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on
Zero
Running
on
Zero
| """ | |
| Adapted from https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/evaluations/evaluator.py | |
| """ | |
| import warnings | |
| import numpy as np | |
| from scipy import linalg | |
| class InvalidFIDException(Exception): | |
| pass | |
| class FIDStatistics: | |
| def __init__(self, mu: np.ndarray, sigma: np.ndarray): | |
| self.mu = mu | |
| self.sigma = sigma | |
| def frechet_distance(self, other, eps=1e-6): | |
| """ | |
| Compute the Frechet distance between two sets of statistics. | |
| """ | |
| # https://github.com/bioinf-jku/TTUR/blob/73ab375cdf952a12686d9aa7978567771084da42/fid.py#L132 | |
| mu1, sigma1 = self.mu, self.sigma | |
| mu2, sigma2 = other.mu, other.sigma | |
| mu1 = np.atleast_1d(mu1) | |
| mu2 = np.atleast_1d(mu2) | |
| sigma1 = np.atleast_2d(sigma1) | |
| sigma2 = np.atleast_2d(sigma2) | |
| assert ( | |
| mu1.shape == mu2.shape | |
| ), f"Training and test mean vectors have different lengths: {mu1.shape}, {mu2.shape}" | |
| assert ( | |
| sigma1.shape == sigma2.shape | |
| ), f"Training and test covariances have different dimensions: {sigma1.shape}, {sigma2.shape}" | |
| diff = mu1 - mu2 | |
| # product might be almost singular | |
| covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) | |
| if not np.isfinite(covmean).all(): | |
| msg = ( | |
| "fid calculation produces singular product; adding %s to diagonal of cov estimates" | |
| % eps | |
| ) | |
| warnings.warn(msg) | |
| offset = np.eye(sigma1.shape[0]) * eps | |
| covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) | |
| # numerical error might give slight imaginary component | |
| if np.iscomplexobj(covmean): | |
| if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): | |
| m = np.max(np.abs(covmean.imag)) | |
| raise ValueError("Imaginary component {}".format(m)) | |
| covmean = covmean.real | |
| tr_covmean = np.trace(covmean) | |
| return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean | |
| def compute_statistics(feats: np.ndarray) -> FIDStatistics: | |
| mu = np.mean(feats, axis=0) | |
| sigma = np.cov(feats, rowvar=False) | |
| return FIDStatistics(mu, sigma) | |
| def compute_inception_score(preds: np.ndarray, split_size: int = 5000) -> float: | |
| # https://github.com/openai/improved-gan/blob/4f5d1ec5c16a7eceb206f42bfc652693601e1d5c/inception_score/model.py#L46 | |
| scores = [] | |
| for i in range(0, len(preds), split_size): | |
| part = preds[i : i + split_size] | |
| kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0))) | |
| kl = np.mean(np.sum(kl, 1)) | |
| scores.append(np.exp(kl)) | |
| return float(np.mean(scores)) | |