42 lines
1.9 KiB
Python
42 lines
1.9 KiB
Python
"""Train the feature detectors offline and write integer weights for altai_infer (inference runs in KNP).
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k-means on 6×6×3 patches of the vehicles training images (patch centring and ZCA, no contrast division), then
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centring, ZCA and the prototype are folded into one weight vector per detector type on raw pixels, the offset
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and alpha into a per-step bias (objects/spiking_pipeline.py); weights are scaled to integers in ±wscale.
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usage: ~/anvenv/bin/python build_model.py <model_dir> [--k 200 --stride 2 --alpha 0.25 --wscale 127]
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"""
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import argparse
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import sys
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from pathlib import Path
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import numpy as np
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sys.path.insert(0, "/mnt/c/knp/objects")
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import kmeans_features as kf # noqa: E402
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import spiking_pipeline as sp # noqa: E402
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ap = argparse.ArgumentParser()
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ap.add_argument("out")
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ap.add_argument("--k", type=int, default=200)
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ap.add_argument("--stride", type=int, default=2)
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ap.add_argument("--alpha", type=float, default=0.25)
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ap.add_argument("--wscale", type=float, default=127)
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ap.add_argument("--classes", type=int, default=4)
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ap.add_argument("--data", default="/home/int/run/cifar/vehicles")
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a = ap.parse_args()
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out = Path(a.out)
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out.mkdir(parents=True, exist_ok=True)
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kf.CONTRAST_DIVIDE = False
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xt = np.load(f"{a.data}/train.npz")["rgb"]
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model = kf.learn(xt, k=a.k)
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w, b = sp.fold(model, a.alpha) # [108, K], [K] for patches in [0, 1]
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scale = a.wscale / np.abs(w).max()
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wq, bq = np.rint(w * scale).astype(np.int32), np.rint(b * scale).astype(np.int32)
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p = kf.patches_at(xt[:300].astype(np.float32) / 255, range(0, 27, a.stride), range(0, 27, a.stride)).reshape(-1, 108)
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act = p @ w + b
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thr = int(round(float(np.percentile(act[act > 0], 99)) * scale))
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wq.T.copy().tofile(out / "det_w.bin")
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bq.tofile(out / "det_b.bin")
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(out / "meta.txt").write_text(f"{a.k} {a.stride} {thr} {a.classes} 0\n")
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np.savez(out / "kmeans.npz", **model)
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print(f"K {a.k} stride {a.stride}: weight scale {scale:.2f}, detector threshold {thr}, bias range {bq.min()}..{bq.max()}")
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