inception_score.py

# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
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"""Inception Score (IS) from the paper "Improved techniques for training
GANs". Matches the original implementation by Salimans et al. at
https://github.com/openai/improved-gan/blob/master/inception_score/model.py"""

import numpy as np
from . import metric_utils

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

def compute_is(opts, num_gen, num_splits):
    # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
    detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl'
    detector_kwargs = dict(no_output_bias=True) # Match the original implementation by not applying bias in the softmax layer.

    gen_probs = metric_utils.compute_feature_stats_for_generator(
        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
        capture_all=True, max_items=num_gen).get_all()

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

    scores = []
    for i in range(num_splits):
        part = gen_probs[i * num_gen // num_splits : (i + 1) * num_gen // num_splits]
        kl = part * (np.log(part) - np.log(np.mean(part, axis=0, keepdims=True)))
        kl = np.mean(np.sum(kl, axis=1))
        scores.append(np.exp(kl))
    return float(np.mean(scores)), float(np.std(scores))

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