323 lines
15 KiB
C++
323 lines
15 KiB
C++
#include <filesystem>
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namespace fs = std::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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#include "gpu_unit_test.hpp"
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#include "toolbox_unit_test.hpp"
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#include "ViolaJones.hpp"
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#include "ViolaJonesGPU.hpp"
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#include "ViolaJonesCPU.hpp"
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#if GPU_BOOSTED
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#define LABEL "GPU"
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#define apply_features apply_features_gpu
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#define set_integral_image set_integral_image_gpu
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#define argsort_2d argsort_2d_gpu
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#else
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#define LABEL "CPU"
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#define apply_features apply_features_cpu
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#define set_integral_image set_integral_image_cpu
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#define argsort_2d argsort_2d_cpu
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#endif
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std::tuple<np::Array<int32_t>, np::Array<uint16_t>, np::Array<uint8_t>, np::Array<int32_t>, np::Array<uint8_t>> preprocessing() {
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// Creating state saver folders if they don't exist already
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if (SAVE_STATE)
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for (const char* const folder_name : { "models", "out" })
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fs::create_directory(folder_name);
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const std::chrono::system_clock::time_point preproc_timestamp = perf_counter_ns();
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const std::array<int32_t, 3> preproc_gaps = { 49, -18, 29 };
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header({ "Preprocessing", "Time spent (ns)", "Formatted time spent" }, preproc_gaps);
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const auto [ X_train, y_train, X_test, y_test ] = state_saver<uint8_t, 4>("Loading sets", preproc_gaps[0], {"X_train", "y_train", "X_test", "y_test"},
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FORCE_REDO, SAVE_STATE, OUT_DIR, load_datasets);
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#if __DEBUG
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print("X_train");
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print(X_train.shape);
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print(X_train, { IDX_INSPECT });
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print("X_test");
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print(X_test.shape);
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print(X_test, { IDX_INSPECT });
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print("y_train");
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print(y_train.shape);
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print(y_train, { IDX_INSPECT, IDX_INSPECT + IDX_INSPECT_OFFSET });
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print("y_test");
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print(y_test.shape);
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print(y_test, { IDX_INSPECT, IDX_INSPECT + IDX_INSPECT_OFFSET });
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#endif
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const np::Array<uint8_t> feats = state_saver<uint8_t>("Building features", preproc_gaps[0], "feats",
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FORCE_REDO, SAVE_STATE, OUT_DIR, build_features, X_train.shape[1], X_train.shape[2]);
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#if __DEBUG
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print("feats");
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print(feats.shape);
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print_feat(feats, { IDX_INSPECT });
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#endif
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const np::Array<uint32_t> X_train_ii = state_saver<uint32_t>("Converting training set to integral images (" LABEL ")", preproc_gaps[0], "X_train_ii_" LABEL,
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FORCE_REDO, SAVE_STATE, OUT_DIR, set_integral_image, X_train);
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const np::Array<uint32_t> X_test_ii = state_saver<uint32_t>("Converting testing set to integral images (" LABEL ")", preproc_gaps[0], "X_test_ii_" LABEL,
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FORCE_REDO, SAVE_STATE, OUT_DIR, set_integral_image, X_test);
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#if __DEBUG
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print("X_train_ii");
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print(X_train_ii.shape);
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print(X_train_ii, { IDX_INSPECT });
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print("X_test_ii");
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print(X_test_ii.shape);
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print(X_test_ii, { IDX_INSPECT });
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#endif
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const np::Array<int32_t> X_train_feat = state_saver<int32_t>("Applying features to training set (" LABEL ")", preproc_gaps[0], "X_train_feat_" LABEL,
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FORCE_REDO, SAVE_STATE, OUT_DIR, apply_features, feats, X_train_ii);
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const np::Array<int32_t> X_test_feat = state_saver<int32_t>("Applying features to testing set (" LABEL ")", preproc_gaps[0], "X_test_feat_" LABEL,
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FORCE_REDO, SAVE_STATE, OUT_DIR, apply_features, feats, X_test_ii);
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#if __DEBUG
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print("X_train_feat");
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print(X_train_feat.shape);
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print(X_train_feat, { IDX_INSPECT, IDX_INSPECT + IDX_INSPECT_OFFSET });
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print("X_test_feat");
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print(X_test_feat.shape);
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print(X_test_feat, { IDX_INSPECT, IDX_INSPECT + IDX_INSPECT_OFFSET });
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#endif
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// const Array<int> indices = measure_time_save<Array<int>>("Selecting best features", "indices", select_percentile, X_train_feat, d.y_train);
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// const Array<int> indices = measure_time<Array<int>>("Selecting best features", select_percentile, X_train_feat, d.y_train);
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#if __DEBUG
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// print_feature(indices);
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#endif
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const np::Array<uint16_t> X_train_feat_argsort = state_saver<uint16_t>("Precalculating training set argsort (" LABEL ")", preproc_gaps[0], "X_train_feat_argsort_" LABEL,
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FORCE_REDO, SAVE_STATE, OUT_DIR, argsort_2d, X_train_feat);
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#if __DEBUG
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print("X_train_feat_argsort");
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print(X_train_feat_argsort.shape);
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print(X_train_feat_argsort, { IDX_INSPECT, IDX_INSPECT + IDX_INSPECT_OFFSET });
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#endif
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// const np::Array<uint16_t> X_test_feat_argsort = state_saver<uint16_t>("Precalculating testing set argsort (" LABEL ")", preproc_gaps[0], "X_test_feat_argsort_" LABEL,
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// FORCE_REDO, SAVE_STATE, OUT_DIR, argsort_2d, X_test_feat);
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#if __DEBUG
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// printf("X_test_feat_argsort\n");
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// print(X_test_feat_argsort.shape);
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// print(X_test_feat_argsort, { IDX_INSPECT, IDX_INSPECT + IDX_INSPECT_OFFSET });
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#endif
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const long long time_spent = duration_ns(perf_counter_ns() - preproc_timestamp);
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formatted_line(preproc_gaps, "├", "┼", "─", "┤");
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formatted_row(preproc_gaps, { "Preprocessing summary", thousand_sep(time_spent).c_str(), format_time_ns(time_spent).c_str() });
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footer(preproc_gaps);
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return { X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test };
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}
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std::array<std::array<np::Array<float64_t>, 2>, TS.size()> train(const np::Array<int32_t>& X_train_feat, const np::Array<uint16_t>& X_train_feat_argsort, const np::Array<uint8_t>& y_train) noexcept {
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const std::chrono::system_clock::time_point training_timestamp = perf_counter_ns();
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const std::array<int32_t, 3> training_gaps = { 26, -18, 29 };
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header({ "Training", "Time spent (ns)", "Formatted time spent" }, training_gaps);
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std::array<std::array<np::Array<float64_t>, 2>, TS.size()> models;
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size_t i = 0;
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for (const size_t T : TS) {
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char title[BUFFER_SIZE] = { 0 };
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char alphas_title[BUFFER_SIZE] = { 0 };
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char final_classifiers_title[BUFFER_SIZE] = { 0 };
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snprintf(title, BUFFER_SIZE, "ViolaJones T = %-4lu (%s)", T, LABEL);
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snprintf(alphas_title, BUFFER_SIZE, "alphas_%lu_%s", T, LABEL);
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snprintf(final_classifiers_title, BUFFER_SIZE, "final_classifiers_%lu_%s", T, LABEL);
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const auto [ alphas, final_classifiers ] = state_saver<float64_t, 2>(title, training_gaps[0], { alphas_title, final_classifiers_title },
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FORCE_REDO, SAVE_STATE, MODEL_DIR, train_viola_jones, T, X_train_feat, X_train_feat_argsort, y_train);
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#if __DEBUG
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print("alphas");
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print(alphas);
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print("final_classifiers");
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print(final_classifiers);
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#endif
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models[i++] = { alphas, final_classifiers };
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}
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const long long time_spent = duration_ns(perf_counter_ns() - training_timestamp);
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formatted_line(training_gaps, "├", "┼", "─", "┤");
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formatted_row(training_gaps, { "Training summary", thousand_sep(time_spent).c_str(), format_time_ns(time_spent).c_str() });
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footer(training_gaps);
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return models;
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}
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void testing_and_evaluating(const std::array<std::array<np::Array<float64_t>, 2>, TS.size()>& models, const np::Array<int32_t>& X_train_feat, const np::Array<uint8_t>& y_train, const np::Array<int32_t>& X_test_feat, const np::Array<uint8_t>& y_test) {
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const std::array<int32_t, 5> testing_gaps = { 26, -19, 24, -19, 24 };
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header({ "Testing", "Time spent (ns) (E)", "Formatted time spent (E)", "Time spent (ns) (T)", "Formatted time spent (T)" }, testing_gaps);
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std::array<std::array<float64_t, 8>, TS.size()> results;
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size_t i = 0;
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long long total_train_timestamp = 0;
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long long total_test_timestamp = 0;
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for (const auto& [ alphas, final_classifiers ] : models) {
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char title[BUFFER_SIZE] = { 0 };
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snprintf(title, BUFFER_SIZE, "ViolaJones T = %-4i (%s)", TS[i], LABEL);
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std::chrono::system_clock::time_point start = perf_counter_ns();
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const np::Array<uint8_t> y_pred_train = classify_viola_jones(alphas, final_classifiers, X_train_feat);
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const long long t_pred_train = duration_ns(perf_counter_ns() - start);
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total_train_timestamp += t_pred_train;
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const float64_t e_acc = accuracy_score(y_train, y_pred_train);
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const float64_t e_f1 = f1_score(y_train, y_pred_train);
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float64_t e_FN, e_FP;
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std::tie(std::ignore, e_FN, e_FP, std::ignore) = confusion_matrix(y_train, y_pred_train);
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start = perf_counter_ns();
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const np::Array<uint8_t> y_pred_test = classify_viola_jones(alphas, final_classifiers, X_test_feat);
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const long long t_pred_test = duration_ns(perf_counter_ns() - start);
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total_test_timestamp += t_pred_test;
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const float64_t t_acc = accuracy_score(y_test, y_pred_test);
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const float64_t t_f1 = f1_score(y_test, y_pred_test);
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float64_t t_FN, t_FP;
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std::tie(std::ignore, t_FN, t_FP, std::ignore) = confusion_matrix(y_test, y_pred_test);
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results[i++] = { e_acc, e_f1, e_FN, e_FP, t_acc, t_f1, t_FN, t_FP };
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formatted_row(testing_gaps, { title, thousand_sep(t_pred_train).c_str(), format_time_ns(t_pred_train).c_str(), thousand_sep(t_pred_test).c_str(), format_time_ns(t_pred_test).c_str() });
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}
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formatted_line(testing_gaps, "├", "┼", "─", "┤");
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formatted_row(testing_gaps, { "Testing summary", thousand_sep(total_train_timestamp).c_str(), format_time_ns(total_train_timestamp).c_str(), thousand_sep(total_test_timestamp).c_str(), format_time_ns(total_test_timestamp).c_str() });
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footer(testing_gaps);
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const std::array<int32_t, 9> evaluating_gaps = { 19, -7, -6, -6, -6, -7, -6, -6, -6 };
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header({ "Evaluating", "ACC (E)", "F1 (E)", "FN (E)", "FP (E)", "ACC (T)", "F1 (T)", "FN (T)", "FP (T)"}, evaluating_gaps);
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i = 0;
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for (const size_t T : TS) {
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char title[BUFFER_SIZE] = { 0 };
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snprintf(title, BUFFER_SIZE, "ViolaJones T = %-4lu", T);
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const auto [e_acc, e_f1, e_FN, e_FP, t_acc, t_f1, t_FN, t_FP] = results[i++];
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printf("│ %-19s │ %'6.2f%% │ %'6.2f │ %'6.0f │ %'6.0f │ %6.2f%% │ %'6.2f │ %'6.0f │ %'6.0f │\n", title, e_acc * 100, e_f1, e_FN, e_FP, t_acc * 100, t_f1, t_FN, t_FP);
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}
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footer(evaluating_gaps);
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}
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void unit_test(void) {
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const std::chrono::system_clock::time_point unit_timestamp = perf_counter_ns();
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const std::array<int32_t, 4> unit_gaps = { 37, -10, -18, 29};
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header({ "Unit testing", "Test state", "Time spent (ns)", "Formatted time spent" }, unit_gaps);
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char title[BUFFER_SIZE] = { 0 };
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char tmp_title[BUFFER_SIZE / 2] = { 0 };
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char file_cpu[BUFFER_SIZE] = { 0 };
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char file_gpu[BUFFER_SIZE] = { 0 };
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uint64_t n_total = 0, n_success = 0;
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const auto test_fnc = [&unit_gaps, &n_total, &n_success](const char* const title, const auto& fnc) noexcept {
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++n_total;
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const std::chrono::system_clock::time_point start = perf_counter_ns();
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const bool state = fnc();
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const long long time_spent = duration_ns(perf_counter_ns() - start);
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if(state){
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formatted_row(unit_gaps, { title, "Passed", thousand_sep(time_spent).c_str(), format_time_ns(time_spent).c_str() });
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++n_success;
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} else
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formatted_row(unit_gaps, { title, "Failed", thousand_sep(time_spent).c_str(), format_time_ns(time_spent).c_str() });
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};
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for (const char* label : { "train", "test" }) {
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sprintf(file_cpu, OUT_DIR "/X_%s_ii_CPU.bin", label);
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sprintf(file_gpu, OUT_DIR "/X_%s_ii_GPU.bin", label);
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if (fs::exists(file_cpu) && fs::exists(file_gpu)) {
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const np::Array<uint32_t> X_train_ii_cpu = load<uint32_t>(file_cpu);
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const np::Array<uint32_t> X_train_ii_gpu = load<uint32_t>(file_gpu);
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sprintf(tmp_title, "X_%s_ii", label);
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sprintf(title, "%-22s - CPU vs GPU", tmp_title);
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test_fnc(title, [&X_train_ii_cpu, &X_train_ii_gpu]{ return unit_test_cpu_vs_gpu<uint32_t>(X_train_ii_cpu, X_train_ii_gpu); });
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}
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char file_feat[BUFFER_SIZE] = { 0 };
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sprintf(file_feat, OUT_DIR "/X_%s_feat_CPU.bin", label);
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if (fs::exists(file_feat)) {
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const np::Array<int32_t> X_feat = load<int32_t>(file_feat);
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sprintf(file_gpu, OUT_DIR "/X_%s_feat_GPU.bin", label);
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if (fs::exists(file_gpu)) {
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const np::Array<int32_t> X_feat_gpu = load<int32_t>(file_gpu);
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sprintf(tmp_title, "X_%s_feat", label);
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sprintf(title, "%-22s - CPU vs GPU", tmp_title);
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test_fnc(title, [&X_feat, &X_feat_gpu]{ return unit_test_cpu_vs_gpu<int32_t>(X_feat, X_feat_gpu); });
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}
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sprintf(file_cpu, OUT_DIR "/X_%s_feat_argsort_CPU.bin", label);
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np::Array<uint16_t> X_feat_argsort_cpu;
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uint8_t loaded = 0;
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if (fs::exists(file_cpu)) {
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X_feat_argsort_cpu = std::move(load<uint16_t>(file_cpu));
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++loaded;
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sprintf(tmp_title, "X_%s_feat_argsort", label);
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sprintf(title, "%-22s - CPU argsort", tmp_title);
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test_fnc(title, [&X_feat, &X_feat_argsort_cpu]{ return unit_test_argsort_2d<int32_t>(X_feat, X_feat_argsort_cpu); });
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}
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sprintf(file_gpu, OUT_DIR "/X_%s_feat_argsort_GPU.bin", label);
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np::Array<uint16_t> X_feat_argsort_gpu;
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if (fs::exists(file_gpu)) {
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X_feat_argsort_gpu = std::move(load<uint16_t>(file_gpu));
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++loaded;
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sprintf(tmp_title, "X_%s_feat_argsort", label);
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sprintf(title, "%-22s - GPU argsort", tmp_title);
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test_fnc(title, [&X_feat, &X_feat_argsort_gpu]{ return unit_test_argsort_2d<int32_t>(X_feat, X_feat_argsort_gpu); });
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}
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if (loaded == 2){
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sprintf(tmp_title, "X_%s_feat_argsort", label);
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sprintf(title, "%-22s - CPU vs GPU", tmp_title);
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test_fnc(title, [&X_feat_argsort_cpu, &X_feat_argsort_gpu]{ return unit_test_cpu_vs_gpu<uint16_t>(X_feat_argsort_cpu, X_feat_argsort_gpu); });
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}
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}
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}
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for (const size_t T : TS)
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for (const char* label : { "alphas", "final_classifiers" }) {
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sprintf(file_cpu, MODEL_DIR "/%s_%lu_CPU.bin", label, T);
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sprintf(file_gpu, MODEL_DIR "/%s_%lu_GPU.bin", label, T);
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if (fs::exists(file_cpu) && fs::exists(file_gpu)){
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const np::Array<float64_t> cpu = load<float64_t>(file_cpu);
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const np::Array<float64_t> gpu = load<float64_t>(file_gpu);
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sprintf(tmp_title, "%s_%ld", label, T);
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sprintf(title, "%-22s - CPU vs GPU", tmp_title);
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test_fnc(title, [&cpu, &gpu]{ return unit_test_cpu_vs_gpu<float64_t>(cpu, gpu); });
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}
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}
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const long long time_spent = duration_ns(perf_counter_ns() - unit_timestamp);
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snprintf(title, BUFFER_SIZE, "%ld/%ld", n_success, n_total);
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formatted_line(unit_gaps, "├", "┼", "─", "┤");
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formatted_row(unit_gaps, { "Unit testing summary", title, thousand_sep(time_spent).c_str(), format_time_ns(time_spent).c_str() });
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footer(unit_gaps);
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}
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int main(){
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setlocale(LC_NUMERIC, ""); // Allow proper number display
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const std::chrono::system_clock::time_point unit_timestamp = perf_counter_ns();
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const std::array<int32_t, 3> unit_gaps = { 27, -18, 29 };
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header({ "Unit testing", "Time spent (ns)", "Formatted time spent" }, unit_gaps);
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#if GPU_BOOSTED
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benchmark_function_void("Testing GPU capabilities 1D", unit_gaps[0], test_working, 50000);
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benchmark_function_void("Testing GPU capabilities 2D", unit_gaps[0], test_working_2d, 200, 500);
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benchmark_function_void("Testing GPU capabilities 3D", unit_gaps[0], test_working_3d, 30, 40, 500);
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#endif
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benchmark_function_void("Testing format_time", unit_gaps[0], format_time_test);
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benchmark_function_void("Testing format_time_ns", unit_gaps[0], format_time_ns_test);
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benchmark_function_void("Testing format_byte_size", unit_gaps[0], format_byte_size_test);
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benchmark_function_void("Testing thousand_sep", unit_gaps[0], thousand_sep_test);
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const long long time_spent = duration_ns(perf_counter_ns() - unit_timestamp);
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formatted_line(unit_gaps, "├", "┼", "─", "┤");
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formatted_row(unit_gaps, { "Unit testing summary", thousand_sep(time_spent).c_str(), format_time_ns(time_spent).c_str() });
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footer(unit_gaps);
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const auto [ X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test ] = preprocessing();
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const std::array<std::array<np::Array<float64_t>, 2>, TS.size()> models = train(X_train_feat, X_train_feat_argsort, y_train);
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testing_and_evaluating(models, X_train_feat, y_train, X_test_feat, y_test);
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unit_test();
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return EXIT_SUCCESS;
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}
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