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import numpy as np |
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from scipy import linalg |
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def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): |
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mu1 = np.atleast_1d(mu1) |
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mu2 = np.atleast_1d(mu2) |
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sigma1 = np.atleast_2d(sigma1) |
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sigma2 = np.atleast_2d(sigma2) |
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assert mu1.shape == mu2.shape, 'Training and test mean vectors have different lengths' |
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assert sigma1.shape == sigma2.shape, 'Training and test covariances have different dimensions' |
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diff = mu1 - mu2 |
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covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) |
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if not np.isfinite(covmean).all(): |
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print('fid calculation produces singular product; adding %s to diagonal of cov estimates' % eps) |
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offset = np.eye(sigma1.shape[0]) * eps |
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covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) |
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if np.iscomplexobj(covmean): |
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if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): |
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m = np.max(np.abs(covmean.imag)) |
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raise ValueError('Imaginary component {}'.format(m)) |
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covmean = covmean.real |
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return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * np.trace(covmean) |
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def calculate_fid_given_features(feature1, feature2): |
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mu1 = np.mean(feature1, axis=0) |
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sigma1 = np.cov(feature1, rowvar=False) |
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mu2 = np.mean(feature2, axis=0) |
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sigma2 = np.cov(feature2, rowvar=False) |
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fid_value = calculate_frechet_distance(mu1, sigma1, mu2, sigma2) |
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return fid_value |
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