Moved DEBUG option to config files
This commit is contained in:
saundersp
140
python/projet.py
140
python/projet.py
@ -12,9 +12,9 @@ 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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from config import FORCE_REDO, COMPILE_WITH_C, GPU_BOOSTED, TS, SAVE_STATE, MODEL_DIR, __DEBUG
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if __DEBUG:
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from config import IDX_INSPECT, IDX_INSPECT_OFFSET
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if GPU_BOOSTED:
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from ViolaJonesGPU import apply_features, set_integral_image, argsort
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@ -26,12 +26,6 @@ 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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@ -45,25 +39,23 @@ def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) ->
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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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if __DEBUG:
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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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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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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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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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@ -71,14 +63,13 @@ def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) ->
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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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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_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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#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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@ -96,40 +87,35 @@ def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) ->
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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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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_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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# 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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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), FORCE_REDO, SAVE_STATE)
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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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if __DEBUG:
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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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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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print(f"\n| {'Training':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |\n|{'-'*51}|{'-'*20}|{'-'*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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alphas, final_classifiers = state_saver(f"ViolaJones T = {T:<3} ({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, MODEL_DIR)
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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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return X_train_feat, X_test_feat
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@ -183,43 +169,37 @@ def _main_() -> None:
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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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print(f"| {'Preprocessing':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |\n|{'-'*51}|{'-'*20}|{'-'*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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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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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 = picke_multi_loader([f"X_train_feat_{label}", f"X_test_feat_{label}"], OUT_DIR)
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# indices = picke_multi_loader(["indices"], OUT_DIR)[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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if __DEBUG:
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toolbox_unit_test()
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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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