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Diffstat (limited to 'python/openvino/runtime/common/utils/include/samples')
10 files changed, 2391 insertions, 0 deletions
diff --git a/python/openvino/runtime/common/utils/include/samples/args_helper.hpp b/python/openvino/runtime/common/utils/include/samples/args_helper.hpp new file mode 100644 index 0000000..6626140 --- /dev/null +++ b/python/openvino/runtime/common/utils/include/samples/args_helper.hpp @@ -0,0 +1,112 @@ +// Copyright (C) 2018-2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +/** + * @brief a header file with common samples functionality + * @file args_helper.hpp + */ + +#pragma once + +// clang-format off +#include <string> +#include <vector> + +#include "openvino/openvino.hpp" + +#include "samples/slog.hpp" +// clang-format on + +/** + * @brief This function checks input args and existence of specified files in a given folder + * @param arg path to a file to be checked for existence + * @return files updated vector of verified input files + */ +void readInputFilesArguments(std::vector<std::string>& files, const std::string& arg); + +/** + * @brief This function find -i/--images key in input args + * It's necessary to process multiple values for single key + * @return files updated vector of verified input files + */ +void parseInputFilesArguments(std::vector<std::string>& files); +std::map<std::string, std::string> parseArgMap(std::string argMap); + +void printInputAndOutputsInfo(const ov::Model& network); + +void configurePrePostProcessing(std::shared_ptr<ov::Model>& function, + const std::string& ip, + const std::string& op, + const std::string& iop, + const std::string& il, + const std::string& ol, + const std::string& iol, + const std::string& iml, + const std::string& oml, + const std::string& ioml); + +void printInputAndOutputsInfo(const ov::Model& network); +ov::element::Type getPrecision2(const std::string& value); + +template <class T> +void printInputAndOutputsInfoShort(const T& network) { + slog::info << "Network inputs:" << slog::endl; + for (auto&& input : network.inputs()) { + std::string in_name; + std::string node_name; + + // Workaround for "tensor has no name" issue + try { + for (const auto& name : input.get_names()) { + in_name += name + " , "; + } + in_name = in_name.substr(0, in_name.size() - 3); + } catch (const ov::Exception&) { + } + + try { + node_name = input.get_node()->get_friendly_name(); + } catch (const ov::Exception&) { + } + + if (in_name == "") { + in_name = "***NO_NAME***"; + } + if (node_name == "") { + node_name = "***NO_NAME***"; + } + + slog::info << " " << in_name << " (node: " << node_name << ") : " << input.get_element_type() << " / " + << ov::layout::get_layout(input).to_string() << " / " << input.get_partial_shape() << slog::endl; + } + + slog::info << "Network outputs:" << slog::endl; + for (auto&& output : network.outputs()) { + std::string out_name; + std::string node_name; + + // Workaround for "tensor has no name" issue + try { + for (const auto& name : output.get_names()) { + out_name += name + " , "; + } + out_name = out_name.substr(0, out_name.size() - 3); + } catch (const ov::Exception&) { + } + try { + node_name = output.get_node()->get_input_node_ptr(0)->get_friendly_name(); + } catch (const ov::Exception&) { + } + + if (out_name == "") { + out_name = "***NO_NAME***"; + } + if (node_name == "") { + node_name = "***NO_NAME***"; + } + + slog::info << " " << out_name << " (node: " << node_name << ") : " << output.get_element_type() << " / " + << ov::layout::get_layout(output).to_string() << " / " << output.get_partial_shape() << slog::endl; + } +} diff --git a/python/openvino/runtime/common/utils/include/samples/classification_results.h b/python/openvino/runtime/common/utils/include/samples/classification_results.h new file mode 100644 index 0000000..e1bc20f --- /dev/null +++ b/python/openvino/runtime/common/utils/include/samples/classification_results.h @@ -0,0 +1,205 @@ +// Copyright (C) 2018-2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +/** + * @brief a header file with output classification results + * @file classification_results.h + */ +#pragma once + +#include <algorithm> +#include <iomanip> +#include <iostream> +#include <string> +#include <utility> +#include <vector> + +#include "openvino/openvino.hpp" + +/** + * @class ClassificationResult + * @brief A ClassificationResult creates an output table with results + */ +class ClassificationResult { +private: + const std::string _classidStr = "classid"; + const std::string _probabilityStr = "probability"; + const std::string _labelStr = "label"; + size_t _nTop; + ov::Tensor _outTensor; + const std::vector<std::string> _labels; + const std::vector<std::string> _imageNames; + const size_t _batchSize; + std::vector<unsigned> _results; + + void printHeader() { + std::cout << _classidStr << " " << _probabilityStr; + if (!_labels.empty()) + std::cout << " " << _labelStr; + std::string classidColumn(_classidStr.length(), '-'); + std::string probabilityColumn(_probabilityStr.length(), '-'); + std::string labelColumn(_labelStr.length(), '-'); + std::cout << std::endl << classidColumn << " " << probabilityColumn; + if (!_labels.empty()) + std::cout << " " << labelColumn; + std::cout << std::endl; + } + + /** + * @brief Gets the top n results from a tensor + * + * @param n Top n count + * @param input 1D tensor that contains probabilities + * @param output Vector of indexes for the top n places + */ + template <class T> + void topResults(unsigned int n, const ov::Tensor& input, std::vector<unsigned>& output) { + ov::Shape shape = input.get_shape(); + size_t input_rank = shape.size(); + OPENVINO_ASSERT(input_rank != 0 && shape[0] != 0, "Input tensor has incorrect dimensions!"); + size_t batchSize = shape[0]; + std::vector<unsigned> indexes(input.get_size() / batchSize); + + n = static_cast<unsigned>(std::min<size_t>((size_t)n, input.get_size())); + output.resize(n * batchSize); + + for (size_t i = 0; i < batchSize; i++) { + const size_t offset = i * (input.get_size() / batchSize); + const T* batchData = input.data<const T>(); + batchData += offset; + + std::iota(std::begin(indexes), std::end(indexes), 0); + std::partial_sort(std::begin(indexes), + std::begin(indexes) + n, + std::end(indexes), + [&batchData](unsigned l, unsigned r) { + return batchData[l] > batchData[r]; + }); + for (unsigned j = 0; j < n; j++) { + output.at(i * n + j) = indexes.at(j); + } + } + } + + /** + * @brief Gets the top n results from a blob + * + * @param n Top n count + * @param input 1D blob that contains probabilities + * @param output Vector of indexes for the top n places + */ + void topResults(unsigned int n, const ov::Tensor& input, std::vector<unsigned>& output) { +#define TENSOR_TOP_RESULT(elem_type) \ + case ov::element::Type_t::elem_type: { \ + using tensor_type = ov::fundamental_type_for<ov::element::Type_t::elem_type>; \ + topResults<tensor_type>(n, input, output); \ + break; \ + } + + switch (input.get_element_type()) { + TENSOR_TOP_RESULT(f32); + TENSOR_TOP_RESULT(f64); + TENSOR_TOP_RESULT(f16); + TENSOR_TOP_RESULT(i16); + TENSOR_TOP_RESULT(u8); + TENSOR_TOP_RESULT(i8); + TENSOR_TOP_RESULT(u16); + TENSOR_TOP_RESULT(i32); + TENSOR_TOP_RESULT(u32); + TENSOR_TOP_RESULT(i64); + TENSOR_TOP_RESULT(u64); + default: + OPENVINO_ASSERT(false, "cannot locate tensor with element type: ", input.get_element_type()); + } + +#undef TENSOR_TOP_RESULT + } + +public: + explicit ClassificationResult(const ov::Tensor& output_tensor, + const std::vector<std::string>& image_names = {}, + size_t batch_size = 1, + size_t num_of_top = 10, + const std::vector<std::string>& labels = {}) + : _nTop(num_of_top), + _outTensor(output_tensor), + _labels(labels), + _imageNames(image_names), + _batchSize(batch_size), + _results() { + OPENVINO_ASSERT(_imageNames.size() == _batchSize, "Batch size should be equal to the number of images."); + + topResults(_nTop, _outTensor, _results); + } + + /** + * @brief prints formatted classification results + */ + void show() { + /** Print the result iterating over each batch **/ + std::ios::fmtflags fmt(std::cout.flags()); + std::cout << std::endl << "Top " << _nTop << " results:" << std::endl << std::endl; + for (size_t image_id = 0; image_id < _batchSize; ++image_id) { + std::string out(_imageNames[image_id].begin(), _imageNames[image_id].end()); + std::cout << "Image " << out; + std::cout.flush(); + std::cout.clear(); + std::cout << std::endl << std::endl; + printHeader(); + + for (size_t id = image_id * _nTop, cnt = 0; id < (image_id + 1) * _nTop; ++cnt, ++id) { + std::cout.precision(7); + // Getting probability for resulting class + const auto index = _results.at(id) + image_id * (_outTensor.get_size() / _batchSize); + const auto result = _outTensor.data<const float>()[index]; + + std::cout << std::setw(static_cast<int>(_classidStr.length())) << std::left << _results.at(id) << " "; + std::cout << std::left << std::setw(static_cast<int>(_probabilityStr.length())) << std::fixed << result; + + if (!_labels.empty()) { + std::cout << " " + _labels[_results.at(id)]; + } + std::cout << std::endl; + } + std::cout << std::endl; + } + std::cout.flags(fmt); + } + + void print() { + /** Print the result iterating over each batch **/ + std::ios::fmtflags fmt(std::cout.flags()); + std::cout << std::endl << "Top " << _nTop << " results:" << std::endl << std::endl; + for (size_t image_id = 0; image_id < _batchSize; ++image_id) { + std::string out(_imageNames[image_id].begin(), _imageNames[image_id].end()); + std::cout << "Image " << out; + std::cout.flush(); + std::cout.clear(); + std::cout << std::endl << std::endl; + printHeader(); + + for (size_t id = image_id * _nTop, cnt = 0; id < (image_id + 1) * _nTop; ++cnt, ++id) { + std::cout.precision(7); + // Getting probability for resulting class + const auto result = _outTensor.data<float>(); + std::cout << std::setw(static_cast<int>(_classidStr.length())) << std::left << _results.at(id) << " "; + std::cout << std::left << std::setw(static_cast<int>(_probabilityStr.length())) << std::fixed << result; + + if (!_labels.empty()) { + std::cout << " " + _labels[_results.at(id)]; + } + std::cout << std::endl; + } + std::cout << std::endl; + } + std::cout.flags(fmt); + } + + /** + * @brief returns the classification results in a vector + */ + std::vector<unsigned> getResults() { + return _results; + } +}; diff --git a/python/openvino/runtime/common/utils/include/samples/common.hpp b/python/openvino/runtime/common/utils/include/samples/common.hpp new file mode 100644 index 0000000..448fd96 --- /dev/null +++ b/python/openvino/runtime/common/utils/include/samples/common.hpp @@ -0,0 +1,1429 @@ +// Copyright (C) 2018-2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +/** + * @brief a header file with common samples functionality + * @file common.hpp + */ + +#pragma once + +#include <algorithm> +#include <cctype> +#include <fstream> +#include <functional> +#include <iomanip> +#include <iostream> +#include <limits> +#include <list> +#include <map> +#include <random> +#include <string> +#include <utility> +#include <vector> + +using std::setprecision; + +// clang-format off +#include <inference_engine.hpp> +#include "openvino/openvino.hpp" +#include "slog.hpp" +// clang-format on + +// @brief performance counters sort +static constexpr char pcSort[] = "sort"; +static constexpr char pcNoSort[] = "no_sort"; +static constexpr char pcSimpleSort[] = "simple_sort"; + +#ifndef UNUSED +# if defined(_MSC_VER) && !defined(__clang__) +# define UNUSED +# else +# define UNUSED __attribute__((unused)) +# endif +#endif + +/** + * @brief Unicode string wrappers + */ +#if defined(ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32) +# define tchar wchar_t +# define tstring std::wstring +# define tmain wmain +# define TSTRING2STRING(tstr) wstring2string(tstr) +#else +# define tchar char +# define tstring std::string +# define tmain main +# define TSTRING2STRING(tstr) tstr +#endif + +#if defined(ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32) + +/** + * @brief Convert wstring to string + * @param ref on wstring + * @return string + */ +inline std::string wstring2string(const std::wstring& wstr) { + std::string str; + for (auto&& wc : wstr) + str += static_cast<char>(wc); + return str; +} +#endif + +/** + * @brief trim from start (in place) + * @param s - string to trim + */ +inline void ltrim(std::string& s) { + s.erase(s.begin(), std::find_if(s.begin(), s.end(), [](int c) { + return !std::isspace(c); + })); +} + +/** + * @brief trim from end (in place) + * @param s - string to trim + */ +inline void rtrim(std::string& s) { + s.erase(std::find_if(s.rbegin(), + s.rend(), + [](int c) { + return !std::isspace(c); + }) + .base(), + s.end()); +} + +/** + * @brief trim from both ends (in place) + * @param s - string to trim + */ +inline std::string& trim(std::string& s) { + ltrim(s); + rtrim(s); + return s; +} +/** + * @brief Gets filename without extension + * @param filepath - full file name + * @return filename without extension + */ +inline std::string fileNameNoExt(const std::string& filepath) { + auto pos = filepath.rfind('.'); + if (pos == std::string::npos) + return filepath; + return filepath.substr(0, pos); +} + +/** + * @brief Get extension from filename + * @param filename - name of the file which extension should be extracted + * @return string with extracted file extension + */ +inline std::string fileExt(const std::string& filename) { + auto pos = filename.rfind('.'); + if (pos == std::string::npos) + return ""; + return filename.substr(pos + 1); +} + +inline slog::LogStream& operator<<(slog::LogStream& os, const ov::Version& version) { + os << "Build ................................. "; + os << version.buildNumber << slog::endl; + + return os; +} + +inline slog::LogStream& operator<<(slog::LogStream& os, const std::map<std::string, ov::Version>& versions) { + for (auto&& version : versions) { + os << version.first << slog::endl; + os << version.second << slog::endl; + } + + return os; +} + +/** + * @class Color + * @brief A Color class stores channels of a given color + */ +class Color { +private: + unsigned char _r; + unsigned char _g; + unsigned char _b; + +public: + /** + * A default constructor. + * @param r - value for red channel + * @param g - value for green channel + * @param b - value for blue channel + */ + Color(unsigned char r, unsigned char g, unsigned char b) : _r(r), _g(g), _b(b) {} + + inline unsigned char red() { + return _r; + } + + inline unsigned char blue() { + return _b; + } + + inline unsigned char green() { + return _g; + } +}; + +// TODO : keep only one version of writeOutputBMP + +/** + * @brief Writes output data to image + * @param name - image name + * @param data - output data + * @param classesNum - the number of classes + * @return false if error else true + */ +static UNUSED void writeOutputBmp(std::vector<std::vector<size_t>> data, size_t classesNum, std::ostream& outFile) { + unsigned int seed = (unsigned int)time(NULL); + // Known colors for training classes from Cityscape dataset + static std::vector<Color> colors = { + {128, 64, 128}, {232, 35, 244}, {70, 70, 70}, {156, 102, 102}, {153, 153, 190}, {153, 153, 153}, + {30, 170, 250}, {0, 220, 220}, {35, 142, 107}, {152, 251, 152}, {180, 130, 70}, {60, 20, 220}, + {0, 0, 255}, {142, 0, 0}, {70, 0, 0}, {100, 60, 0}, {90, 0, 0}, {230, 0, 0}, + {32, 11, 119}, {0, 74, 111}, {81, 0, 81}}; + + while (classesNum > colors.size()) { + static std::mt19937 rng(seed); + std::uniform_int_distribution<int> dist(0, 255); + Color color(dist(rng), dist(rng), dist(rng)); + colors.push_back(color); + } + + unsigned char file[14] = { + 'B', + 'M', // magic + 0, + 0, + 0, + 0, // size in bytes + 0, + 0, // app data + 0, + 0, // app data + 40 + 14, + 0, + 0, + 0 // start of data offset + }; + unsigned char info[40] = { + 40, 0, 0, 0, // info hd size + 0, 0, 0, 0, // width + 0, 0, 0, 0, // height + 1, 0, // number color planes + 24, 0, // bits per pixel + 0, 0, 0, 0, // compression is none + 0, 0, 0, 0, // image bits size + 0x13, 0x0B, 0, 0, // horz resolution in pixel / m + 0x13, 0x0B, 0, 0, // vert resolution (0x03C3 = 96 dpi, 0x0B13 = 72 dpi) + 0, 0, 0, 0, // #colors in palette + 0, 0, 0, 0, // #important colors + }; + + auto height = data.size(); + auto width = data.at(0).size(); + + OPENVINO_ASSERT( + height < (size_t)std::numeric_limits<int32_t>::max && width < (size_t)std::numeric_limits<int32_t>::max, + "File size is too big: ", + height, + " X ", + width); + + int padSize = static_cast<int>(4 - (width * 3) % 4) % 4; + int sizeData = static_cast<int>(width * height * 3 + height * padSize); + int sizeAll = sizeData + sizeof(file) + sizeof(info); + + file[2] = (unsigned char)(sizeAll); + file[3] = (unsigned char)(sizeAll >> 8); + file[4] = (unsigned char)(sizeAll >> 16); + file[5] = (unsigned char)(sizeAll >> 24); + + info[4] = (unsigned char)(width); + info[5] = (unsigned char)(width >> 8); + info[6] = (unsigned char)(width >> 16); + info[7] = (unsigned char)(width >> 24); + + int32_t negativeHeight = -(int32_t)height; + info[8] = (unsigned char)(negativeHeight); + info[9] = (unsigned char)(negativeHeight >> 8); + info[10] = (unsigned char)(negativeHeight >> 16); + info[11] = (unsigned char)(negativeHeight >> 24); + + info[20] = (unsigned char)(sizeData); + info[21] = (unsigned char)(sizeData >> 8); + info[22] = (unsigned char)(sizeData >> 16); + info[23] = (unsigned char)(sizeData >> 24); + + outFile.write(reinterpret_cast<char*>(file), sizeof(file)); + outFile.write(reinterpret_cast<char*>(info), sizeof(info)); + + unsigned char pad[3] = {0, 0, 0}; + + for (size_t y = 0; y < height; y++) { + for (size_t x = 0; x < width; x++) { + unsigned char pixel[3]; + size_t index = data.at(y).at(x); + pixel[0] = colors.at(index).red(); + pixel[1] = colors.at(index).green(); + pixel[2] = colors.at(index).blue(); + outFile.write(reinterpret_cast<char*>(pixel), 3); + } + outFile.write(reinterpret_cast<char*>(pad), padSize); + } +} + +/** + * @brief Writes output data to BMP image + * @param name - image name + * @param data - output data + * @param height - height of the target image + * @param width - width of the target image + * @return false if error else true + */ +static UNUSED bool writeOutputBmp(std::string name, unsigned char* data, size_t height, size_t width) { + std::ofstream outFile; + outFile.open(name, std::ofstream::binary); + if (!outFile.is_open()) { + return false; + } + + unsigned char file[14] = { + 'B', + 'M', // magic + 0, + 0, + 0, + 0, // size in bytes + 0, + 0, // app data + 0, + 0, // app data + 40 + 14, + 0, + 0, + 0 // start of data offset + }; + unsigned char info[40] = { + 40, 0, 0, 0, // info hd size + 0, 0, 0, 0, // width + 0, 0, 0, 0, // height + 1, 0, // number color planes + 24, 0, // bits per pixel + 0, 0, 0, 0, // compression is none + 0, 0, 0, 0, // image bits size + 0x13, 0x0B, 0, 0, // horz resolution in pixel / m + 0x13, 0x0B, 0, 0, // vert resolution (0x03C3 = 96 dpi, 0x0B13 = 72 dpi) + 0, 0, 0, 0, // #colors in palette + 0, 0, 0, 0, // #important colors + }; + + OPENVINO_ASSERT( + height < (size_t)std::numeric_limits<int32_t>::max && width < (size_t)std::numeric_limits<int32_t>::max, + "File size is too big: ", + height, + " X ", + width); + + int padSize = static_cast<int>(4 - (width * 3) % 4) % 4; + int sizeData = static_cast<int>(width * height * 3 + height * padSize); + int sizeAll = sizeData + sizeof(file) + sizeof(info); + + file[2] = (unsigned char)(sizeAll); + file[3] = (unsigned char)(sizeAll >> 8); + file[4] = (unsigned char)(sizeAll >> 16); + file[5] = (unsigned char)(sizeAll >> 24); + + info[4] = (unsigned char)(width); + info[5] = (unsigned char)(width >> 8); + info[6] = (unsigned char)(width >> 16); + info[7] = (unsigned char)(width >> 24); + + int32_t negativeHeight = -(int32_t)height; + info[8] = (unsigned char)(negativeHeight); + info[9] = (unsigned char)(negativeHeight >> 8); + info[10] = (unsigned char)(negativeHeight >> 16); + info[11] = (unsigned char)(negativeHeight >> 24); + + info[20] = (unsigned char)(sizeData); + info[21] = (unsigned char)(sizeData >> 8); + info[22] = (unsigned char)(sizeData >> 16); + info[23] = (unsigned char)(sizeData >> 24); + + outFile.write(reinterpret_cast<char*>(file), sizeof(file)); + outFile.write(reinterpret_cast<char*>(info), sizeof(info)); + + unsigned char pad[3] = {0, 0, 0}; + + for (size_t y = 0; y < height; y++) { + for (size_t x = 0; x < width; x++) { + unsigned char pixel[3]; + pixel[0] = data[y * width * 3 + x * 3]; + pixel[1] = data[y * width * 3 + x * 3 + 1]; + pixel[2] = data[y * width * 3 + x * 3 + 2]; + + outFile.write(reinterpret_cast<char*>(pixel), 3); + } + outFile.write(reinterpret_cast<char*>(pad), padSize); + } + return true; +} + +/** + * @brief Adds colored rectangles to the image + * @param data - data where rectangles are put + * @param height - height of the rectangle + * @param width - width of the rectangle + * @param rectangles - vector points for the rectangle, should be 4x compared to num classes + * @param classes - vector of classes + * @param thickness - thickness of a line (in pixels) to be used for bounding boxes + */ +static UNUSED void addRectangles(unsigned char* data, + size_t height, + size_t width, + std::vector<int> rectangles, + std::vector<int> classes, + int thickness = 1) { + std::vector<Color> colors = {// colors to be used for bounding boxes + {128, 64, 128}, {232, 35, 244}, {70, 70, 70}, {156, 102, 102}, {153, 153, 190}, + {153, 153, 153}, {30, 170, 250}, {0, 220, 220}, {35, 142, 107}, {152, 251, 152}, + {180, 130, 70}, {60, 20, 220}, {0, 0, 255}, {142, 0, 0}, {70, 0, 0}, + {100, 60, 0}, {90, 0, 0}, {230, 0, 0}, {32, 11, 119}, {0, 74, 111}, + {81, 0, 81}}; + + if (rectangles.size() % 4 != 0 || rectangles.size() / 4 != classes.size()) { + return; + } + + for (size_t i = 0; i < classes.size(); i++) { + int x = rectangles.at(i * 4); + int y = rectangles.at(i * 4 + 1); + int w = rectangles.at(i * 4 + 2); + int h = rectangles.at(i * 4 + 3); + + int cls = classes.at(i) % colors.size(); // color of a bounding box line + + if (x < 0) + x = 0; + if (y < 0) + y = 0; + if (w < 0) + w = 0; + if (h < 0) + h = 0; + + if (static_cast<std::size_t>(x) >= width) { + x = static_cast<int>(width - 1); + w = 0; + thickness = 1; + } + if (static_cast<std::size_t>(y) >= height) { + y = static_cast<int>(height - 1); + h = 0; + thickness = 1; + } + + if (static_cast<std::size_t>(x + w) >= width) { + w = static_cast<int>(width - x - 1); + } + if (static_cast<std::size_t>(y + h) >= height) { + h = static_cast<int>(height - y - 1); + } + + thickness = std::min(std::min(thickness, w / 2 + 1), h / 2 + 1); + + size_t shift_first; + size_t shift_second; + for (int t = 0; t < thickness; t++) { + shift_first = (y + t) * width * 3; + shift_second = (y + h - t) * width * 3; + for (int ii = x; ii < x + w + 1; ii++) { + data[shift_first + ii * 3] = colors.at(cls).red(); + data[shift_first + ii * 3 + 1] = colors.at(cls).green(); + data[shift_first + ii * 3 + 2] = colors.at(cls).blue(); + data[shift_second + ii * 3] = colors.at(cls).red(); + data[shift_second + ii * 3 + 1] = colors.at(cls).green(); + data[shift_second + ii * 3 + 2] = colors.at(cls).blue(); + } + } + + for (int t = 0; t < thickness; t++) { + shift_first = (x + t) * 3; + shift_second = (x + w - t) * 3; + for (int ii = y; ii < y + h + 1; ii++) { + data[shift_first + ii * width * 3] = colors.at(cls).red(); + data[shift_first + ii * width * 3 + 1] = colors.at(cls).green(); + data[shift_first + ii * width * 3 + 2] = colors.at(cls).blue(); + data[shift_second + ii * width * 3] = colors.at(cls).red(); + data[shift_second + ii * width * 3 + 1] = colors.at(cls).green(); + data[shift_second + ii * width * 3 + 2] = colors.at(cls).blue(); + } + } + } +} + +// DLA PATCH BEGIN - Re-implement functions needed for dla_benchmark that was removed from OPENVINO 2022.3.0 +inline std::size_t getTensorWidth(const InferenceEngine::TensorDesc& desc) { + const auto& layout = desc.getLayout(); + const auto& dims = desc.getDims(); + const auto& size = dims.size(); + if ((size >= 2) && (layout == InferenceEngine::Layout::NCHW || layout == InferenceEngine::Layout::NHWC || layout == InferenceEngine::Layout::NCDHW || + layout == InferenceEngine::Layout::NDHWC || layout == InferenceEngine::Layout::OIHW || layout == InferenceEngine::Layout::GOIHW || + layout == InferenceEngine::Layout::OIDHW || layout == InferenceEngine::Layout::GOIDHW || layout == InferenceEngine::Layout::CHW || + layout == InferenceEngine::Layout::HW)) { + // Regardless of layout, dimensions are stored in fixed order + return dims.back(); + } else { + IE_THROW() << "Tensor does not have width dimension"; + } + return 0; +} + +inline std::size_t getTensorHeight(const InferenceEngine::TensorDesc& desc) { + const auto& layout = desc.getLayout(); + const auto& dims = desc.getDims(); + const auto& size = dims.size(); + if ((size >= 2) && (layout == InferenceEngine::Layout::NCHW || layout == InferenceEngine::Layout::NHWC || layout == InferenceEngine::Layout::NCDHW || + layout == InferenceEngine::Layout::NDHWC || layout == InferenceEngine::Layout::OIHW || layout == InferenceEngine::Layout::GOIHW || + layout == InferenceEngine::Layout::OIDHW || layout == InferenceEngine::Layout::GOIDHW || layout == InferenceEngine::Layout::CHW || + layout == InferenceEngine::Layout::HW)) { + // Regardless of layout, dimensions are stored in fixed order + return dims.at(size - 2); + } else { + IE_THROW() << "Tensor does not have height dimension"; + } + return 0; +} + +inline std::size_t getTensorChannels(const InferenceEngine::TensorDesc& desc) { + const auto& layout = desc.getLayout(); + if (layout == InferenceEngine::Layout::NCHW || layout == InferenceEngine::Layout::NHWC || layout == InferenceEngine::Layout::NCDHW || + layout == InferenceEngine::Layout::NDHWC || layout == InferenceEngine::Layout::C || layout == InferenceEngine::Layout::CHW || + layout == InferenceEngine::Layout::NC || layout == InferenceEngine::Layout::CN) { + // Regardless of layout, dimensions are stored in fixed order + const auto& dims = desc.getDims(); + switch (desc.getLayoutByDims(dims)) { + case InferenceEngine::Layout::C: + return dims.at(0); + case InferenceEngine::Layout::NC: + return dims.at(1); + case InferenceEngine::Layout::CHW: + return dims.at(0); + case InferenceEngine::Layout::NCHW: + return dims.at(1); + case InferenceEngine::Layout::NCDHW: + return dims.at(1); + case InferenceEngine::Layout::SCALAR: // [[fallthrough]] + case InferenceEngine::Layout::BLOCKED: // [[fallthrough]] + default: + IE_THROW() << "Tensor does not have channels dimension"; + } + } else { + IE_THROW() << "Tensor does not have channels dimension"; + } + return 0; +} + +inline std::size_t getTensorBatch(const InferenceEngine::TensorDesc& desc) { + const auto& layout = desc.getLayout(); + if (layout == InferenceEngine::Layout::NCHW || layout == InferenceEngine::Layout::NHWC || layout == InferenceEngine::Layout::NCDHW || + layout == InferenceEngine::Layout::NDHWC || layout == InferenceEngine::Layout::NC || layout == InferenceEngine::Layout::CN) { + // Regardless of layout, dimensions are stored in fixed order + const auto& dims = desc.getDims(); + switch (desc.getLayoutByDims(dims)) { + case InferenceEngine::Layout::NC: + return dims.at(0); + case InferenceEngine::Layout::NCHW: + return dims.at(0); + case InferenceEngine::Layout::NCDHW: + return dims.at(0); + case InferenceEngine::Layout::CHW: // [[fallthrough]] + case InferenceEngine::Layout::C: // [[fallthrough]] + case InferenceEngine::Layout::SCALAR: // [[fallthrough]] + case InferenceEngine::Layout::BLOCKED: // [[fallthrough]] + default: + IE_THROW() << "Tensor does not have channels dimension"; + } + } else { + IE_THROW() << "Tensor does not have channels dimension"; + } + return 0; +} + +// DLA PATCH END + +/** + * Write output data to image + * \param name - image name + * \param data - output data + * \param classesNum - the number of classes + * \return false if error else true + */ + +static UNUSED bool writeOutputBmp(unsigned char* data, size_t height, size_t width, std::ostream& outFile) { + unsigned char file[14] = { + 'B', + 'M', // magic + 0, + 0, + 0, + 0, // size in bytes + 0, + 0, // app data + 0, + 0, // app data + 40 + 14, + 0, + 0, + 0 // start of data offset + }; + unsigned char info[40] = { + 40, 0, 0, 0, // info hd size + 0, 0, 0, 0, // width + 0, 0, 0, 0, // height + 1, 0, // number color planes + 24, 0, // bits per pixel + 0, 0, 0, 0, // compression is none + 0, 0, 0, 0, // image bits size + 0x13, 0x0B, 0, 0, // horz resolution in pixel / m + 0x13, 0x0B, 0, 0, // vert resolution (0x03C3 = 96 dpi, 0x0B13 = 72 dpi) + 0, 0, 0, 0, // #colors in palette + 0, 0, 0, 0, // #important colors + }; + + OPENVINO_ASSERT( + height < (size_t)std::numeric_limits<int32_t>::max && width < (size_t)std::numeric_limits<int32_t>::max, + "File size is too big: ", + height, + " X ", + width); + + int padSize = static_cast<int>(4 - (width * 3) % 4) % 4; + int sizeData = static_cast<int>(width * height * 3 + height * padSize); + int sizeAll = sizeData + sizeof(file) + sizeof(info); + + file[2] = (unsigned char)(sizeAll); + file[3] = (unsigned char)(sizeAll >> 8); + file[4] = (unsigned char)(sizeAll >> 16); + file[5] = (unsigned char)(sizeAll >> 24); + + info[4] = (unsigned char)(width); + info[5] = (unsigned char)(width >> 8); + info[6] = (unsigned char)(width >> 16); + info[7] = (unsigned char)(width >> 24); + + int32_t negativeHeight = -(int32_t)height; + info[8] = (unsigned char)(negativeHeight); + info[9] = (unsigned char)(negativeHeight >> 8); + info[10] = (unsigned char)(negativeHeight >> 16); + info[11] = (unsigned char)(negativeHeight >> 24); + + info[20] = (unsigned char)(sizeData); + info[21] = (unsigned char)(sizeData >> 8); + info[22] = (unsigned char)(sizeData >> 16); + info[23] = (unsigned char)(sizeData >> 24); + + outFile.write(reinterpret_cast<char*>(file), sizeof(file)); + outFile.write(reinterpret_cast<char*>(info), sizeof(info)); + + unsigned char pad[3] = {0, 0, 0}; + + for (size_t y = 0; y < height; y++) { + for (size_t x = 0; x < width; x++) { + unsigned char pixel[3]; + pixel[0] = data[y * width * 3 + x * 3]; + pixel[1] = data[y * width * 3 + x * 3 + 1]; + pixel[2] = data[y * width * 3 + x * 3 + 2]; + outFile.write(reinterpret_cast<char*>(pixel), 3); + } + outFile.write(reinterpret_cast<char*>(pad), padSize); + } + + return true; +} + +static UNUSED void printPerformanceCounts(const std::map<std::string, ov::ProfilingInfo>& performanceMap, + std::ostream& stream, + std::string deviceName, + bool bshowHeader = true) { + std::chrono::microseconds totalTime = std::chrono::microseconds::zero(); + // Print performance counts + if (bshowHeader) { + stream << std::endl << "performance counts:" << std::endl << std::endl; + } + std::ios::fmtflags fmt(std::cout.flags()); + + for (const auto& it : performanceMap) { + std::string toPrint(it.first); + const int maxLayerName = 30; + + if (it.first.length() >= maxLayerName) { + toPrint = it.first.substr(0, maxLayerName - 4); + toPrint += "..."; + } + + stream << std::setw(maxLayerName) << std::left << toPrint; + switch (it.second.status) { + case ov::ProfilingInfo::Status::EXECUTED: + stream << std::setw(15) << std::left << "EXECUTED"; + break; + case ov::ProfilingInfo::Status::NOT_RUN: + stream << std::setw(15) << std::left << "NOT_RUN"; + break; + case ov::ProfilingInfo::Status::OPTIMIZED_OUT: + stream << std::setw(15) << std::left << "OPTIMIZED_OUT"; + break; + } + stream << std::setw(30) << std::left << "layerType: " + std::string(it.second.node_type) + " "; + stream << std::setw(20) << std::left << "realTime: " + std::to_string(it.second.real_time.count()); + stream << std::setw(20) << std::left << "cpu: " + std::to_string(it.second.cpu_time.count()); + stream << " execType: " << it.second.exec_type << std::endl; + if (it.second.real_time.count() > 0) { + totalTime += it.second.real_time; + } + } + stream << std::setw(20) << std::left << "Total time: " + std::to_string(totalTime.count()) << " microseconds" + << std::endl; + std::cout << std::endl; + std::cout << "Full device name: " << deviceName << std::endl; + std::cout << std::endl; + std::cout.flags(fmt); +} + +/** + * @brief This class represents an object that is found by an object detection net + */ +class DetectedObject { +public: + int objectType; + float xmin, xmax, ymin, ymax, prob; + bool difficult; + + DetectedObject(int _objectType, + float _xmin, + float _ymin, + float _xmax, + float _ymax, + float _prob, + bool _difficult = false) + : objectType(_objectType), + xmin(_xmin), + xmax(_xmax), + ymin(_ymin), + ymax(_ymax), + prob(_prob), + difficult(_difficult) {} + + DetectedObject(const DetectedObject& other) = default; + + static float ioU(const DetectedObject& detectedObject1_, const DetectedObject& detectedObject2_) { + // Add small space to eliminate empty squares + float epsilon = 0; // 1e-5f; + + DetectedObject detectedObject1(detectedObject1_.objectType, + (detectedObject1_.xmin - epsilon), + (detectedObject1_.ymin - epsilon), + (detectedObject1_.xmax - epsilon), + (detectedObject1_.ymax - epsilon), + detectedObject1_.prob); + DetectedObject detectedObject2(detectedObject2_.objectType, + (detectedObject2_.xmin + epsilon), + (detectedObject2_.ymin + epsilon), + (detectedObject2_.xmax), + (detectedObject2_.ymax), + detectedObject2_.prob); + + if (detectedObject1.objectType != detectedObject2.objectType) { + // objects are different, so the result is 0 + return 0.0f; + } + + if (detectedObject1.xmax < detectedObject1.xmin) + return 0.0; + if (detectedObject1.ymax < detectedObject1.ymin) + return 0.0; + if (detectedObject2.xmax < detectedObject2.xmin) + return 0.0; + if (detectedObject2.ymax < detectedObject2.ymin) + return 0.0; + + float xmin = (std::max)(detectedObject1.xmin, detectedObject2.xmin); + float ymin = (std::max)(detectedObject1.ymin, detectedObject2.ymin); + float xmax = (std::min)(detectedObject1.xmax, detectedObject2.xmax); + float ymax = (std::min)(detectedObject1.ymax, detectedObject2.ymax); + + // Caffe adds 1 to every length if the box isn't normalized. So do we... + float addendum; + if (xmax > 1 || ymax > 1) + addendum = 1; + else + addendum = 0; + + // intersection + float intr; + if ((xmax >= xmin) && (ymax >= ymin)) { + intr = (addendum + xmax - xmin) * (addendum + ymax - ymin); + } else { + intr = 0.0f; + } + + // union + float square1 = (addendum + detectedObject1.xmax - detectedObject1.xmin) * + (addendum + detectedObject1.ymax - detectedObject1.ymin); + float square2 = (addendum + detectedObject2.xmax - detectedObject2.xmin) * + (addendum + detectedObject2.ymax - detectedObject2.ymin); + + float unn = square1 + square2 - intr; + + return static_cast<float>(intr) / unn; + } + + DetectedObject scale(float scale_x, float scale_y) const { + return DetectedObject(objectType, + xmin * scale_x, + ymin * scale_y, + xmax * scale_x, + ymax * scale_y, + prob, + difficult); + } +}; + +class ImageDescription { +public: + const std::list<DetectedObject> alist; + const bool check_probs; + + explicit ImageDescription(const std::list<DetectedObject>& _alist, bool _check_probs = false) + : alist(_alist), + check_probs(_check_probs) {} + + static float ioUMultiple(const ImageDescription& detectedObjects, const ImageDescription& desiredObjects) { + const ImageDescription *detectedObjectsSmall, *detectedObjectsBig; + bool check_probs = desiredObjects.check_probs; + + if (detectedObjects.alist.size() < desiredObjects.alist.size()) { + detectedObjectsSmall = &detectedObjects; + detectedObjectsBig = &desiredObjects; + } else { + detectedObjectsSmall = &desiredObjects; + detectedObjectsBig = &detectedObjects; + } + + std::list<DetectedObject> doS = detectedObjectsSmall->alist; + std::list<DetectedObject> doB = detectedObjectsBig->alist; + + float fullScore = 0.0f; + while (doS.size() > 0) { + float score = 0.0f; + std::list<DetectedObject>::iterator bestJ = doB.end(); + for (auto j = doB.begin(); j != doB.end(); j++) { + float curscore = DetectedObject::ioU(*doS.begin(), *j); + if (score < curscore) { + score = curscore; + bestJ = j; + } + } + + float coeff = 1.0; + if (check_probs) { + if (bestJ != doB.end()) { + float mn = std::min((*bestJ).prob, (*doS.begin()).prob); + float mx = std::max((*bestJ).prob, (*doS.begin()).prob); + + coeff = mn / mx; + } + } + + doS.pop_front(); + if (bestJ != doB.end()) + doB.erase(bestJ); + fullScore += coeff * score; + } + fullScore /= detectedObjectsBig->alist.size(); + + return fullScore; + } + + ImageDescription scale(float scale_x, float scale_y) const { + std::list<DetectedObject> slist; + for (auto& dob : alist) { + slist.push_back(dob.scale(scale_x, scale_y)); + } + return ImageDescription(slist, check_probs); + } +}; + +struct AveragePrecisionCalculator { +private: + enum MatchKind { TruePositive, FalsePositive }; + + /** + * Here we count all TP and FP matches for all the classes in all the images. + */ + std::map<int, std::vector<std::pair<double, MatchKind>>> matches; + + std::map<int, int> N; + + double threshold; + + static bool SortBBoxDescend(const DetectedObject& bbox1, const DetectedObject& bbox2) { + return bbox1.prob > bbox2.prob; + } + + static bool SortPairDescend(const std::pair<double, MatchKind>& p1, const std::pair<double, MatchKind>& p2) { + return p1.first > p2.first; + } + +public: + explicit AveragePrecisionCalculator(double _threshold) : threshold(_threshold) {} + + // gt_bboxes -> des + // bboxes -> det + + void consumeImage(const ImageDescription& detectedObjects, const ImageDescription& desiredObjects) { + // Collecting IoU values + std::vector<bool> visited(desiredObjects.alist.size(), false); + std::vector<DetectedObject> bboxes{std::begin(detectedObjects.alist), std::end(detectedObjects.alist)}; + std::sort(bboxes.begin(), bboxes.end(), SortBBoxDescend); + + for (auto&& detObj : bboxes) { + // Searching for the best match to this detection + // Searching for desired object + float overlap_max = -1; + int jmax = -1; + auto desmax = desiredObjects.alist.end(); + + int j = 0; + for (auto desObj = desiredObjects.alist.begin(); desObj != desiredObjects.alist.end(); desObj++, j++) { + double iou = DetectedObject::ioU(detObj, *desObj); + if (iou > overlap_max) { + overlap_max = static_cast<float>(iou); + jmax = j; + desmax = desObj; + } + } + + MatchKind mk; + if (overlap_max >= threshold) { + if (!desmax->difficult) { + if (!visited[jmax]) { + mk = TruePositive; + visited[jmax] = true; + } else { + mk = FalsePositive; + } + matches[detObj.objectType].push_back(std::make_pair(detObj.prob, mk)); + } + } else { + mk = FalsePositive; + matches[detObj.objectType].push_back(std::make_pair(detObj.prob, mk)); + } + } + + for (auto desObj = desiredObjects.alist.begin(); desObj != desiredObjects.alist.end(); desObj++) { + if (!desObj->difficult) { + N[desObj->objectType]++; + } + } + } + + std::map<int, double> calculateAveragePrecisionPerClass() const { + /** + * Precision-to-TP curve per class (a variation of precision-to-recall curve without + * dividing into N) + */ + std::map<int, std::map<int, double>> precisionToTP; + + std::map<int, double> res; + + for (auto m : matches) { + // Sorting + std::sort(m.second.begin(), m.second.end(), SortPairDescend); + + int clazz = m.first; + int TP = 0, FP = 0; + + std::vector<double> prec; + std::vector<double> rec; + + for (auto mm : m.second) { + // Here we are descending in a probability value + MatchKind mk = mm.second; + if (mk == TruePositive) + TP++; + else if (mk == FalsePositive) + FP++; + + double precision = static_cast<double>(TP) / (TP + FP); + double recall = 0; + if (N.find(clazz) != N.end()) { + recall = static_cast<double>(TP) / N.at(clazz); + } + + prec.push_back(precision); + rec.push_back(recall); + } + + int num = static_cast<int>(rec.size()); + + // 11point from Caffe + double ap = 0; + std::vector<float> max_precs(11, 0.); + int start_idx = num - 1; + for (int j = 10; j >= 0; --j) { + for (int i = start_idx; i >= 0; --i) { + if (rec[i] < j / 10.) { + start_idx = i; + if (j > 0) { + max_precs[j - 1] = max_precs[j]; + } + break; + } else { + if (max_precs[j] < prec[i]) { + max_precs[j] = static_cast<float>(prec[i]); + } + } + } + } + for (int j = 10; j >= 0; --j) { + ap += max_precs[j] / 11; + } + res[clazz] = ap; + } + + return res; + } +}; + +/** + * @brief Adds colored rectangles to the image + * @param data - data where rectangles are put + * @param height - height of the rectangle + * @param width - width of the rectangle + * @param detectedObjects - vector of detected objects + */ +static UNUSED void addRectangles(unsigned char* data, + size_t height, + size_t width, + std::vector<DetectedObject> detectedObjects) { + std::vector<Color> colors = {{128, 64, 128}, {232, 35, 244}, {70, 70, 70}, {156, 102, 102}, {153, 153, 190}, + {153, 153, 153}, {30, 170, 250}, {0, 220, 220}, {35, 142, 107}, {152, 251, 152}, + {180, 130, 70}, {60, 20, 220}, {0, 0, 255}, {142, 0, 0}, {70, 0, 0}, + {100, 60, 0}, {90, 0, 0}, {230, 0, 0}, {32, 11, 119}, {0, 74, 111}, + {81, 0, 81}}; + + for (size_t i = 0; i < detectedObjects.size(); i++) { + int cls = detectedObjects[i].objectType % colors.size(); + + int xmin = static_cast<int>(detectedObjects[i].xmin * width); + int xmax = static_cast<int>(detectedObjects[i].xmax * width); + int ymin = static_cast<int>(detectedObjects[i].ymin * height); + int ymax = static_cast<int>(detectedObjects[i].ymax * height); + + size_t shift_first = ymin * width * 3; + size_t shift_second = ymax * width * 3; + for (int x = xmin; x < xmax; x++) { + data[shift_first + x * 3] = colors.at(cls).red(); + data[shift_first + x * 3 + 1] = colors.at(cls).green(); + data[shift_first + x * 3 + 2] = colors.at(cls).blue(); + data[shift_second + x * 3] = colors.at(cls).red(); + data[shift_second + x * 3 + 1] = colors.at(cls).green(); + data[shift_second + x * 3 + 2] = colors.at(cls).blue(); + } + + shift_first = xmin * 3; + shift_second = xmax * 3; + for (int y = ymin; y < ymax; y++) { + data[shift_first + y * width * 3] = colors.at(cls).red(); + data[shift_first + y * width * 3 + 1] = colors.at(cls).green(); + data[shift_first + y * width * 3 + 2] = colors.at(cls).blue(); + data[shift_second + y * width * 3] = colors.at(cls).red(); + data[shift_second + y * width * 3 + 1] = colors.at(cls).green(); + data[shift_second + y * width * 3 + 2] = colors.at(cls).blue(); + } + } +} + +inline void showAvailableDevices() { + ov::Core core; + std::vector<std::string> devices = core.get_available_devices(); + + std::cout << std::endl; + std::cout << "Available target devices:"; + for (const auto& device : devices) { + std::cout << " " << device; + } + std::cout << std::endl; +} + +/** + * @brief Parse text config file. The file must have the following format (with space a delimeter): + * CONFIG_NAME1 CONFIG_VALUE1 + * CONFIG_NAME2 CONFIG_VALUE2 + * + * @param configName - filename for a file with config options + * @param comment - lines starting with symbol `comment` are skipped + */ +std::map<std::string, std::string> parseConfig(const std::string& configName, char comment = '#'); + +inline std::string getFullDeviceName(ov::Core& core, std::string device) { + try { + return core.get_property(device, ov::device::full_name); + } catch (ov::Exception&) { + return {}; + } +} + +static UNUSED void printPerformanceCounts(std::vector<ov::ProfilingInfo> performanceData, + std::ostream& stream, + std::string deviceName, + bool bshowHeader = true) { + std::chrono::microseconds totalTime = std::chrono::microseconds::zero(); + // Print performance counts + if (bshowHeader) { + stream << std::endl << "performance counts:" << std::endl << std::endl; + } + std::ios::fmtflags fmt(std::cout.flags()); + for (const auto& it : performanceData) { + std::string toPrint(it.node_name); + const int maxLayerName = 30; + + if (it.node_name.length() >= maxLayerName) { + toPrint = it.node_name.substr(0, maxLayerName - 5); + toPrint += "..."; + } + + stream << std::setw(maxLayerName) << std::left << toPrint << " "; + switch (it.status) { + case ov::ProfilingInfo::Status::EXECUTED: + stream << std::setw(15) << std::left << "EXECUTED "; + break; + case ov::ProfilingInfo::Status::NOT_RUN: + stream << std::setw(15) << std::left << "NOT_RUN "; + break; + case ov::ProfilingInfo::Status::OPTIMIZED_OUT: + stream << std::setw(15) << std::left << "OPTIMIZED_OUT "; + break; + } + stream << std::setw(30) << std::left << "layerType: " + std::string(it.node_type) + " "; + stream << std::setw(30) << std::left << "execType: " + std::string(it.exec_type) + " "; + stream << std::setw(25) << std::left << "realTime (ms): " + std::to_string(it.real_time.count() / 1000.0) + " "; + stream << std::setw(25) << std::left << "cpuTime (ms): " + std::to_string(it.cpu_time.count() / 1000.0) + " "; + stream << std::endl; + if (it.real_time.count() > 0) { + totalTime += it.real_time; + } + } + stream << std::setw(25) << std::left << "Total time: " + std::to_string(totalTime.count() / 1000.0) + << " milliseconds" << std::endl; + std::cout << std::endl; + std::cout << "Full device name: " << deviceName << std::endl; + std::cout << std::endl; + std::cout.flags(fmt); +} + +static UNUSED void printPerformanceCounts(ov::InferRequest request, + std::ostream& stream, + std::string deviceName, + bool bshowHeader = true) { + auto performanceMap = request.get_profiling_info(); + printPerformanceCounts(performanceMap, stream, deviceName, bshowHeader); +} + +static inline std::string double_to_string(const double number) { + std::stringstream ss; + ss << std::fixed << std::setprecision(2) << number; + return ss.str(); +} + +template <typename T> +using uniformDistribution = typename std::conditional< + std::is_floating_point<T>::value, + std::uniform_real_distribution<T>, + typename std::conditional<std::is_integral<T>::value, std::uniform_int_distribution<T>, void>::type>::type; + +template <typename T, typename T2> +static inline void fill_random(ov::Tensor& tensor, + T rand_min = std::numeric_limits<uint8_t>::min(), + T rand_max = std::numeric_limits<uint8_t>::max()) { + std::mt19937 gen(0); + size_t tensor_size = tensor.get_size(); + if (0 == tensor_size) { + throw std::runtime_error( + "Models with dynamic shapes aren't supported. Input tensors must have specific shapes before inference"); + } + T* data = tensor.data<T>(); + uniformDistribution<T2> distribution(rand_min, rand_max); + for (size_t i = 0; i < tensor_size; i++) { + data[i] = static_cast<T>(distribution(gen)); + } +} + +static inline void fill_tensor_random(ov::Tensor tensor) { + switch (tensor.get_element_type()) { + case ov::element::f32: + fill_random<float, float>(tensor); + break; + case ov::element::f64: + fill_random<double, double>(tensor); + break; + case ov::element::f16: + fill_random<short, short>(tensor); + break; + case ov::element::i32: + fill_random<int32_t, int32_t>(tensor); + break; + case ov::element::i64: + fill_random<int64_t, int64_t>(tensor); + break; + case ov::element::u8: + // uniform_int_distribution<uint8_t> is not allowed in the C++17 + // standard and vs2017/19 + fill_random<uint8_t, uint32_t>(tensor); + break; + case ov::element::i8: + // uniform_int_distribution<int8_t> is not allowed in the C++17 standard + // and vs2017/19 + fill_random<int8_t, int32_t>(tensor, std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()); + break; + case ov::element::u16: + fill_random<uint16_t, uint16_t>(tensor); + break; + case ov::element::i16: + fill_random<int16_t, int16_t>(tensor); + break; + case ov::element::boolean: + fill_random<uint8_t, uint32_t>(tensor, 0, 1); + break; + default: + throw ov::Exception("Input type is not supported for a tensor"); + } +} + +static UNUSED void printPerformanceCountsNoSort(std::vector<ov::ProfilingInfo> performanceData, + std::ostream& stream, + std::string deviceName, + bool bshowHeader = true) { + std::chrono::microseconds totalTime = std::chrono::microseconds::zero(); + // Print performance counts + if (bshowHeader) { + stream << std::endl << "performance counts:" << std::endl << std::endl; + } + std::ios::fmtflags fmt(std::cout.flags()); + + for (const auto& it : performanceData) { + if (it.real_time.count() > 0) { + totalTime += it.real_time; + } + } + if (totalTime.count() != 0) { + for (const auto& it : performanceData) { + std::string toPrint(it.node_name); + const int maxLayerName = 30; + + if (it.node_name.length() >= maxLayerName) { + toPrint = it.node_name.substr(0, maxLayerName - 5); + toPrint += "..."; + } + + stream << std::setw(maxLayerName) << std::left << toPrint << " "; + switch (it.status) { + case ov::ProfilingInfo::Status::EXECUTED: + stream << std::setw(15) << std::left << "EXECUTED "; + break; + case ov::ProfilingInfo::Status::NOT_RUN: + stream << std::setw(15) << std::left << "NOT_RUN "; + break; + case ov::ProfilingInfo::Status::OPTIMIZED_OUT: + stream << std::setw(15) << std::left << "OPTIMIZED_OUT "; + break; + } + stream << std::setw(30) << std::left << "layerType: " + std::string(it.node_type) + " "; + stream << std::setw(30) << std::left << "execType: " + std::string(it.exec_type) + " "; + stream << std::setw(25) << std::left + << "realTime (ms): " + std::to_string(it.real_time.count() / 1000.0) + " "; + stream << std::setw(25) << std::left + << "cpuTime (ms): " + std::to_string(it.cpu_time.count() / 1000.0) + " "; + + double opt_proportion = it.real_time.count() * 100.0 / totalTime.count(); + std::stringstream opt_proportion_ss; + opt_proportion_ss << std::fixed << std::setprecision(2) << opt_proportion; + std::string opt_proportion_str = opt_proportion_ss.str(); + if (opt_proportion_str == "0.00") { + opt_proportion_str = "N/A"; + } + stream << std::setw(20) << std::left << "proportion: " + opt_proportion_str + "%"; + + stream << std::endl; + } + } + stream << std::setw(25) << std::left << "Total time: " + std::to_string(totalTime.count() / 1000.0) + << " milliseconds" << std::endl; + std::cout << std::endl; + std::cout << "Full device name: " << deviceName << std::endl; + std::cout << std::endl; + std::cout.flags(fmt); +} + +static UNUSED bool sort_pc_descend(const ov::ProfilingInfo& profiling1, const ov::ProfilingInfo& profiling2) { + return profiling1.real_time > profiling2.real_time; +} + +static UNUSED void printPerformanceCountsDescendSort(std::vector<ov::ProfilingInfo> performanceData, + std::ostream& stream, + std::string deviceName, + bool bshowHeader = true) { + std::chrono::microseconds totalTime = std::chrono::microseconds::zero(); + // Print performance counts + if (bshowHeader) { + stream << std::endl << "performance counts:" << std::endl << std::endl; + } + std::ios::fmtflags fmt(std::cout.flags()); + + for (const auto& it : performanceData) { + if (it.real_time.count() > 0) { + totalTime += it.real_time; + } + } + if (totalTime.count() != 0) { + // sort perfcounter + std::vector<ov::ProfilingInfo> sortPerfCounts{std::begin(performanceData), std::end(performanceData)}; + std::sort(sortPerfCounts.begin(), sortPerfCounts.end(), sort_pc_descend); + + for (const auto& it : sortPerfCounts) { + std::string toPrint(it.node_name); + const int maxLayerName = 30; + + if (it.node_name.length() >= maxLayerName) { + toPrint = it.node_name.substr(0, maxLayerName - 5); + toPrint += "..."; + } + + stream << std::setw(maxLayerName) << std::left << toPrint << " "; + switch (it.status) { + case ov::ProfilingInfo::Status::EXECUTED: + stream << std::setw(15) << std::left << "EXECUTED "; + break; + case ov::ProfilingInfo::Status::NOT_RUN: + stream << std::setw(15) << std::left << "NOT_RUN "; + break; + case ov::ProfilingInfo::Status::OPTIMIZED_OUT: + stream << std::setw(15) << std::left << "OPTIMIZED_OUT "; + break; + } + stream << std::setw(30) << std::left << "layerType: " + std::string(it.node_type) + " "; + stream << std::setw(30) << std::left << "execType: " + std::string(it.exec_type) + " "; + stream << std::setw(25) << std::left + << "realTime (ms): " + std::to_string(it.real_time.count() / 1000.0) + " "; + stream << std::setw(25) << std::left + << "cpuTime (ms): " + std::to_string(it.cpu_time.count() / 1000.0) + " "; + + double opt_proportion = it.real_time.count() * 100.0 / totalTime.count(); + std::stringstream opt_proportion_ss; + opt_proportion_ss << std::fixed << std::setprecision(2) << opt_proportion; + std::string opt_proportion_str = opt_proportion_ss.str(); + if (opt_proportion_str == "0.00") { + opt_proportion_str = "N/A"; + } + stream << std::setw(20) << std::left << "proportion: " + opt_proportion_str + "%"; + + stream << std::endl; + } + } + stream << std::setw(25) << std::left << "Total time: " + std::to_string(totalTime.count() / 1000.0) + << " milliseconds" << std::endl; + std::cout << std::endl; + std::cout << "Full device name: " << deviceName << std::endl; + std::cout << std::endl; + std::cout.flags(fmt); +} + +static UNUSED void printPerformanceCountsSimpleSort(std::vector<ov::ProfilingInfo> performanceData, + std::ostream& stream, + std::string deviceName, + bool bshowHeader = true) { + std::chrono::microseconds totalTime = std::chrono::microseconds::zero(); + // Print performance counts + if (bshowHeader) { + stream << std::endl << "performance counts:" << std::endl << std::endl; + } + std::ios::fmtflags fmt(std::cout.flags()); + + for (const auto& it : performanceData) { + if (it.real_time.count() > 0) { + totalTime += it.real_time; + } + } + if (totalTime.count() != 0) { + // sort perfcounter + std::vector<ov::ProfilingInfo> sortPerfCounts{std::begin(performanceData), std::end(performanceData)}; + std::sort(sortPerfCounts.begin(), sortPerfCounts.end(), sort_pc_descend); + + for (const auto& it : sortPerfCounts) { + if (it.status == ov::ProfilingInfo::Status::EXECUTED) { + std::string toPrint(it.node_name); + const int maxLayerName = 30; + + if (it.node_name.length() >= maxLayerName) { + toPrint = it.node_name.substr(0, maxLayerName - 5); + toPrint += "..."; + } + + stream << std::setw(maxLayerName) << std::left << toPrint << " "; + stream << std::setw(15) << std::left << "EXECUTED "; + stream << std::setw(30) << std::left << "layerType: " + std::string(it.node_type) + " "; + stream << std::setw(30) << std::left << "execType: " + std::string(it.exec_type) + " "; + stream << std::setw(25) << std::left + << "realTime (ms): " + std::to_string(it.real_time.count() / 1000.0) + " "; + stream << std::setw(25) << std::left + << "cpuTime (ms): " + std::to_string(it.cpu_time.count() / 1000.0) + " "; + + double opt_proportion = it.real_time.count() * 100.0 / totalTime.count(); + std::stringstream opt_proportion_ss; + opt_proportion_ss << std::fixed << std::setprecision(2) << opt_proportion; + std::string opt_proportion_str = opt_proportion_ss.str(); + if (opt_proportion_str == "0.00") { + opt_proportion_str = "N/A"; + } + stream << std::setw(20) << std::left << "proportion: " + opt_proportion_str + "%"; + + stream << std::endl; + } + } + } + stream << std::setw(25) << std::left << "Total time: " + std::to_string(totalTime.count() / 1000.0) + << " milliseconds" << std::endl; + std::cout << std::endl; + std::cout << "Full device name: " << deviceName << std::endl; + std::cout << std::endl; + std::cout.flags(fmt); +} + +static UNUSED void printPerformanceCountsSort(std::vector<ov::ProfilingInfo> performanceData, + std::ostream& stream, + std::string deviceName, + std::string sorttype, + bool bshowHeader = true) { + if (sorttype == pcNoSort) { + printPerformanceCountsNoSort(performanceData, stream, deviceName, bshowHeader); + } else if (sorttype == pcSort) { + printPerformanceCountsDescendSort(performanceData, stream, deviceName, bshowHeader); + } else if (sorttype == pcSimpleSort) { + printPerformanceCountsSimpleSort(performanceData, stream, deviceName, bshowHeader); + } +} diff --git a/python/openvino/runtime/common/utils/include/samples/console_progress.hpp b/python/openvino/runtime/common/utils/include/samples/console_progress.hpp new file mode 100644 index 0000000..f62aeed --- /dev/null +++ b/python/openvino/runtime/common/utils/include/samples/console_progress.hpp @@ -0,0 +1,107 @@ +// Copyright (C) 2018-2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include <cstdio> +#include <iomanip> +#include <sstream> + +/** + * @class ConsoleProgress + * @brief A ConsoleProgress class provides functionality for printing progress dynamics + */ +class ConsoleProgress { + static const size_t DEFAULT_DETALIZATION = 20; + static const size_t DEFAULT_PERCENT_TO_UPDATE_PROGRESS = 1; + + size_t total; + size_t cur_progress = 0; + size_t prev_progress = 0; + bool stream_output; + size_t detalization; + size_t percent_to_update; + +public: + /** + * @brief A constructor of ConsoleProgress class + * @param _total - maximum value that is correspondent to 100% + * @param _detalization - number of symbols(.) to use to represent progress + */ + explicit ConsoleProgress(size_t _total, + bool _stream_output = false, + size_t _percent_to_update = DEFAULT_PERCENT_TO_UPDATE_PROGRESS, + size_t _detalization = DEFAULT_DETALIZATION) + : total(_total), + detalization(_detalization), + percent_to_update(_percent_to_update) { + stream_output = _stream_output; + if (total == 0) { + total = 1; + } + } + + /** + * @brief Shows progress with current data. Progress is shown from the beginning of the current + * line. + */ + void showProgress() const { + std::stringstream strm; + if (!stream_output) { + strm << '\r'; + } + strm << "Progress: ["; + size_t i = 0; + for (; i < detalization * cur_progress / total; i++) { + strm << "."; + } + for (; i < detalization; i++) { + strm << " "; + } + strm << "] " << std::setw(3) << 100 * cur_progress / total << "% done"; + if (stream_output) { + strm << std::endl; + } + std::fputs(strm.str().c_str(), stdout); + std::fflush(stdout); + } + + /** + * @brief Updates current value and progressbar + */ + void updateProgress() { + if (cur_progress > total) + cur_progress = total; + size_t prev_percent = 100 * prev_progress / total; + size_t cur_percent = 100 * cur_progress / total; + + if (prev_progress == 0 || cur_progress == total || prev_percent + percent_to_update <= cur_percent) { + showProgress(); + prev_progress = cur_progress; + } + } + + /** + * @brief Adds value to currently represented and redraw progressbar + * @param add - value to add + */ + void addProgress(int add) { + if (add < 0 && -add > static_cast<int>(cur_progress)) { + add = -static_cast<int>(cur_progress); + } + cur_progress += add; + updateProgress(); + } + + /** + * @brief Output end line. + * @return + */ + void finish() { + std::stringstream strm; + strm << std::endl; + std::fputs(strm.str().c_str(), stdout); + std::fflush(stdout); + } +}; diff --git a/python/openvino/runtime/common/utils/include/samples/csv_dumper.hpp b/python/openvino/runtime/common/utils/include/samples/csv_dumper.hpp new file mode 100644 index 0000000..5c80134 --- /dev/null +++ b/python/openvino/runtime/common/utils/include/samples/csv_dumper.hpp @@ -0,0 +1,98 @@ +// Copyright (C) 2018-2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include <ctime> +#include <fstream> +#include <iostream> +#include <sstream> +#include <string> + +#include "samples/slog.hpp" + +/** + * @class CsvDumper + * @brief A CsvDumper class provides functionality for dumping the values in CSV files + */ +class CsvDumper { + std::ofstream file; + std::string filename; + bool canDump = true; + char delimiter = ';'; + + std::string generateFilename() { + std::stringstream filename; + filename << "dumpfile-"; + filename << time(nullptr); + filename << ".csv"; + return filename.str(); + } + +public: + /** + * @brief A constructor. Disables dumping in case dump file cannot be created + * @param enabled - True if dumping is enabled by default. + * @param name - name of file to dump to. File won't be created if first parameter is false. + */ + explicit CsvDumper(bool enabled = true, const std::string& name = "") : canDump(enabled) { + if (!canDump) { + return; + } + filename = (name == "" ? generateFilename() : name); + file.open(filename, std::ios::out); + if (!file) { + slog::warn << "Cannot create dump file! Disabling dump." << slog::endl; + canDump = false; + } + } + + /** + * @brief Sets a delimiter to use in csv file + * @param c - Delimiter char + * @return + */ + void setDelimiter(char c) { + delimiter = c; + } + + /** + * @brief Overloads operator to organize streaming values to file. Does nothing if dumping is + * disabled Adds delimiter at the end of value provided + * @param add - value to add to dump + * @return reference to same object + */ + template <class T> + CsvDumper& operator<<(const T& add) { + if (canDump) { + file << add << delimiter; + } + return *this; + } + + /** + * @brief Finishes line in dump file. Does nothing if dumping is disabled + */ + void endLine() { + if (canDump) { + file << "\n"; + } + } + + /** + * @brief Gets information if dump is enabled. + * @return true if dump is enabled and file was successfully created + */ + bool dumpEnabled() { + return canDump; + } + + /** + * @brief Gets name of a dump file + * @return name of a dump file + */ + std::string getFilename() const { + return filename; + } +}; diff --git a/python/openvino/runtime/common/utils/include/samples/latency_metrics.hpp b/python/openvino/runtime/common/utils/include/samples/latency_metrics.hpp new file mode 100644 index 0000000..bca39d0 --- /dev/null +++ b/python/openvino/runtime/common/utils/include/samples/latency_metrics.hpp @@ -0,0 +1,42 @@ +// Copyright (C) 2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include <map> +#include <string> +#include <utility> +#include <vector> + +// clang-format off +#include "samples/common.hpp" +#include "samples/slog.hpp" +// clang-format on + +/// @brief Responsible for calculating different latency metrics +class LatencyMetrics { +public: + LatencyMetrics() {} + + LatencyMetrics(const std::vector<double>& latencies, + const std::string& data_shape = "", + size_t percentile_boundary = 50) + : data_shape(data_shape), + percentile_boundary(percentile_boundary) { + fill_data(latencies, percentile_boundary); + } + + void write_to_stream(std::ostream& stream) const; + void write_to_slog() const; + + double median_or_percentile = 0; + double avg = 0; + double min = 0; + double max = 0; + std::string data_shape; + +private: + void fill_data(std::vector<double> latencies, size_t percentile_boundary); + size_t percentile_boundary = 50; +}; diff --git a/python/openvino/runtime/common/utils/include/samples/ocv_common.hpp b/python/openvino/runtime/common/utils/include/samples/ocv_common.hpp new file mode 100644 index 0000000..94f3b1f --- /dev/null +++ b/python/openvino/runtime/common/utils/include/samples/ocv_common.hpp @@ -0,0 +1,92 @@ +// Copyright (C) 2018-2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +/** + * @brief a header file with common samples functionality using OpenCV + * @file ocv_common.hpp + */ + +#pragma once + +#include <opencv2/opencv.hpp> + +#include "openvino/openvino.hpp" +#include "samples/common.hpp" + +/** + * @brief Sets image data stored in cv::Mat object to a given Blob object. + * @param orig_image - given cv::Mat object with an image data. + * @param blob - Blob object which to be filled by an image data. + * @param batchIndex - batch index of an image inside of the blob. + */ +template <typename T> +void matU8ToBlob(const cv::Mat& orig_image, InferenceEngine::Blob::Ptr& blob, int batchIndex = 0) { + InferenceEngine::SizeVector blobSize = blob->getTensorDesc().getDims(); + const size_t width = blobSize[3]; + const size_t height = blobSize[2]; + const size_t channels = blobSize[1]; + InferenceEngine::MemoryBlob::Ptr mblob = InferenceEngine::as<InferenceEngine::MemoryBlob>(blob); + OPENVINO_ASSERT(mblob, + "We expect blob to be inherited from MemoryBlob in matU8ToBlob, " + "but by fact we were not able to cast inputBlob to MemoryBlob"); + // locked memory holder should be alive all time while access to its buffer happens + auto mblobHolder = mblob->wmap(); + + T* blob_data = mblobHolder.as<T*>(); + + cv::Mat resized_image(orig_image); + if (static_cast<int>(width) != orig_image.size().width || static_cast<int>(height) != orig_image.size().height) { + cv::resize(orig_image, resized_image, cv::Size(width, height)); + } + + int batchOffset = batchIndex * width * height * channels; + + for (size_t c = 0; c < channels; c++) { + for (size_t h = 0; h < height; h++) { + for (size_t w = 0; w < width; w++) { + blob_data[batchOffset + c * width * height + h * width + w] = resized_image.at<cv::Vec3b>(h, w)[c]; + } + } + } +} + +/** + * @brief Wraps data stored inside of a passed cv::Mat object by new Blob pointer. + * @note: No memory allocation is happened. The blob just points to already existing + * cv::Mat data. + * @param mat - given cv::Mat object with an image data. + * @return resulting Blob pointer. + */ +static UNUSED InferenceEngine::Blob::Ptr wrapMat2Blob(const cv::Mat& mat) { + size_t channels = mat.channels(); + size_t height = mat.size().height; + size_t width = mat.size().width; + + size_t strideH = mat.step.buf[0]; + size_t strideW = mat.step.buf[1]; + + bool is_dense = strideW == channels && strideH == channels * width; + + OPENVINO_ASSERT(is_dense, "Doesn't support conversion from not dense cv::Mat"); + + InferenceEngine::TensorDesc tDesc(InferenceEngine::Precision::U8, + {1, channels, height, width}, + InferenceEngine::Layout::NHWC); + + return InferenceEngine::make_shared_blob<uint8_t>(tDesc, mat.data); +} + +static UNUSED ov::Tensor wrapMat2Tensor(const cv::Mat& mat) { + const size_t channels = mat.channels(); + const size_t height = mat.size().height; + const size_t width = mat.size().width; + + const size_t strideH = mat.step.buf[0]; + const size_t strideW = mat.step.buf[1]; + + const bool is_dense = strideW == channels && strideH == channels * width; + OPENVINO_ASSERT(is_dense, "Doesn't support conversion from not dense cv::Mat"); + + return ov::Tensor(ov::element::u8, ov::Shape{1, height, width, channels}, mat.data); +} diff --git a/python/openvino/runtime/common/utils/include/samples/os/windows/w_dirent.h b/python/openvino/runtime/common/utils/include/samples/os/windows/w_dirent.h new file mode 100644 index 0000000..40d1c5b --- /dev/null +++ b/python/openvino/runtime/common/utils/include/samples/os/windows/w_dirent.h @@ -0,0 +1,176 @@ +// Copyright (C) 2018-2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#if defined(_WIN32) + +# ifndef WIN32_LEAN_AND_MEAN +# define WIN32_LEAN_AND_MEAN +# define WIN32_LEAN_AND_MEAN_UNDEF +# endif + +# ifndef NOMINMAX +# define NOMINMAX +# define NOMINMAX_UNDEF +# endif + +# if defined(_M_IX86) && !defined(_X86_) && !defined(_AMD64_) +# define _X86_ +# endif + +# if defined(_M_X64) && !defined(_X86_) && !defined(_AMD64_) +# define _AMD64_ +# endif + +# if defined(_M_ARM) && !defined(_ARM_) && !defined(_ARM64_) +# define _ARM_ +# endif + +# if defined(_M_ARM64) && !defined(_ARM_) && !defined(_ARM64_) +# define _ARM64_ +# endif + +// clang-format off + #include <string.h> + #include <windef.h> + #include <fileapi.h> + #include <Winbase.h> + #include <sys/stat.h> +// clang-format on + +// Copied from linux libc sys/stat.h: +# define S_ISREG(m) (((m)&S_IFMT) == S_IFREG) +# define S_ISDIR(m) (((m)&S_IFMT) == S_IFDIR) + +/// @brief structure to store directory names +struct dirent { + char* d_name; + + explicit dirent(const wchar_t* wsFilePath) { + size_t i; + auto slen = wcslen(wsFilePath); + d_name = static_cast<char*>(malloc(slen + 1)); + wcstombs_s(&i, d_name, slen + 1, wsFilePath, slen); + } + ~dirent() { + free(d_name); + } +}; + +/// @brief class to store directory data (files meta) +class DIR { + WIN32_FIND_DATAA FindFileData; + HANDLE hFind; + dirent* next; + + static inline bool endsWith(const std::string& src, const char* with) { + int wl = static_cast<int>(strlen(with)); + int so = static_cast<int>(src.length()) - wl; + if (so < 0) + return false; + return 0 == strncmp(with, &src[so], wl); + } + +public: + DIR(const DIR& other) = delete; + DIR(DIR&& other) = delete; + DIR& operator=(const DIR& other) = delete; + DIR& operator=(DIR&& other) = delete; + + explicit DIR(const char* dirPath) : next(nullptr) { + std::string ws = dirPath; + if (endsWith(ws, "\\")) + ws += "*"; + else + ws += "\\*"; + hFind = FindFirstFileA(ws.c_str(), &FindFileData); + FindFileData.dwReserved0 = hFind != INVALID_HANDLE_VALUE; + } + + ~DIR() { + if (!next) + delete next; + next = nullptr; + FindClose(hFind); + } + + /** + * @brief Check file handler is valid + * @return status True(success) or False(fail) + */ + bool isValid() const { + return (hFind != INVALID_HANDLE_VALUE && FindFileData.dwReserved0); + } + + /** + * @brief Add directory to directory names struct + * @return pointer to directory names struct + */ + dirent* nextEnt() { + if (next != nullptr) + delete next; + next = nullptr; + + if (!FindFileData.dwReserved0) + return nullptr; + + wchar_t wbuf[4096]; + + size_t outSize; + mbstowcs_s(&outSize, wbuf, 4094, FindFileData.cFileName, 4094); + next = new dirent(wbuf); + FindFileData.dwReserved0 = FindNextFileA(hFind, &FindFileData); + return next; + } +}; + +/** + * @brief Create directory data struct element + * @param string directory path + * @return pointer to directory data struct element + */ +static DIR* opendir(const char* dirPath) { + auto dp = new DIR(dirPath); + if (!dp->isValid()) { + delete dp; + return nullptr; + } + return dp; +} + +/** + * @brief Walk throw directory data struct + * @param pointer to directory data struct + * @return pointer to directory data struct next element + */ +static struct dirent* readdir(DIR* dp) { + return dp->nextEnt(); +} + +/** + * @brief Remove directory data struct + * @param pointer to struct directory data + * @return void + */ +static void closedir(DIR* dp) { + delete dp; +} + +# ifdef WIN32_LEAN_AND_MEAN_UNDEF +# undef WIN32_LEAN_AND_MEAN +# undef WIN32_LEAN_AND_MEAN_UNDEF +# endif + +# ifdef NOMINMAX_UNDEF +# undef NOMINMAX_UNDEF +# undef NOMINMAX +# endif + +#else + +# include <dirent.h> +# include <sys/types.h> + +#endif diff --git a/python/openvino/runtime/common/utils/include/samples/slog.hpp b/python/openvino/runtime/common/utils/include/samples/slog.hpp new file mode 100644 index 0000000..3f237e5 --- /dev/null +++ b/python/openvino/runtime/common/utils/include/samples/slog.hpp @@ -0,0 +1,102 @@ +// Copyright (C) 2018-2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +/** + * @brief a header file with logging facility for common samples + * @file log.hpp + */ + +#pragma once + +#include <ostream> +#include <string> +#include <vector> + +namespace slog { +/** + * @class LogStreamEndLine + * @brief The LogStreamEndLine class implements an end line marker for a log stream + */ +class LogStreamEndLine {}; + +static constexpr LogStreamEndLine endl; + +/** + * @class LogStreamBoolAlpha + * @brief The LogStreamBoolAlpha class implements bool printing for a log stream + */ +class LogStreamBoolAlpha {}; + +static constexpr LogStreamBoolAlpha boolalpha; + +/** + * @class LogStreamFlush + * @brief The LogStreamFlush class implements flushing for a log stream + */ +class LogStreamFlush {}; + +static constexpr LogStreamFlush flush; + +/** + * @class LogStream + * @brief The LogStream class implements a stream for sample logging + */ +class LogStream { + std::string _prefix; + std::ostream* _log_stream; + bool _new_line; + +public: + /** + * @brief A constructor. Creates an LogStream object + * @param prefix The prefix to print + */ + LogStream(const std::string& prefix, std::ostream& log_stream); + + /** + * @brief A stream output operator to be used within the logger + * @param arg Object for serialization in the logger message + */ + template <class T> + LogStream& operator<<(const T& arg) { + if (_new_line) { + (*_log_stream) << "[ " << _prefix << " ] "; + _new_line = false; + } + + (*_log_stream) << arg; + return *this; + } + + /** + * @brief Overload output stream operator to print vectors in pretty form + * [value1, value2, ...] + */ + template <typename T> + LogStream& operator<<(const std::vector<T>& v) { + (*_log_stream) << "[ "; + + for (auto&& value : v) + (*_log_stream) << value << " "; + + (*_log_stream) << "]"; + + return *this; + } + + // Specializing for LogStreamEndLine to support slog::endl + LogStream& operator<<(const LogStreamEndLine&); + + // Specializing for LogStreamBoolAlpha to support slog::boolalpha + LogStream& operator<<(const LogStreamBoolAlpha&); + + // Specializing for LogStreamFlush to support slog::flush + LogStream& operator<<(const LogStreamFlush&); +}; + +extern LogStream info; +extern LogStream warn; +extern LogStream err; + +} // namespace slog diff --git a/python/openvino/runtime/common/utils/include/samples/vpu/vpu_tools_common.hpp b/python/openvino/runtime/common/utils/include/samples/vpu/vpu_tools_common.hpp new file mode 100644 index 0000000..ba0665f --- /dev/null +++ b/python/openvino/runtime/common/utils/include/samples/vpu/vpu_tools_common.hpp @@ -0,0 +1,28 @@ +// Copyright (C) 2018-2022 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include <fstream> +#include <map> +#include <string> + +static std::map<std::string, std::string> parseConfig(const std::string& configName, char comment = '#') { + std::map<std::string, std::string> config = {}; + + std::ifstream file(configName); + if (!file.is_open()) { + return config; + } + + std::string key, value; + while (file >> key >> value) { + if (key.empty() || key[0] == comment) { + continue; + } + config[key] = value; + } + + return config; +} |