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# python3.7
"""Contains the functions to compute Frechet Inception Distance (FID).
FID metric is introduced in paper
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash
Equilibrium. Heusel et al. NeurIPS 2017.
See details at https://arxiv.org/pdf/1706.08500.pdf
"""
import numpy as np
import scipy.linalg
__all__ = ['extract_feature', 'compute_fid']
def extract_feature(inception_model, images):
"""Extracts feature from input images with given model.
NOTE: The input images are assumed to be with pixel range [-1, 1].
Args:
inception_model: The model used to extract features.
images: The input image tensor to extract features from.
Returns:
A `numpy.ndarray`, containing the extracted features.
"""
features = inception_model(images, output_logits=False)
features = features.detach().cpu().numpy()
assert features.ndim == 2 and features.shape[1] == 2048
return features
def compute_fid(fake_features, real_features):
"""Computes FID based on the features extracted from fake and real data.
Given the mean and covariance (m_f, C_f) of fake data and (m_r, C_r) of real
data, the FID metric can be computed by
d^2 = ||m_f - m_r||_2^2 + Tr(C_f + C_r - 2(C_f C_r)^0.5)
Args:
fake_features: The features extracted from fake data.
real_features: The features extracted from real data.
Returns:
A real number, suggesting the FID value.
"""
m_f = np.mean(fake_features, axis=0)
C_f = np.cov(fake_features, rowvar=False)
m_r = np.mean(real_features, axis=0)
C_r = np.cov(real_features, rowvar=False)
fid = np.sum((m_f - m_r) ** 2) + np.trace(
C_f + C_r - 2 * scipy.linalg.sqrtm(np.dot(C_f, C_r)))
return np.real(fid)
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