feat(calibration): RuView per-room calibration service (reference impl)
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>
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# RuView Calibration Service (reference implementation)
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Turn a **shared WiFi-CSI pose base model** into a room-specific one with a **30-second labeled
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calibration** and a **~11 KB per-room LoRA adapter**. This is the deployable resolution of the
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cross-subject / cross-environment generalization problem (full study: [ADR-150 §3.3–3.6](../../docs/adr/ADR-150-rf-foundation-encoder.md)).
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## Why
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Zero-shot WiFi pose generalizes poorly to a **new room or new person** — an unseen room can drop a
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strong model to near-random. But that gap is **not** algorithmically closeable (CORAL, DANN,
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instance-norm, contrastive foundation-pretraining all failed) and **not** closeable by collecting
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more subjects (saturates ~64%). It **is** closeable, cheaply, at deployment time: a handful of
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labeled frames from the actual room pin down its multipath instantly.
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| Deployment case | Zero-shot | + in-room calibration |
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|-----------------|----------:|----------------------:|
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| Same room, new person (cross-subject) | 64% | **76%** (200 samples) |
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| **New room + new person (cross-environment)** | **~10%** | **60% @ 5 samples → 73% @ 200** |
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**Verified demo (this code, source-only base on an unseen MM-Fi room E04):**
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`zero-shot 3.09% → after 200-sample calibration 74.29%` (+71 pts).
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## How it works
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A frozen shared **base** (transformer + temporal attention pool + skeleton-graph head, the published
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[`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)) plus a
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tiny **LoRA adapter** (rank 8 on the input projection + pose head — **11,200 params ≈ 11 KB int8 /
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22 KB fp16**) fitted per room. Thousands of room-adapters hang off one base.
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## Usage
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```bash
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# 1) Capture a short labeled clip in the deployment room -> calib.npz {X:[N,3,114,10], Y:[N,17,2]}
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# (~100–200 samples recommended; below ~20 the adapter can underperform zero-shot)
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# 2) Fit the per-room adapter (~11 KB):
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python calibrate.py --base pose_mmfi_best.pt --data calib.npz --out room.adapter.npz
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# 3) Run calibrated inference (base + room adapter):
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python infer.py --base pose_mmfi_best.pt --adapter room.adapter.npz --data frames.npz --out kp.npy
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# omit --adapter to run the uncalibrated (zero-shot) base
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```
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`X` is CSI amplitude `[N, 3 antennas, 114 subcarriers, 10 frames]` (per-sample standardization is
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applied internally). `Y` is `[N,17,2]` COCO keypoints in `[0,1]`.
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## Calibration budget (measured, rank-8 LoRA, 3 seeds — ADR-150 §3.5)
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| Labeled samples/room | cross-subject | cross-environment |
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|---------------------:|--------------:|------------------:|
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| 0 (zero-shot) | 64% | ~10% |
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| 5 | — | 60% |
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| 20 | 66% | 66% |
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| 50 | 70% | 70% |
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| 200 | 72% | 73% |
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Knee at ~50 samples (~70%); **below ~20 samples the adapter can hurt** (too few to fit reliably).
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## Notes
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- **Calibration only helps when the base hasn't already seen the room.** The published flagship was
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trained on MM-Fi `random_split`, so calibrating it on an MM-Fi subject is a near-no-op (it already
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saw them); for a genuinely new real-world room it is zero-shot and calibration applies. To
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*reproduce the demo* on a held-out MM-Fi room, train a source-only base (exclude the target
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environment) — see `ADR-150 §3.6` and the few-shot harness in `aether-arena/staging/`.
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- Adapter is saved fp16 (~22 KB); quantize to int8 for the ~11 KB on-device form.
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- Inference is real-time on CPU (the 75 K-param `micro` variant runs in 0.135 ms single-thread x86;
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see [`docs/benchmarks/wifi-pose-efficiency-frontier.md`](../../docs/benchmarks/wifi-pose-efficiency-frontier.md)).
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"""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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"""Run calibrated WiFi-CSI pose inference: shared base + a per-room LoRA adapter.
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python infer.py --base pose_mmfi_best.pt --adapter room_A.adapter.npz --data frames.npz
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`frames.npz` contains `X` [N,3,114,10] CSI amplitude. Prints/saves [N,17,2] keypoints in [0,1].
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Omit --adapter to run the uncalibrated (zero-shot) base. With a room adapter, expect SOTA-level
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accuracy in that room/person; without one, zero-shot degrades in unseen rooms (ADR-150 §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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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)
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ap.add_argument("--adapter", default=None, help="per-room .adapter.npz (omit for zero-shot)")
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ap.add_argument("--data", required=True, help=".npz with X [N,3,114,10]")
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ap.add_argument("--out", default=None, help="optional .npy to save [N,17,2] keypoints")
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ap.add_argument("--rank", type=int, default=8)
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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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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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if a.adapter:
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net.add_lora(r=a.rank).to(dev)
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z = np.load(a.adapter)
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net.load_lora({k: z[k].astype(np.float32) for k in z.files if k.endswith(".A") or k.endswith(".B")})
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net.eval()
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X = torch.tensor(np.load(a.data)["X"].astype(np.float32)).to(dev)
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Xs = standardize(X)
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out = []
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with torch.no_grad():
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for i in range(0, len(Xs), 4096):
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out.append(net(Xs[i:i + 4096]).cpu().numpy())
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kp = np.concatenate(out).reshape(-1, 17, 2)
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print(f"inferred {len(kp)} frames | adapter={'yes' if a.adapter else 'NONE (zero-shot)'}")
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if a.out:
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np.save(a.out, kp)
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print(f"saved keypoints -> {a.out}")
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if __name__ == "__main__":
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main()
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"""WiFi-CSI pose model + LoRA adapter for the RuView calibration service.
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Architecture matches the published flagship checkpoint
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[`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)
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(`pose_mmfi_best.pt`): transformer encoder + temporal attention pooling + skeleton-graph head.
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The calibration service freezes this base and fits a tiny per-room **LoRA adapter** (rank 8 on the
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input projection + pose head ≈ 11 KB) from ~100–200 labeled in-room samples. Empirically that lifts
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cross-subject 64→72% and cross-environment 11→73% (ADR-150 §3.3–3.6).
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"""
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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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# COCO-17 skeleton edges for the graph-refinement head.
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EDGES = [(0, 1), (0, 2), (1, 3), (2, 4), (5, 6), (5, 7), (7, 9), (6, 8), (8, 10),
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(5, 11), (6, 12), (11, 12), (11, 13), (13, 15), (12, 14), (14, 16)]
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_A = np.eye(17, dtype=np.float32)
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for _i, _j in EDGES:
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_A[_i, _j] = _A[_j, _i] = 1.0
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_A = _A / _A.sum(1, keepdims=True)
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class LoRA(nn.Module):
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"""Low-rank adapter wrapping a frozen Linear: y = W·x + (x·A·B)·(alpha/r)."""
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def __init__(self, base: nn.Linear, r: int = 8, alpha: int = 16):
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super().__init__()
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self.base = base
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for p in self.base.parameters():
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p.requires_grad = False
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self.A = nn.Parameter(torch.zeros(base.in_features, r))
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self.B = nn.Parameter(torch.zeros(r, base.out_features))
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nn.init.normal_(self.A, std=0.02)
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self.scale = alpha / r
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def forward(self, x):
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return self.base(x) + (x @ self.A @ self.B) * self.scale
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class GR(nn.Module):
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"""Skeleton-graph refinement: nudges joints toward anatomically consistent positions."""
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def __init__(self, d=256, h=96):
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super().__init__()
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self.je = nn.Parameter(torch.randn(17, 32) * 0.02)
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self.inp = nn.Linear(d + 34, h)
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self.g1 = nn.Linear(h, h)
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self.g2 = nn.Linear(h, h)
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self.out = nn.Linear(h, 2)
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self.register_buffer("A", torch.tensor(_A))
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def forward(self, z, kp0):
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B = z.shape[0]
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f = torch.relu(self.inp(torch.cat(
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[z.unsqueeze(1).expand(-1, 17, -1), self.je.unsqueeze(0).expand(B, -1, -1), kp0], -1)))
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f = torch.relu(self.g1(torch.einsum('ij,bjh->bih', self.A, f)))
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f = torch.relu(self.g2(torch.einsum('ij,bjh->bih', self.A, f)))
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return kp0 + 0.3 * torch.tanh(self.out(f))
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class PoseNet(nn.Module):
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"""Flagship pose model. Input [B,3,114,10] CSI amplitude (per-sample standardized) -> [B,34]."""
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def __init__(self, na=3, nsc=114, nt=10, d=256, L=4, H=8):
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super().__init__()
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self.proj = nn.Linear(na * nsc, d)
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self.pos = nn.Parameter(torch.randn(1, nt, d) * 0.02)
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enc = nn.TransformerEncoderLayer(d, H, d * 2, dropout=0.2, batch_first=True, activation='gelu')
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self.tf = nn.TransformerEncoder(enc, L)
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self.att = nn.Linear(d, 1)
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self.head = nn.Sequential(nn.Linear(d, 256), nn.GELU(), nn.Dropout(0.3), nn.Linear(256, 34))
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self.gr = GR(d)
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self.na, self.nsc, self.nt = na, nsc, nt
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def forward(self, x):
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B = x.shape[0]
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t = x.permute(0, 3, 1, 2).reshape(B, self.nt, self.na * self.nsc)
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h = self.tf(self.proj(t) + self.pos)
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w = torch.softmax(self.att(h), 1)
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z = (h * w).sum(1)
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kp0 = torch.sigmoid(self.head(z)).reshape(B, 17, 2)
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return self.gr(z, kp0).reshape(B, 34)
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def add_lora(self, r=8, alpha=16):
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"""Wrap the input projection + pose head with LoRA adapters (the ~11 KB calibration set)."""
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self.proj = LoRA(self.proj, r, alpha)
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self.head[0] = LoRA(self.head[0], r, alpha)
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self.head[3] = LoRA(self.head[3], r, alpha)
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return self
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def lora_state(self) -> dict:
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"""Extract just the LoRA A/B tensors (the per-room adapter to save)."""
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return {k: v.detach().cpu().numpy() for k, v in self.state_dict().items()
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if k.endswith(".A") or k.endswith(".B")}
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def load_lora(self, adapter: dict):
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sd = self.state_dict()
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for k, v in adapter.items():
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sd[k] = torch.tensor(v)
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self.load_state_dict(sd)
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return self
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def standardize(x: torch.Tensor) -> torch.Tensor:
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"""Per-sample standardization used in training/inference."""
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return (x - x.mean((1, 2, 3), keepdim=True)) / (x.std((1, 2, 3), keepdim=True) + 1e-6)
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