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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. | |
| """Precision/Recall (PR) from the paper "Improved Precision and Recall | |
| Metric for Assessing Generative Models". Matches the original implementation | |
| by Kynkaanniemi et al. at | |
| https://github.com/kynkaat/improved-precision-and-recall-metric/blob/master/precision_recall.py""" | |
| import torch | |
| from . import metric_utils | |
| #---------------------------------------------------------------------------- | |
| def compute_distances(row_features, col_features, num_gpus, rank, col_batch_size): | |
| assert 0 <= rank < num_gpus | |
| num_cols = col_features.shape[0] | |
| num_batches = ((num_cols - 1) // col_batch_size // num_gpus + 1) * num_gpus | |
| col_batches = torch.nn.functional.pad(col_features, [0, 0, 0, -num_cols % num_batches]).chunk(num_batches) | |
| dist_batches = [] | |
| for col_batch in col_batches[rank :: num_gpus]: | |
| dist_batch = torch.cdist(row_features.unsqueeze(0), col_batch.unsqueeze(0))[0] | |
| for src in range(num_gpus): | |
| dist_broadcast = dist_batch.clone() | |
| if num_gpus > 1: | |
| torch.distributed.broadcast(dist_broadcast, src=src) | |
| dist_batches.append(dist_broadcast.cpu() if rank == 0 else None) | |
| return torch.cat(dist_batches, dim=1)[:, :num_cols] if rank == 0 else None | |
| #---------------------------------------------------------------------------- | |
| def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_batch_size): | |
| detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt' | |
| detector_kwargs = dict(return_features=True) | |
| 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_torch().to(torch.float16).to(opts.device) | |
| 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_torch().to(torch.float16).to(opts.device) | |
| results = dict() | |
| for name, manifold, probes in [('precision', real_features, gen_features), ('recall', gen_features, real_features)]: | |
| kth = [] | |
| for manifold_batch in manifold.split(row_batch_size): | |
| dist = compute_distances(row_features=manifold_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size) | |
| kth.append(dist.to(torch.float32).kthvalue(nhood_size + 1).values.to(torch.float16) if opts.rank == 0 else None) | |
| kth = torch.cat(kth) if opts.rank == 0 else None | |
| pred = [] | |
| for probes_batch in probes.split(row_batch_size): | |
| dist = compute_distances(row_features=probes_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size) | |
| pred.append((dist <= kth).any(dim=1) if opts.rank == 0 else None) | |
| results[name] = float(torch.cat(pred).to(torch.float32).mean() if opts.rank == 0 else 'nan') | |
| return results['precision'], results['recall'] | |
| #---------------------------------------------------------------------------- | |