python : Updated code with better display, documentation and format_time
This commit is contained in:
saundersp
@ -1,29 +1,29 @@
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from toolbox import picke_multi_loader, format_time_ns, unit_test_argsort_2d
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from toolbox import pickle_multi_loader, format_time_ns, unit_test_argsort_2d, header, footer, formatted_line, formatted_row
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from typing import List, Tuple
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from time import perf_counter_ns
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from sys import stderr
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import numpy as np
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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", "PY", "PGPU"], 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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Given ViolaJones is a fully deterministic algorithm. The results, regardless the device, should be the same
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(given the floating point fluctuations), this function check this assertion.
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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", "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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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', '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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return print('Not enough devices to test')
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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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unit_gaps = [37, -10, -18, 29]
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header(unit_gaps, ['Unit testing', 'Test state', 'Time spent (ns)', 'Formatted time spent'])
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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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unit_timestamp = perf_counter_ns()
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n_total, n_success = 0, 0
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def test_fnc(title, fnc):
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nonlocal n_total, n_success
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@ -32,96 +32,102 @@ def unit_test(TS: List[int], labels: List[str] = ["CPU", "GPU", "PY", "PGPU"], t
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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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formatted_row(unit_gaps, [title, 'Passed', f'{e:,}', format_time_ns(e)])
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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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formatted_row(unit_gaps, [title, 'Failed', f'{e:,}', format_time_ns(e)])
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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 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}...', file = stderr, end = '\r')
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bs = pickle_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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formatted_row(unit_gaps, [f'{title:<22} - {l1:<12}', 'Skipped', 'None', 'None'])
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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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formatted_row(unit_gaps, [f'{title:<22} - {l1:<4} vs {l2:<4}', 'Skipped', 'None', 'None'])
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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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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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title = f'X_{set_name}_feat_argsort'
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print(f'Loading {title}...', file = stderr, 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"X_{set_name}_feat_{label}"], OUT_DIR)[0]
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feat_tmp = pickle_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"{title}_{label}"], OUT_DIR)[0])
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bs.append(pickle_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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if __DEBUG:
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print(f"| {title:<22} - {l1:<12} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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formatted_row(unit_gaps, [f'{title:<22} - {l1:<12}', 'Skipped', 'None', 'None'])
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continue
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if feat is not None:
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test_fnc(f"{title:<22} - {l1:<4} argsort", lambda: unit_test_argsort_2d(feat, b1))
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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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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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formatted_row(unit_gaps, [f'{title:<22} - {l1:<4} vs {l2:<4}', 'Skipped', 'None', 'None'])
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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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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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print(f"{filename}_{T}...", end = "\r")
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bs = picke_multi_loader([f"{filename}_{T}_{label}" for label in labels])
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for filename in ['alphas', 'final_classifiers']:
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print(f'{filename}_{T}...', file = stderr, end = '\r')
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bs = pickle_multi_loader([f'{filename}_{T}_{label}' for label in labels])
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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"| {filename + '_' + str(T):<22} - {l1:<12} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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formatted_row(unit_gaps, [f"{filename + '_' + str(T):<22} - {l1:<12}", 'Skipped', 'None', 'None'])
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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"| {filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4} | {'Skipped':>10} | {'None':>18} | {'None':<29} |")
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formatted_row(unit_gaps, [f"{filename + '_' + str(T):<22} - {l1:<4} vs {l2:<4}", 'Skipped', 'None', 'None'])
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continue
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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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print(f"| {'Unit testing summary':<37} | {str(n_success) + '/' + str(n_total):>10} | {e:>18,} | {format_time_ns(e):<29} |")
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time_spent = perf_counter_ns() - unit_timestamp
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if n_total == 0:
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formatted_row(unit_gaps, ['Unit testing summary', 'No files', f'{time_spent:,}', format_time_ns(time_spent)])
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else:
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formatted_line(unit_gaps, '├', '┼', '─', '┤')
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formatted_row(unit_gaps, ['Unit testing summary', f'{n_success}/{n_total}', f'{time_spent:,}', format_time_ns(time_spent)])
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footer(unit_gaps)
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def load_datasets(data_dir: str = DATA_DIR) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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"""Load the datasets.
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Args:
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data_dir (str, optional): [description]. Defaults to DATA_DIR (see config.py).
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data_dir (str, optional): [description]. Defaults to DATA_DIR (see config.py)
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Returns:
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Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: [description]
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Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: X_train, y_train, X_test, y_test
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"""
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bytes_to_int_list = lambda b: list(map(int, b.rstrip().split(" ")))
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bytes_to_int_list = lambda b: list(map(int, b.rstrip().split(' ')))
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def load(set_name: str) -> np.ndarray:
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with open(f"{data_dir}/{set_name}.bin", "r") as f:
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with open(f'{data_dir}/{set_name}.bin', 'r') as f:
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shape = bytes_to_int_list(f.readline())
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return np.asarray(bytes_to_int_list(f.readline()), dtype = np.uint8).reshape(shape)
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return load("X_train"), load("y_train"), load("X_test"), load("y_test")
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return load('X_train'), load('y_train'), load('X_test'), load('y_test')
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