170 lines
6.9 KiB
C++
170 lines
6.9 KiB
C++
#pragma once
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#include <filesystem>
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#include "data.hpp"
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#include "toolbox.hpp"
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//#include "config.hpp"
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template <typename T>
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bool unit_test_cpu_vs_gpu(const np::Array<T>& cpu, const np::Array<T>& gpu) noexcept {
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if (cpu.shape != gpu.shape) {
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#if __DEBUG
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fprintf(stderr, "Unequal shape !\n");
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#endif
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return false;
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}
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size_t eq = 0;
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const size_t length = np::prod(cpu.shape);
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for (size_t i = 0; i < length; ++i)
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if (cpu[i] == gpu[i])
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++eq;
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#if __DEBUG
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if (eq != length)
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printf("Incorrect results, Number of equalities : %s/%s <=> %.2f%% !\n", thousand_sep(eq).c_str(), thousand_sep(length).c_str(),
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static_cast<float64_t>(eq) / static_cast<float64_t>(length) * 100.0);
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#endif
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return eq == length;
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}
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template <typename T>
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bool unit_test_argsort_2d(const np::Array<T>& a, const np::Array<uint16_t>& indices) noexcept {
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if (a.shape != indices.shape) {
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#if __DEBUG
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fprintf(stderr, "Unequal shape !\n");
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#endif
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return false;
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}
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size_t correct = a.shape[0]; // First elements are always correctly sorted
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const size_t total = np::prod(a.shape);
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for(size_t i = 0; i < total; i += a.shape[1])
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for(size_t j = 0; j < a.shape[1] - 1; ++j){
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const size_t k = i + j;
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if(a[i + indices[k]] <= a[i + indices[k + 1]])
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++correct;
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}
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#if __DEBUG
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if (correct != total)
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printf("Incorrect results, Number of equalities : %s/%s <=> %.2f%% !\n", thousand_sep(correct).c_str(), thousand_sep(total).c_str(),
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static_cast<float64_t>(correct) / static_cast<float64_t>(total) * 100.0);
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#endif
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return correct == total;
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}
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template <typename T, typename F, typename... Args>
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T benchmark_function(const char* const step_name, const int32_t& column_width, const F& fnc, Args &&...args) noexcept {
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#if __DEBUG == false
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fprintf(stderr, "%s...\r", step_name);
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fflush(stderr); // manual flush is mandatory, otherwise it will not be shown immediately because the output is buffered
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#endif
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const std::chrono::system_clock::time_point start = perf_counter_ns();
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const T res = fnc(std::forward<Args>(args)...);
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const long long time_spent = duration_ns(perf_counter_ns() - start);
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formatted_row<3>({ column_width, -18, 29 }, { step_name, thousand_sep(time_spent).c_str(), format_time_ns(time_spent).c_str() });
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return res;
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}
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template <typename F, typename... Args>
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void benchmark_function_void(const char* const step_name, const int32_t& column_width, const F& fnc, Args &&...args) noexcept {
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#if __DEBUG == false
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fprintf(stderr, "%s...\r", step_name);
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fflush(stderr); // manual flush is mandatory, otherwise it will not be shown immediately because the output is buffered
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#endif
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const std::chrono::system_clock::time_point start = perf_counter_ns();
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fnc(std::forward<Args>(args)...);
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const long long time_spent = duration_ns(perf_counter_ns() - start);
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formatted_row<3>({ column_width, -18, 29 }, { step_name, thousand_sep(time_spent).c_str(), format_time_ns(time_spent).c_str() });
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}
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template <typename T, typename F, typename... Args>
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np::Array<T> state_saver(const char* const step_name, const int32_t& column_width, const char* const filename, const bool& force_redo, const bool& save_state, const char* const out_dir, const F& fnc, Args &&...args) noexcept {
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char filepath[BUFFER_SIZE] = { 0 };
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snprintf(filepath, BUFFER_SIZE, "%s/%s.bin", out_dir, filename);
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np::Array<T> bin;
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if (!std::filesystem::exists(filepath) || force_redo) {
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//bin = std::move(benchmark_function<np::Array<T>>(step_name, column_width, fnc, std::forward<Args>(args)...));
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bin = benchmark_function<np::Array<T>>(step_name, column_width, fnc, std::forward<Args>(args)...);
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if(save_state){
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#if __DEBUG == false
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fprintf(stderr, "Saving results of %s\r", step_name);
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fflush(stderr);
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#endif
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save<T>(bin, filepath);
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#if __DEBUG == false
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fprintf(stderr, "%*c\r", 100, ' '); // Clear previous clear
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fflush(stderr);
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#endif
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}
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} else {
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#if __DEBUG == false
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fprintf(stderr, "Loading results of %s\r", step_name);
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fflush(stderr);
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#endif
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//bin = std::move(load<T>(filepath));
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bin = load<T>(filepath);
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formatted_row<3>({ column_width, -18, 29 }, { step_name, "None", "loaded saved state" });
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}
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return bin;
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}
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template <typename T, size_t N, typename F, typename... Args>
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std::array<np::Array<T>, N> state_saver(const char* const step_name, const int32_t& column_width, const std::vector<const char*>& filenames, const bool& force_redo, const bool& save_state, const char* const out_dir, const F& fnc, Args &&...args) noexcept {
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char filepath[BUFFER_SIZE] = { 0 };
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bool abs = false;
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for (const char* filename : filenames){
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sprintf(filepath, "%s/%s.bin", out_dir, filename);
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if (!fs::exists(filepath)) {
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abs = true;
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break;
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}
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}
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std::array<np::Array<T>, N> bin;
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if (abs || force_redo) {
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//bin = std::move(benchmark_function<std::array<np::Array<T>, N>>(step_name, column_width, fnc, std::forward<Args>(args)...));
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bin = benchmark_function<std::array<np::Array<T>, N>>(step_name, column_width, fnc, std::forward<Args>(args)...);
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if (save_state){
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#if __DEBUG == false
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fprintf(stderr, "Saving results of %s\r", step_name);
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fflush(stderr);
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#endif
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size_t i = 0;
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for (const char* filename : filenames){
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sprintf(filepath, "%s/%s.bin", out_dir, filename);
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save<T>(bin[i++], filepath);
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}
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#if __DEBUG == false
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fprintf(stderr, "%*c\r", 100, ' '); // Clear previous print
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fflush(stderr);
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#endif
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}
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} else {
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#if __DEBUG == false
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fprintf(stderr, "Loading results of %s\r", step_name);
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fflush(stderr);
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#endif
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size_t i = 0;
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for (const char* filename : filenames){
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bin[i++] = std::move(load<T>(filepath));
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snprintf(filepath, BUFFER_SIZE, "%s/%s.bin", out_dir, filename);
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}
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formatted_row<3>({ column_width, -18, 29 }, { step_name, "None", "loaded saved state" });
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}
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return bin;
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}
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np::Array<uint16_t> argsort_2d_cpu(const np::Array<int32_t>&) noexcept;
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np::Array<uint8_t> build_features(const uint16_t&, const uint16_t&) noexcept;
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np::Array<int> select_percentile(const np::Array<uint8_t>&, const np::Array<uint8_t>&) noexcept;
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np::Array<uint8_t> classify_viola_jones(const np::Array<float64_t>&, const np::Array<float64_t>&, const np::Array<int32_t>&) noexcept;
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np::Array<float64_t> init_weights(const np::Array<uint8_t>&) noexcept;
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std::tuple<int32_t, float64_t, np::Array<float64_t>> select_best(const np::Array<float64_t>&, const np::Array<float64_t>&, const np::Array<int32_t>&,
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const np::Array<uint8_t>&) noexcept;
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std::array<np::Array<float64_t>, 2> train_viola_jones(const size_t&, const np::Array<int32_t>&, const np::Array<uint16_t>&, const np::Array<uint8_t>&) noexcept;
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float64_t accuracy_score(const np::Array<uint8_t>&, const np::Array<uint8_t>&) noexcept;
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float64_t precision_score(const np::Array<uint8_t>&, const np::Array<uint8_t>&) noexcept;
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float64_t recall_score(const np::Array<uint8_t>&, const np::Array<uint8_t>&) noexcept;
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float64_t f1_score(const np::Array<uint8_t>&, const np::Array<uint8_t>&) noexcept;
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std::tuple<uint16_t, uint16_t, uint16_t, uint16_t> confusion_matrix(const np::Array<uint8_t>&, const np::Array<uint8_t>&) noexcept;
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