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c3d0544 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | # SPDX-FileCopyrightText: Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES.
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# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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import torch
from physicsnemo.metrics.general.wasserstein import wasserstein_from_normal
def calculate_fid_from_inception_stats(
mu: torch.Tensor, sigma: torch.Tensor, mu_ref: torch.Tensor, sigma_ref: torch.Tensor
) -> torch.Tensor:
"""
Calculate the Fréchet Inception Distance (FID) between two sets
of Inception statistics.
The Fréchet Inception Distance is a measure of the similarity between two datasets
based on their Inception features (mu and sigma). It is commonly used to evaluate
the quality of generated images in generative models.
Parameters
----------
mu: torch.Tensor:
Mean of Inception statistics for the generated dataset.
sigma: torch.Tensor:
Covariance matrix of Inception statistics for the generated dataset.
mu_ref: torch.Tensor
Mean of Inception statistics for the reference dataset.
sigma_ref: torch.Tensor
Covariance matrix of Inception statistics for the reference dataset.
Returns
-------
float
The Fréchet Inception Distance (FID) between the two datasets.
"""
return wasserstein_from_normal(mu, sigma, mu_ref, sigma_ref)
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