67d25d7099
* Add sync_bnehcmark * Fix Unix comilation * niter->time * Explain main loop * samples: factor out common * Code style * clang-format -i * return 0; -> return EXIT_SUCCESS;, +x * Update throughput_benchmark * Add READMEs * Fix READMEs refs * Add sync_benchmark.py * Add niter, infer_new_request, -pc * from datetime import timedelta * Fix niter and seconds_to_run * Add disclaimer about benchmark_app performance * Update samples/cpp/benchmark/sync_benchmark/README.md * Add dynamic_shape_bert_benhcmark * Add dynamic_shape_detection_benchmark * Adopt for detr-resnet50 * Remove sync_benchmark2, throughput_benchmark2, perf counters * clang-format -i * Fix flake8 * Add README.md * Add links to sample_dynamic_shape_bert_benchmark * Add softmax * nameless LatencyMetrics * parent.parent -> parents[2] * Add bert_benhcmark sample * Code style * Add bert_benhcmark/README.md * rm -r samples/python/benchmark/dynamic_shape_bert_benhcmark/ * rm -r samples/cpp/benchmark/dynamic_shape_detection_benchmark/ * bert_benhcmark/README.md: remove dynamic shape * Remove add_subdirectory(dynamic_shape_detection_benchmark) * flake8 * samples: Add a note about CUMULATIVE_THROUGHPUT, don’t expect get_property() to throw, don’t introduce json dependency for samples/cpp/common * / namespace * Add article * namespace -> static * Update README, seconds_ro_run 10, niter 10, no inter alinment * percentile->median * benchmark samples: use generate(), align logs, update READMEs * benchmakr samples: remove percentile() * samples/python/benchmark/bert_benhcmark/bert_benhcmark.py: report average sequence length and processing time * Python samples: move requirements.txt to every sample * Remove numpy from requirements.txt * Remove Building section from Python samples, install only required extras from openvino-dev, set up environment for bert_benhcmark, report duration for bert_benhcmark * Install openvino-dev for Hello Reshape SSD C++ Sample
146 lines
7.5 KiB
C++
146 lines
7.5 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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#pragma once
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#include <chrono>
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#include <iomanip>
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#include <map>
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#include <openvino/openvino.hpp>
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#include <samples/slog.hpp>
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#include <string>
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#include <vector>
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typedef std::chrono::high_resolution_clock Time;
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typedef std::chrono::nanoseconds ns;
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inline uint64_t get_duration_in_milliseconds(uint32_t duration) {
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return duration * 1000LL;
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}
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inline uint64_t get_duration_in_nanoseconds(uint32_t duration) {
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return duration * 1000000000LL;
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}
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inline double get_duration_ms_till_now(Time::time_point& startTime) {
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return std::chrono::duration_cast<ns>(Time::now() - startTime).count() * 0.000001;
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};
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namespace benchmark_app {
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struct InputInfo {
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ov::element::Type type;
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ov::PartialShape partialShape;
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ov::Shape dataShape;
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ov::Layout layout;
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std::vector<float> scale;
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std::vector<float> mean;
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bool is_image() const;
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bool is_image_info() const;
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size_t width() const;
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size_t height() const;
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size_t channels() const;
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size_t batch() const;
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size_t depth() const;
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std::vector<std::string> fileNames;
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};
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using InputsInfo = std::map<std::string, InputInfo>;
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using PartialShapes = std::map<std::string, ngraph::PartialShape>;
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} // namespace benchmark_app
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std::vector<std::string> parse_devices(const std::string& device_string);
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uint32_t device_default_device_duration_in_seconds(const std::string& device);
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std::map<std::string, std::string> parse_value_per_device(const std::vector<std::string>& devices,
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const std::string& values_string);
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void parse_value_for_virtual_device(const std::string& device, std::map<std::string, std::string>& values_string);
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std::string get_shapes_string(const benchmark_app::PartialShapes& shapes);
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size_t get_batch_size(const benchmark_app::InputsInfo& inputs_info);
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std::vector<std::string> split(const std::string& s, char delim);
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std::map<std::string, std::vector<float>> parse_scale_or_mean(const std::string& scale_mean,
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const benchmark_app::InputsInfo& inputs_info);
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std::pair<std::string, std::vector<std::string>> parse_input_files(const std::string& file_paths_string);
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std::map<std::string, std::vector<std::string>> parse_input_arguments(const std::vector<std::string>& args);
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std::map<std::string, std::vector<std::string>> parse_input_parameters(const std::string& parameter_string,
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const ov::ParameterVector& input_info);
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/// <summary>
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/// Parses command line data and data obtained from the function and returns configuration of each input
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/// </summary>
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/// <param name="shape_string">command-line shape string</param>
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/// <param name="layout_string">command-line layout string</param>
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/// <param name="batch_size">command-line batch string</param>
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/// <param name="tensors_shape_string">command-line data_shape string</param>
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/// <param name="scale_string">command-line iscale string</param>
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/// <param name="mean_string">command-line imean string</param>
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/// <param name="input_info">inputs vector obtained from ov::Model</param>
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/// <param name="reshape_required">returns true to this parameter if reshape is required</param>
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/// <returns>vector of benchmark_app::InputsInfo elements.
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/// Each element is a configuration item for every test configuration case
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/// (number of cases is calculated basing on data_shape and other parameters).
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/// Each element is a map (input_name, configuration) containing data for each input</returns>
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std::vector<benchmark_app::InputsInfo> get_inputs_info(const std::string& shape_string,
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const std::string& layout_string,
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const size_t batch_size,
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const std::string& data_shapes_string,
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const std::map<std::string, std::vector<std::string>>& fileNames,
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const std::string& scale_string,
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const std::string& mean_string,
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const std::vector<ov::Output<const ov::Node>>& input_info,
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bool& reshape_required);
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/// <summary>
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/// Parses command line data and data obtained from the function and returns configuration of each input
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/// </summary>
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/// <param name="shape_string">command-line shape string</param>
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/// <param name="layout_string">command-line layout string</param>
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/// <param name="batch_size">command-line batch string</param>
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/// <param name="tensors_shape_string">command-line data_shape string</param>
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/// <param name="scale_string">command-line iscale string</param>
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/// <param name="mean_string">command-line imean string</param>
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/// <param name="input_info">inputs vector obtained from ov::Model</param>
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/// <param name="reshape_required">returns true to this parameter if reshape is required</param>
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/// <returns>vector of benchmark_app::InputsInfo elements.
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/// Each element is a configuration item for every test configuration case
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/// (number of cases is calculated basing on data_shape and other parameters).
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/// Each element is a map (input_name, configuration) containing data for each
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/// input</returns>
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std::vector<benchmark_app::InputsInfo> get_inputs_info(const std::string& shape_string,
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const std::string& layout_string,
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const size_t batch_size,
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const std::string& data_shapes_string,
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const std::map<std::string, std::vector<std::string>>& fileNames,
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const std::string& scale_string,
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const std::string& mean_string,
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const std::vector<ov::Output<const ov::Node>>& input_info);
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void dump_config(const std::string& filename, const std::map<std::string, ov::AnyMap>& config);
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void load_config(const std::string& filename, std::map<std::string, ov::AnyMap>& config);
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extern const std::vector<std::string> supported_image_extensions;
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extern const std::vector<std::string> supported_binary_extensions;
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bool is_binary_file(const std::string& filePath);
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bool is_image_file(const std::string& filePath);
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bool contains_binaries(const std::vector<std::string>& filePaths);
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std::vector<std::string> filter_files_by_extensions(const std::vector<std::string>& filePaths,
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const std::vector<std::string>& extensions);
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std::string parameter_name_to_tensor_name(
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const std::string& name,
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const std::vector<ov::Output<const ov::Node>>& inputs_info,
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const std::vector<ov::Output<const ov::Node>>& outputs_info = std::vector<ov::Output<const ov::Node>>());
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template <class T>
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void convert_io_names_in_map(
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T& map,
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const std::vector<ov::Output<const ov::Node>>& inputs_info,
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const std::vector<ov::Output<const ov::Node>>& outputs_info = std::vector<ov::Output<const ov::Node>>()) {
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T new_map;
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for (auto& item : map) {
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new_map[item.first == "" ? "" : parameter_name_to_tensor_name(item.first, inputs_info, outputs_info)] =
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std::move(item.second);
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}
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map = new_map;
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}
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