c4eeecfec5
* Removed Intel MYRIAD plugin * Removed Intel MYIAD from CI files * Removed Intel MYRIAD from cmake folder * Removed MYRIAD, HDDL from samples * Removed MYRIAD, HDDL from scripts folder * Removed MYRIAD from bindings folder (C and Python API) * Removed MYRIAD tests * Removed MYRIAD from tests folder * Removed MYRIAD from tools folder * Removed HDDL (VAD), MYRIAD (NSC2) from documentation * Fixed build for AUTO unit tests * Fixed clang code style * Fixed comments and issues * removed MYRIAD from AUTO tests * Disabled MULTI tests in CI * Update docs/OV_Runtime_UG/auto_device_selection.md Co-authored-by: Yuan Xu <yuan1.xu@intel.com> * Update docs/get_started/get_started_demos.md Co-authored-by: Yuan Xu <yuan1.xu@intel.com> * Update docs/OV_Runtime_UG/deployment/local-distribution.md Co-authored-by: Yuan Xu <yuan1.xu@intel.com> Co-authored-by: Yuan Xu <yuan1.xu@intel.com>
73 lines
2.9 KiB
C++
73 lines
2.9 KiB
C++
// Copyright (C) 2022 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <string>
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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 "samples/args_helper.hpp"
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#include "samples/common.hpp"
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#include "samples/latency_metrics.hpp"
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#include "samples/slog.hpp"
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// clang-format on
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using Ms = std::chrono::duration<double, std::ratio<1, 1000>>;
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int main(int argc, char* argv[]) {
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try {
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slog::info << "OpenVINO:" << slog::endl;
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slog::info << ov::get_openvino_version();
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if (argc != 2) {
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slog::info << "Usage : " << argv[0] << " <path_to_model>" << slog::endl;
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return EXIT_FAILURE;
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}
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// Optimize for latency. Most of the devices are configured for latency by default,
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// but there are exceptions like GNA
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ov::AnyMap latency{{ov::hint::performance_mode.name(), ov::hint::PerformanceMode::LATENCY}};
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// Create ov::Core and use it to compile a model.
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// Pick a device by replacing CPU, for example AUTO:GPU,CPU.
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// Using MULTI device is pointless in sync scenario
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// because only one instance of ov::InferRequest is used
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ov::Core core;
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ov::CompiledModel compiled_model = core.compile_model(argv[1], "CPU", latency);
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ov::InferRequest ireq = compiled_model.create_infer_request();
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// Fill input data for the ireq
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for (const ov::Output<const ov::Node>& model_input : compiled_model.inputs()) {
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fill_tensor_random(ireq.get_tensor(model_input));
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}
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// Warm up
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ireq.infer();
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// Benchmark for seconds_to_run seconds and at least niter iterations
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std::chrono::seconds seconds_to_run{10};
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size_t niter = 10;
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std::vector<double> latencies;
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latencies.reserve(niter);
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auto start = std::chrono::steady_clock::now();
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auto time_point = start;
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auto time_point_to_finish = start + seconds_to_run;
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while (time_point < time_point_to_finish || latencies.size() < niter) {
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ireq.infer();
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auto iter_end = std::chrono::steady_clock::now();
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latencies.push_back(std::chrono::duration_cast<Ms>(iter_end - time_point).count());
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time_point = iter_end;
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}
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auto end = time_point;
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double duration = std::chrono::duration_cast<Ms>(end - start).count();
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// Report results
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slog::info << "Count: " << latencies.size() << " iterations" << slog::endl
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<< "Duration: " << duration << " ms" << slog::endl
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<< "Latency:" << slog::endl;
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size_t percent = 50;
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LatencyMetrics{latencies, "", percent}.write_to_slog();
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slog::info << "Throughput: " << double_to_string(latencies.size() * 1000 / duration) << " FPS" << slog::endl;
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} catch (const std::exception& ex) {
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slog::err << ex.what() << slog::endl;
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return EXIT_FAILURE;
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}
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return EXIT_SUCCESS;
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}
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