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https://github.com/ruvnet/RuView
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ruv
4db727649a
Operationalizes the campaign's central finding (ADR-150 §3.3-3.6): a frozen shared base + a ~11KB per-room LoRA adapter from ~100-200 labeled samples recovers SOTA-level pose in any new room/person. Verified end-to-end: source-only base zero-shot 3.09% on unseen room -> 74.29% after 200-sample calibration. Files: model.py (PoseNet+LoRA), calibrate.py, infer.py, README with measured calibration budget. Co-Authored-By: claude-flow <ruv@ruv.net>
72 lines
3.0 KiB
Python
72 lines
3.0 KiB
Python
"""RuView per-room calibration — fit a ~11 KB LoRA adapter from a short labeled in-room capture.
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python calibrate.py --base pose_mmfi_best.pt --data room_calib.npz --out room_A.adapter.npz
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`room_calib.npz` must contain `X` [N,3,114,10] CSI amplitude and `Y` [N,17,2] (or [N,34]) keypoints
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in [0,1] — the labeled calibration samples from the deployment room (~100–200 recommended; ≥20).
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Outputs a tiny adapter (.npz, ~11 KB) that, loaded over the shared base at inference, recovers
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SOTA-level pose for that room/person (ADR-150 §3.5–3.6).
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"""
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import argparse
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import numpy as np
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import torch
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import torch.nn as nn
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from model import PoseNet, standardize
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--base", required=True, help="base checkpoint (pose_mmfi_best.pt)")
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ap.add_argument("--data", required=True, help="labeled calibration .npz with X and Y")
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ap.add_argument("--out", required=True, help="output adapter .npz")
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ap.add_argument("--rank", type=int, default=8)
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ap.add_argument("--iters", type=int, default=600)
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ap.add_argument("--lr", type=float, default=8e-4)
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ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
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a = ap.parse_args()
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z = np.load(a.data)
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X = torch.tensor(z["X"].astype(np.float32))
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Y = torch.tensor(z["Y"].reshape(len(z["Y"]), 34).astype(np.float32))
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n = len(X)
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if n < 20:
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print(f"WARNING: only {n} calibration samples — below ~20 the adapter may underperform "
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f"zero-shot (ADR-150 §3.5). Recommend ~100–200.")
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dev = a.device
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net = PoseNet().to(dev)
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net.load_state_dict(torch.load(a.base, map_location=dev), strict=False)
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net.add_lora(r=a.rank).to(dev)
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for k, p in net.named_parameters():
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p.requires_grad = k.endswith(".A") or k.endswith(".B")
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trainable = [p for p in net.parameters() if p.requires_grad]
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n_tr = sum(p.numel() for p in trainable)
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Xs = standardize(X.to(dev))
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Yt = Y.to(dev)
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opt = torch.optim.AdamW(trainable, lr=a.lr, weight_decay=0.0)
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lossf = nn.SmoothL1Loss(beta=0.1)
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bs = min(128, n)
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net.train()
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for it in range(a.iters):
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bi = torch.randint(0, n, (bs,), device=dev)
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xb = Xs[bi]
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# light augmentation (subcarrier dropout + noise) — matches training-time regularization
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m = (torch.rand(xb.shape[0], xb.shape[1], 1, 1, device=dev) > 0.15).float()
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xb = xb * m + 0.03 * torch.randn_like(xb) * torch.rand(xb.shape[0], 1, 1, 1, device=dev)
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opt.zero_grad()
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lossf(net(xb), Yt[bi]).backward()
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opt.step()
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adapter = net.lora_state()
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nbytes = sum(v.astype(np.float16).nbytes for v in adapter.values())
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np.savez(a.out, **{k: v.astype(np.float16) for k, v in adapter.items()},
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_meta=np.array([a.rank, n, n_tr], dtype=np.int64))
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print(f"saved {a.out} | rank {a.rank} | {n_tr:,} params | ~{nbytes/1024:.1f} KB fp16 | "
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f"from {n} labeled samples")
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if __name__ == "__main__":
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main()
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