mirror of
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>
108 lines
4.3 KiB
Python
108 lines
4.3 KiB
Python
"""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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