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saundersp
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python/projet.py
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226
python/projet.py
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#!/usr/bin/env python
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# Author: @saundersp
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from ViolaJones import train_viola_jones, classify_viola_jones
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from toolbox import state_saver, picke_multi_loader, format_time_ns, benchmark_function, toolbox_unit_test, unit_test_argsort_2d
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from sklearn.metrics import accuracy_score, f1_score, confusion_matrix
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from sklearn.feature_selection import SelectPercentile, f_classif
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from common import load_datasets, unit_test
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from ViolaJones import build_features, get_best_anova_features
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from typing import Tuple
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from time import perf_counter_ns
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from os import makedirs
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import numpy as np
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#np.seterr(all = 'raise')
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from config import FORCE_REDO, COMPILE_WITH_C, GPU_BOOSTED, TS, SAVE_STATE
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if GPU_BOOSTED:
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from ViolaJonesGPU import apply_features, set_integral_image, argsort
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label = 'GPU' if COMPILE_WITH_C else 'PGPU'
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# The parallel prefix sum doesn't use the whole GPU so numba output some annoying warnings, this disables it
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from numba import config
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config.CUDA_LOW_OCCUPANCY_WARNINGS = 0
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else:
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from ViolaJonesCPU import apply_features, set_integral_image, argsort
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label = 'CPU' if COMPILE_WITH_C else 'PY'
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# FIXME Debug code
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# IDX_INSPECT = 0
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# IDX_INSPECT = 2
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IDX_INSPECT = 4548
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IDX_INSPECT_OFFSET = 100
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def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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"""Train the weak classifiers.
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Args:
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X_train (np.ndarray): Training images.
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X_test (np.ndarray): Testing Images.
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y_train (np.ndarray): Training labels.
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Returns:
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Tuple[np.ndarray, np.ndarray]: Training and testing features.
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"""
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feats = state_saver("Building features", "feats", lambda: build_features(X_train.shape[1], X_train.shape[2]), FORCE_REDO, SAVE_STATE)
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# FIXME Debug code
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# print("feats")
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# print(feats.shape)
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# print(feats[IDX_INSPECT].ravel())
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# return 0, 0
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X_train_ii = state_saver(f"Converting training set to integral images ({label})", f"X_train_ii_{label}",
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lambda: set_integral_image(X_train), FORCE_REDO, SAVE_STATE)
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X_test_ii = state_saver(f"Converting testing set to integral images ({label})", f"X_test_ii_{label}",
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lambda: set_integral_image(X_test), FORCE_REDO, SAVE_STATE)
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# FIXME Debug code
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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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# return 0, 0
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X_train_feat = state_saver(f"Applying features to training set ({label})", f"X_train_feat_{label}",
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lambda: apply_features(feats, X_train_ii), FORCE_REDO, SAVE_STATE)
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X_test_feat = state_saver(f"Applying features to testing set ({label})", f"X_test_feat_{label}",
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lambda: apply_features(feats, X_test_ii), FORCE_REDO, SAVE_STATE)
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del X_train_ii, X_test_ii, feats
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# FIXME Debug code
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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_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_OFFSET])
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# return 0, 0
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#indices = state_saver("Selecting best features training set", "indices", force_redo = True, save_state = SAVE_STATE,
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# fnc = lambda: SelectPercentile(f_classif, percentile = 10).fit(X_train_feat.T, y_train).get_support(indices = True))
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#indices = state_saver("Selecting best features training set", "indices", force_redo = FORCE_REDO, save_state = SAVE_STATE,
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# fnc = lambda: get_best_anova_features(X_train_feat, y_train))
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#indices = benchmark_function("Selecting best features (manual)", lambda: get_best_anova_features(X_train_feat, y_train))
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# FIXME Debug code
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# print("indices")
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# print(indices.shape)
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# print(indices[IDX_INSPECT: IDX_INSPECT + IDX_INSPECT_OFFSET])
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# assert indices.shape[0] == indices_new.shape[0], f"Indices length not equal : {indices.shape} != {indices_new.shape}"
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# assert (eq := indices == indices_new).all(), f"Indices not equal : {eq.sum() / indices.shape[0]}"
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# return 0, 0
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# X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices]
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#return 0, 0
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X_train_feat_argsort = state_saver(f"Precalculating training set argsort ({label})", f"X_train_feat_argsort_{label}",
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lambda: argsort(X_train_feat), FORCE_REDO, SAVE_STATE)
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# FIXME Debug code
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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_OFFSET])
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# benchmark_function("Arg unit test", lambda: unit_test_argsort_2d(X_train_feat, X_train_feat_argsort))
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# return 0, 0
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# X_test_feat_argsort = state_saver(f"Precalculating testing set argsort ({label})", f"X_test_feat_argsort_{label}",
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# lambda: argsort(X_test_feat), True, False)
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# FIXME Debug code
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# print("X_test_feat_argsort")
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# print(X_test_feat_argsort.shape)
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# print(X_test_feat_argsort[IDX_INSPECT, : IDX_INSPECT_OFFSET])
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# benchmark_function("Arg unit test", lambda: unit_test_argsort_2d(X_test_feat, X_test_feat_argsort))
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# return 0, 0
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# del X_test_feat_argsort
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print(f"\n| {'Training':<49} | {'Time spent (ns)':<17} | {'Formatted time spent':<29} |\n|{'-'*51}|{'-'*19}|{'-'*31}|")
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for T in TS:
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# alphas, final_classifiers = state_saver(f"ViolaJones T = {T:<3} ({label})", [f"alphas_{T}_{label}", f"final_classifiers_{T}_{label}"],
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state_saver(f"ViolaJones T = {T:<4} ({label})", [f"alphas_{T}_{label}", f"final_classifiers_{T}_{label}"],
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lambda: train_viola_jones(T, X_train_feat, X_train_feat_argsort, y_train), FORCE_REDO, SAVE_STATE, "./models")
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# FIXME Debug code
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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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return X_train_feat, X_test_feat
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def bench_accuracy(label, X_train_feat: np.ndarray, X_test_feat: np.ndarray, y_train: np.ndarray, y_test: np.ndarray) -> None:
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"""Benchmark the trained classifiers on the training and testing sets.
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Args:
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X_train_feat (np.ndarray): Training features.
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X_test_feat (np.ndarray): Testing features.
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y_train (np.ndarray): Training labels.
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y_test (np.ndarray): Testing labels.
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"""
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print(f"\n| {'Testing':<26} | Time spent (ns) (E) | {'Formatted time spent (E)':<29}", end = " | ")
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print(f"Time spent (ns) (T) | {'Formatted time spent (T)':<29} |")
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print(f"|{'-'*28}|{'-'*21}|{'-'*31}|{'-'*21}|{'-'*31}|")
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perfs = []
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for T in TS:
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(alphas, final_classifiers) = picke_multi_loader([f"alphas_{T}_{label}", f"final_classifiers_{T}_{label}"])
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s = perf_counter_ns()
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y_pred_train = classify_viola_jones(alphas, final_classifiers, X_train_feat)
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t_pred_train = perf_counter_ns() - s
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e_acc = accuracy_score(y_train, y_pred_train)
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e_f1 = f1_score(y_train, y_pred_train)
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(_, e_FP), (e_FN, _) = confusion_matrix(y_train, y_pred_train)
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s = perf_counter_ns()
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y_pred_test = classify_viola_jones(alphas, final_classifiers, X_test_feat)
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t_pred_test = perf_counter_ns() - s
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t_acc = accuracy_score(y_test, y_pred_test)
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t_f1 = f1_score(y_test, y_pred_test)
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(_, t_FP), (t_FN, _) = confusion_matrix(y_test, y_pred_test)
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perfs.append((e_acc, e_f1, e_FN, e_FP, t_acc, t_f1, t_FN, t_FP))
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print(f"| {'ViolaJones T = ' + str(T):<19} {'(' + label + ')':<6}", end = " | ")
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print(f"{t_pred_train:>19,} | {format_time_ns(t_pred_train):<29}", end = " | ")
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print(f"{t_pred_test:>19,} | {format_time_ns(t_pred_test):<29} |")
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print(f"\n| {'Evaluating':<19} | ACC (E) | F1 (E) | FN (E) | FP (E) | ACC (T) | F1 (T) | FN (T) | FP (T) | ")
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print(f"|{'-'*21}|{'-'*9}|{'-'*8}|{'-'*8}|{'-'*8}|{'-'*9}|{'-'*8}|{'-'*8}|{'-'*8}|")
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for T, (e_acc, e_f1, e_FN, e_FP, t_acc, t_f1, t_FN, t_FP) in zip(TS, perfs):
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print(f"| {'ViolaJones T = ' + str(T):<19} | {e_acc:>7.2%} | {e_f1:>6.2f} | {e_FN:>6,} | {e_FP:>6,}", end = " | ")
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print(f"{t_acc:>7.2%} | {t_f1:>6.2f} | {t_FN:>6,} | {t_FP:>6,} |")
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def _main_() -> None:
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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 folder_name in ["models", "out"]:
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makedirs(folder_name, exist_ok = True)
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print(f"| {'Preprocessing':<49} | {'Time spent (ns)':<17} | {'Formatted time spent':<29} |\n|{'-'*51}|{'-'*19}|{'-'*31}|")
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X_train, y_train, X_test, y_test = state_saver("Loading sets", ["X_train", "y_train", "X_test", "y_test"],
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load_datasets, FORCE_REDO, SAVE_STATE)
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# FIXME Debug option (image width * log_10(length) + extra characters)
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# np.set_printoptions(linewidth = 19 * 6 + 3)
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# FIXME Debug code
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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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# return
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X_train_feat, X_test_feat = bench_train(X_train, X_test, y_train)
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# FIXME Debug code
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# return
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# X_train_feat, X_test_feat = picke_multi_loader([f"X_train_feat_{label}", f"X_test_feat_{label}"], "./out")
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# indices = picke_multi_loader(["indices"], "./out")[0]
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# X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices]
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bench_accuracy(label, X_train_feat, X_test_feat, y_train, y_test)
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if __name__ == "__main__":
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#toolbox_unit_test()
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_main_()
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# Only execute unit test after having trained the specified labels
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unit_test(TS, ["GPU", "CPU", "PY", "PGPU"])
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pass
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