diff options
Diffstat (limited to 'utils.py')
| -rw-r--r-- | utils.py | 45 |
1 files changed, 42 insertions, 3 deletions
@@ -1,4 +1,6 @@ import numpy as np +import scipy.linalg +import matplotlib.pyplot as plt def fid(reals, fakes): @@ -8,7 +10,6 @@ def fid(reals, fakes): reals (numpy.array): Real images. fakes (numpy.array): Fake images. """ - print(reals.shape, fakes.shape) reals = reals.reshape(reals.shape[0], -1) fakes = fakes.reshape(fakes.shape[0], -1) @@ -19,7 +20,45 @@ def fid(reals, fakes): diff = mu_real - mu_fake covmean = np.dot(sigma_real, sigma_fake.T) - covmean = np.sqrt(covmean * (covmean > 0)) - print(np.trace(covmean)) + 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) + + plt.figure() + colors = ['#ff0000', '#00ff00', '#0000ff'] + for i in range(reals.shape[0]): + plt.plot(hist_real[i], label='real', color=colors[i]) + plt.plot(hist_fake[i], label='fake', color=colors[i], linestyle='dashed') + plt.legend() + plt.savefig('hist.tmp.png') + + 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))
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