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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. | |
| """Kernel Inception Distance (KID) from the paper "Demystifying MMD | |
| GANs". Matches the original implementation by Binkowski et al. at | |
| https://github.com/mbinkowski/MMD-GAN/blob/master/gan/compute_scores.py""" | |
| import numpy as np | |
| from . import metric_utils | |
| #---------------------------------------------------------------------------- | |
| def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size): | |
| # 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. | |
| real_features = metric_utils.compute_feature_stats_for_dataset( | |
| opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, | |
| rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all() | |
| gen_features = metric_utils.compute_feature_stats_for_generator( | |
| opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, | |
| rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all() | |
| if opts.rank != 0: | |
| return float('nan') | |
| n = real_features.shape[1] | |
| m = min(min(real_features.shape[0], gen_features.shape[0]), max_subset_size) | |
| t = 0 | |
| for _subset_idx in range(num_subsets): | |
| x = gen_features[np.random.choice(gen_features.shape[0], m, replace=False)] | |
| y = real_features[np.random.choice(real_features.shape[0], m, replace=False)] | |
| a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3 | |
| b = (x @ y.T / n + 1) ** 3 | |
| t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m | |
| kid = t / num_subsets / m | |
| return float(kid) | |
| #---------------------------------------------------------------------------- | |