stylegan2_ada_pytorch/metrics/frechet_inception_distance.py

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
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# license agreement from NVIDIA CORPORATION is strictly prohibited.

"""Frechet Inception Distance (FID) from the paper
"GANs trained by a two time-scale update rule converge to a local Nash
equilibrium". Matches the original implementation by Heusel et al. at
https://github.com/bioinf-jku/TTUR/blob/master/fid.py"""

import numpy as np
import scipy.linalg
from . import metric_utils

# ----------------------------------------------------------------------------


def compute_fid(opts, max_real, num_gen):
    # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
    detector_url = "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt"
    detector_kwargs = dict(
        return_features=True
    )  # Return raw features before the softmax layer.

    mu_real, sigma_real = metric_utils.compute_feature_stats_for_dataset(
        opts=opts,
        detector_url=detector_url,
        detector_kwargs=detector_kwargs,
        rel_lo=0,
        rel_hi=0,
        capture_mean_cov=True,
        max_items=max_real,
    ).get_mean_cov()

    mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator(
        opts=opts,
        detector_url=detector_url,
        detector_kwargs=detector_kwargs,
        rel_lo=0,
        rel_hi=1,
        capture_mean_cov=True,
        max_items=num_gen,
    ).get_mean_cov()

    if opts.rank != 0:
        return float("nan")

    m = np.square(mu_gen - mu_real).sum()
    s, _ = scipy.linalg.sqrtm(
        np.dot(sigma_gen, sigma_real), disp=False
    )  # pylint: disable=no-member
    fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2))
    return float(fid)


# ----------------------------------------------------------------------------