mirror of
https://github.com/ruvnet/RuView
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17471e93ff
* feat(calibration): NodeGeometry transceiver-geometry recording (ADR-152 §2.1.1) PerceptAlign-motivated geometry capture at enrollment: per-node optional records (position, antenna orientation, inter-node distances, acquisition method) — recorded when known, never required. Event-sourced via EnrollmentEvent::GeometryRecorded (latest recording wins); persisted on SpecialistBank with serde defaults so pre-ADR-152 bank JSON loads cleanly (fixture-proven, and geometry-free banks serialize byte-shape-identical to the old schema); threaded through MultiNodeMixture as data only — the learned geometry embeddings and algorithmic fusion use are §2.1.2, deliberately deferred until the ADR-151 P6 LoRA heads exist. Geometry recorded from now on means banks captured today remain usable for layout-conditioned training later — you can't retroactively add geometry to data you didn't record. 8 new tests (3 geometry, 2 anchor, 2 bank, 1 multistatic) + full-loop extension (2-node geometry, one tape-measured + one unknown, surviving the bank JSON round-trip the runtime loads from). 50/50 calibration (both feature configs) + 23 CLI tests green. Co-Authored-By: RuFlo <ruv@ruv.net> * feat(training): two-checkerboard camera↔room calibration for ADR-079 labels (ADR-152 §2.1.3) Defends the camera-supervised pipeline against PerceptAlign's "coordinate overfitting": MediaPipe keypoints were emitted in raw camera coordinates with no shared frame and no transceiver-geometry metadata — the exact label shape that memorizes deployment layout and collapses cross-layout. - scripts/calibrate-camera-room.py + calibration_lib.py: OpenCV two-checkerboard calibration → versioned bundle JSON (intrinsics, camera→room extrinsics, checkerboard spec, transceiver geometry, sha256 calibration_id). Intrinsics resolve from file > cache > multi-view computation > loud-warning 2-view fallback. - collect-ground-truth.py --calibration <bundle>: every sample gains keypoints_room (unit bearing rays from the camera center in the room frame — documented projective alignment; raw image coords preserved so training chooses), camera_origin_room, calibration_id, and the transceiver geometry stamp. Without the flag, output is byte-identical to before (tested) + a one-line ADR-152 warning. Design finding (recorded for ADR-152): a single planar checkerboard's corner grid is centrosymmetric — the reversed corner ordering fits a ghost camera pose with IDENTICAL reprojection error, so per-board flip disambiguation is mathematically ill-posed. solve_two_board_extrinsics solves the joint wall+floor set over all 4 flip combinations, where the minimum is unique — an independent reason the TWO-checkerboard method is required, beyond what PerceptAlign states. 15 headless pytest tests green (synthetic corners: extrinsics recovery incl. ghost resolution, bundle round-trip + hash stability, ray transforms w/ distortion + cross-resolution, no-calibration byte identity). Co-Authored-By: RuFlo <ruv@ruv.net> * feat(benchmarks): WiFlow-STD reproduction harness + measurement (a) results (ADR-152 §2.2) Shipped checkpoint REFUTED (0.08% PCK@20, wrong keypoint normalization); 6 reproducibility defects documented (broken imports, corrupted dataset tail with float32-max garbage that NaN-poisons fp16 BatchNorm, unreachable test phase). After repairs, retraining with upstream defaults reproduces 96.09% PCK@20 full-test / 96.61% corruption-free (published 97.25%) on RTX 5080. Claims graded MEASURED-EQUIVALENT; 2.23M params + ~0.055 GFLOPs verified. Third-party code/weights/data stay out of tree (gitignored). Co-Authored-By: claude-flow <ruv@ruv.net> * feat: ADR-152 Rust integrations + ADR-153 802.11bf protocol model - calibration: GeometryEmbedding — 32-slot permutation-invariant NodeGeometry featurization for future LoRA-head conditioning (ADR-152 §2.1.2); derived SpecialistBank::geometry_embedding() accessor; 59 tests - train: MaePretrainConfig + patchify/random-mask with UNSW measured recipe (80% masking, (30,3) patches; ADR-152 §2.3, arXiv 2511.18792); strict no-truncate/no-NaN policy; proptest properties - train: WiFlowStdModel — tch-gated port of the verified ~96%-PCK@20 WiFlow-STD architecture (ADR-152 §2.2 beyond-SOTA); ungated param formula pinned to 2,225,042; 15/17-keypoint support; 239 crate tests - hardware: ieee80211bf forward-compatibility protocol model (ADR-153): SpecProfile gates, SensingCapabilities negotiation, required ConsentMode, session FSM, SensingTransport + SimTransport + OpportunisticCsiBridge; full acceptance checklist covered; 156+4 tests - deps: ruvector bumps per ADR-152 §2.6 survey (mincut/solver 2.0.6, attention 2.1.0, gnn 2.2.0); vendor/ruvector synced to a083bd77f - docs: ADR-153 accepted; ADR-152 §2.2 status, §2.4 amendment, §2.6 added Workspace: 162 test suites green (--no-default-features); Python proof PASS. Known pre-existing flake: homecore-api env_empty_falls_back_to_defaults (unserialized env-var mutation) — untouched, follow-up. Co-Authored-By: claude-flow <ruv@ruv.net> * docs: CHANGELOG + CLAUDE.md entries for ADR-152 integrations and ADR-153 Co-Authored-By: claude-flow <ruv@ruv.net> * fix(train): repair tch-backend bit-rot — gated path compiles and tests run again Mechanical API refresh against current tch: Vec::from(Tensor) -> try_from (+ explicit flatten), numel() usize cast, Rem/div ops -> remainder() / divide_scalar_mode(floor) — the latter fixed a silent true-division bug in heatmap argmax decoding; clamp(1.0, f64::MAX) -> clamp_min (torch 2.x scalar overflow panic); petgraph EdgeRef import; missing EvalMetrics and verify_checkpoint_dir APIs that tests documented. wiflow_std roundtrip test uses safetensors (.pt _save_parameters roundtrip broken in torch 2.11 Windows). Gated: 349 passed (incl. all 20 wiflow_std); ungated: unchanged. Known pre-existing: gaussian-heatmap convention mismatch (2 tests), proof seed race under parallel threads — documented, deliberate follow-ups. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(train): WiFlow-STD PyTorch->tch weight import + numerical parity proof export_to_safetensors.py maps the retrained checkpoint (295 tensors -> 248 mapped, param sum exactly 2,225,042; num_batches_tracked dropped) into a tch-loadable safetensors plus a deterministic parity fixture. Gated #[ignore] integration test loads it strictly and asserts forward-pass agreement: max abs diff 1.192e-7 on the seed-42 fixture. dump_variable_names test makes the tch name layout authoritative. Zero architecture discrepancies found. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: workflow-review findings — BN gamma init, ThresholdParams serde, init docs Concurrent validation workflow (2 review lanes + adversarial verification, 13 agents): 5 confirmed findings, 3 refuted. Fixes: - wiflow_std: pin BatchNorm gamma to 1.0 (tch default draws Uniform(0,1) — silently halves activations in from-scratch training; loaded checkpoints unaffected, parity re-verified after the change) - wiflow_std: document the conv-init divergences vs the reference's effective kaiming_normal(fan_out) re-init (from-scratch dynamics only) - ieee80211bf: ThresholdParams deserialization validates via try_from so the <=100 invariant holds for untrusted payloads (+ rejection test) Benchmarks (release, ruvzen): GeometryEmbedding 1.84us/call (542k/s), MAE tokenization 7.38us/window (135k/s), 802.11bf FSM 8.9M events/s — nothing suspicious. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr): ADR-152 §2.1.4 gate resolved — PerceptAlign repo MIT, dataset on HF Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): edge optimization measured + measurement (b) blocked + 92.9% retraction Edge optimization (ADR-152 optimize track): ONNX Runtime fp32 is the CPU latency win (3.2 ms/window, ~3.4x faster than torch, parity 2.4e-7); ORT dynamic int8 reaches 2.44 MB (paper's ~2.2 MB claim plausible only via conv-capable toolchains; -0.16pt PCK@20, +18% MPJPE, 2x slower); torch dynamic quant converts 0% of this conv-only model; fp16 halves storage free but is slower on CPU. Measurement (b) BLOCKED-ON-DATA: only 1,077 paired ESP32 windows exist (stop rule <2k). Forensic recheck of the surviving April holdout RETRACTS the ADR-079 '92.9% PCK@20' figure: constant-output model, absolute (not torso) threshold, 69 near-static frames — mean predictor scores 100% under that protocol; torso-PCK@20 is 19.1%. Corroborates PR #535. Stale citations removed from user-guide, readme-details, ADR-152 §2.1.3; no-citation rule extended to ADR-079 accuracy claims. Unblock: >=2k-window multi-pose paired session + torso-PCK re-baseline. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(user-guide): corrected camera-supervised collection tutorial Step 0 CSI-rate check + session-length math (window yield = frames/20 — the May session's 8x under-delivery was a ~12 Hz CSI rate, not an aligner bug); two-checkerboard calibration step (ADR-152 §2.1.3); pose-variety and confidence guidance; torso-normalized PCK + temporal-split + pred-variance eval protocol (lessons from the 92.9% retraction); scale presets re-keyed to realistic window counts. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): static PTQ int8 (calibrated) results + overnight capture script Conv-only static QDQ beats dynamic int8 on accuracy (PCK@20 96.61-96.63% vs 96.52%, MPJPE +10% vs +18% over fp32) at ~equal size/latency; all-ops QDQ strictly worse (int8 activations through attention glue). Entropy calibration verified bit-identical to MinMax on this data. Deployment: ONNX fp32 for speed (3.2ms), static conv-only QDQ for smallest (2.53MB). Also: scripts/overnight-empty-capture.py — segmented UDP CSI recorder for empty-room baselines (no glob collisions, detach-safe). Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): measurement (b) MEASURED — optimization transfer only, mean-pose baseline wins WiFlow-STD fine-tuned on 2,046 fresh single-room ESP32 paired windows (temporal 70/15/15, 70->540 adapter, K=17): pretrained-init 65% PCK@20 vs scratch 0% (optimization transfer) but frozen-trunk ~0% (no feature transfer), and NOTHING beats the mean-pose baseline (95.9% PCK@20 — single subject, near-static normalized coords). Honesty gates held: pred std 0.0113 (non-constant model) but mean-baseline dominance means no citable CSI->pose capability from this data. ADR-152 open question 1 answered partially; definitive answer needs multi-subject/position data. Two new aligner findings: heterogeneous csi_shape with silent zero-padding (~20%), and extractCsiMatrix's transposed shape label (frame-major data, [nSc, nFrames] label) — fixes pending. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): efficiency sweep MEASURED — half model dominates full reference Compact WiFlow-STD variants on the same data/split/protocol: half (843,834 params, 0.38x) strictly dominates the 2.23M reference (PCK@20 96.62 vs 96.61, PCK@50 99.47 vs 99.11, MPJPE 0.00898 vs 0.0094) — the published architecture is over-parameterized for its own benchmark. quarter (338k) 96.05%; tiny (56,290 params, 1/39.5) holds 94.11% — a ~220KB fp32 edge candidate. In-domain caveats recorded; cross-domain untested. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(train): compact WiFlow-STD presets in Rust + tiny edge artifact (ADR-152) WiFlowStdConfig gains half()/quarter()/tiny() mirroring the overnight sweep exactly: TcnGroupsMode (Fixed/Gcd/Depthwise), input_pw_groups, derived stride schedule and decoder-mid (all default to upstream behavior; legacy serde JSON unaffected). Param formulas pin to trained ground truth first try: 843,834 / 338,600 / 56,290; default 2,225,042 pin and 1.192e-7 parity unchanged. 248 tests green. Tiny edge artifact (tiny_edge_bench.py): ONNX fp32 = 295 KB, 0.66 ms/win (~1,500/s CPU), 94.11% PCK@20 (matches sweep clean-test exactly; parity 1.49e-7). Static int8 is a bad trade at this scale (-1.43pt, +19% MPJPE, -16% size, slower) — recorded as negative result. Export note: width-16 breaks AdaptiveAvgPool((15,1)) TorchScript export; replaced by exact mean+matmul equivalent, proven by parity. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: resolve all 10 confirmed code-review findings (7-angle review, 20/20 verified) wiflow_std: min_feature_width (default 15) replaces the keypoints->stride coupling — for_keypoints(17) now provably builds the trained [2,2,2,2] graph and pools 15->17, matching the validated Python protocol (pinned by tests); param_count() total on invalid configs; random_mask returns Result and rejects non-finite/out-of-range ratios; trainer checkpoints switched to safetensors (.pt VarStore roundtrip broken on Windows torch 2.11). ieee80211bf: SBP proxy now re-triggers instances and relays reports via Action::RelaySbpReport -> SensingFrame::SbpReport (clients consume via their existing path); missed_instances reset on success = consecutive semantics; SessionTable gains a guarded SBP entry point + unknown-id drop counter; initiator-role sessions reject inbound setup/SBP requests (RejectedNotSupported) closing the idle hijack; StartSetup/StartSbp outside Idle return InvalidStateForCommand; SBP validation unified through evaluate_setup with a 1:1 SetupStatus->SbpStatus mapping. events.rs split out to honor the 500-line cap. calibration/cli: enrollment geometry now actually reaches trained banks — both production call sites attach .with_geometry; --geometry flag on train-room and POST /enroll/geometry + train-body geometry on calibrate-serve give production a recording surface; geometry-free banks log the ADR-152 §2.1.2 note. benchmarks: corruption masks committed as ground truth (unregenerable after in-place cleaning; verified bit-identical regeneration from the pristine copy) + generate_corruption_masks.py producer; _bench_common.py dedups the 5x-copied shim/evaluate/seed/remap (post-refactor PCK@20 re-verified equal to the last digit); remote scripts get the mmap patch; tiny_edge --calib validated multiple-of-64; onnx_bench --help no longer executes (and overwrote) the export — artifact restored byte-exact. Workspace: 2,963 tests passed, 0 failed; Python proof PASS. Co-Authored-By: claude-flow <ruv@ruv.net> * ci: build workspace tests without debuginfo — runner disk exhaustion The combined 38-crate debug target exceeds the GitHub runner's disk ('final link failed: No space left on device'); the same tree measured 151GB locally with full debuginfo. CARGO_PROFILE_{DEV,TEST}_DEBUG=0 shrinks the target ~5-10x; debuginfo serves no purpose in CI test runs. Co-Authored-By: claude-flow <ruv@ruv.net>
627 lines
24 KiB
Rust
627 lines
24 KiB
Rust
//! `enroll` / `train-room` / `room-status` / `room-watch` — ADR-151 Stages 2–5 CLI.
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//!
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//! Drives the `wifi-densepose-calibration` pipeline against a live ESP32 CSI
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//! stream (requires `edge_tier=0` raw CSI). `enroll` walks the guided anchors and
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//! writes labelled features; `train-room` fits the specialist bank; `room-watch`
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//! runs the mixture runtime and prints live room state.
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use anyhow::{bail, Result};
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use clap::Args;
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use std::time::{Duration, Instant, SystemTime, UNIX_EPOCH};
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use tokio::net::UdpSocket;
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use wifi_densepose_calibration::{
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Anchor, AnchorLabel, AnchorQualityGate, AnchorRecorder, EnrollmentEvent, EnrollmentSession,
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MixtureOfSpecialists, MultiNodeMixture, NodeGeometry, SpecialistBank,
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};
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use wifi_densepose_calibration::extract::{AnchorFeature, Features};
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use wifi_densepose_core::types::CsiFrame;
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use wifi_densepose_signal::BaselineCalibration;
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use crate::calibrate::parse_csi_packet;
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const RECV_BUF: usize = 2048;
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// ---------------------------------------------------------------------------
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// Shared helpers
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// ---------------------------------------------------------------------------
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fn now_unix() -> i64 {
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SystemTime::now()
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.duration_since(UNIX_EPOCH)
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.map(|d| d.as_secs() as i64)
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.unwrap_or(0)
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}
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/// Per-frame scalar: mean amplitude across all subcarriers/streams.
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///
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/// Carries presence/motion energy plus the breathing amplitude modulation.
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/// (Validated live on the ESP32 — picks up breathing where a max-variance
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/// subcarrier instead locks onto motion artifacts. A phase-based carrier on a
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/// *stable* subcarrier is the proper higher-SNR refinement — ADR-151 §4.)
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fn frame_scalar(frame: &CsiFrame) -> f32 {
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let a = &frame.amplitude;
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if a.is_empty() {
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return 0.0;
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}
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(a.sum() / a.len() as f64) as f32
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}
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fn load_baseline(path: &str) -> Result<BaselineCalibration> {
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let bytes = std::fs::read(path)
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.map_err(|e| anyhow::anyhow!("cannot read baseline {path}: {e} — run `calibrate` first"))?;
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BaselineCalibration::from_bytes(&bytes)
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.map_err(|e| anyhow::anyhow!("invalid baseline {path}: {e}"))
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}
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/// Persisted enrollment output (labelled features + audit log).
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#[derive(serde::Serialize, serde::Deserialize)]
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struct EnrollmentData {
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room_id: String,
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baseline_id: String,
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fs_hz: f32,
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anchors: Vec<AnchorFeature>,
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session: EnrollmentSession,
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}
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// ---------------------------------------------------------------------------
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// enroll
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// ---------------------------------------------------------------------------
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/// Arguments for `enroll`.
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#[derive(Args, Debug, Clone)]
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pub struct EnrollArgs {
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/// UDP port for ESP32 CSI frames (raw CSI; provision with `--edge-tier 0`).
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#[arg(long, default_value_t = 5005)]
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pub udp_port: u16,
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/// Bind address for the UDP socket.
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#[arg(long, default_value = "0.0.0.0")]
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pub bind: String,
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/// Path to the empty-room baseline produced by `calibrate`.
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#[arg(long, default_value = "./baseline.bin")]
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pub baseline: String,
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/// PHY tier (ht20 / ht40 / he20 / he40).
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#[arg(long, default_value = "ht20")]
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pub tier: String,
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/// Room label.
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#[arg(long, default_value = "default")]
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pub room_id: String,
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/// Output enrollment file.
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#[arg(long, default_value = "./enrollment.json")]
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pub output: String,
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/// CSI sample rate (Hz) used for periodicity extraction.
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#[arg(long, default_value_t = 15.0)]
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pub fs_hz: f32,
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/// Max attempts per anchor before moving on.
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#[arg(long, default_value_t = 2)]
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pub attempts: u32,
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}
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/// Capture one anchor: returns (accepted feature?, anchor verdict, reason).
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async fn capture_anchor(
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socket: &UdpSocket,
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baseline: &BaselineCalibration,
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gate: &AnchorQualityGate,
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label: AnchorLabel,
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tier: &str,
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fs_hz: f32,
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room_id: &str,
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) -> Result<(Option<AnchorFeature>, Anchor, Option<String>)> {
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eprintln!("\n[enroll] {} — {}", label.as_str(), label.prompt());
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for c in (1..=3).rev() {
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eprintln!("[enroll] starting in {c}…");
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tokio::time::sleep(Duration::from_secs(1)).await;
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}
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eprintln!("[enroll] capturing {} s…", label.duration_s());
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let mut recorder = AnchorRecorder::new(label);
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let mut series: Vec<f32> = Vec::new();
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let mut buf = vec![0u8; RECV_BUF];
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let deadline = Instant::now() + Duration::from_secs(label.duration_s() as u64);
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while Instant::now() < deadline {
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let timeout = Duration::from_millis(500);
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if let Ok(Ok(n)) = tokio::time::timeout(timeout, socket.recv(&mut buf)).await {
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if let Some(frame) = parse_csi_packet(&buf[..n], tier) {
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recorder.record_frame(baseline, &frame);
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series.push(frame_scalar(&frame));
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}
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}
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}
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let (anchor, reason) = recorder.finalize(gate, now_unix());
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let feature = if anchor.quality.accepted {
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Some(AnchorFeature::from_series(room_id, label, &series, fs_hz))
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} else {
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None
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};
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Ok((feature, anchor, reason))
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}
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/// Execute `enroll`.
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pub async fn enroll(args: EnrollArgs) -> Result<()> {
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let baseline = load_baseline(&args.baseline)?;
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let baseline_id = baseline.calibration_uuid().to_string();
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let gate = AnchorQualityGate::default();
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let addr = format!("{}:{}", args.bind, args.udp_port);
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let socket = UdpSocket::bind(&addr)
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.await
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.map_err(|e| anyhow::anyhow!("cannot bind {addr}: {e}"))?;
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eprintln!("[enroll] room='{}' baseline={} on udp://{addr}", args.room_id, &baseline_id[..8]);
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eprintln!("[enroll] follow each prompt; bad captures are re-prompted.");
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let mut session = EnrollmentSession::new(&args.room_id, &baseline_id, now_unix());
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let mut features: Vec<AnchorFeature> = Vec::new();
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for label in AnchorLabel::SEQUENCE {
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let mut accepted = false;
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for attempt in 1..=args.attempts {
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let (feat, anchor, reason) =
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capture_anchor(&socket, &baseline, &gate, label, &args.tier, args.fs_hz, &args.room_id)
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.await?;
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if anchor.quality.accepted {
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eprintln!(
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"[enroll] ✓ accepted (presence_z={:.2} motion={:.0}% frames={})",
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anchor.quality.presence_z,
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anchor.quality.motion_rate * 100.0,
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anchor.quality.frames
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);
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if let Some(f) = feat {
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features.push(f);
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}
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session.apply(EnrollmentEvent::AnchorAccepted { anchor });
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accepted = true;
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break;
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} else {
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let why = reason.unwrap_or_default();
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eprintln!("[enroll] ✗ rejected: {why}");
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session.apply(EnrollmentEvent::AnchorRejected {
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label,
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reason: why,
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at: now_unix(),
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});
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if attempt < args.attempts {
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eprintln!("[enroll] retrying ({}/{})…", attempt + 1, args.attempts);
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}
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}
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}
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if !accepted {
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eprintln!("[enroll] moving on without '{}'", label.as_str());
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}
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}
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if session.is_complete() {
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session.apply(EnrollmentEvent::Completed { at: now_unix() });
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}
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let (got, total) = session.progress();
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let data = EnrollmentData {
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room_id: args.room_id.clone(),
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baseline_id,
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fs_hz: args.fs_hz,
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anchors: features,
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session,
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};
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std::fs::write(
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&args.output,
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serde_json::to_string_pretty(&data).map_err(|e| anyhow::anyhow!("serialize: {e}"))?,
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)
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.map_err(|e| anyhow::anyhow!("cannot write {}: {e}", args.output))?;
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eprintln!(
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"\n[enroll] done: {got}/{total} anchors accepted → {} (next: `train-room`)",
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args.output
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);
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Ok(())
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}
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// ---------------------------------------------------------------------------
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// train-room
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// ---------------------------------------------------------------------------
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/// Arguments for `train-room`.
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#[derive(Args, Debug, Clone)]
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pub struct TrainRoomArgs {
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/// Enrollment file from `enroll`.
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#[arg(long, default_value = "./enrollment.json")]
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pub enrollment: String,
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/// Output specialist-bank file.
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#[arg(long, default_value = "./room-bank.json")]
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pub output: String,
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/// Optional transceiver-geometry file: a JSON array of `NodeGeometry`
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/// records (ADR-152 §2.1.1). Recorded into the enrollment session before
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/// training so the bank carries the layout it was trained under.
|
||
#[arg(long)]
|
||
pub geometry: Option<String>,
|
||
}
|
||
|
||
/// Execute `train-room`.
|
||
///
|
||
/// If the enrollment session carries a transceiver-geometry snapshot (recorded
|
||
/// at enroll time or supplied here via `--geometry`), it is threaded into the
|
||
/// bank (ADR-152 §2.1.1); a geometry-free enrollment still trains a valid bank.
|
||
pub async fn train_room(args: TrainRoomArgs) -> Result<()> {
|
||
let raw = std::fs::read_to_string(&args.enrollment)
|
||
.map_err(|e| anyhow::anyhow!("cannot read {}: {e} — run `enroll` first", args.enrollment))?;
|
||
let mut data: EnrollmentData =
|
||
serde_json::from_str(&raw).map_err(|e| anyhow::anyhow!("invalid enrollment: {e}"))?;
|
||
if data.anchors.is_empty() {
|
||
bail!("no accepted anchors in {} — re-run enroll", args.enrollment);
|
||
}
|
||
|
||
if let Some(path) = &args.geometry {
|
||
let graw = std::fs::read_to_string(path)
|
||
.map_err(|e| anyhow::anyhow!("cannot read geometry {path}: {e}"))?;
|
||
let geometry: Vec<NodeGeometry> = serde_json::from_str(&graw).map_err(|e| {
|
||
anyhow::anyhow!("invalid geometry {path}: {e} (expected a JSON array of NodeGeometry records)")
|
||
})?;
|
||
data.session.record_geometry(geometry, now_unix());
|
||
}
|
||
|
||
let mut bank = SpecialistBank::train(&data.room_id, &data.baseline_id, &data.anchors, now_unix())
|
||
.map_err(|e| anyhow::anyhow!("training failed: {e}"))?;
|
||
match data.session.geometry() {
|
||
Some(g) if !g.is_empty() => {
|
||
bank = bank.with_geometry(g.to_vec());
|
||
eprintln!(
|
||
"[train-room] geometry: {} node(s) snapshotted into the bank (ADR-152 §2.1.1)",
|
||
bank.geometry.len()
|
||
);
|
||
}
|
||
_ => eprintln!(
|
||
"[train-room] no transceiver geometry recorded — bank will not support geometry conditioning (ADR-152 §2.1.2)"
|
||
),
|
||
}
|
||
std::fs::write(&args.output, bank.to_json().map_err(|e| anyhow::anyhow!("{e}"))?)
|
||
.map_err(|e| anyhow::anyhow!("cannot write {}: {e}", args.output))?;
|
||
|
||
eprintln!(
|
||
"[train-room] room='{}' trained {} specialists from {} anchors → {}",
|
||
bank.room_id,
|
||
bank.trained_kinds().len(),
|
||
bank.anchor_count,
|
||
args.output
|
||
);
|
||
for k in bank.trained_kinds() {
|
||
eprintln!("[train-room] • {k:?}");
|
||
}
|
||
Ok(())
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// room-status
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// Arguments for `room-status`.
|
||
#[derive(Args, Debug, Clone)]
|
||
pub struct RoomStatusArgs {
|
||
/// Specialist-bank file.
|
||
#[arg(long, default_value = "./room-bank.json")]
|
||
pub bank: String,
|
||
}
|
||
|
||
/// Execute `room-status`.
|
||
pub async fn room_status(args: RoomStatusArgs) -> Result<()> {
|
||
let raw = std::fs::read_to_string(&args.bank)
|
||
.map_err(|e| anyhow::anyhow!("cannot read {}: {e}", args.bank))?;
|
||
let bank = SpecialistBank::from_json(&raw).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||
println!("room: {}", bank.room_id);
|
||
println!("baseline: {}", bank.baseline_id);
|
||
println!("trained_at: {}", bank.trained_at_unix_s);
|
||
println!("anchors: {}", bank.anchor_count);
|
||
println!("specialists: {:?}", bank.trained_kinds());
|
||
Ok(())
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// room-watch
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// Arguments for `room-watch`.
|
||
#[derive(Args, Debug, Clone)]
|
||
pub struct RoomWatchArgs {
|
||
/// Specialist-bank file (single-node mode).
|
||
#[arg(long, default_value = "./room-bank.json")]
|
||
pub bank: String,
|
||
/// Multistatic mode: map a node id to its bank as `N:path` (repeatable).
|
||
/// When supplied, frames are grouped by node id and fused (ADR-029/151).
|
||
#[arg(long = "node-bank", value_name = "N:PATH")]
|
||
pub node_bank: Vec<String>,
|
||
/// UDP port for ESP32 CSI frames (raw CSI).
|
||
#[arg(long, default_value_t = 5005)]
|
||
pub udp_port: u16,
|
||
/// Bind address.
|
||
#[arg(long, default_value = "0.0.0.0")]
|
||
pub bind: String,
|
||
/// PHY tier.
|
||
#[arg(long, default_value = "ht20")]
|
||
pub tier: String,
|
||
/// CSI sample rate (Hz).
|
||
#[arg(long, default_value_t = 15.0)]
|
||
pub fs_hz: f32,
|
||
/// Rolling window length (frames) for each inference.
|
||
#[arg(long, default_value_t = 200)]
|
||
pub window: usize,
|
||
/// Seconds to run (0 = until Ctrl-C).
|
||
#[arg(long, default_value_t = 0)]
|
||
pub seconds: u32,
|
||
}
|
||
|
||
/// Execute `room-watch` — live (multistatic) mixture-of-specialists readout.
|
||
pub async fn room_watch(args: RoomWatchArgs) -> Result<()> {
|
||
if !args.node_bank.is_empty() {
|
||
return room_watch_multi(args).await;
|
||
}
|
||
let raw = std::fs::read_to_string(&args.bank)
|
||
.map_err(|e| anyhow::anyhow!("cannot read {}: {e}", args.bank))?;
|
||
let bank = SpecialistBank::from_json(&raw).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||
let baseline_id = bank.baseline_id.clone();
|
||
let mix = MixtureOfSpecialists::new(bank);
|
||
|
||
let addr = format!("{}:{}", args.bind, args.udp_port);
|
||
let socket = UdpSocket::bind(&addr)
|
||
.await
|
||
.map_err(|e| anyhow::anyhow!("cannot bind {addr}: {e}"))?;
|
||
eprintln!("[room-watch] inferring on udp://{addr} (window={} frames)", args.window);
|
||
|
||
let mut buf = vec![0u8; RECV_BUF];
|
||
let mut win: std::collections::VecDeque<f32> = std::collections::VecDeque::new();
|
||
let start = Instant::now();
|
||
let mut last_print = Instant::now();
|
||
|
||
loop {
|
||
if args.seconds > 0 && start.elapsed() >= Duration::from_secs(args.seconds as u64) {
|
||
break;
|
||
}
|
||
if let Ok(Ok(n)) = tokio::time::timeout(Duration::from_millis(500), socket.recv(&mut buf)).await {
|
||
if let Some(frame) = parse_csi_packet(&buf[..n], &args.tier) {
|
||
win.push_back(frame_scalar(&frame));
|
||
while win.len() > args.window {
|
||
win.pop_front();
|
||
}
|
||
}
|
||
}
|
||
if last_print.elapsed() >= Duration::from_secs(1) && win.len() >= 32 {
|
||
let series: Vec<f32> = win.iter().copied().collect();
|
||
let f = Features::from_series(&series, args.fs_hz);
|
||
let s = mix.infer(&f, &baseline_id);
|
||
let pres = s.presence.as_ref().map(|r| r.label.clone().unwrap_or_default()).unwrap_or("-".into());
|
||
let post = s.posture.as_ref().and_then(|r| r.label.clone()).unwrap_or("-".into());
|
||
let br = s.breathing.as_ref().map(|r| format!("{:.1}bpm", r.value)).unwrap_or("-".into());
|
||
let hr = s.heartbeat.as_ref().map(|r| format!("{:.0}bpm", r.value)).unwrap_or("-".into());
|
||
let rest = s.restlessness.as_ref().map(|r| format!("{:.2}", r.value)).unwrap_or("-".into());
|
||
let flags = format!(
|
||
"{}{}",
|
||
if s.vetoed { " VETO" } else { "" },
|
||
if s.stale { " STALE" } else { "" }
|
||
);
|
||
println!(
|
||
"presence={pres:<7} posture={post:<8} breathing={br:<8} heart={hr:<7} restless={rest}{flags}"
|
||
);
|
||
last_print = Instant::now();
|
||
}
|
||
}
|
||
Ok(())
|
||
}
|
||
|
||
/// Multistatic `room-watch`: fuse several co-located nodes (ADR-029/151).
|
||
async fn room_watch_multi(args: RoomWatchArgs) -> Result<()> {
|
||
use std::collections::{BTreeMap, VecDeque};
|
||
|
||
let mut mix = MultiNodeMixture::new();
|
||
let mut node_ids: Vec<u8> = Vec::new();
|
||
for spec in &args.node_bank {
|
||
let (id_s, path) = spec
|
||
.split_once(':')
|
||
.ok_or_else(|| anyhow::anyhow!("--node-bank must be N:path (got {spec:?})"))?;
|
||
let id: u8 = id_s
|
||
.parse()
|
||
.map_err(|_| anyhow::anyhow!("bad node id in {spec:?}"))?;
|
||
let raw = std::fs::read_to_string(path)
|
||
.map_err(|e| anyhow::anyhow!("cannot read {path}: {e}"))?;
|
||
let bank = SpecialistBank::from_json(&raw).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||
let baseline = bank.baseline_id.clone();
|
||
mix.add_node(id, bank, baseline);
|
||
node_ids.push(id);
|
||
}
|
||
eprintln!("[room-watch] multistatic over nodes {node_ids:?}");
|
||
|
||
let addr = format!("{}:{}", args.bind, args.udp_port);
|
||
let socket = UdpSocket::bind(&addr)
|
||
.await
|
||
.map_err(|e| anyhow::anyhow!("cannot bind {addr}: {e}"))?;
|
||
eprintln!("[room-watch] fusing on udp://{addr} (window={} frames)", args.window);
|
||
|
||
let mut buf = vec![0u8; RECV_BUF];
|
||
let mut wins: BTreeMap<u8, VecDeque<f32>> = BTreeMap::new();
|
||
let start = Instant::now();
|
||
let mut last_print = Instant::now();
|
||
|
||
loop {
|
||
if args.seconds > 0 && start.elapsed() >= Duration::from_secs(args.seconds as u64) {
|
||
break;
|
||
}
|
||
if let Ok(Ok(n)) =
|
||
tokio::time::timeout(Duration::from_millis(500), socket.recv(&mut buf)).await
|
||
{
|
||
if n < 5 {
|
||
continue;
|
||
}
|
||
let node_id = buf[4];
|
||
if !node_ids.contains(&node_id) {
|
||
continue;
|
||
}
|
||
if let Some(frame) = parse_csi_packet(&buf[..n], &args.tier) {
|
||
let w = wins.entry(node_id).or_default();
|
||
w.push_back(frame_scalar(&frame));
|
||
while w.len() > args.window {
|
||
w.pop_front();
|
||
}
|
||
}
|
||
}
|
||
if last_print.elapsed() >= Duration::from_secs(1) {
|
||
let per_node: BTreeMap<u8, Features> = wins
|
||
.iter()
|
||
.filter(|(_, w)| w.len() >= 32)
|
||
.map(|(id, w)| {
|
||
let series: Vec<f32> = w.iter().copied().collect();
|
||
(*id, Features::from_series(&series, args.fs_hz))
|
||
})
|
||
.collect();
|
||
if !per_node.is_empty() {
|
||
let active: Vec<u8> = per_node.keys().copied().collect();
|
||
let s = mix.infer(&per_node);
|
||
let pres = s.presence.as_ref().and_then(|r| r.label.clone()).unwrap_or("-".into());
|
||
let post = s.posture.as_ref().and_then(|r| r.label.clone()).unwrap_or("-".into());
|
||
let br = s.breathing.as_ref().map(|r| format!("{:.1}bpm", r.value)).unwrap_or("-".into());
|
||
let flags = format!(
|
||
"{}{}",
|
||
if s.vetoed { " VETO" } else { "" },
|
||
if s.stale { " STALE" } else { "" }
|
||
);
|
||
println!(
|
||
"nodes={active:?} presence={pres:<7} posture={post:<8} breathing={br:<8}{flags}"
|
||
);
|
||
}
|
||
last_print = Instant::now();
|
||
}
|
||
}
|
||
Ok(())
|
||
}
|
||
|
||
#[cfg(test)]
|
||
mod tests {
|
||
use super::*;
|
||
|
||
fn feature(label: AnchorLabel, variance: f32, motion: f32) -> AnchorFeature {
|
||
AnchorFeature {
|
||
room_id: "t".into(),
|
||
label,
|
||
features: Features {
|
||
mean: 1.0,
|
||
variance,
|
||
motion,
|
||
breathing_score: 0.0,
|
||
breathing_hz: 0.0,
|
||
heart_score: 0.0,
|
||
heart_hz: 0.0,
|
||
},
|
||
}
|
||
}
|
||
|
||
/// Write a minimal valid enrollment file (two anchors, no geometry event).
|
||
fn write_enrollment(dir: &std::path::Path) -> String {
|
||
let data = EnrollmentData {
|
||
room_id: "t".into(),
|
||
baseline_id: "base-1".into(),
|
||
fs_hz: 15.0,
|
||
anchors: vec![
|
||
feature(AnchorLabel::Empty, 1.0, 0.1),
|
||
feature(AnchorLabel::StandStill, 10.0, 0.2),
|
||
],
|
||
session: EnrollmentSession::new("t", "base-1", 1000),
|
||
};
|
||
let path = dir.join("enrollment.json");
|
||
std::fs::write(&path, serde_json::to_string(&data).unwrap()).unwrap();
|
||
path.to_string_lossy().into_owned()
|
||
}
|
||
|
||
fn trained_bank(out: &std::path::Path) -> SpecialistBank {
|
||
SpecialistBank::from_json(&std::fs::read_to_string(out).unwrap()).unwrap()
|
||
}
|
||
|
||
/// ADR-152 §2.1.1: `--geometry` records into the session and the bank
|
||
/// snapshots it — enrollment geometry reaches the trained bank.
|
||
#[tokio::test]
|
||
async fn train_room_threads_geometry_when_provided() {
|
||
let dir = tempfile::tempdir().unwrap();
|
||
let enrollment = write_enrollment(dir.path());
|
||
let geometry = vec![
|
||
NodeGeometry::new(1, "tape-measure").with_position(0.0, 0.0, 1.0),
|
||
NodeGeometry::unknown(2),
|
||
];
|
||
let gpath = dir.path().join("geometry.json");
|
||
std::fs::write(&gpath, serde_json::to_string(&geometry).unwrap()).unwrap();
|
||
let out = dir.path().join("bank.json");
|
||
|
||
train_room(TrainRoomArgs {
|
||
enrollment,
|
||
output: out.to_string_lossy().into_owned(),
|
||
geometry: Some(gpath.to_string_lossy().into_owned()),
|
||
})
|
||
.await
|
||
.unwrap();
|
||
|
||
assert_eq!(trained_bank(&out).geometry, geometry);
|
||
}
|
||
|
||
/// A geometry-free enrollment still trains a valid bank (optional by
|
||
/// design) — it just carries no snapshot.
|
||
#[tokio::test]
|
||
async fn train_room_without_geometry_yields_geometry_free_bank() {
|
||
let dir = tempfile::tempdir().unwrap();
|
||
let enrollment = write_enrollment(dir.path());
|
||
let out = dir.path().join("bank.json");
|
||
|
||
train_room(TrainRoomArgs {
|
||
enrollment,
|
||
output: out.to_string_lossy().into_owned(),
|
||
geometry: None,
|
||
})
|
||
.await
|
||
.unwrap();
|
||
|
||
let bank = trained_bank(&out);
|
||
assert!(bank.geometry.is_empty());
|
||
assert!(bank.presence.is_some(), "bank still trains without geometry");
|
||
}
|
||
|
||
/// Geometry recorded at enroll time (in the session event log) is picked up
|
||
/// without the `--geometry` flag.
|
||
#[tokio::test]
|
||
async fn train_room_uses_session_geometry() {
|
||
let dir = tempfile::tempdir().unwrap();
|
||
let geometry = vec![NodeGeometry::new(3, "floor-plan").with_position(1.0, 2.0, 1.5)];
|
||
let mut session = EnrollmentSession::new("t", "base-1", 1000);
|
||
session.record_geometry(geometry.clone(), 1000);
|
||
let data = EnrollmentData {
|
||
room_id: "t".into(),
|
||
baseline_id: "base-1".into(),
|
||
fs_hz: 15.0,
|
||
anchors: vec![
|
||
feature(AnchorLabel::Empty, 1.0, 0.1),
|
||
feature(AnchorLabel::StandStill, 10.0, 0.2),
|
||
],
|
||
session,
|
||
};
|
||
let epath = dir.path().join("enrollment.json");
|
||
std::fs::write(&epath, serde_json::to_string(&data).unwrap()).unwrap();
|
||
let out = dir.path().join("bank.json");
|
||
|
||
train_room(TrainRoomArgs {
|
||
enrollment: epath.to_string_lossy().into_owned(),
|
||
output: out.to_string_lossy().into_owned(),
|
||
geometry: None,
|
||
})
|
||
.await
|
||
.unwrap();
|
||
|
||
assert_eq!(trained_bank(&out).geometry, geometry);
|
||
}
|
||
|
||
#[tokio::test]
|
||
async fn train_room_rejects_invalid_geometry_file() {
|
||
let dir = tempfile::tempdir().unwrap();
|
||
let enrollment = write_enrollment(dir.path());
|
||
let gpath = dir.path().join("geometry.json");
|
||
std::fs::write(&gpath, r#"{"not":"an array"}"#).unwrap();
|
||
|
||
let err = train_room(TrainRoomArgs {
|
||
enrollment,
|
||
output: dir.path().join("bank.json").to_string_lossy().into_owned(),
|
||
geometry: Some(gpath.to_string_lossy().into_owned()),
|
||
})
|
||
.await
|
||
.unwrap_err();
|
||
assert!(err.to_string().contains("invalid geometry"), "{err}");
|
||
}
|
||
}
|