python : clearer main algorithm progression && revamp final test display
This commit is contained in:
saundersp
parent
b507e1b0fd
commit
5371c6f201
115
python/common.py
115
python/common.py
@ -2,9 +2,9 @@ from toolbox import picke_multi_loader, format_time_ns, unit_test_argsort_2d
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from typing import List, Tuple
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from time import perf_counter_ns
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import numpy as np
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from config import OUT_DIR, DATA_DIR
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from config import OUT_DIR, DATA_DIR, __DEBUG
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def unit_test(TS: List[int], labels: List[str] = ["CPU", "GPU"], tol: float = 1e-8) -> None:
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def unit_test(TS: List[int], labels: List[str] = ["CPU", "GPU", "PY", "PGPU"], tol: float = 1e-8) -> None:
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"""Test if the each result is equals to other devices.
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Given ViolaJones is a deterministic algorithm, the results no matter the device should be the same
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@ -12,83 +12,78 @@ def unit_test(TS: List[int], labels: List[str] = ["CPU", "GPU"], tol: float = 1e
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Args:
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TS (List[int]): Number of trained weak classifiers.
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labels (List[str], optional): List of the trained device names. Defaults to ["CPU", "GPU"].
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labels (List[str], optional): List of the trained device names. Defaults to ["CPU", "GPU", "PY", "PGPU"] (see config.py for more info).
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tol (float, optional): Float difference tolerance. Defaults to 1e-8.
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"""
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if len(labels) < 2:
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return print("Not enough devices to test")
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fnc_s = perf_counter_ns()
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n_total= 0
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n_success = 0
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print(f"\n| {'Unit testing':<37} | {'Test state':<10} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |")
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print(f"|{'-'*39}|{'-'*12}|{'-'*20}|{'-'*31}|")
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for filename in ["X_train_feat", "X_test_feat", "X_train_ii", "X_test_ii"]:
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print(f"{filename}...", end = "\r")
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bs = picke_multi_loader([f"{filename}_{label}" for label in labels], OUT_DIR)
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fnc_s = perf_counter_ns()
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n_total = 0
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n_success = 0
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for i, (b1, l1) in enumerate(zip(bs, labels)):
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if b1 is None:
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#print(f"| {filename:<22} - {l1:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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continue
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for j, (b2, l2) in enumerate(zip(bs, labels)):
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if i >= j:
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continue
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if b2 is None:
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#print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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continue
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n_total += 1
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s = perf_counter_ns()
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state = np.abs(b1 - b2).mean() < tol
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e = perf_counter_ns() - s
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if state:
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print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Passed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
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n_success += 1
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else:
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print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Failed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
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def test_fnc(title, fnc):
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nonlocal n_total, n_success
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n_total += 1
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s = perf_counter_ns()
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state = fnc()
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e = perf_counter_ns() - s
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if state:
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print(f"| {title:<37} | {'Passed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
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n_success += 1
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else:
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print(f"| {title:<37} | {'Failed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
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for filename, featname in zip(["X_train_feat_argsort", "X_test_feat_argsort"], ["X_train_feat", "X_test_feat"]):
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print(f"Loading {filename}...", end = "\r")
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for set_name in ["train", "test"]:
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for filename in ["ii", "feat"]:
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title = f"X_{set_name}_{filename}"
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print(f"{filename}...", end = "\r")
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bs = picke_multi_loader([f"{title}_{label}" for label in labels], OUT_DIR)
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for i, (b1, l1) in enumerate(zip(bs, labels)):
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if b1 is None:
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if __DEBUG:
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print(f"| {title:<22} - {l1:<12} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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continue
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for j, (b2, l2) in enumerate(zip(bs, labels)):
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if i >= j:
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continue
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if b2 is None:
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if __DEBUG:
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print(f"| {title:<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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continue
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test_fnc(f"{title:<22} - {l1:<4} vs {l2:<4}", lambda: np.abs(b1 - b2).mean() < tol)
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title = f"X_{set_name}_feat_argsort"
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print(f"Loading {title}...", end = "\r")
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feat = None
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bs = []
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for label in labels:
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if feat is None:
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feat_tmp = picke_multi_loader([f"{featname}_{label}"], OUT_DIR)[0]
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feat_tmp = picke_multi_loader([f"X_{set_name}_feat_{label}"], OUT_DIR)[0]
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if feat_tmp is not None:
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feat = feat_tmp
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bs.append(picke_multi_loader([f"{filename}_{label}"], OUT_DIR)[0])
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bs.append(picke_multi_loader([f"{title}_{label}"], OUT_DIR)[0])
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for i, (b1, l1) in enumerate(zip(bs, labels)):
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if b1 is None:
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#print(f"| {filename:<22} - {l1:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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if __DEBUG:
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print(f"| {title:<22} - {l1:<12} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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continue
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if feat is not None:
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n_total += 1
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s = perf_counter_ns()
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state = unit_test_argsort_2d(feat, b1)
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e = perf_counter_ns() - s
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if state:
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print(f"| {filename:<22} - {l1:<4} argsort | {'Passed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
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n_success += 1
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else:
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print(f"| {filename:<22} - {l1:<4} argsort | {'Failed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
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test_fnc(f"{title:<22} - {l1:<4} argsort", lambda: unit_test_argsort_2d(feat, b1))
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for j, (b2, l2) in enumerate(zip(bs, labels)):
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if i >= j:
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continue
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if b2 is None:
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#print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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if __DEBUG:
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print(f"| {title:<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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continue
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n_total += 1
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s = perf_counter_ns()
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state = np.abs(b1 - b2).mean() < tol
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e = perf_counter_ns() - s
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if state:
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print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Passed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
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n_success += 1
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else:
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print(f"| {filename:<22} - {l1:<4} vs {l2:<4} | {'Failed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
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test_fnc(f"{title:<22} - {l1:<4} vs {l2:<4}", lambda: np.abs(b1 - b2).mean() < tol)
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for T in TS:
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for filename in ["alphas", "final_classifiers"]:
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@ -97,23 +92,17 @@ def unit_test(TS: List[int], labels: List[str] = ["CPU", "GPU"], tol: float = 1e
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for i, (b1, l1) in enumerate(zip(bs, labels)):
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if b1 is None:
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#print(f"| {filename + '_' + str(T):<22} - {l1:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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if __DEBUG:
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print(f"| {filename + '_' + str(T):<22} - {l1:<12} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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continue
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for j, (b2, l2) in enumerate(zip(bs, labels)):
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if i >= j:
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continue
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if b2 is None:
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#print(f"| {filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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if __DEBUG:
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print(f"| {filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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continue
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n_total += 1
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s = perf_counter_ns()
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state = np.abs(b1 - b2).mean() < tol
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e = perf_counter_ns() - s
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if state:
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print(f"| {filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4} | {'Passed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
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n_success += 1
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else:
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print(f"| {filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4} | {'Failed':>10} | {e:>18,} | {format_time_ns(e):<29} |")
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test_fnc(f"{filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4}", lambda: np.abs(b1 - b2).mean() < tol)
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print(f"|{'-'*39}|{'-'*12}|{'-'*20}|{'-'*31}|")
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e = perf_counter_ns() - fnc_s
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109
python/projet.py
109
python/projet.py
@ -26,17 +26,36 @@ 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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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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def preprocessing() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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"""Load the dataset, calculate features and integral images, apply features to images and calculate argsort of the featured images.
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Returns:
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Tuple[np.ndarray, np.ndarray]: Training and testing features.
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Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test
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"""
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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)':<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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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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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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if __DEBUG:
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@ -77,13 +96,12 @@ def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) ->
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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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#if __DEBUG:
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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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# X_train_feat, X_test_feat = X_train_feat[indices], X_test_feat[indices]
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@ -104,8 +122,17 @@ def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) ->
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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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return X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test
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def train(X_train_feat: np.ndarray, X_train_feat_argsort: np.ndarray, y_train: np.ndarray) -> None:
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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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"""
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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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@ -117,15 +144,13 @@ def bench_train(X_train: np.ndarray, X_test: np.ndarray, y_train: np.ndarray) ->
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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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def testing_and_evaluating(X_train_feat: np.ndarray, y_train: np.ndarray, X_test_feat: 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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X_test_feat (np.ndarray): Testing features.
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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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@ -162,45 +187,21 @@ def bench_accuracy(label, X_train_feat: np.ndarray, X_test_feat: np.ndarray, y_t
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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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def main() -> None:
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print(f"| {'Unit testing':<49} | {'Time spent (ns)':<18} | {'Formatted time spent':<29} |")
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print(f"|{'-'*51}|{'-'*20}|{'-'*31}|")
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benchmark_function("Testing format_time_ns", format_time_ns_test)
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print()
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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)':<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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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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X_train_feat, X_train_feat_argsort, y_train, X_test_feat, y_test = preprocessing()
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train(X_train_feat, X_train_feat_argsort, y_train)
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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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testing_and_evaluating(X_train_feat, y_train, X_test_feat, y_test)
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unit_test(TS)
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if __name__ == "__main__":
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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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pass
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main()
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