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import numpy as np
import scipy.linalg
import matplotlib.pyplot as plt
def fid(reals, fakes):
"""FID score calculation.
Args:
reals (numpy.array): Real images.
fakes (numpy.array): Fake images.
"""
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 = np.dot(sigma_real, sigma_fake.T)
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
ncovmean = scipy.linalg.sqrtm((sigma_real + offset).dot(sigma_fake + offset))
covmean = ncovmean
if np.iscomplexobj(covmean):
covmean = covmean.real
return diff @ diff + np.trace(sigma_real) + np.trace(sigma_fake) - 2 * np.trace(covmean)
def kl(reals, fakes):
"""KL divergence calculation.
Args:
reals (numpy.array): Real images.
fakes (numpy.array): Fake images.
"""
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)
return np.mean(np.log(hist_real / hist_fake))
def jsd(reals, fakes):
"""Jensen-Shannon divergence calculation.
Args:
reals (numpy.array): Real images.
fakes (numpy.array): Fake images.
"""
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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