#!/usr/bin/env python # Author: @saundersp from ViolaJones import train_viola_jones, classify_viola_jones #from toolbox import state_saver, pickle_multi_loader, format_time_ns, benchmark_function, unit_test_argsort_2d from toolbox import state_saver, format_time_ns, benchmark_function, unit_test_argsort_2d from toolbox import header, footer, formatted_row, formatted_line from toolbox_unit_test import format_time_test, format_time_ns_test from sklearn.metrics import accuracy_score, f1_score, confusion_matrix #from sklearn.feature_selection import SelectPercentile, f_classif from common import load_datasets, unit_test from ViolaJones import build_features # , get_best_anova_features from typing import Tuple, List from time import perf_counter_ns from os import makedirs import numpy as np from config import FORCE_REDO, COMPILE_WITH_C, GPU_BOOSTED, TS, SAVE_STATE, MODEL_DIR, __DEBUG if __DEBUG: from config import IDX_INSPECT, IDX_INSPECT_OFFSET if GPU_BOOSTED: from ViolaJonesGPU import apply_features, set_integral_image, argsort_2d label = 'GPU' if COMPILE_WITH_C else 'PGPU' # The parallel prefix sum doesn't use the whole GPU so numba output some annoying warnings, this disables it from numba import config config.CUDA_LOW_OCCUPANCY_WARNINGS = 0 else: from ViolaJonesCPU import apply_features, set_integral_image, argsort_2d label = 'CPU' if COMPILE_WITH_C else 'PY' def preprocessing() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Execute the preprocessing phase The preprocessing phase consist of the following steps : - Load the dataset - Calculate features - Calculate integral images - Apply features to images - Calculate argsort of the featured images Returns: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: Tuple containing in order : training features, training features sorted indexes, training labels, testing features, testing labels """ # Creating state saver folders if they don't exist already if SAVE_STATE: for folder_name in ['models', 'out']: makedirs(folder_name, exist_ok = True) preproc_timestamp = perf_counter_ns() preproc_gaps = [49, -18, 29] header(preproc_gaps, ['Preprocessing', 'Time spent (ns)', 'Formatted time spent']) X_train, y_train, X_test, y_test = state_saver('Loading sets', preproc_gaps[0], ['X_train', 'y_train', 'X_test', 'y_test'], load_datasets, FORCE_REDO, SAVE_STATE) if __DEBUG: print('X_train') print(X_train.shape) print(X_train[IDX_INSPECT]) print('X_test') print(X_test.shape) print(X_test[IDX_INSPECT]) print('y_train') print(y_train.shape) print(y_train[IDX_INSPECT: IDX_INSPECT + IDX_INSPECT_OFFSET]) print('y_test') print(y_test.shape) print(y_test[IDX_INSPECT: IDX_INSPECT + IDX_INSPECT_OFFSET]) feats = state_saver('Building features', preproc_gaps[0], 'feats', lambda: build_features(X_train.shape[1], X_train.shape[2]), FORCE_REDO, SAVE_STATE) if __DEBUG: print('feats') print(feats.shape) print(feats[IDX_INSPECT].ravel()) X_train_ii = state_saver(f'Converting training set to integral images ({label})', preproc_gaps[0], f'X_train_ii_{label}', lambda: set_integral_image(X_train), FORCE_REDO, SAVE_STATE) X_test_ii = state_saver(f'Converting testing set to integral images ({label})', preproc_gaps[0], f'X_test_ii_{label}', lambda: set_integral_image(X_test), FORCE_REDO, SAVE_STATE) if __DEBUG: print('X_train_ii') print(X_train_ii.shape) print(X_train_ii[IDX_INSPECT]) print('X_test_ii') print(X_test_ii.shape) print(X_test_ii[IDX_INSPECT]) X_train_feat = state_saver(f'Applying features to training set ({label})', preproc_gaps[0], f'X_train_feat_{label}', lambda: apply_features(feats, X_train_ii), FORCE_REDO, SAVE_STATE) X_test_feat = state_saver(f'Applying features to testing set ({label})', preproc_gaps[0], f'X_test_feat_{label}', lambda: apply_features(feats, X_test_ii), FORCE_REDO, SAVE_STATE) del X_train_ii, X_test_ii, feats if __DEBUG: print('X_train_feat') print(X_train_feat.shape) print(X_train_feat[IDX_INSPECT, : IDX_INSPECT_OFFSET]) print('X_test_feat') print(X_test_feat.shape) print(X_test_feat[IDX_INSPECT, : IDX_INSPECT_OFFSET]) #indices = state_saver('Selecting best features training set', 'indices', force_redo = FORCE_REDO, save_state = SAVE_STATE, # fnc = lambda: SelectPercentile(f_classif, percentile = 10).fit(X_train_feat.T, y_train).get_support(indices = True)) #indices = state_saver('Selecting best features training set', 'indices', force_redo = FORCE_REDO, save_state = SAVE_STATE, # fnc = lambda: get_best_anova_features(X_train_feat, y_train)) #indices = benchmark_function('Selecting best features (manual)', lambda: get_best_anova_features(X_train_feat, y_train)) #if __DEBUG: # print('indices') # print(indices.shape) # print(indices[IDX_INSPECT: IDX_INSPECT + IDX_INSPECT_OFFSET]) # assert indices.shape[0] == indices_new.shape[0], f'Indices length not equal : {indices.shape} != {indices_new.shape}' # assert (eq := indices == indices_new).all(), f'Indices not equal : {eq.sum() / indices.shape[0]}' # X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices] X_train_feat_argsort = state_saver(f'Precalculating training set argsort ({label})', preproc_gaps[0], f'X_train_feat_argsort_{label}', lambda: argsort_2d(X_train_feat), FORCE_REDO, SAVE_STATE) if __DEBUG: print('X_train_feat_argsort') print(X_train_feat_argsort.shape) print(X_train_feat_argsort[IDX_INSPECT, : IDX_INSPECT_OFFSET]) benchmark_function('Arg unit test', preproc_gaps[0], lambda: unit_test_argsort_2d(X_train_feat, X_train_feat_argsort)) X_test_feat_argsort = state_saver(f'Precalculating testing set argsort ({label})', preproc_gaps[0], f'X_test_feat_argsort_{label}', lambda: argsort_2d(X_test_feat), FORCE_REDO, SAVE_STATE) if __DEBUG: print('X_test_feat_argsort') print(X_test_feat_argsort.shape) print(X_test_feat_argsort[IDX_INSPECT, : IDX_INSPECT_OFFSET]) benchmark_function('Arg unit test', lambda: unit_test_argsort_2d(X_test_feat, X_test_feat_argsort)) time_spent = perf_counter_ns() - preproc_timestamp formatted_line(preproc_gaps, '├', '┼', '─', '┤') formatted_row(preproc_gaps, ['Preprocessing summary', f'{time_spent:,}', format_time_ns(time_spent)]) footer(preproc_gaps) return X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test def train(X_train_feat: np.ndarray, X_train_feat_argsort: np.ndarray, y_train: np.ndarray) -> List[np.ndarray]: """Train the weak classifiers. Args: X_train (np.ndarray): Training images X_train_feat_argsort (np.ndarray): Sorted indexes of the training images features y_train (np.ndarray): Training labels Returns: List[np.ndarray]: List of trained models """ training_timestamp = perf_counter_ns() training_gaps = [26, -18, 29] header(training_gaps, ['Training', 'Time spent (ns)', 'Formatted time spent']) models = [] for T in TS: alphas, final_classifiers = state_saver(f'ViolaJones T = {T:<4} ({label})', training_gaps[0], [f'alphas_{T}_{label}', f'final_classifiers_{T}_{label}'], lambda: train_viola_jones(T, X_train_feat, X_train_feat_argsort, y_train), FORCE_REDO, SAVE_STATE, MODEL_DIR) models.append([alphas, final_classifiers]) if __DEBUG: print('alphas') print(alphas) print('final_classifiers') print(final_classifiers) time_spent = perf_counter_ns() - training_timestamp formatted_line(training_gaps, '├', '┼', '─', '┤') formatted_row(training_gaps, ['Training summary', f'{time_spent:,}', format_time_ns(time_spent)]) footer(training_gaps) return models def testing_and_evaluating(models: List[np.ndarray], X_train_feat: np.ndarray, y_train: np.ndarray, X_test_feat: np.ndarray, y_test: np.ndarray) -> None: """Benchmark the trained classifiers on the training and testing sets. Args: models (List[np.ndarray]): List of trained models X_train_feat (np.ndarray): Training features y_train (np.ndarray): Training labels X_test_feat (np.ndarray): Testing features y_test (np.ndarray): Testing labels """ testing_gaps = [26, -19, 24, -19, 24] header(testing_gaps, ['Testing', 'Time spent (ns) (E)', 'Formatted time spent (E)', 'Time spent (ns) (T)', 'Formatted time spent (T)']) performances = [] total_train_timestamp = 0 total_test_timestamp = 0 for T, (alphas, final_classifiers) in zip(TS, models): s = perf_counter_ns() y_pred_train = classify_viola_jones(alphas, final_classifiers, X_train_feat) t_pred_train = perf_counter_ns() - s total_train_timestamp += t_pred_train e_acc = accuracy_score(y_train, y_pred_train) e_f1 = f1_score(y_train, y_pred_train) (_, e_FP), (e_FN, _) = confusion_matrix(y_train, y_pred_train) s = perf_counter_ns() y_pred_test = classify_viola_jones(alphas, final_classifiers, X_test_feat) t_pred_test = perf_counter_ns() - s total_test_timestamp += t_pred_test t_acc = accuracy_score(y_test, y_pred_test) t_f1 = f1_score(y_test, y_pred_test) (_, t_FP), (t_FN, _) = confusion_matrix(y_test, y_pred_test) performances.append((e_acc, e_f1, e_FN, e_FP, t_acc, t_f1, t_FN, t_FP)) formatted_row(testing_gaps, [f"{'ViolaJones T = ' + str(T):<19} {'(' + label + ')':<6}", f'{t_pred_train:,}', format_time_ns(t_pred_train), f'{t_pred_test:,}', format_time_ns(t_pred_test)]) formatted_line(testing_gaps, '├', '┼', '─', '┤') formatted_row(testing_gaps, ['Testing summary', f'{total_train_timestamp:,}', format_time_ns(total_train_timestamp), f'{total_test_timestamp:,}', format_time_ns(total_test_timestamp)]) footer(testing_gaps) evaluating_gaps = [19, 7, 6, 6, 6, 7, 6, 6, 6] header(evaluating_gaps, ['Evaluating', 'ACC (E)', 'F1 (E)', 'FN (E)', 'FP (E)', 'ACC (T)', 'F1 (T)', 'FN (T)', 'FP (T)']) for T, (e_acc, e_f1, e_FN, e_FP, t_acc, t_f1, t_FN, t_FP) in zip(TS, performances): print(f'│ ViolaJones T = {T:<4} │ {e_acc:>7.2%} │ {e_f1:>6.2f} │ {e_FN:>6,} │ {e_FP:>6,}', end = ' │ ') print(f'{t_acc:>7.2%} │ {t_f1:>6.2f} │ {t_FN:>6,} │ {t_FP:>6,} │') footer(evaluating_gaps) def main() -> None: unit_timestamp = perf_counter_ns() unit_gaps = [27, -18, 29] header(unit_gaps, ['Unit testing', 'Time spent (ns)', 'Formatted time spent']) benchmark_function('testing format_time', unit_gaps[0], format_time_test) benchmark_function('testing format_time_ns', unit_gaps[0], format_time_ns_test) time_spent = perf_counter_ns() - unit_timestamp formatted_line(unit_gaps, '├', '┼', '─', '┤') formatted_row(unit_gaps, ['Unit testing summary', f'{time_spent:,}', format_time_ns(time_spent)]) footer(unit_gaps) X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test = preprocessing() models = train(X_train_feat, X_train_feat_argsort, y_train) # X_train_feat, X_test_feat = pickle_multi_loader([f'X_train_feat_{label}', f'X_test_feat_{label}'], OUT_DIR) # indices = pickle_multi_loader(['indices'], OUT_DIR)[0] # X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices] testing_and_evaluating(models, X_train_feat, y_train, X_test_feat, y_test) unit_test(TS) if __name__ == '__main__': main()