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Running
on
Zero
Running
on
Zero
| import torch | |
| import numpy as np | |
| import scipy.linalg | |
| # FID评价保真度，越小越好 | |
| def calculate_fid( | |
| featuresdict_1, featuresdict_2, feat_layer_name | |
| ): # using 2048 layer to calculate | |
| eps = 1e-6 | |
| features_1 = featuresdict_1[feat_layer_name] | |
| features_2 = featuresdict_2[feat_layer_name] | |
| assert torch.is_tensor(features_1) and features_1.dim() == 2 | |
| assert torch.is_tensor(features_2) and features_2.dim() == 2 | |
| stat_1 = { | |
| "mu": np.mean(features_1.numpy(), axis=0), | |
| "sigma": np.cov(features_1.numpy(), rowvar=False), | |
| } | |
| stat_2 = { | |
| "mu": np.mean(features_2.numpy(), axis=0), | |
| "sigma": np.cov(features_2.numpy(), rowvar=False), | |
| } | |
| # print("Computing Frechet Distance (PANNs)") | |
| mu1, sigma1 = stat_1["mu"], stat_1["sigma"] | |
| mu2, sigma2 = stat_2["mu"], stat_2["sigma"] | |
| assert mu1.shape == mu2.shape and mu1.dtype == mu2.dtype | |
| assert sigma1.shape == sigma2.shape and sigma1.dtype == sigma2.dtype | |
| 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 | |
| ), "Training and test mean vectors have different lengths" | |
| assert ( | |
| sigma1.shape == sigma2.shape | |
| ), "Training and test covariances have different dimensions" | |
| diff = mu1 - mu2 | |
| # Product might be almost singular | |
| covmean, _ = scipy.linalg.sqrtm(sigma1.dot(sigma2), disp=False) | |
| if not np.isfinite(covmean).all(): | |
| print( | |
| f"WARNING: fid calculation produces singular product; adding {eps} to diagonal of cov" | |
| ) | |
| offset = np.eye(sigma1.shape[0]) * eps | |
| covmean = scipy.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)) | |
| assert False, "Imaginary component {}".format(m) | |
| covmean = covmean.real | |
| tr_covmean = np.trace(covmean) | |
| fid = diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean | |
| return { | |
| "frechet_distance": float(fid), | |
| } | |