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python : clearer main algorithm progression && revamp final test display

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saundersp 2023-07-25 12:41:03 +02:00
parent b507e1b0fd
commit 5371c6f201
2 changed files with 107 additions and 117 deletions
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115
python/common.py
View File

@ -2,9 +2,9 @@ from toolbox import picke_multi_loader, format_time_ns, unit_test_argsort_2d
from typing import List, Tuple from typing import List, Tuple
from time import perf_counter_ns from time import perf_counter_ns
import numpy as np import numpy as np
from config import OUT_DIR, DATA_DIR from config import OUT_DIR, DATA_DIR, __DEBUG
def unit_test(TS: List[int], labels: List[str] = ["CPU", "GPU"], tol: float = 1e-8) -> None: def unit_test(TS: List[int], labels: List[str] = ["CPU", "GPU", "PY", "PGPU"], tol: float = 1e-8) -> None:
"""Test if the each result is equals to other devices. """Test if the each result is equals to other devices.
Given ViolaJones is a deterministic algorithm, the results no matter the device should be the same Given ViolaJones is a deterministic algorithm, the results no matter the device should be the same
@ -12,83 +12,78 @@ def unit_test(TS: List[int], labels: List[str] = ["CPU", "GPU"], tol: float = 1e
Args: Args:
TS (List[int]): Number of trained weak classifiers. TS (List[int]): Number of trained weak classifiers.
labels (List[str], optional): List of the trained device names. Defaults to ["CPU", "GPU"]. labels (List[str], optional): List of the trained device names. Defaults to ["CPU", "GPU", "PY", "PGPU"] (see config.py for more info).
tol (float, optional): Float difference tolerance. Defaults to 1e-8. tol (float, optional): Float difference tolerance. Defaults to 1e-8.
""" """
if len(labels) < 2: if len(labels) < 2:
return print("Not enough devices to test") return print("Not enough devices to test")
fnc_s = perf_counter_ns()
n_total= 0
n_success = 0
print(f"\n| {'Unit testing':<37} | {'Test state':<10} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |") print(f"\n| {'Unit testing':<37} | {'Test state':<10} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |")
print(f"|{'-'*39}|{'-'*12}|{'-'*20}|{'-'*31}|") print(f"|{'-'*39}|{'-'*12}|{'-'*20}|{'-'*31}|")
for filename in ["X_train_feat", "X_test_feat", "X_train_ii", "X_test_ii"]: fnc_s = perf_counter_ns()
print(f"{filename}...", end = "\r") n_total = 0
bs = picke_multi_loader([f"{filename}_{label}" for label in labels], OUT_DIR) n_success = 0
for i, (b1, l1) in enumerate(zip(bs, labels)): def test_fnc(title, fnc):
if b1 is None: nonlocal n_total, n_success
#print(f"| {filename:<22} - {l1:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |") n_total += 1
continue s = perf_counter_ns()
for j, (b2, l2) in enumerate(zip(bs, labels)): state = fnc()
if i >= j: e = perf_counter_ns() - s
continue if state:
if b2 is None: print(f"| {title:<37} | {'Passed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
#print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |") n_success += 1
continue else:
n_total += 1 print(f"| {title:<37} | {'Failed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
s = perf_counter_ns()
state = np.abs(b1 - b2).mean() < tol
e = perf_counter_ns() - s
if state:
print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Passed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
n_success += 1
else:
print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Failed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
for filename, featname in zip(["X_train_feat_argsort", "X_test_feat_argsort"], ["X_train_feat", "X_test_feat"]): for set_name in ["train", "test"]:
print(f"Loading {filename}...", end = "\r") for filename in ["ii", "feat"]:
title = f"X_{set_name}_{filename}"
print(f"{filename}...", end = "\r")
bs = picke_multi_loader([f"{title}_{label}" for label in labels], OUT_DIR)
for i, (b1, l1) in enumerate(zip(bs, labels)):
if b1 is None:
if __DEBUG:
print(f"| {title:<22} - {l1:<12} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
continue
for j, (b2, l2) in enumerate(zip(bs, labels)):
if i >= j:
continue
if b2 is None:
if __DEBUG:
print(f"| {title:<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
continue
test_fnc(f"{title:<22} - {l1:<4} vs {l2:<4}", lambda: np.abs(b1 - b2).mean() < tol)
title = f"X_{set_name}_feat_argsort"
print(f"Loading {title}...", end = "\r")
feat = None feat = None
bs = [] bs = []
for label in labels: for label in labels:
if feat is None: if feat is None:
feat_tmp = picke_multi_loader([f"{featname}_{label}"], OUT_DIR)[0] feat_tmp = picke_multi_loader([f"X_{set_name}_feat_{label}"], OUT_DIR)[0]
if feat_tmp is not None: if feat_tmp is not None:
feat = feat_tmp feat = feat_tmp
bs.append(picke_multi_loader([f"{filename}_{label}"], OUT_DIR)[0]) bs.append(picke_multi_loader([f"{title}_{label}"], OUT_DIR)[0])
for i, (b1, l1) in enumerate(zip(bs, labels)): for i, (b1, l1) in enumerate(zip(bs, labels)):
if b1 is None: if b1 is None:
#print(f"| {filename:<22} - {l1:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |") if __DEBUG:
print(f"| {title:<22} - {l1:<12} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
continue continue
if feat is not None: if feat is not None:
n_total += 1 test_fnc(f"{title:<22} - {l1:<4} argsort", lambda: unit_test_argsort_2d(feat, b1))
s = perf_counter_ns()
state = unit_test_argsort_2d(feat, b1)
e = perf_counter_ns() - s
if state:
print(f"| {filename:<22} - {l1:<4} argsort | {'Passed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
n_success += 1
else:
print(f"| {filename:<22} - {l1:<4} argsort | {'Failed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
for j, (b2, l2) in enumerate(zip(bs, labels)): for j, (b2, l2) in enumerate(zip(bs, labels)):
if i >= j: if i >= j:
continue continue
if b2 is None: if b2 is None:
#print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |") if __DEBUG:
print(f"| {title:<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
continue continue
n_total += 1 test_fnc(f"{title:<22} - {l1:<4} vs {l2:<4}", lambda: np.abs(b1 - b2).mean() < tol)
s = perf_counter_ns()
state = np.abs(b1 - b2).mean() < tol
e = perf_counter_ns() - s
if state:
print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Passed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
n_success += 1
else:
print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Failed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
for T in TS: for T in TS:
for filename in ["alphas", "final_classifiers"]: for filename in ["alphas", "final_classifiers"]:
@ -97,23 +92,17 @@ def unit_test(TS: List[int], labels: List[str] = ["CPU", "GPU"], tol: float = 1e
for i, (b1, l1) in enumerate(zip(bs, labels)): for i, (b1, l1) in enumerate(zip(bs, labels)):
if b1 is None: if b1 is None:
#print(f"| {filename + '_' + str(T):<22} - {l1:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |") if __DEBUG:
print(f"| {filename + '_' + str(T):<22} - {l1:<12} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
continue continue
for j, (b2, l2) in enumerate(zip(bs, labels)): for j, (b2, l2) in enumerate(zip(bs, labels)):
if i >= j: if i >= j:
continue continue
if b2 is None: if b2 is None:
#print(f"| {filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |") if __DEBUG:
print(f"| {filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
continue continue
n_total += 1 test_fnc(f"{filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4}", lambda: np.abs(b1 - b2).mean() < tol)
s = perf_counter_ns()
state = np.abs(b1 - b2).mean() < tol
e = perf_counter_ns() - s
if state:
print(f"| {filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4} | {'Passed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
n_success += 1
else:
print(f"| {filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4} | {'Failed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
print(f"|{'-'*39}|{'-'*12}|{'-'*20}|{'-'*31}|") print(f"|{'-'*39}|{'-'*12}|{'-'*20}|{'-'*31}|")
e = perf_counter_ns() - fnc_s e = perf_counter_ns() - fnc_s

109
python/projet.py
View File

@ -26,17 +26,36 @@ else:
from ViolaJonesCPU import apply_features, set_integral_image, argsort from ViolaJonesCPU import apply_features, set_integral_image, argsort
label = 'CPU' if COMPILE_WITH_C else 'PY' label = 'CPU' if COMPILE_WITH_C else 'PY'
def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: def preprocessing() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Train the weak classifiers. """Load the dataset, calculate features and integral images, apply features to images and calculate argsort of the featured images.
Args:
X_train (np.ndarray): Training images.
X_test (np.ndarray): Testing Images.
y_train (np.ndarray): Training labels.
Returns: Returns:
Tuple[np.ndarray, np.ndarray]: Training and testing features. Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test
""" """
# 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)
print(f"| {'Preprocessing':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |\n|{'-'*51}|{'-'*20}|{'-'*31}|")
X_train, y_train, X_test, y_test = state_saver("Loading sets", ["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", "feats", lambda: build_features(X_train.shape[1], X_train.shape[2]), FORCE_REDO, SAVE_STATE) feats = state_saver("Building features", "feats", lambda: build_features(X_train.shape[1], X_train.shape[2]), FORCE_REDO, SAVE_STATE)
if __DEBUG: if __DEBUG:
@ -77,13 +96,12 @@ def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) ->
# fnc = lambda: get_best_anova_features(X_train_feat, y_train)) # 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)) #indices = benchmark_function("Selecting best features (manual)", lambda: get_best_anova_features(X_train_feat, y_train))
# FIXME Debug code #if __DEBUG:
# print("indices") # print("indices")
# print(indices.shape) # print(indices.shape)
# print(indices[IDX_INSPECT: IDX_INSPECT + IDX_INSPECT_OFFSET]) # 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 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]}" # assert (eq := indices == indices_new).all(), f"Indices not equal : {eq.sum() / indices.shape[0]}"
# return 0, 0
# X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices] # X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices]
@ -104,8 +122,17 @@ def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) ->
print(X_test_feat_argsort.shape) print(X_test_feat_argsort.shape)
print(X_test_feat_argsort[IDX_INSPECT, : IDX_INSPECT_OFFSET]) 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)) benchmark_function("Arg unit test", lambda: unit_test_argsort_2d(X_test_feat, X_test_feat_argsort))
del X_test_feat_argsort
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) -> None:
"""Train the weak classifiers.
Args:
X_train (np.ndarray): Training images.
X_test (np.ndarray): Testing Images.
y_train (np.ndarray): Training labels.
"""
print(f"\n| {'Training':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |\n|{'-'*51}|{'-'*20}|{'-'*31}|") print(f"\n| {'Training':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |\n|{'-'*51}|{'-'*20}|{'-'*31}|")
for T in TS: for T in TS:
@ -117,15 +144,13 @@ def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) ->
print("final_classifiers") print("final_classifiers")
print(final_classifiers) print(final_classifiers)
return X_train_feat, X_test_feat def testing_and_evaluating(X_train_feat: np.ndarray, y_train: np.ndarray, X_test_feat: np.ndarray, y_test: np.ndarray) -> None:
def bench_accuracy(label, X_train_feat: np.ndarray, X_test_feat: np.ndarray, y_train: np.ndarray, y_test: np.ndarray) -> None:
"""Benchmark the trained classifiers on the training and testing sets. """Benchmark the trained classifiers on the training and testing sets.
Args: Args:
X_train_feat (np.ndarray): Training features. X_train_feat (np.ndarray): Training features.
X_test_feat (np.ndarray): Testing features.
y_train (np.ndarray): Training labels. y_train (np.ndarray): Training labels.
X_test_feat (np.ndarray): Testing features.
y_test (np.ndarray): Testing labels. y_test (np.ndarray): Testing labels.
""" """
print(f"\n| {'Testing':<26} | Time spent (ns) (E) | {'Formatted time spent (E)':<29}", end = " | ") print(f"\n| {'Testing':<26} | Time spent (ns) (E) | {'Formatted time spent (E)':<29}", end = " | ")
@ -162,45 +187,21 @@ def bench_accuracy(label, X_train_feat: np.ndarray, X_test_feat: np.ndarray, y_t
print(f"| {'ViolaJones T = ' + str(T):<19} | {e_acc:>7.2%} | {e_f1:>6.2f} | {e_FN:>6,} | {e_FP:>6,}", end = " | ") print(f"| {'ViolaJones T = ' + str(T):<19} | {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,} |") print(f"{t_acc:>7.2%} | {t_f1:>6.2f} | {t_FN:>6,} | {t_FP:>6,} |")
def _main_() -> None: def main() -> None:
print(f"| {'Unit testing':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |")
print(f"|{'-'*51}|{'-'*20}|{'-'*31}|")
benchmark_function("Testing format_time_ns", format_time_ns_test)
print()
# Creating state saver folders if they don't exist already X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test = preprocessing()
if SAVE_STATE: train(X_train_feat, X_train_feat_argsort, y_train)
for folder_name in ["models", "out"]:
makedirs(folder_name, exist_ok = True)
print(f"| {'Preprocessing':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |\n|{'-'*51}|{'-'*20}|{'-'*31}|")
X_train, y_train, X_test, y_test = state_saver("Loading sets", ["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])
X_train_feat, X_test_feat = bench_train(X_train, X_test, y_train)
# X_train_feat, X_test_feat = picke_multi_loader([f"X_train_feat_{label}", f"X_test_feat_{label}"], OUT_DIR) # X_train_feat, X_test_feat = picke_multi_loader([f"X_train_feat_{label}", f"X_test_feat_{label}"], OUT_DIR)
# indices = picke_multi_loader(["indices"], OUT_DIR)[0] # indices = picke_multi_loader(["indices"], OUT_DIR)[0]
# X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices] # X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices]
bench_accuracy(label, X_train_feat, X_test_feat, y_train, y_test) testing_and_evaluating(X_train_feat, y_train, X_test_feat, y_test)
unit_test(TS)
if __name__ == "__main__": if __name__ == "__main__":
_main_() main()
if __DEBUG:
toolbox_unit_test()
# Only execute unit test after having trained the specified labels
unit_test(TS, ["GPU", "CPU", "PY", "PGPU"])
pass