInputsBuffer,
|
||
inferRequestsQueue.requests.size());
|
||
useGpuMem = true;
|
||
} else if (device_name.find("CPU") == 0) {
|
||
if (newInputType) {
|
||
inputsData = get_tensors(inputFiles, app_inputs_info);
|
||
} else {
|
||
inputsData = get_tensors_static_case(
|
||
inputFiles.empty() ? std::vector<std::string>{} : inputFiles.begin()->second,
|
||
batchSize,
|
||
app_inputs_info[0],
|
||
nireq);
|
||
}
|
||
} else {
|
||
throw ov::Exception("Requested device doesn't support `use_device_mem` option.");
|
||
}
|
||
} else {
|
||
if (newInputType) {
|
||
inputsData = get_tensors(inputFiles, app_inputs_info);
|
||
} else {
|
||
inputsData = get_tensors_static_case(
|
||
inputFiles.empty() ? std::vector<std::string>{} : inputFiles.begin()->second,
|
||
batchSize,
|
||
app_inputs_info[0],
|
||
nireq);
|
||
}
|
||
}
|
||
// ----------------- 10. Measuring performance
|
||
// ------------------------------------------------------------------
|
||
size_t progressCnt = 0;
|
||
size_t progressBarTotalCount = progressBarDefaultTotalCount;
|
||
size_t iteration = 0;
|
||
|
||
std::stringstream ss;
|
||
ss << "Start inference " << FLAGS_api << "hronously";
|
||
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 << get_duration_in_milliseconds(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());
|
||
|
||
if (inferenceOnly) {
|
||
slog::info << "BENCHMARK IS IN INFERENCE ONLY MODE." << slog::endl;
|
||
slog::info << "Input blobs will be filled once before performance measurements." << slog::endl;
|
||
} else {
|
||
slog::info << "BENCHMARK IS IN FULL MODE." << slog::endl;
|
||
slog::info << "Inputs setup stage will be included in performance measurements." << slog::endl;
|
||
}
|
||
|
||
// copy prepared data straight into inferRequest->getTensor()
|
||
// for inference only mode
|
||
if (inferenceOnly) {
|
||
if (nireq < inputsData.begin()->second.size())
|
||
slog::warn << "Only " << nireq << " test configs will be used." << slog::endl;
|
||
size_t i = 0;
|
||
for (auto& inferRequest : inferRequestsQueue.requests) {
|
||
auto inputs = app_inputs_info[i % app_inputs_info.size()];
|
||
for (auto& item : inputs) {
|
||
auto inputName = item.first;
|
||
const auto& inputTensor = inputsData.at(inputName)[i % inputsData.at(inputName).size()];
|
||
// for remote blobs setTensor is used, they are already allocated on the device
|
||
if (useGpuMem) {
|
||
inferRequest->set_tensor(inputName, inputTensor);
|
||
} else {
|
||
auto requestTensor = inferRequest->get_tensor(inputName);
|
||
if (isDynamicNetwork) {
|
||
requestTensor.set_shape(inputTensor.get_shape());
|
||
}
|
||
copy_tensor_data(requestTensor, inputTensor);
|
||
}
|
||
}
|
||
|
||
if (useGpuMem) {
|
||
auto outputTensors =
|
||
::gpu::get_remote_output_tensors(compiledModel, inferRequest->get_output_cl_buffer());
|
||
for (auto& output : compiledModel.outputs()) {
|
||
inferRequest->set_tensor(output.get_any_name(), outputTensors[output.get_any_name()]);
|
||
}
|
||
}
|
||
++i;
|
||
}
|
||
}
|
||
|
||
// warming up - out of scope
|
||
auto inferRequest = inferRequestsQueue.get_idle_request();
|
||
if (!inferRequest) {
|
||
throw ov::Exception("No idle Infer Requests!");
|
||
}
|
||
|
||
if (!inferenceOnly) {
|
||
auto inputs = app_inputs_info[0];
|
||
|
||
for (auto& item : inputs) {
|
||
auto inputName = item.first;
|
||
const auto& data = inputsData.at(inputName)[0];
|
||
inferRequest->set_tensor(inputName, data);
|
||
}
|
||
|
||
if (useGpuMem) {
|
||
auto outputTensors =
|
||
::gpu::get_remote_output_tensors(compiledModel, inferRequest->get_output_cl_buffer());
|
||
for (auto& output : compiledModel.outputs()) {
|
||
inferRequest->set_tensor(output.get_any_name(), outputTensors[output.get_any_name()]);
|
||
}
|
||
}
|
||
}
|
||
|
||
if (FLAGS_api == "sync") {
|
||
inferRequest->infer();
|
||
} else {
|
||
inferRequest->start_async();
|
||
}
|
||
|
||
inferRequestsQueue.wait_all();
|
||
|
||
auto duration_ms = inferRequestsQueue.get_latencies()[0];
|
||
slog::info << "First inference took " << double_to_string(duration_ms) << " ms" << slog::endl;
|
||
|
||
if (statistics) {
|
||
statistics->add_parameters(
|
||
StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{StatisticsVariant("first inference time (ms)", "first_inference_time", duration_ms)});
|
||
}
|
||
inferRequestsQueue.reset_times();
|
||
|
||
size_t processedFramesN = 0;
|
||
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.get_idle_request();
|
||
if (!inferRequest) {
|
||
throw ov::Exception("No idle Infer Requests!");
|
||
}
|
||
|
||
if (!inferenceOnly) {
|
||
auto inputs = app_inputs_info[iteration % app_inputs_info.size()];
|
||
|
||
if (FLAGS_pcseq) {
|
||
inferRequest->set_latency_group_id(iteration % app_inputs_info.size());
|
||
}
|
||
|
||
if (isDynamicNetwork) {
|
||
batchSize = get_batch_size(inputs);
|
||
if (!std::any_of(inputs.begin(),
|
||
inputs.end(),
|
||
[](const std::pair<const std::string, benchmark_app::InputInfo>& info) {
|
||
return ov::layout::has_batch(info.second.layout);
|
||
})) {
|
||
slog::warn
|
||
<< "No batch dimension was found, asssuming batch to be 1. Beware: this might affect "
|
||
"FPS calculation."
|
||
<< slog::endl;
|
||
}
|
||
}
|
||
|
||
for (auto& item : inputs) {
|
||
auto inputName = item.first;
|
||
const auto& data = inputsData.at(inputName)[iteration % inputsData.at(inputName).size()];
|
||
inferRequest->set_tensor(inputName, data);
|
||
}
|
||
|
||
if (useGpuMem) {
|
||
auto outputTensors =
|
||
::gpu::get_remote_output_tensors(compiledModel, inferRequest->get_output_cl_buffer());
|
||
for (auto& output : compiledModel.outputs()) {
|
||
inferRequest->set_tensor(output.get_any_name(), outputTensors[output.get_any_name()]);
|
||
}
|
||
}
|
||
}
|
||
|
||
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->start_async();
|
||
}
|
||
++iteration;
|
||
|
||
execTime = std::chrono::duration_cast<ns>(Time::now() - startTime).count();
|
||
processedFramesN += batchSize;
|
||
|
||
if (niter > 0) {
|
||
progressBar.add_progress(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.add_progress(newProgress);
|
||
progressCnt += newProgress;
|
||
}
|
||
}
|
||
|
||
// wait the latest inference executions
|
||
inferRequestsQueue.wait_all();
|
||
|
||
LatencyMetrics generalLatency(inferRequestsQueue.get_latencies(), "", FLAGS_latency_percentile);
|
||
std::vector<LatencyMetrics> groupLatencies = {};
|
||
if (FLAGS_pcseq && app_inputs_info.size() > 1) {
|
||
const auto& lat_groups = inferRequestsQueue.get_latency_groups();
|
||
for (int i = 0; i < lat_groups.size(); i++) {
|
||
const auto& lats = lat_groups[i];
|
||
|
||
std::string data_shapes_string = "";
|
||
for (auto& item : app_inputs_info[i]) {
|
||
data_shapes_string += item.first + get_shape_string(item.second.dataShape) + ",";
|
||
}
|
||
data_shapes_string =
|
||
data_shapes_string == "" ? "" : data_shapes_string.substr(0, data_shapes_string.size() - 1);
|
||
|
||
groupLatencies.emplace_back(lats, data_shapes_string, FLAGS_latency_percentile);
|
||
}
|
||
}
|
||
|
||
double totalDuration = inferRequestsQueue.get_duration_in_milliseconds();
|
||
double fps = (FLAGS_api == "sync") ? batchSize * 1000.0 / generalLatency.median_or_percentile
|
||
: 1000.0 * processedFramesN / totalDuration;
|
||
|
||
if (statistics) {
|
||
statistics->add_parameters(StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{StatisticsVariant("total execution time (ms)", "execution_time", totalDuration),
|
||
StatisticsVariant("total number of iterations", "iterations_num", iteration)});
|
||
if (device_name.find("MULTI") == std::string::npos) {
|
||
std::string latency_label;
|
||
if (FLAGS_latency_percentile == 50) {
|
||
latency_label = "Median latency (ms)";
|
||
} else {
|
||
latency_label = "latency (" + std::to_string(FLAGS_latency_percentile) + " percentile) (ms)";
|
||
}
|
||
statistics->add_parameters(
|
||
StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{StatisticsVariant(latency_label, "latency_median", generalLatency.median_or_percentile),
|
||
StatisticsVariant("Percentile boundary", "percentile_boundary", FLAGS_latency_percentile),
|
||
StatisticsVariant("Average latency (ms)", "latency_avg", generalLatency.avg),
|
||
StatisticsVariant("Min latency (ms)", "latency_min", generalLatency.min),
|
||
StatisticsVariant("Max latency (ms)", "latency_max", generalLatency.max)});
|
||
|
||
if (FLAGS_pcseq && app_inputs_info.size() > 1) {
|
||
for (size_t i = 0; i < groupLatencies.size(); ++i) {
|
||
statistics->add_parameters(
|
||
StatisticsReport::Category::EXECUTION_RESULTS_GROUPPED,
|
||
{StatisticsVariant("Group Latencies", "group_latencies", groupLatencies[i])});
|
||
}
|
||
}
|
||
}
|
||
statistics->add_parameters(StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{StatisticsVariant("throughput", "throughput", fps)});
|
||
}
|
||
progressBar.finish();
|
||
|
||
// ----------------- 11. Dumping statistics report
|
||
// -------------------------------------------------------------
|
||
next_step();
|
||
|
||
if (!FLAGS_dump_config.empty()) {
|
||
dump_config(FLAGS_dump_config, config);
|
||
slog::info << "OpenVINO Runtime configuration settings were dumped to " << FLAGS_dump_config << slog::endl;
|
||
}
|
||
|
||
if (!FLAGS_exec_graph_path.empty()) {
|
||
try {
|
||
ov::serialize(compiledModel.get_runtime_model(), 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::vector<ov::ProfilingInfo>> perfCounts;
|
||
for (size_t ireq = 0; ireq < nireq; ireq++) {
|
||
auto reqPerfCounts = inferRequestsQueue.requests[ireq]->get_performance_counts();
|
||
if (FLAGS_pc) {
|
||
slog::info << "Performance counts for " << ireq << "-th infer request:" << slog::endl;
|
||
printPerformanceCounts(reqPerfCounts, std::cout, getFullDeviceName(core, FLAGS_d), false);
|
||
}
|
||
perfCounts.push_back(reqPerfCounts);
|
||
}
|
||
if (statistics) {
|
||
statistics->dump_performance_counters(perfCounts);
|
||
}
|
||
}
|
||
|
||
if (statistics)
|
||
statistics->dump();
|
||
|
||
// Performance metrics report
|
||
slog::info << "Count: " << iteration << " iterations" << slog::endl;
|
||
slog::info << "Duration: " << double_to_string(totalDuration) << " ms" << slog::endl;
|
||
if (device_name.find("MULTI") == std::string::npos) {
|
||
slog::info << "Latency: " << slog::endl;
|
||
generalLatency.write_to_slog();
|
||
|
||
if (FLAGS_pcseq && app_inputs_info.size() > 1) {
|
||
slog::info << "Latency for each data shape group:" << slog::endl;
|
||
for (size_t i = 0; i < app_inputs_info.size(); ++i) {
|
||
slog::info << (i + 1) << ".";
|
||
for (auto& item : app_inputs_info[i]) {
|
||
std::stringstream input_shape;
|
||
auto shape = item.second.dataShape;
|
||
std::copy(shape.begin(), shape.end() - 1, std::ostream_iterator<size_t>(input_shape, ","));
|
||
input_shape << shape.back();
|
||
slog::info << " " << item.first << " : " << get_shape_string(item.second.dataShape);
|
||
}
|
||
slog::info << slog::endl;
|
||
|
||
groupLatencies[i].write_to_slog();
|
||
}
|
||
}
|
||
}
|
||
slog::info << "Throughput: " << double_to_string(fps) << " FPS" << slog::endl;
|
||
|
||
} catch (const std::exception& ex) {
|
||
slog::err << ex.what() << slog::endl;
|
||
|
||
if (statistics) {
|
||
statistics->add_parameters(StatisticsReport::Category::EXECUTION_RESULTS,
|
||
{StatisticsVariant("error", "error", ex.what())});
|
||
statistics->dump();
|
||
}
|
||
|
||
return 3;
|
||
}
|
||
|
||
return 0;
|
||
}
|