Method compute_score

extra/models/inception.py:312–321

(self, inception_activations:Tensor, val_stats_path:str)

    return x

  def compute_score(self, inception_activations:Tensor, val_stats_path:str) -> float:
    if self.m1 is None and self.s1 is None:
      with np.load(val_stats_path) as f:
        self.m1, self.s1 = f['mu'][:], f['sigma'][:]
    assert self.m1 is not None and self.s1 is not None

    m2 = inception_activations.mean(axis=0).numpy()
    s2 = np.cov(inception_activations.numpy(), rowvar=False)

    return calculate_frechet_distance(self.m1, self.s1, m2, s2)

def calculate_frechet_distance(mu1:np.ndarray, sigma1:np.ndarray, mu2:np.ndarray, sigma2:np.ndarray, eps:float=1e-6) -> float:
  mu1 = np.atleast_1d(mu1)