Datasets:
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b0d8bdd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # Copyright 2026 Nikhil Raghavendra
# SPDX-License-Identifier: Apache-2.0
"""Write Inception statistics of the real optical targets.
Usage::
python scripts/make_fid_stats.py DATASET_DIR_OR_REPO OUT_DIR
Writes ``OUT_DIR/fid_validation.npz`` and ``OUT_DIR/fid_test.npz``, computed with
the sen-bridge PyTorch FID so ``FID.from_stats`` can score without featurising the
reals.
"""
from __future__ import annotations
import sys
from pathlib import Path
import numpy as np
import torch
from datasets import load_dataset
from sen_bridge import torch as sb
_BATCH = 200
_HALF_RANGE = 127.5
_SPLITS = (("validation", "fid_validation"), ("test", "fid_test"))
def main() -> None:
"""Compute and save the statistics of the validation and test targets."""
data, out = sys.argv[1], Path(sys.argv[2])
out.mkdir(parents=True, exist_ok=True)
for split, name in _SPLITS:
ds = load_dataset(data, split=split)
fid = sb.FID()
for start in range(0, len(ds), _BATCH):
stop = min(start + _BATCH, len(ds))
rows = ds.select(range(start, stop))
images = np.stack([np.asarray(r["optical"]) for r in rows])
tensor = torch.from_numpy(images.astype(np.float32) / _HALF_RANGE - 1)
fid.update(tensor.permute(0, 3, 1, 2), real=True)
fid.save_stats(out / f"{name}.npz", real=True)
print(split, len(ds), "->", out / f"{name}.npz")
if __name__ == "__main__":
main()
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