mirror of
https://github.com/ruvnet/RuView.git
synced 2026-06-02 00:58:56 +02:00
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>
50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
"""Run calibrated WiFi-CSI pose inference: shared base + a per-room LoRA adapter.
|
|
|
|
python infer.py --base pose_mmfi_best.pt --adapter room_A.adapter.npz --data frames.npz
|
|
|
|
`frames.npz` contains `X` [N,3,114,10] CSI amplitude. Prints/saves [N,17,2] keypoints in [0,1].
|
|
Omit --adapter to run the uncalibrated (zero-shot) base. With a room adapter, expect SOTA-level
|
|
accuracy in that room/person; without one, zero-shot degrades in unseen rooms (ADR-150 §3.6).
|
|
"""
|
|
import argparse
|
|
import numpy as np
|
|
import torch
|
|
|
|
from model import PoseNet, standardize
|
|
|
|
|
|
def main():
|
|
ap = argparse.ArgumentParser()
|
|
ap.add_argument("--base", required=True)
|
|
ap.add_argument("--adapter", default=None, help="per-room .adapter.npz (omit for zero-shot)")
|
|
ap.add_argument("--data", required=True, help=".npz with X [N,3,114,10]")
|
|
ap.add_argument("--out", default=None, help="optional .npy to save [N,17,2] keypoints")
|
|
ap.add_argument("--rank", type=int, default=8)
|
|
ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
|
|
a = ap.parse_args()
|
|
dev = a.device
|
|
|
|
net = PoseNet().to(dev)
|
|
net.load_state_dict(torch.load(a.base, map_location=dev), strict=False)
|
|
if a.adapter:
|
|
net.add_lora(r=a.rank).to(dev)
|
|
z = np.load(a.adapter)
|
|
net.load_lora({k: z[k].astype(np.float32) for k in z.files if k.endswith(".A") or k.endswith(".B")})
|
|
net.eval()
|
|
|
|
X = torch.tensor(np.load(a.data)["X"].astype(np.float32)).to(dev)
|
|
Xs = standardize(X)
|
|
out = []
|
|
with torch.no_grad():
|
|
for i in range(0, len(Xs), 4096):
|
|
out.append(net(Xs[i:i + 4096]).cpu().numpy())
|
|
kp = np.concatenate(out).reshape(-1, 17, 2)
|
|
print(f"inferred {len(kp)} frames | adapter={'yes' if a.adapter else 'NONE (zero-shot)'}")
|
|
if a.out:
|
|
np.save(a.out, kp)
|
|
print(f"saved keypoints -> {a.out}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|