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metrics.py
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metrics.py
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import numpy as np
import torch
def mean_absolute_error(image_true, image_generated):
"""Compute mean absolute error.
Args:
image_true: (Tensor) true image
image_generated: (Tensor) generated image
Returns:
mse: (float) mean squared error
"""
return torch.abs(image_true - image_generated).mean()
def peak_signal_to_noise_ratio(image_true, image_generated):
""""Compute peak signal-to-noise ratio.
Args:
image_true: (Tensor) true image
image_generated: (Tensor) generated image
Returns:
psnr: (float) peak signal-to-noise ratio"""
mse = ((image_true - image_generated) ** 2).mean().cpu()
return -10 * np.log10(mse)
def structural_similarity_index(image_true, image_generated, C1=0.01, C2=0.03):
"""Compute structural similarity index.
Args:
image_true: (Tensor) true image
image_generated: (Tensor) generated image
C1: (float) variable to stabilize the denominator
C2: (float) variable to stabilize the denominator
Returns:
ssim: (float) mean squared error"""
mean_true = image_true.mean()
mean_generated = image_generated.mean()
std_true = image_true.std()
std_generated = image_generated.std()
covariance = (
(image_true - mean_true) * (image_generated - mean_generated)).mean()
numerator = (2 * mean_true * mean_generated + C1) * (2 * covariance + C2)
denominator = ((mean_true ** 2 + mean_generated ** 2 + C1) *
(std_true ** 2 + std_generated ** 2 + C2))
return numerator / denominator