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import math
import random
def _sigmoid(x):
return 1 / (1 + math.exp(-x))
def _2_sigmoid_minus_1(x):
return 2 * _sigmoid(x) - 1
def _tanh(x):
return math.tanh(x)
# mapping param for special metrix
special_metric_dict = {
# with T
'MAE': 50,
'RMS': 50,
'MSE': 5,
'RMSE': 5,
'ABSREL': 0.1,
'EPE': 1,
'FID': 25,
'FVD': 100,
'FAD': 10,
'PSNR': 1 / 20, # higher is better
'SAD': 10,
'RTE': 0.5,
'CD': 1,
'MCD': 5,
# without T
'WER': None,
'MS-SSIM': None,
'MOS': None,
}
HIGHER_IS_BETTER = [
'PSNR',
]
def map_function_for_special(metrix: str, score: float) -> float:
"""
Score mapping function for special metrics.
>>> metrix: metrix name, str, e.g., 'MAE'.
>>> score: task score, float, e.g., 5.3.
return: mapped scores, float.
"""
metrix = metrix.upper()
T = special_metric_dict[metrix]
assert score > 0, f'score should be > 0, but found: {score}'
if metrix in HIGHER_IS_BETTER:
y = _tanh(T * score)
elif metrix == 'WER':
y = 1 - score
elif metrix == 'MS-SSIM':
y = (score + 1) / 2
elif metrix == 'MOS':
y = (score - 1) / 4
else: # lower is better
y = _2_sigmoid_minus_1(T / score)
return y * 100 # Convert to percentage scale
# • Normalizing WER:
# y = 1 − x, where x ∈ [0, 1], y ∈ [0, 1].
# • Normalizing MS-SSIM:
# y = (x + 1) / 2 , where x ∈ [−1, 1], y ∈ [0, 1].
# • Normalizing MOS:
# y = x − 1 / 4 , where x ∈ [1, 5], y ∈ [0, 1].
if __name__ == '__main__':
r = random.random()
print(f"{r = }")
print(f"{_sigmoid(r) = }")
print(f"{_2_sigmoid_minus_1(r) = }")
print(f"{_tanh(r) = }")
print(f"{_tanh(r / 2) = }") |