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import numpy as np
import scipy
def fid(reals: np.ndarray, fakes: np.ndarray) -> float:
"""
Calculate Frechet Inception Distance (FID) score.
Args:
reals: Real images [N, C, H, W]
fakes: Generated images [N, C, H, W]
Returns:
FID score (lower is better)
"""
reals = reals.reshape(reals.shape[0], -1)
fakes = fakes.reshape(fakes.shape[0], -1)
mu_real = np.mean(reals, axis=0)
mu_fake = np.mean(fakes, axis=0)
sigma_real = np.cov(reals, rowvar=False)
sigma_fake = np.cov(fakes, rowvar=False)
diff = mu_real - mu_fake
covmean, _ = scipy.linalg.sqrtm(sigma_real.dot(sigma_fake), disp=False)
if not np.isfinite(covmean).all():
eps = 1e-6
offset = np.eye(sigma_real.shape[0]) * eps
covmean = scipy.linalg.sqrtm((sigma_real + offset).dot(sigma_fake + offset))
if np.iscomplexobj(covmean):
covmean = covmean.real
return diff @ diff + np.trace(sigma_real) + np.trace(sigma_fake) - 2 * np.trace(covmean)
def kl_divergence(reals: np.ndarray, fakes: np.ndarray) -> float:
"""
Calculate KL divergence between real and fake image distributions.
Args:
reals: Real images [N, C, H, W]
fakes: Generated images [N, C, H, W]
Returns:
KL divergence value
"""
reals = reals.transpose(1, 0, 2, 3).reshape(reals.shape[1], -1)
fakes = fakes.transpose(1, 0, 2, 3).reshape(fakes.shape[1], -1)
hist_real = np.apply_along_axis(
lambda a: np.histogram(a, bins=40, range=(-1, 1))[0], 1, reals
)
hist_fake = np.apply_along_axis(
lambda a: np.histogram(a, bins=40, range=(-1, 1))[0], 1, fakes
)
# Add smoothing
hist_real = hist_real + 1
hist_fake = hist_fake + 1
hist_real = hist_real / np.sum(hist_real)
hist_fake = hist_fake / np.sum(hist_fake)
return np.mean(np.log(hist_real / hist_fake))
def jsd(reals: np.ndarray, fakes: np.ndarray) -> float:
"""
Calculate Jensen-Shannon divergence between real and fake image distributions.
Args:
reals: Real images [N, C, H, W]
fakes: Generated images [N, C, H, W]
Returns:
JSD value
"""
reals = reals.transpose(1, 0, 2, 3).reshape(reals.shape[1], -1)
fakes = fakes.transpose(1, 0, 2, 3).reshape(fakes.shape[1], -1)
hist_real = np.apply_along_axis(
lambda a: np.histogram(a, bins=40, range=(-1, 1))[0], 1, reals
)
hist_fake = np.apply_along_axis(
lambda a: np.histogram(a, bins=40, range=(-1, 1))[0], 1, fakes
)
hist_real = hist_real + 1
hist_fake = hist_fake + 1
hist_real = hist_real / np.sum(hist_real)
hist_fake = hist_fake / np.sum(hist_fake)
hist_avg = (hist_real + hist_fake) / 2
return 0.5 * (np.mean(np.log(hist_real / hist_avg)) + np.mean(np.log(hist_fake / hist_avg)))
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