63 lines
3.6 KiB
Python
63 lines
3.6 KiB
Python
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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION is strictly prohibited.
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"""Precision/Recall (PR) from the paper "Improved Precision and Recall
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Metric for Assessing Generative Models". Matches the original implementation
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by Kynkaanniemi et al. at
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https://github.com/kynkaat/improved-precision-and-recall-metric/blob/master/precision_recall.py"""
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import torch
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from . import metric_utils
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#----------------------------------------------------------------------------
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def compute_distances(row_features, col_features, num_gpus, rank, col_batch_size):
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assert 0 <= rank < num_gpus
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num_cols = col_features.shape[0]
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num_batches = ((num_cols - 1) // col_batch_size // num_gpus + 1) * num_gpus
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col_batches = torch.nn.functional.pad(col_features, [0, 0, 0, -num_cols % num_batches]).chunk(num_batches)
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dist_batches = []
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for col_batch in col_batches[rank :: num_gpus]:
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dist_batch = torch.cdist(row_features.unsqueeze(0), col_batch.unsqueeze(0))[0]
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for src in range(num_gpus):
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dist_broadcast = dist_batch.clone()
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if num_gpus > 1:
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torch.distributed.broadcast(dist_broadcast, src=src)
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dist_batches.append(dist_broadcast.cpu() if rank == 0 else None)
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return torch.cat(dist_batches, dim=1)[:, :num_cols] if rank == 0 else None
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#----------------------------------------------------------------------------
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def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_batch_size):
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detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl'
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detector_kwargs = dict(return_features=True)
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real_features = metric_utils.compute_feature_stats_for_dataset(
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opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
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rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all_torch().to(torch.float16).to(opts.device)
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gen_features = metric_utils.compute_feature_stats_for_generator(
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opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
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rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all_torch().to(torch.float16).to(opts.device)
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results = dict()
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for name, manifold, probes in [('precision', real_features, gen_features), ('recall', gen_features, real_features)]:
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kth = []
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for manifold_batch in manifold.split(row_batch_size):
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dist = compute_distances(row_features=manifold_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size)
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kth.append(dist.to(torch.float32).kthvalue(nhood_size + 1).values.to(torch.float16) if opts.rank == 0 else None)
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kth = torch.cat(kth) if opts.rank == 0 else None
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pred = []
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for probes_batch in probes.split(row_batch_size):
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dist = compute_distances(row_features=probes_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size)
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pred.append((dist <= kth).any(dim=1) if opts.rank == 0 else None)
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results[name] = float(torch.cat(pred).to(torch.float32).mean() if opts.rank == 0 else 'nan')
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return results['precision'], results['recall']
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#----------------------------------------------------------------------------
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