8f43927575
examples/ is based on KasperskyLab/knp at 55470ed6 with local changes to the mnist-learn altai/blifat models, cuda-backend CMake and model_loader. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
31 lines
1.5 KiB
Python
31 lines
1.5 KiB
Python
"""Generate fixed descriptors for saved RGB splits, preserving exact labels/order."""
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import argparse
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import hashlib
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import json
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from pathlib import Path
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import numpy as np
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from features import descriptor
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ap = argparse.ArgumentParser()
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ap.add_argument('source', type=Path)
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ap.add_argument('output', type=Path)
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a = ap.parse_args()
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a.output.mkdir(exist_ok=True, parents=True)
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for kind in ('hog', 'hybrid', 'sparse_hog'):
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arrays, labels = {}, {}
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for split in ('train', 'val', 'test'):
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raw = np.load(a.source / f'{split}-original.npz')
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arrays[split] = descriptor(raw['rgb'], hybrid=kind == 'hybrid', sparse=kind == 'sparse_hog')
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labels[split] = raw['labels']
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arrays[split].tofile(a.output / f'{kind}_{split}.bin')
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np.savetxt(a.output / f'{kind}_{split}.target', labels[split], fmt='%d')
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for suffix, split in [('validation', 'val'), ('train_test', 'test')]:
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np.concatenate([arrays['train'], arrays[split]]).tofile(a.output / f'{kind}_{suffix}.bin')
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np.savetxt(a.output / f'{kind}_{suffix}.target', np.concatenate([labels['train'], labels[split]]), fmt='%d')
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metadata = {'encoding': kind, 'features': 1940 if kind == 'hybrid' else 1764, 'padded_to': 2048,
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'fixed_untrained_transform': True,
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'sizes': {s: len(x) for s, x in arrays.items()},
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'sha256': {s: hashlib.sha256(x.tobytes()).hexdigest() for s, x in arrays.items()}}
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(a.output / f'{kind}-manifest.json').write_text(json.dumps(metadata, indent=2) + '\n')
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print(json.dumps(metadata), flush=True)
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