ce8f164fea
* Fix -api sync for single -data_shape Tickets 111187 and 111185 I wasn’t able to find C++ equivalent of Python’s `info.original_shape.is_static`. Later I realized that it shouldn’t be considered because -shape cmd arg should have higher priority for shape inference than model’s shape. So I removed it from Python. Replace `if benchmark.inference_only and batch_size.is_dynamic:` with `if allow_inference_only_or_sync and batch_size.is_dynamic:` to reset batch_size to static in case of dynamic shape with single -data_shape * Check only app_input_info.size() == 1 because if it's gretaer than 1, input shape is dynamic and there are more that one static shapes. Apply TODO
165 lines
8.6 KiB
C++
165 lines
8.6 KiB
C++
// Copyright (C) 2018-2023 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 <unordered_set>
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#include <vector>
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#ifdef USE_OPENCV
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const std::unordered_set<std::string> supported_image_extensions =
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{"bmp", "dib", "jpeg", "jpg", "jpe", "jp2", "png", "pbm", "pgm", "ppm", "sr", "ras", "tiff", "tif"};
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#else
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const std::unordered_set<std::string> supported_image_extensions = {"bmp"};
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#endif
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const std::unordered_set<std::string> supported_numpy_extensions = {"npy"};
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const std::unordered_set<std::string> supported_binary_extensions = {"bin"};
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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(uint64_t duration) {
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return duration * 1000LL;
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}
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inline uint64_t get_duration_in_nanoseconds(uint64_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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bool can_measure_as_static(const std::vector<benchmark_app::InputsInfo>& app_input_info);
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bool is_virtual_device(const std::string& device_name);
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bool is_virtual_device_found(const std::vector<std::string>& device_names);
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void update_device_properties_setting(const std::string& device_name,
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ov::AnyMap& config,
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std::pair<std::string, ov::Any> device_property);
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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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template <typename T>
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void update_device_config_for_virtual_device(const std::string& value,
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ov::AnyMap& device_config,
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ov::Property<T, ov::PropertyMutability::RW> property);
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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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std::string get_extension(const std::string& name);
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bool is_binary_file(const std::string& filePath);
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bool is_numpy_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::unordered_set<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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