583f2bdd13
BOMs barf on UTF-8 decoding within PyTorch:
File "D:\Soft\Miniconda3\lib\site-packages\torch\utils_cpp_extension_versioner.py", line 16, in hash_source_files
hash_value = update_hash(hash_value, file.read())
UnicodeDecodeError: 'gbk' codec can't decode byte 0xbf in position 2: illegal multibyte sequence
Should fix #10, #14
62 lines
3.6 KiB
Python
62 lines
3.6 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.
|
|
|
|
"""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://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl'
|
|
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']
|
|
|
|
#----------------------------------------------------------------------------
|