39 lines
1.9 KiB
Python
39 lines
1.9 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. 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://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))
|
|
|
|
#----------------------------------------------------------------------------
|