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# 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
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""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://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt'
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))
#----------------------------------------------------------------------------
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