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Running
on
Zero
Running
on
Zero
File size: 2,232 Bytes
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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),
}
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