(mu1, sigma1, mu2, sigma2, eps=1e-6)
return diff.dot(diff) + np.trace(sigma1) + np.trace(
        sigma2) - 2 * tr_covmean
Numpy implementation of the Frechet Distance.
    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
    and X_2 ~ N(mu_2, C_2) is
            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).

    Stable version by Dougal J. Sutherland.

    Args:
        mu1 : Numpy array containing the activations of the pool_3 layer of the
            inception net ( like returned by the function 'get_predictions')
            for generated samples.
        mu2: The sample mean over activations of the pool_3 layer, precalcualted
            on an representive data set.
        sigma1 (ndarray): The covariance matrix over activations of the pool_3 layer for
            generated samples.
        sigma2: The covariance matrix over activations of the pool_3 layer,
            precalcualted on an representive data set.

    Returns:
        np.float64: The Frechet Distance.
Numpy implementation of the Frechet Distance.
    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
    and X_2 ~ N(mu_2, C_2) is
            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
Numpy implementation of the Frechet Distance . The Frechet distance between two multivariate Gaussians X_1 ~ N ( mu_1 C_1 ) and X_2 ~ N ( mu_2 C_2 ) is d^2 = ||mu_1 - mu_2||^2 + Tr ( C_1 + C_2 - 2 * sqrt ( C_1 * C_2 )) .
def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
    """
    Numpy implementation of the Frechet Distance.
    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
    and X_2 ~ N(mu_2, C_2) is
            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).

    Stable version by Dougal J. Sutherland.

    Args:
        mu1 : Numpy array containing the activations of the pool_3 layer of the
            inception net ( like returned by the function 'get_predictions')
            for generated samples.
        mu2: The sample mean over activations of the pool_3 layer, precalcualted
            on an representive data set.
        sigma1 (ndarray): The covariance matrix over activations of the pool_3 layer for
            generated samples.
        sigma2: The covariance matrix over activations of the pool_3 layer,
            precalcualted on an representive data set.

    Returns:
        np.float64: The Frechet Distance.
    """
    if mu1.shape != mu2.shape or sigma1.shape != sigma2.shape:
        raise ValueError(
            "(mu1, sigma1) should have exactly the same shape as (mu2, sigma2)."
        )

    mu1 = np.atleast_1d(mu1)
    mu2 = np.atleast_1d(mu2)

    sigma1 = np.atleast_2d(sigma1)
    sigma2 = np.atleast_2d(sigma2)

    diff = mu1 - mu2

    # Product might be almost singular
    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
    if not np.isfinite(covmean).all():
        print(
            "WARNING: fid calculation produces singular product; adding {} to diagonal of cov estimates"
            .format(eps))

        offset = np.eye(sigma1.shape[0]) * eps
        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))

    # Numerical error might give slight imaginary component
    if np.iscomplexobj(covmean):
        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
            m = np.max(np.abs(covmean.imag))
            raise ValueError("Imaginary component {}".format(m))
        covmean = covmean.real

    tr_covmean = np.trace(covmean)

    return diff.dot(diff) + np.trace(sigma1) + np.trace(
        sigma2) - 2 * tr_covmean
def calculate_frechet_distance ( mu1 , sigma1 , mu2 , sigma2 , eps = 1e-6 ) : if mu1 . shape != mu2 . shape or sigma1 . shape != sigma2 . shape : raise ValueError ( "(mu1, sigma1) should have exactly the same shape as (mu2, sigma2)." ) mu1 = np . atleast_1d ( mu1 ) mu2 = np . atleast_1d ( mu2 ) sigma1 = np . atleast_2d ( sigma1 ) sigma2 = np . atleast_2d ( sigma2 ) diff = mu1 - mu2 # Product might be almost singular covmean , _ = linalg . sqrtm ( sigma1 . dot ( sigma2 ) , disp = False ) if not np . isfinite ( covmean ) . all ( ) : print ( "WARNING: fid calculation produces singular product; adding {} to diagonal of cov estimates" . format ( eps ) ) offset = np . eye ( sigma1 . shape [ 0 ] ) * eps covmean = linalg . sqrtm ( ( sigma1 + offset ) . dot ( sigma2 + offset ) ) # Numerical error might give slight imaginary component if np . iscomplexobj ( covmean ) : if not np . allclose ( np . diagonal ( covmean ) . imag , 0 , atol = 1e-3 ) : m = np . max ( np . abs ( covmean . imag ) ) raise ValueError ( "Imaginary component {}" . format ( m ) ) covmean = covmean . real tr_covmean = np . trace ( covmean ) return diff . dot ( diff ) + np . trace ( sigma1 ) + np . trace ( sigma2 ) - 2 * tr_covmean