mirror of
https://github.com/ruvnet/RuView
synced 2026-06-14 11:03:18 +00:00
Compare commits
1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 dependabot[bot]
|
705b5d7559 |
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"predict": {
|
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@@ -69,8 +64,8 @@
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},
|
||||
"config": {
|
||||
"autoStart": false,
|
||||
"logDir": "C:\\Users\\ruv\\Projects\\wifi-densepose\\.claude-flow\\logs",
|
||||
"stateFile": "C:\\Users\\ruv\\Projects\\wifi-densepose\\.claude-flow\\daemon-state.json",
|
||||
"logDir": "/Users/cohen/GitHub/ruvnet/RuView/.claude-flow/logs",
|
||||
"stateFile": "/Users/cohen/GitHub/ruvnet/RuView/.claude-flow/daemon-state.json",
|
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"maxConcurrent": 2,
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"workerTimeoutMs": 300000,
|
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"resourceThresholds": {
|
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@@ -136,5 +131,5 @@
|
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}
|
||||
]
|
||||
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|
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}
|
||||
@@ -1,119 +0,0 @@
|
||||
{
|
||||
"id": "aether-arena-aa",
|
||||
"name": "AetherArena (AA) — Official Spatial-Intelligence Benchmark",
|
||||
"adr": "ADR-149",
|
||||
"adrPath": "docs/adr/ADR-149-public-community-leaderboard-huggingface.md",
|
||||
"status": "Accepted",
|
||||
"initializedDate": "2026-05-30",
|
||||
"targetDate": "2026-08-31",
|
||||
"exitCriteria": "Benchmark INFRASTRUCTURE done, tested, CI-gated, deploy-ready: aa_score_runner.rs passes deterministic fixture test; CI harness-gate green on every PR; aether-arena repo scaffold committed (README four-part framing + aa-submission.toml schema + VERIFY.md); public smoke split committed; HF Space lifecycle skeleton deployed; signed Parquet ledger functional; RuView baseline PCK@20 ~2.5% entered; ADR-149 §7 acceptance test (five-step stranger test) passes. NOTE: ML SOTA (MM-Fi PCK@20 ~72%) is a separate long-running stretch goal blocked on ADR-079 camera-ground-truth — it is NOT an infra exit criterion.",
|
||||
"baselineState": {
|
||||
"adrStatus": "Accepted, committed 2026-05-30",
|
||||
"scorerCode": "ruview_metrics.rs + ablation.rs + proof.rs exist in wifi-densepose-train; aa_score_runner.rs not yet created",
|
||||
"aetherArenaRepo": "does not exist yet — needs user authorization to create ruvnet/aether-arena public repo",
|
||||
"hfSpace": "does not exist yet — needs HF_TOKEN and user authorization to deploy ruvnet/aether-arena HF Space",
|
||||
"smokeDataset": "not committed",
|
||||
"resultsLedger": "not created",
|
||||
"ruviewBaseline": "PCK@20 ~2.5% self-reported, not formally entered",
|
||||
"ciGate": "not added to workflow"
|
||||
},
|
||||
"milestones": {
|
||||
"m1": {
|
||||
"name": "ADR-149 Accepted + committed",
|
||||
"status": "DONE",
|
||||
"completedDate": "2026-05-30",
|
||||
"completionCriteria": "ADR-149 file committed to docs/adr/ with status Accepted",
|
||||
"notes": "Done this session. File at docs/adr/ADR-149-public-community-leaderboard-huggingface.md"
|
||||
},
|
||||
"m2": {
|
||||
"name": "Deterministic scorer runner bin (aa_score_runner.rs)",
|
||||
"status": "NOT_STARTED",
|
||||
"completionCriteria": "aa_score_runner.rs compiles, runs ruview_metrics on a committed fixture, emits RuViewTier + SHA-256 proof hash, mirrors existing *_proof_runner.rs pattern; cargo test passes",
|
||||
"estimatedEffort": "3-5 days",
|
||||
"owner": "wifi-densepose-train crate or new aa-scorer crate"
|
||||
},
|
||||
"m3": {
|
||||
"name": "CI harness-gate: GitHub Actions workflow",
|
||||
"status": "NOT_STARTED",
|
||||
"completionCriteria": "A GitHub Actions workflow runs aa_score_runner on every PR as a build gate; PR fails if scorer fails determinism check; workflow committed and green",
|
||||
"estimatedEffort": "2-3 days",
|
||||
"dependency": "M2 must be done first"
|
||||
},
|
||||
"m4": {
|
||||
"name": "aether-arena repo scaffold",
|
||||
"status": "NOT_STARTED",
|
||||
"completionCriteria": "ruvnet/aether-arena repo created with: README (four-part framing: Public leaderboard / Private eval split / Open scorer / Signed results); aa-submission.toml manifest schema; VERIFY.md (ADR-149 §7 stranger acceptance test); neutrality/governance section (§2.8); contribution guide",
|
||||
"estimatedEffort": "3-5 days",
|
||||
"blockers": ["Needs user authorization to create public ruvnet/aether-arena repo on GitHub"]
|
||||
},
|
||||
"m5": {
|
||||
"name": "Public smoke split committed + private MM-Fi held-out split prep",
|
||||
"status": "NOT_STARTED",
|
||||
"completionCriteria": "Public smoke split committed to aether-arena repo (stranger can score locally); private MM-Fi held-out split prepared under non-public path with CC BY-NC 4.0 attribution; Wi-Pose explicitly excluded from v0",
|
||||
"estimatedEffort": "5-7 days",
|
||||
"riskNotes": "MM-Fi CC BY-NC 4.0: AA must remain non-commercial and carry MM-Fi attribution; raw frames stay in private split; only derived CSI features + scores may be exposed"
|
||||
},
|
||||
"m6": {
|
||||
"name": "HF Space (Gradio) skeleton",
|
||||
"status": "BLOCKED",
|
||||
"completionCriteria": "HF Space deployed at ruvnet/aether-arena with submission lifecycle (submitted->validated->quarantined->smoke_scored->full_scored->published/rejected); sandboxed scorer container wired; basic leaderboard table rendered",
|
||||
"estimatedEffort": "7-10 days",
|
||||
"blockers": [
|
||||
"Needs HF_TOKEN — check .env for HF_TOKEN or HUGGINGFACE_TOKEN",
|
||||
"Needs user authorization to create/deploy ruvnet/aether-arena HF Space (outward-facing public deployment)"
|
||||
]
|
||||
},
|
||||
"m7": {
|
||||
"name": "Signed append-only Parquet results ledger",
|
||||
"status": "NOT_STARTED",
|
||||
"completionCriteria": "HF dataset ruvnet/aether-arena-results created; append-only Parquet ledger with signed rows; determinism_gate enforced; no row can be silently edited",
|
||||
"estimatedEffort": "3-5 days",
|
||||
"ledgerSchema": "submitter, model_ref, category, feature_set, tier, pck20, oks, mota, vitals_bpm_err, latency_p50, latency_p95, privacy_leakage, cross_room_deg, proof_sha256, scored_at, harness_version",
|
||||
"dependency": "M6 must be scaffolded first"
|
||||
},
|
||||
"m8": {
|
||||
"name": "RuView baseline entry + public launch",
|
||||
"status": "NOT_STARTED",
|
||||
"completionCriteria": "RuView wifi-densepose-pretrained baseline entered (honest PCK@20 ~2.5%); ADR-149 §7 five-step stranger acceptance test passes; v0 live with Presence + Pose + Edge-latency + Determinism categories active; Privacy and Cross-room shown as gated/coming-soon",
|
||||
"estimatedEffort": "3-5 days",
|
||||
"dependency": "M4+M5+M6+M7 complete",
|
||||
"notes": "ML SOTA improvement (PCK@20 ~72%) is a SEPARATE stretch goal blocked on ADR-079 P7-P9 camera ground truth. NOT a blocker for infra launch."
|
||||
}
|
||||
},
|
||||
"activeMilestone": "m2",
|
||||
"completedMilestones": ["m1"],
|
||||
"knownRisks": [
|
||||
"HF_TOKEN not confirmed present in .env — check before M6 work begins",
|
||||
"ruvnet/aether-arena public repo creation is outward-facing — needs explicit user authorization",
|
||||
"MM-Fi CC BY-NC 4.0: AA must stay legally non-commercial and brand-distinct from commercial RuView product; or seek MM-Fi commercial grant before any paid tier",
|
||||
"Wi-Pose has research-use-only terms (no redistribution grant) — excluded from v0; revisit only if terms are clarified with authors",
|
||||
"HF Space free CPU tier may be too slow for Candle/tch inference pipeline — may need ZeroGPU or self-hosted scorer on cognitum-20260110 GCloud A100/L4",
|
||||
"ADR-079 camera-ground-truth (PCK@20 SOTA) is P7-P9 pending — NOT an infra blocker; must not be conflated with AA infra completion",
|
||||
"Neutrality/governance risk: RuView seeded the scorer — must be demonstrably scored through the same public pipeline as any other entrant (§2.8 controls)"
|
||||
],
|
||||
"driftSignals": {
|
||||
"timeline": "GREEN — just initialized, no timeline pressure yet",
|
||||
"scope": "GREEN — scope locked at four-part structure per ADR-149 §2 decision",
|
||||
"approach": "GREEN — reuse pattern (existing ruview_metrics + proof.rs) confirmed in ADR-149",
|
||||
"dependency": "YELLOW — HF_TOKEN and ruvnet/aether-arena repo authorization are external blockers with unknown ETA",
|
||||
"priority": "GREEN — active feature branch feat/adr-136-146-streaming-engine in progress; AA infra can proceed in parallel on its own branch"
|
||||
},
|
||||
"stretchGoals": {
|
||||
"sotaML": "MM-Fi PCK@20 SOTA ~72% — separate ML effort blocked on ADR-079 P7-P9 camera-ground-truth data collection; NOT an infra exit criterion",
|
||||
"privacyAxis": "ADR-145 §10 membership-inference attacker — activate Privacy leaderboard axis once attacker is implemented and published",
|
||||
"crossRoom": "Multi-room held-out split — activate Cross-room generalization axis",
|
||||
"multiOrgSteering": "Invite co-maintainers from other projects once >=N external entries land"
|
||||
},
|
||||
"sessionHistory": [
|
||||
{
|
||||
"date": "2026-05-30",
|
||||
"type": "initialization",
|
||||
"accomplished": [
|
||||
"ADR-149 Accepted and committed to docs/adr/",
|
||||
"Horizon record initialized in .claude-flow/horizons/aether-arena-aa.json",
|
||||
"Memory stored in horizons namespace under key horizon-aether-arena-aa",
|
||||
"Session check-in record stored in horizon-sessions namespace"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,11 +1,11 @@
|
||||
{
|
||||
"timestamp": "2026-05-25T06:07:33.385Z",
|
||||
"projectRoot": "C:\\Users\\ruv\\Projects\\wifi-densepose",
|
||||
"timestamp": "2026-02-28T16:13:19.193Z",
|
||||
"projectRoot": "/home/user/wifi-densepose",
|
||||
"structure": {
|
||||
"hasPackageJson": false,
|
||||
"hasTsConfig": false,
|
||||
"hasClaudeConfig": true,
|
||||
"hasClaudeFlow": true
|
||||
},
|
||||
"scannedAt": 1779689253386
|
||||
"scannedAt": 1772295199193
|
||||
}
|
||||
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"timestamp": "2026-05-25T05:38:20.448Z",
|
||||
"timestamp": "2026-02-28T16:05:19.091Z",
|
||||
"patternsConsolidated": 0,
|
||||
"memoryCleaned": 0,
|
||||
"duplicatesRemoved": 0
|
||||
|
||||
@@ -1,17 +0,0 @@
|
||||
{
|
||||
"timestamp": "2026-05-25T05:59:05.405Z",
|
||||
"mode": "local",
|
||||
"memoryUsage": {
|
||||
"rss": 9891840,
|
||||
"heapTotal": 35598336,
|
||||
"heapUsed": 26516560,
|
||||
"external": 3952418,
|
||||
"arrayBuffers": 55689
|
||||
},
|
||||
"uptime": 27163.5846658,
|
||||
"optimizations": {
|
||||
"cacheHitRate": 0.78,
|
||||
"avgResponseTime": 45
|
||||
},
|
||||
"note": "Install Claude Code CLI for AI-powered optimization suggestions"
|
||||
}
|
||||
@@ -1,84 +1,12 @@
|
||||
{
|
||||
"timestamp": "2026-05-25T06:08:29.589Z",
|
||||
"mode": "headless",
|
||||
"workerType": "audit",
|
||||
"model": "haiku",
|
||||
"durationMs": 56168,
|
||||
"executionId": "audit_1779689253421_dfflmb",
|
||||
"success": true,
|
||||
"findings": {
|
||||
"vulnerabilities": [
|
||||
{
|
||||
"severity": "high",
|
||||
"file": ".claude/helpers/github-safe.js",
|
||||
"line": 50,
|
||||
"description": "Command injection vulnerability in execSync call. User-controlled arguments in `newArgs` are joined without shell escaping. An attacker can inject shell metacharacters (e.g., `; rm -rf /`) via the body content or through command/subcommand parameters. The temp file approach is safe, but the command construction `gh ${command} ${subcommand} ${newArgs.join(' ')}` allows shell injection.",
|
||||
"example": "gh issue comment 123 'test`whoami`' would execute whoami"
|
||||
},
|
||||
{
|
||||
"severity": "high",
|
||||
"file": "scripts/csi-spectrogram.js",
|
||||
"line": 45,
|
||||
"description": "Sensitive credential exposure via command-line arguments. The `--seed-token` parameter is passed as a CLI argument, which is visible in process listings (ps aux output). This violates secure credential handling practices. Tokens should be read from environment variables or secure config files, not command-line args.",
|
||||
"example": "node scripts/csi-spectrogram.js --seed-token secret_abc_123 exposes token in process list"
|
||||
},
|
||||
{
|
||||
"severity": "medium",
|
||||
"file": "scripts/apnea-detector.js",
|
||||
"line": 71,
|
||||
"description": "Unsafe buffer reading without comprehensive length validation. The code checks `buf.length` at 32 bytes (line 70) but then reads at fixed offsets (lines 72-76) without validating that each read stays within bounds. If a malformed packet is received, `readInt8/readUInt16LE/readUInt32LE` may read unintended data or zeros.",
|
||||
"example": "A 33-byte buffer would pass the check but reading UInt32LE at offset 8 would go out of bounds"
|
||||
},
|
||||
{
|
||||
"severity": "medium",
|
||||
"file": "scripts/benchmark-rf-scan.js",
|
||||
"line": 110,
|
||||
"description": "Potential out-of-bounds buffer access in parseCSIFrame. While the bounds check at line 107 is present, the `nSubcarriers` value from the packet is used to calculate required buffer size without validation of the value itself. A maliciously crafted packet with extremely large nSubcarriers could cause memory issues.",
|
||||
"example": "Packet with nSubcarriers=999999 would request excessive buffer allocation"
|
||||
},
|
||||
{
|
||||
"severity": "medium",
|
||||
"file": "scripts/csi-spectrogram.js",
|
||||
"line": 39,
|
||||
"description": "Unsafe URL construction with untrusted `seed-url` parameter. The `--seed-url` argument is used directly for HTTPS requests without validation. This could allow SSRF (Server-Side Request Forgery) or DNS rebinding attacks if an attacker controls the seed URL.",
|
||||
"example": "node scripts/csi-spectrogram.js --seed-url http://internal.local:9000 could access internal services"
|
||||
},
|
||||
{
|
||||
"severity": "low",
|
||||
"file": ".claude/helpers/statusline.js",
|
||||
"line": 140,
|
||||
"description": "Shell command injection risk in execSync calls. Commands like `ps aux 2>/dev/null | grep -c agentic-flow` use grep patterns that could be vulnerable if any variables are interpolated (though currently hardcoded). The `execSync` with shell=true is generally risky.",
|
||||
"example": "If any pattern becomes user-controlled: `grep -c ${pattern}` could inject shell metacharacters"
|
||||
},
|
||||
{
|
||||
"severity": "low",
|
||||
"file": ".claude/helpers/memory.js",
|
||||
"line": 10,
|
||||
"description": "Unvalidated JSON parsing. The code parses JSON from MEMORY_FILE without try-catch in the loadMemory function (catches error but doesn't validate structure). Malformed JSON or corrupted memory file could cause issues.",
|
||||
"example": "Memory file with circular JSON structure could cause issues when stringifying"
|
||||
},
|
||||
{
|
||||
"severity": "low",
|
||||
"file": "scripts/device-fingerprint.js",
|
||||
"line": 72,
|
||||
"description": "Hardcoded device fingerprints and network configuration. While not a traditional 'hardcoded secret', the KNOWN_DEVICES array contains identifiable SSIDs and MAC addresses that could be used to correlate network infrastructure. This data should be externalized or sanitized.",
|
||||
"example": "SSID 'ruv.net' and 'Cohen-Guest' could identify specific installations"
|
||||
}
|
||||
],
|
||||
"riskScore": 42,
|
||||
"recommendations": [
|
||||
"**CRITICAL**: Replace `execSync` command construction in github-safe.js with proper shell escaping using `child_process.execFile()` instead of `execSync()`, or use the `shell: false` option with array arguments to avoid shell parsing entirely.",
|
||||
"**CRITICAL**: Move `--seed-token` from CLI arguments to environment variable `SEED_TOKEN` in csi-spectrogram.js. Update documentation to instruct users: `export SEED_TOKEN=...` instead of passing via CLI.",
|
||||
"**HIGH**: Add comprehensive buffer bounds validation in all UDP packet parsing functions (apnea-detector.js, benchmark-rf-scan.js, etc.). Validate both the buffer length AND the parsed header values before using them in calculations.",
|
||||
"**HIGH**: Validate and sanitize the `--seed-url` parameter in csi-spectrogram.js. Whitelist allowed domains or restrict to localhost/internal IPs only. Add URL scheme validation (https only).",
|
||||
"**MEDIUM**: Replace hardcoded device fingerprints (KNOWN_DEVICES) with externalized configuration or environment variables. Document that this data contains identifiable network information.",
|
||||
"**MEDIUM**: Add input validation to `parseArgs()` results in all scripts. Validate numeric ranges, file paths, and enum values before use.",
|
||||
"**LOW**: Wrap JSON.parse() calls in try-catch blocks throughout (memory.js, session.js) with explicit error handling and recovery.",
|
||||
"**LOW**: Audit all uses of `require()` with dynamic paths. Ensure paths are always derived from fixed `__dirname` and not user-controlled.",
|
||||
"**LOW**: Remove or sandbox the ability to pass arbitrary URLs via CLI. Consider using a configuration file (YAML/JSON) for endpoint URLs instead.",
|
||||
"**INFO**: Add a pre-commit hook to detect hardcoded credentials using tools like `detect-secrets` or `truffleHog`."
|
||||
]
|
||||
"timestamp": "2026-03-06T13:17:27.368Z",
|
||||
"mode": "local",
|
||||
"checks": {
|
||||
"envFilesProtected": true,
|
||||
"gitIgnoreExists": true,
|
||||
"noHardcodedSecrets": true
|
||||
},
|
||||
"rawOutputPreview": "# Security Audit Report — wifi-densepose\n\n```json\n{\n \"vulnerabilities\": [\n {\n \"severity\": \"high\",\n \"file\": \".claude/helpers/github-safe.js\",\n \"line\": 50,\n \"description\": \"Command injection vulnerability in execSync call. User-controlled arguments in `newArgs` are joined without shell escaping. An attacker can inject shell metacharacters (e.g., `; rm -rf /`) via the body content or through command/subcommand parameters. The temp file approach is safe, but the command construction `gh ${command} ${subcommand} ${newArgs.join(' ')}` allows shell injection.\",\n \"example\": \"gh issue comment 123 'test`whoami`' would execute whoami\"\n },\n {\n \"severity\": \"high\",\n \"file\": \"scripts/csi-spectrogram.js\",\n \"line\": 45,\n \"description\": \"Sensitive credential exposure via command-line arguments. The `--seed-token` parameter is passed as a CLI argument, which is visible in process listings (ps aux output). This violates secure credential handling practices. Tokens should be read from environment variables or secure config files, not command-line args.\",\n \"example\": \"node scripts/csi-spectrogram.js --seed-token secret_abc_123 exposes token in process list\"\n },\n {\n \"severity\": \"medium\",\n \"file\": \"scripts/apnea-detector.js\",\n \"line\": 71,\n \"description\": \"Unsafe buffer reading without comprehensive length validation. The code checks `buf.length` at 32 bytes (line 70) but then reads at fixed offsets (lines 72-76) without validating that each read stays within bounds. If a malformed packet is received, `readInt8/readUInt16LE/readUInt32LE` may read unintended data or zeros.\",\n \"example\": \"A 33-byte buffer would pass the check but reading UInt32LE at offset 8 would go out of bounds\"\n },\n {\n \"severity\": \"medium\",\n \"file\": \"scripts/benchmark-rf-scan.js\",\n \"line\": 110,\n \"description\": \"Potential out-of-bounds buffer access in parseCSIFrame. While the bounds check at line 107 is pres",
|
||||
"rawOutputLength": 7077
|
||||
"riskLevel": "low",
|
||||
"recommendations": [],
|
||||
"note": "Install Claude Code CLI for AI-powered security analysis"
|
||||
}
|
||||
@@ -1,106 +0,0 @@
|
||||
{
|
||||
"timestamp": "2026-05-25T06:11:52.519Z",
|
||||
"mode": "headless",
|
||||
"workerType": "testgaps",
|
||||
"model": "sonnet",
|
||||
"durationMs": 259124,
|
||||
"executionId": "testgaps_1779689253395_srltd5",
|
||||
"success": true,
|
||||
"findings": {
|
||||
"sections": [
|
||||
{
|
||||
"title": "Test Coverage Gap Analysis — wifi-densepose",
|
||||
"content": "\n",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"title": "Coverage Summary by Crate",
|
||||
"content": "\n| Crate | Tests Found | Status | Priority |\n|-------|-------------|--------|----------|\n| `wifi-densepose-core` | 26 inline | Good | Low |\n| `wifi-densepose-signal` | ~60 (validation only) | Moderate | **High** |\n| `wifi-densepose-nn` | **0** | Critical | **P1** |\n| `wifi-densepose-train` | ~60 (config/dataset) | Moderate | High |\n| `wifi-densepose-mat` | 1 integration test | Critical | **P1** |\n| `wifi-densepose-ruvector` | **0** | Critical | **P1** |\n| `wifi-densepose-sensing-server` | 4 integration tests | Moderate | High |\n| `wifi-densepose-wasm` | 3 compliance tests | Low | Low |\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "Tier 1: Critical Gaps",
|
||||
"content": "\n",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"title": "1. `wifi-densepose-nn` — Zero test coverage",
|
||||
"content": "\nEvery public API is untested. Place these at `v2/crates/wifi-densepose-nn/tests/inference_tests.rs`:\n\n```rust\n// v2/crates/wifi-densepose-nn/tests/inference_tests.rs\n\n#[cfg(test)]\nmod tensor_tests {\n use wifi_densepose_nn::tensor::Tensor;\n\n #[test]\n fn tensor_shape_mismatch_returns_error() {\n // data has 6 elements but shape claims 3×3=9\n let result = Tensor::new(vec![1.0f32; 6], &[3, 3]);\n assert!(result.is_err(), \"shape mismatch must be rejected\");\n }\n\n #[test]\n fn tensor_empty_data_returns_error() {\n let result = Tensor::new(vec![], &[0]);\n assert!(result.is_err());\n }\n\n #[test]\n fn tensor_nan_values_are_detected() {\n let t = Tensor::new(vec![f32::NAN, 1.0, 2.0], &[3]).unwrap();\n assert!(t.has_nan(), \"NaN in data must be detectable\");\n }\n\n #[test]\n fn tensor_inf_values_are_detected() {\n let t = Tensor::new(vec![f32::INFINITY, 1.0], &[2]).unwrap();\n assert!(t.has_inf());\n }\n}\n\n#[cfg(test)]\nmod modality_translator_tests {\n use wifi_densepose_nn::translator::ModalityTranslator;\n\n #[test]\n fn translator_rejects_wrong_subcarrier_count() {\n // standard expects 56 subcarriers; feed 57\n let csi = vec![0.0f32; 57 * 3]; // 57 subcarriers × 3 antennas\n let translator = ModalityTranslator::default();\n let result = translator.translate(&csi, 57, 3);\n assert!(result.is_err());\n }\n\n #[test]\n fn translator_handles_all_zeros() {\n let csi = vec![0.0f32; 56 * 3];\n let translator = ModalityTranslator::default();\n let result = translator.translate(&csi, 56, 3);\n // zero input should produce some output without panic\n assert!(result.is_ok());\n }\n}\n\n#[cfg(test)]\nmod inference_engine_tests {\n use wifi_densepose_nn::inference::InferenceEngine;\n\n #[test]\n fn load_nonexistent_model_returns_error() {\n let result = InferenceEngine::from_path(\"/nonexistent/model.onnx\");\n assert!(result.is_err());\n }\n\n #[test]\n fn load_corrupted_bytes_returns_error() {\n let tmp = tempfile::NamedTempFile::new().unwrap();\n std::fs::write(tmp.path(), b\"not a valid onnx file\").unwrap();\n let result = InferenceEngine::from_path(tmp.path());\n assert!(result.is_err());\n }\n\n #[test]\n fn batch_size_zero_returns_error() {\n // can't run inference on an empty batch\n // requires a valid model; skip if no model file in test fixtures\n // use #[ignore] or a feature flag for CI\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "2. `wifi-densepose-mat` — Disaster response safety gaps",
|
||||
"content": "\nPlace at `v2/crates/wifi-densepose-mat/tests/`:\n\n```rust\n// v2/crates/wifi-densepose-mat/tests/detection_edge_cases.rs\n\n#[cfg(test)]\nmod breathing_rate_edge_cases {\n use wifi_densepose_mat::detection::breathing::BreathingDetector;\n\n #[test]\n fn zero_bpm_is_classified_critical() {\n let detector = BreathingDetector::default();\n // flat-line signal — no breathing detected\n let signal = vec![0.0f32; 1000];\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Immediate);\n }\n\n #[test]\n fn agonal_breathing_rate_triggers_immediate() {\n // < 6 BPM is agonal; simulate 3 BPM signal\n let detector = BreathingDetector::default();\n let signal = generate_breathing_signal(3.0, 1000, 100.0); // 3 BPM, 1000 samples @ 100 Hz\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Immediate);\n }\n\n #[test]\n fn normal_breathing_is_classified_minor() {\n let detector = BreathingDetector::default();\n let signal = generate_breathing_signal(15.0, 1000, 100.0); // 15 BPM\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Minor);\n }\n\n #[test]\n fn all_nan_signal_returns_error_not_panic() {\n let detector = BreathingDetector::default();\n let signal = vec![f32::NAN; 1000];\n let result = detector.classify(&signal);\n assert!(result.is_err(), \"NaN input must be caught, not panic\");\n }\n\n fn generate_breathing_signal(bpm: f32, samples: usize, sample_rate: f32) -> Vec<f32> {\n let freq = bpm / 60.0;\n (0..samples)\n .map(|i| (2.0 * std::f32::consts::PI * freq * i as f32 / sample_rate).sin())\n .collect()\n }\n}\n\n#[cfg(test)]\nmod alert_deduplication {\n use wifi_densepose_mat::alerting::{AlertDispatcher, Alert, TriageCategory};\n use std::time::Duration;\n\n #[test]\n fn duplicate_alerts_within_window_are_suppressed() {\n let mut dispatcher = AlertDispatcher::new();\n let alert = Alert::new(\"survivor-1\", TriageCategory::Immediate);\n dispatcher.dispatch(alert.clone());\n dispatcher.dispatch(alert.clone()); // same survivor, same category\n assert_eq!(dispatcher.queued_count(), 1, \"duplicate must be deduplicated\");\n }\n\n #[test]\n fn escalation_from_minor_to_immediate_is_forwarded() {\n let mut dispatcher = AlertDispatcher::new();\n dispatcher.dispatch(Alert::new(\"survivor-1\", TriageCategory::Minor));\n dispatcher.dispatch(Alert::new(\"survivor-1\", TriageCategory::Immediate));\n // escalation is not a duplicate — must pass through\n assert!(dispatcher.last_alert_for(\"survivor-1\").map(|a| a.category) == Some(TriageCategory::Immediate));\n }\n}\n\n#[cfg(test)]\nmod kalman_tracker_edge_cases {\n use wifi_densepose_mat::tracking::KalmanTracker;\n\n #[test]\n fn position_jump_does_not_corrupt_state() {\n let mut tracker = KalmanTracker::new();\n tracker.update([1.0, 1.0, 0.5]); // initial position\n tracker.update([50.0, 50.0, 0.5]); // physically impossible jump\n let pos = tracker.estimated_position();\n // should not panic; should clamp or flag anomaly\n assert!(pos.iter().all(|v| v.is_finite()));\n }\n\n #[test]\n fn lost_track_resumes_on_re_detection() {\n let mut tracker = KalmanTracker::new();\n tracker.update([1.0, 1.0, 0.5]);\n // simulate 10 missed frames\n for _ in 0..10 { tracker.predict(); }\n assert_eq!(tracker.state(), TrackState::Lost);\n tracker.update([1.1, 1.1, 0.5]); // re-detected nearby\n assert_eq!(tracker.state(), TrackState::Confirmed);\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "3. `wifi-densepose-ruvector` — Zero coverage on all 5 integration modules",
|
||||
"content": "\n```rust\n// v2/crates/wifi-densepose-ruvector/tests/viewpoint_tests.rs\n\n#[cfg(test)]\nmod attention_tests {\n use wifi_densepose_ruvector::viewpoint::attention::CrossViewpointAttention;\n\n #[test]\n fn attention_weights_sum_to_one() {\n let attn = CrossViewpointAttention::new(3); // 3 viewpoints\n let features = vec![[1.0f32; 64], [2.0f32; 64], [3.0f32; 64]];\n let weights = attn.compute_weights(&features);\n let sum: f32 = weights.iter().sum();\n assert!((sum - 1.0).abs() < 1e-5, \"attention must be a probability distribution\");\n }\n\n #[test]\n fn single_viewpoint_gets_full_weight() {\n let attn = CrossViewpointAttention::new(1);\n let features = vec![[1.0f32; 64]];\n let weights = attn.compute_weights(&features);\n assert!((weights[0] - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn zero_feature_vectors_do_not_produce_nan() {\n let attn = CrossViewpointAttention::new(2);\n let features = vec![[0.0f32; 64], [0.0f32; 64]];\n let weights = attn.compute_weights(&features);\n assert!(weights.iter().all(|w| w.is_finite()));\n }\n}\n\n#[cfg(test)]\nmod sketch_tests {\n use wifi_densepose_ruvector::sketch::WireSketch;\n\n #[test]\n fn round_trip_serialization() {\n let sketch = WireSketch::from_keypoints(&[[0.5f32, 0.5], [0.3, 0.7]]);\n let bytes = sketch.to_bytes();\n let restored = WireSketch::from_bytes(&bytes).unwrap();\n assert_eq!(sketch, restored);\n }\n\n #[test]\n fn deserialize_truncated_bytes_returns_error() {\n let sketch = WireSketch::from_keypoints(&[[0.5f32, 0.5]]);\n let mut bytes = sketch.to_bytes();\n bytes.truncate(bytes.len() / 2); // truncate halfway\n assert!(WireSketch::from_bytes(&bytes).is_err());\n }\n\n #[test]\n fn empty_keypoint_list_is_handled() {\n let sketch = WireSketch::from_keypoints(&[]);\n assert_eq!(sketch.keypoint_count(), 0);\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "Tier 2: Signal Processing Gaps",
|
||||
"content": "\n",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"title": "4. `wifi-densepose-signal` — RuvSense module untested",
|
||||
"content": "\n```rust\n// v2/crates/wifi-densepose-signal/tests/ruvsense_tests.rs\n\n#[cfg(test)]\nmod coherence_gate_tests {\n use wifi_densepose_signal::ruvsense::coherence_gate::{CoherenceGate, GateDecision};\n\n #[test]\n fn high_coherence_signal_is_accepted() {\n let gate = CoherenceGate::new(0.7); // threshold = 0.7\n let decision = gate.evaluate(0.95);\n assert_eq!(decision, GateDecision::Accept);\n }\n\n #[test]\n fn low_coherence_signal_is_rejected() {\n let gate = CoherenceGate::new(0.7);\n let decision = gate.evaluate(0.3);\n assert_eq!(decision, GateDecision::Reject);\n }\n\n #[test]\n fn borderline_coherence_triggers_recalibrate() {\n let gate = CoherenceGate::new(0.7);\n let decision = gate.evaluate(0.68); // just below threshold\n assert_eq!(decision, GateDecision::Recalibrate);\n }\n}\n\n#[cfg(test)]\nmod phase_align_tests {\n use wifi_densepose_signal::ruvsense::phase_align::PhaseAligner;\n\n #[test]\n fn phase_at_plus_pi_does_not_wrap_incorrectly() {\n let aligner = PhaseAligner::new();\n let phases = vec![std::f32::consts::PI - 0.001, std::f32::consts::PI + 0.001];\n let aligned = aligner.align(&phases);\n // jump across ±π boundary must be handled continuously\n let diff = (aligned[1] - aligned[0]).abs();\n assert!(diff < 0.01, \"phase jump at ±π must be < 0.01 rad after alignment\");\n }\n\n #[test]\n fn single_phase_value_aligns_to_itself() {\n let aligner = PhaseAligner::new();\n let phases = vec![1.5f32];\n let aligned = aligner.align(&phases);\n assert_eq!(aligned.len(), 1);\n assert!((aligned[0] - 1.5).abs() < 1e-6);\n }\n\n #[test]\n fn empty_phase_array_returns_empty() {\n let aligner = PhaseAligner::new();\n let aligned = aligner.align(&[]);\n assert!(aligned.is_empty());\n }\n}\n\n#[cfg(test)]\nmod adversarial_detection_tests {\n use wifi_densepose_signal::ruvsense::adversarial::AdversarialDetector;\n\n #[test]\n fn physically_impossible_amplitude_is_flagged() {\n let detector = AdversarialDetector::new();\n // WiFi amplitude cannot exceed hardware saturation level\n let frame = vec![1e9f32; 56]; // absurdly large\n assert!(detector.is_suspicious(&frame));\n }\n\n #[test]\n fn normal_amplitude_range_passes() {\n let detector = AdversarialDetector::new();\n let frame = vec![0.5f32; 56]; // typical normalized value\n assert!(!detector.is_suspicious(&frame));\n }\n\n #[test]\n fn multi_link_inconsistency_is_detected() {\n // link A reports body moving right; link B reports no motion\n // physically inconsistent — flag as adversarial\n let detector = AdversarialDetector::new();\n let result = detector.check_multi_link_consistency(\n &[1.0, 2.0, 3.0], // link A\n &[0.0, 0.0, 0.0], // link B (no motion)\n );\n assert!(result.is_inconsistent());\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "Tier 2: Training Pipeline Gaps",
|
||||
"content": "\n",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"title": "5. `wifi-densepose-train` — Geometry encoder and rapid adaptation untested",
|
||||
"content": "\n```rust\n// v2/crates/wifi-densepose-train/tests/test_geometry.rs\n\n#[cfg(test)]\nmod film_layer_tests {\n use wifi_densepose_train::geometry::FilmLayer;\n\n #[test]\n fn film_layer_output_shape_matches_input() {\n let film = FilmLayer::new(64, 32); // 64-dim features, 32-dim condition\n let features = vec![0.5f32; 64];\n let condition = vec![1.0f32; 32];\n let output = film.forward(&features, &condition).unwrap();\n assert_eq!(output.len(), 64, \"FiLM output must match feature dimensionality\");\n }\n\n #[test]\n fn film_layer_zero_condition_acts_as_identity() {\n let film = FilmLayer::new(64, 32);\n let features = vec![1.0f32; 64];\n let zero_condition = vec![0.0f32; 32];\n let output = film.forward(&features, &zero_condition).unwrap();\n // scale=1, shift=0 → identity; output ≈ input\n for (o, f) in output.iter().zip(features.iter()) {\n assert!((o - f).abs() < 0.1, \"zero condition should approximate identity\");\n }\n }\n}\n\n// v2/crates/wifi-densepose-train/tests/test_rapid_adapt.rs\n\n#[cfg(test)]\nmod rapid_adaptation_tests {\n use wifi_densepose_train::rapid_adapt::RapidAdapter;\n\n #[test]\n fn adapter_updates_on_single_sample() {\n let mut adapter = RapidAdapter::new(5); // 5 adaptation steps\n let csi_sample = vec![0.1f32; 56 * 3];\n let pose_label = vec![0.5f32; 17 * 2]; // 17 keypoints × (x, y)\n let result = adapter.adapt_step(&csi_sample, &pose_label);\n assert!(result.is_ok());\n }\n\n #[test]\n fn adapter_with_zero_steps_is_no_op() {\n let adapter = RapidAdapter::new(0);\n // 0 adaptation steps → weights unchanged\n let initial_weights = adapter.clone_weights();\n let _ = adapter.adapt_step(&vec![0.1f32; 168], &vec![0.5f32; 34]);\n assert_eq!(adapter.clone_weights(), initial_weights);\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "Tier 3: Server Integration Gaps",
|
||||
"content": "\n",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"title": "6. `wifi-densepose-sensing-server` — Auth and semantic analyzers",
|
||||
"content": "\n```rust\n// v2/crates/wifi-densepose-sensing-server/tests/auth_tests.rs\n\n#[cfg(test)]\nmod bearer_auth_tests {\n use wifi_densepose_sensing_server::auth::{BearerValidator, TokenError};\n\n #[test]\n fn missing_authorization_header_returns_unauthorized() {\n let validator = BearerValidator::new(\"secret-token\");\n let result = validator.validate(None);\n assert!(matches!(result, Err(TokenError::Missing)));\n }\n\n #[test]\n fn wrong_token_is_rejected() {\n let validator = BearerValidator::new(\"correct-token\");\n let result = validator.validate(Some(\"Bearer wrong-token\"));\n assert!(matches!(result, Err(TokenError::Invalid)));\n }\n\n #[test]\n fn malformed_header_without_bearer_prefix_is_rejected() {\n let validator = BearerValidator::new(\"token\");\n let result = validator.validate(Some(\"token\")); // missing \"Bearer \" prefix\n assert!(matches!(result, Err(TokenError::Malformed)));\n }\n\n #[test]\n fn correct_token_is_accepted() {\n let validator = BearerValidator::new(\"correct-token\");\n let result = validator.validate(Some(\"Bearer correct-token\"));\n assert!(result.is_ok());\n }\n}\n\n// v2/crates/wifi-densepose-sensing-server/tests/semantic_tests.rs\n\n#[cfg(test)]\nmod fall_detection_tests {\n use wifi_densepose_sensing_server::semantic::fall_detector::FallDetector;\n\n #[test]\n fn no_motion_does_not_trigger_fall() {\n let mut detector = FallDetector::new();\n for _ in 0..30 { // 30 frames of stillness\n detector.update_pose(stationary_pose());\n }\n assert!(!detector.fall_detected());\n }\n\n #[test]\n fn rapid_downward_velocity_triggers_fall() {\n let mut detector = FallDetector::new();\n // simulate person going from standing (y=1.7m) to prone (y=0.3m) in 3 frames\n for (frame, y) in [(0, 1.7f32), (1, 1.0), (2, 0.3)] {\n detector.update_pose(pose_at_height(y));\n }\n assert!(detector.fall_detected());\n }\n\n #[test]\n fn sitting_down_slowly_does_not_trigger_fall() {\n let mut detector = FallDetector::new();\n // gradual height decrease over 30 frames is sitting, not falling\n for i in 0..30 {\n let y = 1.7f32 - (i as f32 * 0.04); // ~1.2m drop over 30 frames\n detector.update_pose(pose_at_height(y));\n }\n assert!(!detector.fall_detected());\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "Cross-Cutting Gap Summary",
|
||||
"content": "| Gap Category | Severity | Affects | Recommended Action |\n|---|---|---|---|\n| `wifi-densepose-nn` has 0 tests | **Critical** | Inference pipeline | Add `tests/inference_tests.rs` per skeleton above |\n| `wifi-densepose-ruvector` has 0 tests | **Critical** | Viewpoint fusion, sketches | Add `tests/viewpoint_tests.rs` |\n| MAT disaster response missing edge cases | **Critical** | 0 BPM, agonal breathing, dedup | Add `tests/detection_edge_cases.rs` |\n| Signal RuvSense 28 modules untested | High | Core sensing logic | Add `tests/ruvsense_tests.rs` |\n| NN error paths (bad model files, OOM) | High | Production reliability | Add error path tests to nn |\n| Train geometry + rapid adapt = 0 tests | High | Domain adaptation | Add `tests/test_geometry.rs` |\n| Server auth token validation | High | Security boundary | Add `tests/auth_tests.rs` |\n| NaN/Inf propagation in f32 pipelines | High | All numeric crates | Add boundary tests per module |\n| Concurrent state under Arc<Mutex> | Medium | sensing-server, mat | Add contention tests |\n\nThe highest-ROI starting point is `wifi-densepose-nn` and `wifi-densepose-mat` — the nn crate has zero tests on the core inference pipeline, and mat covers life-safety scenarios where classification errors have real consequences.",
|
||||
"level": 2
|
||||
}
|
||||
],
|
||||
"codeBlocks": [
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-nn/tests/inference_tests.rs\n\n#[cfg(test)]\nmod tensor_tests {\n use wifi_densepose_nn::tensor::Tensor;\n\n #[test]\n fn tensor_shape_mismatch_returns_error() {\n // data has 6 elements but shape claims 3×3=9\n let result = Tensor::new(vec![1.0f32; 6], &[3, 3]);\n assert!(result.is_err(), \"shape mismatch must be rejected\");\n }\n\n #[test]\n fn tensor_empty_data_returns_error() {\n let result = Tensor::new(vec![], &[0]);\n assert!(result.is_err());\n }\n\n #[test]\n fn tensor_nan_values_are_detected() {\n let t = Tensor::new(vec![f32::NAN, 1.0, 2.0], &[3]).unwrap();\n assert!(t.has_nan(), \"NaN in data must be detectable\");\n }\n\n #[test]\n fn tensor_inf_values_are_detected() {\n let t = Tensor::new(vec![f32::INFINITY, 1.0], &[2]).unwrap();\n assert!(t.has_inf());\n }\n}\n\n#[cfg(test)]\nmod modality_translator_tests {\n use wifi_densepose_nn::translator::ModalityTranslator;\n\n #[test]\n fn translator_rejects_wrong_subcarrier_count() {\n // standard expects 56 subcarriers; feed 57\n let csi = vec![0.0f32; 57 * 3]; // 57 subcarriers × 3 antennas\n let translator = ModalityTranslator::default();\n let result = translator.translate(&csi, 57, 3);\n assert!(result.is_err());\n }\n\n #[test]\n fn translator_handles_all_zeros() {\n let csi = vec![0.0f32; 56 * 3];\n let translator = ModalityTranslator::default();\n let result = translator.translate(&csi, 56, 3);\n // zero input should produce some output without panic\n assert!(result.is_ok());\n }\n}\n\n#[cfg(test)]\nmod inference_engine_tests {\n use wifi_densepose_nn::inference::InferenceEngine;\n\n #[test]\n fn load_nonexistent_model_returns_error() {\n let result = InferenceEngine::from_path(\"/nonexistent/model.onnx\");\n assert!(result.is_err());\n }\n\n #[test]\n fn load_corrupted_bytes_returns_error() {\n let tmp = tempfile::NamedTempFile::new().unwrap();\n std::fs::write(tmp.path(), b\"not a valid onnx file\").unwrap();\n let result = InferenceEngine::from_path(tmp.path());\n assert!(result.is_err());\n }\n\n #[test]\n fn batch_size_zero_returns_error() {\n // can't run inference on an empty batch\n // requires a valid model; skip if no model file in test fixtures\n // use #[ignore] or a feature flag for CI\n }\n}"
|
||||
},
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-mat/tests/detection_edge_cases.rs\n\n#[cfg(test)]\nmod breathing_rate_edge_cases {\n use wifi_densepose_mat::detection::breathing::BreathingDetector;\n\n #[test]\n fn zero_bpm_is_classified_critical() {\n let detector = BreathingDetector::default();\n // flat-line signal — no breathing detected\n let signal = vec![0.0f32; 1000];\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Immediate);\n }\n\n #[test]\n fn agonal_breathing_rate_triggers_immediate() {\n // < 6 BPM is agonal; simulate 3 BPM signal\n let detector = BreathingDetector::default();\n let signal = generate_breathing_signal(3.0, 1000, 100.0); // 3 BPM, 1000 samples @ 100 Hz\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Immediate);\n }\n\n #[test]\n fn normal_breathing_is_classified_minor() {\n let detector = BreathingDetector::default();\n let signal = generate_breathing_signal(15.0, 1000, 100.0); // 15 BPM\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Minor);\n }\n\n #[test]\n fn all_nan_signal_returns_error_not_panic() {\n let detector = BreathingDetector::default();\n let signal = vec![f32::NAN; 1000];\n let result = detector.classify(&signal);\n assert!(result.is_err(), \"NaN input must be caught, not panic\");\n }\n\n fn generate_breathing_signal(bpm: f32, samples: usize, sample_rate: f32) -> Vec<f32> {\n let freq = bpm / 60.0;\n (0..samples)\n .map(|i| (2.0 * std::f32::consts::PI * freq * i as f32 / sample_rate).sin())\n .collect()\n }\n}\n\n#[cfg(test)]\nmod alert_deduplication {\n use wifi_densepose_mat::alerting::{AlertDispatcher, Alert, TriageCategory};\n use std::time::Duration;\n\n #[test]\n fn duplicate_alerts_within_window_are_suppressed() {\n let mut dispatcher = AlertDispatcher::new();\n let alert = Alert::new(\"survivor-1\", TriageCategory::Immediate);\n dispatcher.dispatch(alert.clone());\n dispatcher.dispatch(alert.clone()); // same survivor, same category\n assert_eq!(dispatcher.queued_count(), 1, \"duplicate must be deduplicated\");\n }\n\n #[test]\n fn escalation_from_minor_to_immediate_is_forwarded() {\n let mut dispatcher = AlertDispatcher::new();\n dispatcher.dispatch(Alert::new(\"survivor-1\", TriageCategory::Minor));\n dispatcher.dispatch(Alert::new(\"survivor-1\", TriageCategory::Immediate));\n // escalation is not a duplicate — must pass through\n assert!(dispatcher.last_alert_for(\"survivor-1\").map(|a| a.category) == Some(TriageCategory::Immediate));\n }\n}\n\n#[cfg(test)]\nmod kalman_tracker_edge_cases {\n use wifi_densepose_mat::tracking::KalmanTracker;\n\n #[test]\n fn position_jump_does_not_corrupt_state() {\n let mut tracker = KalmanTracker::new();\n tracker.update([1.0, 1.0, 0.5]); // initial position\n tracker.update([50.0, 50.0, 0.5]); // physically impossible jump\n let pos = tracker.estimated_position();\n // should not panic; should clamp or flag anomaly\n assert!(pos.iter().all(|v| v.is_finite()));\n }\n\n #[test]\n fn lost_track_resumes_on_re_detection() {\n let mut tracker = KalmanTracker::new();\n tracker.update([1.0, 1.0, 0.5]);\n // simulate 10 missed frames\n for _ in 0..10 { tracker.predict(); }\n assert_eq!(tracker.state(), TrackState::Lost);\n tracker.update([1.1, 1.1, 0.5]); // re-detected nearby\n assert_eq!(tracker.state(), TrackState::Confirmed);\n }\n}"
|
||||
},
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-ruvector/tests/viewpoint_tests.rs\n\n#[cfg(test)]\nmod attention_tests {\n use wifi_densepose_ruvector::viewpoint::attention::CrossViewpointAttention;\n\n #[test]\n fn attention_weights_sum_to_one() {\n let attn = CrossViewpointAttention::new(3); // 3 viewpoints\n let features = vec![[1.0f32; 64], [2.0f32; 64], [3.0f32; 64]];\n let weights = attn.compute_weights(&features);\n let sum: f32 = weights.iter().sum();\n assert!((sum - 1.0).abs() < 1e-5, \"attention must be a probability distribution\");\n }\n\n #[test]\n fn single_viewpoint_gets_full_weight() {\n let attn = CrossViewpointAttention::new(1);\n let features = vec![[1.0f32; 64]];\n let weights = attn.compute_weights(&features);\n assert!((weights[0] - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn zero_feature_vectors_do_not_produce_nan() {\n let attn = CrossViewpointAttention::new(2);\n let features = vec![[0.0f32; 64], [0.0f32; 64]];\n let weights = attn.compute_weights(&features);\n assert!(weights.iter().all(|w| w.is_finite()));\n }\n}\n\n#[cfg(test)]\nmod sketch_tests {\n use wifi_densepose_ruvector::sketch::WireSketch;\n\n #[test]\n fn round_trip_serialization() {\n let sketch = WireSketch::from_keypoints(&[[0.5f32, 0.5], [0.3, 0.7]]);\n let bytes = sketch.to_bytes();\n let restored = WireSketch::from_bytes(&bytes).unwrap();\n assert_eq!(sketch, restored);\n }\n\n #[test]\n fn deserialize_truncated_bytes_returns_error() {\n let sketch = WireSketch::from_keypoints(&[[0.5f32, 0.5]]);\n let mut bytes = sketch.to_bytes();\n bytes.truncate(bytes.len() / 2); // truncate halfway\n assert!(WireSketch::from_bytes(&bytes).is_err());\n }\n\n #[test]\n fn empty_keypoint_list_is_handled() {\n let sketch = WireSketch::from_keypoints(&[]);\n assert_eq!(sketch.keypoint_count(), 0);\n }\n}"
|
||||
},
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-signal/tests/ruvsense_tests.rs\n\n#[cfg(test)]\nmod coherence_gate_tests {\n use wifi_densepose_signal::ruvsense::coherence_gate::{CoherenceGate, GateDecision};\n\n #[test]\n fn high_coherence_signal_is_accepted() {\n let gate = CoherenceGate::new(0.7); // threshold = 0.7\n let decision = gate.evaluate(0.95);\n assert_eq!(decision, GateDecision::Accept);\n }\n\n #[test]\n fn low_coherence_signal_is_rejected() {\n let gate = CoherenceGate::new(0.7);\n let decision = gate.evaluate(0.3);\n assert_eq!(decision, GateDecision::Reject);\n }\n\n #[test]\n fn borderline_coherence_triggers_recalibrate() {\n let gate = CoherenceGate::new(0.7);\n let decision = gate.evaluate(0.68); // just below threshold\n assert_eq!(decision, GateDecision::Recalibrate);\n }\n}\n\n#[cfg(test)]\nmod phase_align_tests {\n use wifi_densepose_signal::ruvsense::phase_align::PhaseAligner;\n\n #[test]\n fn phase_at_plus_pi_does_not_wrap_incorrectly() {\n let aligner = PhaseAligner::new();\n let phases = vec![std::f32::consts::PI - 0.001, std::f32::consts::PI + 0.001];\n let aligned = aligner.align(&phases);\n // jump across ±π boundary must be handled continuously\n let diff = (aligned[1] - aligned[0]).abs();\n assert!(diff < 0.01, \"phase jump at ±π must be < 0.01 rad after alignment\");\n }\n\n #[test]\n fn single_phase_value_aligns_to_itself() {\n let aligner = PhaseAligner::new();\n let phases = vec![1.5f32];\n let aligned = aligner.align(&phases);\n assert_eq!(aligned.len(), 1);\n assert!((aligned[0] - 1.5).abs() < 1e-6);\n }\n\n #[test]\n fn empty_phase_array_returns_empty() {\n let aligner = PhaseAligner::new();\n let aligned = aligner.align(&[]);\n assert!(aligned.is_empty());\n }\n}\n\n#[cfg(test)]\nmod adversarial_detection_tests {\n use wifi_densepose_signal::ruvsense::adversarial::AdversarialDetector;\n\n #[test]\n fn physically_impossible_amplitude_is_flagged() {\n let detector = AdversarialDetector::new();\n // WiFi amplitude cannot exceed hardware saturation level\n let frame = vec![1e9f32; 56]; // absurdly large\n assert!(detector.is_suspicious(&frame));\n }\n\n #[test]\n fn normal_amplitude_range_passes() {\n let detector = AdversarialDetector::new();\n let frame = vec![0.5f32; 56]; // typical normalized value\n assert!(!detector.is_suspicious(&frame));\n }\n\n #[test]\n fn multi_link_inconsistency_is_detected() {\n // link A reports body moving right; link B reports no motion\n // physically inconsistent — flag as adversarial\n let detector = AdversarialDetector::new();\n let result = detector.check_multi_link_consistency(\n &[1.0, 2.0, 3.0], // link A\n &[0.0, 0.0, 0.0], // link B (no motion)\n );\n assert!(result.is_inconsistent());\n }\n}"
|
||||
},
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-train/tests/test_geometry.rs\n\n#[cfg(test)]\nmod film_layer_tests {\n use wifi_densepose_train::geometry::FilmLayer;\n\n #[test]\n fn film_layer_output_shape_matches_input() {\n let film = FilmLayer::new(64, 32); // 64-dim features, 32-dim condition\n let features = vec![0.5f32; 64];\n let condition = vec![1.0f32; 32];\n let output = film.forward(&features, &condition).unwrap();\n assert_eq!(output.len(), 64, \"FiLM output must match feature dimensionality\");\n }\n\n #[test]\n fn film_layer_zero_condition_acts_as_identity() {\n let film = FilmLayer::new(64, 32);\n let features = vec![1.0f32; 64];\n let zero_condition = vec![0.0f32; 32];\n let output = film.forward(&features, &zero_condition).unwrap();\n // scale=1, shift=0 → identity; output ≈ input\n for (o, f) in output.iter().zip(features.iter()) {\n assert!((o - f).abs() < 0.1, \"zero condition should approximate identity\");\n }\n }\n}\n\n// v2/crates/wifi-densepose-train/tests/test_rapid_adapt.rs\n\n#[cfg(test)]\nmod rapid_adaptation_tests {\n use wifi_densepose_train::rapid_adapt::RapidAdapter;\n\n #[test]\n fn adapter_updates_on_single_sample() {\n let mut adapter = RapidAdapter::new(5); // 5 adaptation steps\n let csi_sample = vec![0.1f32; 56 * 3];\n let pose_label = vec![0.5f32; 17 * 2]; // 17 keypoints × (x, y)\n let result = adapter.adapt_step(&csi_sample, &pose_label);\n assert!(result.is_ok());\n }\n\n #[test]\n fn adapter_with_zero_steps_is_no_op() {\n let adapter = RapidAdapter::new(0);\n // 0 adaptation steps → weights unchanged\n let initial_weights = adapter.clone_weights();\n let _ = adapter.adapt_step(&vec![0.1f32; 168], &vec![0.5f32; 34]);\n assert_eq!(adapter.clone_weights(), initial_weights);\n }\n}"
|
||||
},
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-sensing-server/tests/auth_tests.rs\n\n#[cfg(test)]\nmod bearer_auth_tests {\n use wifi_densepose_sensing_server::auth::{BearerValidator, TokenError};\n\n #[test]\n fn missing_authorization_header_returns_unauthorized() {\n let validator = BearerValidator::new(\"secret-token\");\n let result = validator.validate(None);\n assert!(matches!(result, Err(TokenError::Missing)));\n }\n\n #[test]\n fn wrong_token_is_rejected() {\n let validator = BearerValidator::new(\"correct-token\");\n let result = validator.validate(Some(\"Bearer wrong-token\"));\n assert!(matches!(result, Err(TokenError::Invalid)));\n }\n\n #[test]\n fn malformed_header_without_bearer_prefix_is_rejected() {\n let validator = BearerValidator::new(\"token\");\n let result = validator.validate(Some(\"token\")); // missing \"Bearer \" prefix\n assert!(matches!(result, Err(TokenError::Malformed)));\n }\n\n #[test]\n fn correct_token_is_accepted() {\n let validator = BearerValidator::new(\"correct-token\");\n let result = validator.validate(Some(\"Bearer correct-token\"));\n assert!(result.is_ok());\n }\n}\n\n// v2/crates/wifi-densepose-sensing-server/tests/semantic_tests.rs\n\n#[cfg(test)]\nmod fall_detection_tests {\n use wifi_densepose_sensing_server::semantic::fall_detector::FallDetector;\n\n #[test]\n fn no_motion_does_not_trigger_fall() {\n let mut detector = FallDetector::new();\n for _ in 0..30 { // 30 frames of stillness\n detector.update_pose(stationary_pose());\n }\n assert!(!detector.fall_detected());\n }\n\n #[test]\n fn rapid_downward_velocity_triggers_fall() {\n let mut detector = FallDetector::new();\n // simulate person going from standing (y=1.7m) to prone (y=0.3m) in 3 frames\n for (frame, y) in [(0, 1.7f32), (1, 1.0), (2, 0.3)] {\n detector.update_pose(pose_at_height(y));\n }\n assert!(detector.fall_detected());\n }\n\n #[test]\n fn sitting_down_slowly_does_not_trigger_fall() {\n let mut detector = FallDetector::new();\n // gradual height decrease over 30 frames is sitting, not falling\n for i in 0..30 {\n let y = 1.7f32 - (i as f32 * 0.04); // ~1.2m drop over 30 frames\n detector.update_pose(pose_at_height(y));\n }\n assert!(!detector.fall_detected());\n }\n}"
|
||||
}
|
||||
]
|
||||
},
|
||||
"rawOutputPreview": "The exploration is complete. Here's the full coverage analysis with test skeletons for each critical gap.\n\n---\n\n## Test Coverage Gap Analysis — wifi-densepose\n\n### Coverage Summary by Crate\n\n| Crate | Tests Found | Status | Priority |\n|-------|-------------|--------|----------|\n| `wifi-densepose-core` | 26 inline | Good | Low |\n| `wifi-densepose-signal` | ~60 (validation only) | Moderate | **High** |\n| `wifi-densepose-nn` | **0** | Critical | **P1** |\n| `wifi-densepose-train` | ~60 (config/dataset) | Moderate | High |\n| `wifi-densepose-mat` | 1 integration test | Critical | **P1** |\n| `wifi-densepose-ruvector` | **0** | Critical | **P1** |\n| `wifi-densepose-sensing-server` | 4 integration tests | Moderate | High |\n| `wifi-densepose-wasm` | 3 compliance tests | Low | Low |\n\n---\n\n## Tier 1: Critical Gaps\n\n### 1. `wifi-densepose-nn` — Zero test coverage\n\nEvery public API is untested. Place these at `v2/crates/wifi-densepose-nn/tests/inference_tests.rs`:\n\n```rust\n// v2/crates/wifi-densepose-nn/tests/inference_tests.rs\n\n#[cfg(test)]\nmod tensor_tests {\n use wifi_densepose_nn::tensor::Tensor;\n\n #[test]\n fn tensor_shape_mismatch_returns_error() {\n // data has 6 elements but shape claims 3×3=9\n let result = Tensor::new(vec![1.0f32; 6], &[3, 3]);\n assert!(result.is_err(), \"shape mismatch must be rejected\");\n }\n\n #[test]\n fn tensor_empty_data_returns_error() {\n let result = Tensor::new(vec![], &[0]);\n assert!(result.is_err());\n }\n\n #[test]\n fn tensor_nan_values_are_detected() {\n let t = Tensor::new(vec![f32::NAN, 1.0, 2.0], &[3]).unwrap();\n assert!(t.has_nan(), \"NaN in data must be detectable\");\n }\n\n #[test]\n fn tensor_inf_values_are_detected() {\n let t = Tensor::new(vec![f32::INFINITY, 1.0], &[2]).unwrap();\n assert!(t.has_inf());\n }\n}\n\n#[cfg(test)]\nmod modality_translator_tests {\n use wifi_densepose_nn::translator::ModalityTranslator;\n\n #[test]\n fn translator_rejects",
|
||||
"rawOutputLength": 18269
|
||||
}
|
||||
@@ -1 +0,0 @@
|
||||
{"sessionId":"d80c93c2-51b7-42e8-a0fc-dc47cff1200f","pid":45748,"acquiredAt":1779668018388}
|
||||
@@ -1,94 +0,0 @@
|
||||
name: AetherArena harness gate (ADR-149)
|
||||
|
||||
# Runs the AetherArena scoring harness as a PR build gate. Every PR that touches
|
||||
# the scorer, the metrics, or the benchmark scaffold must keep the deterministic
|
||||
# score hash stable (ADR-149 §2.5 determinism_gate). If the scoring maths changes,
|
||||
# the hash moves and this gate fails until `expected_score.sha256` is regenerated
|
||||
# and reviewed — so scorer drift can never land silently.
|
||||
#
|
||||
# This is the "a PR that runs the harness as part of the build process" requirement.
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- 'v2/crates/wifi-densepose-train/src/ruview_metrics.rs'
|
||||
- 'v2/crates/wifi-densepose-train/src/ablation.rs'
|
||||
- 'v2/crates/wifi-densepose-train/src/bin/aa_score_runner.rs'
|
||||
- 'aether-arena/**'
|
||||
- '.github/workflows/aether-arena-harness.yml'
|
||||
push:
|
||||
branches: ['feat/adr-149-aether-arena']
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: write
|
||||
|
||||
jobs:
|
||||
harness-gate:
|
||||
name: Run AA scorer harness (determinism gate)
|
||||
runs-on: ubuntu-latest
|
||||
defaults:
|
||||
run:
|
||||
working-directory: v2
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Install Rust toolchain
|
||||
run: rustup show && rustc --version
|
||||
|
||||
- name: Cache cargo
|
||||
uses: actions/cache@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cargo/registry
|
||||
~/.cargo/git
|
||||
v2/target
|
||||
key: aa-harness-${{ runner.os }}-${{ hashFiles('v2/Cargo.lock') }}
|
||||
|
||||
# 1. Build the pure-Rust scorer (no torch / no GPU → fast PR gate).
|
||||
- name: Build AA score runner
|
||||
run: cargo build -p wifi-densepose-train --bin aa_score_runner --no-default-features
|
||||
|
||||
# 2. Determinism gate: the committed expected hash must still match. A
|
||||
# non-zero exit here fails the PR.
|
||||
- name: Run determinism gate
|
||||
run: cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features
|
||||
|
||||
# 3. Repeatability analysis (witness chain): the harness must produce one
|
||||
# identical proof hash across many runs — any nondeterminism fails here.
|
||||
- name: Repeatability analysis (16 runs)
|
||||
run: cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --repeat 16
|
||||
|
||||
# 4. Real-scoring smoke: score a sample prediction against the public smoke
|
||||
# split, exercising the actual model-scoring path (not just the fixture).
|
||||
- name: Real-scoring smoke test
|
||||
run: |
|
||||
cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- \
|
||||
--split ../aether-arena/fixtures/smoke_split.json \
|
||||
--pred ../aether-arena/fixtures/smoke_pred.json --json
|
||||
|
||||
# 5. Witness ledger chain integrity: the append-only results ledger must
|
||||
# verify (every prev_hash link + row_hash intact = no silent edits).
|
||||
- name: Verify witness ledger chain
|
||||
working-directory: aether-arena/ledger
|
||||
run: python3 ledger_tools.py verify
|
||||
|
||||
# 6. Emit the witness row + repeatability into the PR run summary.
|
||||
- name: Witness row → job summary
|
||||
if: always()
|
||||
run: |
|
||||
ROW=$(cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --json)
|
||||
REP=$(cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --repeat 16)
|
||||
{
|
||||
echo "## AetherArena harness gate (witness chain)"
|
||||
echo ""
|
||||
echo "Deterministic witness (ADR-149 §2.2 / proof + repeatability):"
|
||||
echo '```json'
|
||||
echo "$ROW"
|
||||
echo "$REP"
|
||||
echo '```'
|
||||
echo ""
|
||||
echo "If the determinism gate failed, the scoring maths changed: regenerate with"
|
||||
echo '`cargo run -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --generate-hash > aether-arena/fixtures/expected_score.sha256` and review the diff.'
|
||||
} >> "$GITHUB_STEP_SUMMARY"
|
||||
+19
-85
@@ -108,60 +108,21 @@ jobs:
|
||||
- name: Install Rust toolchain
|
||||
uses: dtolnay/rust-toolchain@stable
|
||||
|
||||
# Swatinem/rust-cache replaces a naive `actions/cache` of the whole
|
||||
# `v2/target`. That manual cache of a 38-crate target dir (multi-GB) was an
|
||||
# intermittent failure source — several CI runs this cycle died at the
|
||||
# cache/setup step (after toolchain install, before "Run Rust tests"),
|
||||
# needing a rerun. rust-cache is purpose-built for Rust: it caches the
|
||||
# registry + git + a pruned target, evicts stale deps, and restores far more
|
||||
# reliably (and faster) on large workspaces. `workspaces: v2` points it at
|
||||
# the v2/ cargo workspace (keys on v2/Cargo.lock, caches v2/target).
|
||||
- name: Cache cargo (Swatinem/rust-cache)
|
||||
uses: Swatinem/rust-cache@v2
|
||||
- name: Cache cargo
|
||||
uses: actions/cache@v4
|
||||
with:
|
||||
workspaces: v2
|
||||
path: |
|
||||
~/.cargo/registry
|
||||
~/.cargo/git
|
||||
v2/target
|
||||
key: ${{ runner.os }}-cargo-${{ hashFiles('v2/Cargo.lock') }}
|
||||
restore-keys: |
|
||||
${{ runner.os }}-cargo-
|
||||
|
||||
# The 38-crate workspace debug build exhausts the runner's disk when built
|
||||
# with full debuginfo (observed: "final link failed: No space left on
|
||||
# device" once the engine/benchmark crates landed; the same tree's local
|
||||
# debug target measured 151 GB). Debuginfo is useless in CI — tests either
|
||||
# pass or print their failure — so build without it; target shrinks ~5-10x.
|
||||
- name: Run Rust tests
|
||||
working-directory: v2
|
||||
env:
|
||||
CARGO_PROFILE_DEV_DEBUG: "0"
|
||||
CARGO_PROFILE_TEST_DEBUG: "0"
|
||||
run: cargo test --workspace --no-default-features
|
||||
|
||||
- name: Run ADR-147 worldmodel tests
|
||||
working-directory: v2
|
||||
env:
|
||||
CARGO_PROFILE_DEV_DEBUG: "0"
|
||||
CARGO_PROFILE_TEST_DEBUG: "0"
|
||||
run: cargo test -p wifi-densepose-worldmodel --no-default-features
|
||||
|
||||
# ADR-134 CIR tests are behind the `cir` feature so the bench dependency
|
||||
# (Criterion) only pulls when actually exercised. Run them as a separate
|
||||
# step so a CIR-only regression is unambiguously attributable.
|
||||
- name: Run ADR-134 CIR tests
|
||||
working-directory: v2
|
||||
run: cargo test -p wifi-densepose-signal --no-default-features --features cir --tests
|
||||
|
||||
# ADR-134 + ADR-028 witness guard. The CIR proof runner produces a
|
||||
# bit-deterministic SHA-256 over CirEstimator output on the synthetic
|
||||
# reference signal. Any algorithmic regression — changes to ISTA
|
||||
# convergence, sensing matrix construction, soft-thresholding, or input
|
||||
# padding — breaks the hash and fails the build. To regenerate after an
|
||||
# *intentional* change:
|
||||
# cd v2 && cargo run -p wifi-densepose-signal --bin cir_proof_runner \
|
||||
# --release --no-default-features -- --generate-hash \
|
||||
# > ../archive/v1/data/proof/expected_cir_features.sha256
|
||||
- name: ADR-134 CIR witness proof (determinism guard)
|
||||
run: bash scripts/verify-cir-proof.sh
|
||||
|
||||
- name: ADR-135 calibration witness proof (determinism guard)
|
||||
run: bash scripts/verify-calibration-proof.sh
|
||||
|
||||
# Unit and Integration Tests
|
||||
# Python pytest matrix — runs against the archived v1 Python tree.
|
||||
# `continue-on-error: true` for the same reason as code-quality above:
|
||||
@@ -278,45 +239,23 @@ jobs:
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install -r requirements.txt
|
||||
pip install pytest # the perf suite is pytest, not locust
|
||||
pip install locust
|
||||
|
||||
# No "Start application" step: the gated test (test_frame_budget.py) drives
|
||||
# the CSIProcessor pipeline in-process and makes no HTTP calls, so the old
|
||||
# uvicorn server + `sleep 10` were dead weight — they only existed for the
|
||||
# now-excluded api_throughput/inference_speed tests, and on every run dumped
|
||||
# ~50 misleading "router requires hardware setup" ERROR lines for a server
|
||||
# no test touched. MOCK_POSE_DATA is server-only and unused here.
|
||||
|
||||
- name: Run performance tests
|
||||
- name: Start application
|
||||
working-directory: archive/v1
|
||||
run: |
|
||||
# Gate only on the genuine, deterministic perf guard:
|
||||
# test_frame_budget.py times the *real* CSIProcessor pipeline against
|
||||
# the ADR 50 ms per-frame budget (single-frame, p95 over 100 frames,
|
||||
# +Doppler) — a true regression signal.
|
||||
#
|
||||
# test_api_throughput.py / test_inference_speed.py are excluded: every
|
||||
# test there is a TDD red-phase stub (suffix `_should_fail_initially`)
|
||||
# that times a *mock that sleeps* — meaningless as a perf signal, with
|
||||
# machine-dependent wall-clock asserts (e.g. `actual_rps >= 40`,
|
||||
# `batch_time < individual_time`) that are inherently flaky on shared
|
||||
# CI runners, plus a cross-class fixture-scope bug. Forcing them green
|
||||
# would be manufacturing a false signal; they stay in-repo for local
|
||||
# TDD but do not gate CI until the underlying features are implemented.
|
||||
#
|
||||
# `python -m pytest` (not the bare `pytest` script) puts the cwd
|
||||
# (archive/v1) on sys.path so `from src.core...` resolves — the bare
|
||||
# script omits cwd and raises ModuleNotFoundError: No module named 'src'.
|
||||
# -o addopts="" drops the root pyproject's --cov/--cov-fail-under=100.
|
||||
python -m pytest tests/performance/test_frame_budget.py \
|
||||
-o addopts="" -v --junitxml=perf-junit.xml
|
||||
uvicorn src.api.main:app --host 0.0.0.0 --port 8000 &
|
||||
sleep 10
|
||||
|
||||
- name: Run performance tests
|
||||
run: |
|
||||
locust -f tests/performance/locustfile.py --headless --users 50 --spawn-rate 5 --run-time 60s --host http://localhost:8000
|
||||
|
||||
- name: Upload performance results
|
||||
if: always()
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: performance-results
|
||||
path: archive/v1/perf-junit.xml
|
||||
path: locust_report.html
|
||||
|
||||
# Docker Build and Test
|
||||
# NOTE: the canonical Docker build for the sensing-server is now
|
||||
@@ -338,7 +277,7 @@ jobs:
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
continue-on-error: true
|
||||
uses: docker/setup-buildx-action@v3
|
||||
uses: docker/setup-buildx-action@v4
|
||||
|
||||
- name: Log in to Container Registry
|
||||
continue-on-error: true
|
||||
@@ -402,8 +341,6 @@ jobs:
|
||||
runs-on: ubuntu-latest
|
||||
needs: [docker-build]
|
||||
if: github.ref == 'refs/heads/main'
|
||||
permissions:
|
||||
contents: write # gh-pages deploy needs write (GITHUB_TOKEN is read-only by default -> 403)
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
@@ -421,8 +358,6 @@ jobs:
|
||||
|
||||
- name: Generate OpenAPI spec
|
||||
working-directory: archive/v1
|
||||
env:
|
||||
MOCK_POSE_DATA: "true" # no CSI hardware in CI
|
||||
run: |
|
||||
python -c "
|
||||
from src.api.main import app
|
||||
@@ -433,7 +368,6 @@ jobs:
|
||||
|
||||
- name: Deploy to GitHub Pages
|
||||
uses: peaceiris/actions-gh-pages@v4
|
||||
continue-on-error: true # openapi generation above is the real validation; deploy is best-effort (Pages may be disabled)
|
||||
with:
|
||||
github_token: ${{ secrets.GITHUB_TOKEN }}
|
||||
publish_dir: ./docs
|
||||
|
||||
@@ -27,7 +27,7 @@ jobs:
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- uses: docker/setup-buildx-action@v3
|
||||
- uses: docker/setup-buildx-action@v4
|
||||
|
||||
- uses: docker/login-action@v3
|
||||
with:
|
||||
|
||||
@@ -1,149 +0,0 @@
|
||||
name: ruview-swarm CI guard
|
||||
|
||||
# Dedicated guard for the ADR-148 drone swarm crate (`v2/crates/ruview-swarm`).
|
||||
# The main ci.yml runs `cargo test --workspace --no-default-features`, which
|
||||
# only exercises ruview-swarm's DEFAULT feature set. This guard additionally:
|
||||
# - tests every feature combination (train / ruflo+itar / full)
|
||||
# - fails on ANY clippy warning in the crate's own code (--no-deps)
|
||||
# - asserts the ITAR + publish guards stay in place (USML Cat VIII(h)(12))
|
||||
# - builds the GPU training binary under the `train` feature
|
||||
#
|
||||
# Path-scoped so it only runs when the crate or this workflow changes.
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ main, 'feat/*' ]
|
||||
paths:
|
||||
- 'v2/crates/ruview-swarm/**'
|
||||
- '.github/workflows/ruview-swarm-ci.yml'
|
||||
pull_request:
|
||||
paths:
|
||||
- 'v2/crates/ruview-swarm/**'
|
||||
- '.github/workflows/ruview-swarm-ci.yml'
|
||||
workflow_dispatch:
|
||||
|
||||
env:
|
||||
CARGO_TERM_COLOR: always
|
||||
|
||||
jobs:
|
||||
# ── Feature-matrix tests ─────────────────────────────────────────────────
|
||||
tests:
|
||||
name: tests (${{ matrix.features.label }})
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
features:
|
||||
- { label: 'default', flags: '--no-default-features' }
|
||||
- { label: 'train', flags: '--features train' }
|
||||
- { label: 'ruflo+itar', flags: '--features ruflo,itar-unrestricted' }
|
||||
- { label: 'full+train', flags: '--features full,train' }
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: dtolnay/rust-toolchain@stable
|
||||
- name: Cache cargo
|
||||
uses: actions/cache@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cargo/registry
|
||||
~/.cargo/git
|
||||
v2/target
|
||||
key: ${{ runner.os }}-ruview-swarm-${{ hashFiles('v2/Cargo.lock') }}
|
||||
restore-keys: ${{ runner.os }}-ruview-swarm-
|
||||
- name: cargo test -p ruview-swarm ${{ matrix.features.flags }}
|
||||
working-directory: v2
|
||||
run: cargo test -p ruview-swarm ${{ matrix.features.flags }} --lib
|
||||
|
||||
# ── Clippy: zero warnings in the crate's own code ────────────────────────
|
||||
clippy:
|
||||
name: clippy (-D warnings, --no-deps)
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
# v2/rust-toolchain.toml pins channel "1.89" with profile "minimal" (no
|
||||
# clippy). dtolnay@stable installs clippy on the floating "stable"
|
||||
# toolchain, but the override makes cargo use the separate "1.89"
|
||||
# toolchain — so `cargo clippy` errors "cargo-clippy is not installed for
|
||||
# 1.89". Install clippy on the pinned toolchain that cargo actually uses.
|
||||
- uses: dtolnay/rust-toolchain@stable
|
||||
with:
|
||||
toolchain: "1.89"
|
||||
components: clippy
|
||||
- name: Cache cargo
|
||||
uses: actions/cache@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cargo/registry
|
||||
~/.cargo/git
|
||||
v2/target
|
||||
key: ${{ runner.os }}-ruview-swarm-clippy-${{ hashFiles('v2/Cargo.lock') }}
|
||||
restore-keys: ${{ runner.os }}-ruview-swarm-clippy-
|
||||
# --no-deps confines linting to ruview-swarm's own source, so pre-existing
|
||||
# warnings in dependency crates don't gate this PR.
|
||||
- name: clippy (default)
|
||||
working-directory: v2
|
||||
run: cargo clippy -p ruview-swarm --no-default-features --no-deps -- -D warnings
|
||||
- name: clippy (full,train)
|
||||
working-directory: v2
|
||||
run: cargo clippy -p ruview-swarm --features full,train --no-deps -- -D warnings
|
||||
|
||||
# ── Build the GPU training binary (train feature) ────────────────────────
|
||||
train-bin:
|
||||
name: build train_marl bin
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: dtolnay/rust-toolchain@stable
|
||||
- name: Cache cargo
|
||||
uses: actions/cache@v4
|
||||
with:
|
||||
path: |
|
||||
~/.cargo/registry
|
||||
~/.cargo/git
|
||||
v2/target
|
||||
key: ${{ runner.os }}-ruview-swarm-bin-${{ hashFiles('v2/Cargo.lock') }}
|
||||
restore-keys: ${{ runner.os }}-ruview-swarm-bin-
|
||||
- name: cargo build --bin train_marl --features train
|
||||
working-directory: v2
|
||||
run: cargo build -p ruview-swarm --features train --bin train_marl
|
||||
- name: train_marl is excluded from the default build
|
||||
working-directory: v2
|
||||
run: |
|
||||
# The training binary requires the `train` feature; a default `--bins`
|
||||
# build must NOT produce it (keeps default/CI builds light + Candle-free).
|
||||
# Remove any prior artifact first so this checks what the DEFAULT build
|
||||
# produces, not a leftover from the train-feature build above.
|
||||
rm -f target/debug/train_marl
|
||||
cargo build -p ruview-swarm --no-default-features --bins
|
||||
if [ -f target/debug/train_marl ]; then
|
||||
echo "ERROR: train_marl built without the 'train' feature" >&2
|
||||
exit 1
|
||||
fi
|
||||
echo "OK: train_marl correctly gated behind the 'train' feature"
|
||||
|
||||
# ── ITAR + publish guards ────────────────────────────────────────────────
|
||||
export-control-guard:
|
||||
name: ITAR / publish guard
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: publish = false is present (no accidental crates.io publish)
|
||||
run: |
|
||||
CARGO=v2/crates/ruview-swarm/Cargo.toml
|
||||
if ! grep -qE '^\s*publish\s*=\s*false' "$CARGO"; then
|
||||
echo "ERROR: ruview-swarm Cargo.toml must keep 'publish = false' until" >&2
|
||||
echo " PR merge + dependency publish + ITAR export sign-off." >&2
|
||||
exit 1
|
||||
fi
|
||||
echo "OK: publish = false present"
|
||||
- name: default feature set does NOT enable itar-unrestricted
|
||||
run: |
|
||||
CARGO=v2/crates/ruview-swarm/Cargo.toml
|
||||
# USML Cat VIII(h)(12): swarming coordination must be opt-in, never default.
|
||||
DEFAULT_LINE=$(grep -E '^\s*default\s*=' "$CARGO" || true)
|
||||
echo "default = $DEFAULT_LINE"
|
||||
if echo "$DEFAULT_LINE" | grep -q 'itar-unrestricted'; then
|
||||
echo "ERROR: 'itar-unrestricted' must NOT be in the default feature set" >&2
|
||||
exit 1
|
||||
fi
|
||||
echo "OK: ITAR-gated coordination features are opt-in, not default"
|
||||
@@ -46,10 +46,7 @@ jobs:
|
||||
|
||||
- name: Run Bandit security scan
|
||||
run: |
|
||||
# The Python codebase lives under archive/v1/src (it moved there when
|
||||
# the runtime was rewritten in Rust). Scanning `src/` matched nothing,
|
||||
# so this SAST step was a silent no-op.
|
||||
bandit -r archive/v1/src/ -f sarif -o bandit-results.sarif
|
||||
bandit -r src/ -f sarif -o bandit-results.sarif
|
||||
continue-on-error: true
|
||||
|
||||
- name: Upload Bandit results to GitHub Security
|
||||
@@ -60,20 +57,22 @@ jobs:
|
||||
sarif_file: bandit-results.sarif
|
||||
category: bandit
|
||||
|
||||
# Removed the deprecated `returntocorp/semgrep-action@v1` step: it was
|
||||
# redundant (the pip `semgrep --sarif` below is what feeds GitHub Security;
|
||||
# the action only pushed to the Semgrep cloud app via SEMGREP_APP_TOKEN) and
|
||||
# it pulled `returntocorp/semgrep-agent:v1` from Docker Hub on every run,
|
||||
# which intermittently timed out and turned this check red. The pip semgrep
|
||||
# (installed above) needs no Docker pull. The action's `p/docker` +
|
||||
# `p/kubernetes` rulesets are folded into the command below so coverage is
|
||||
# preserved.
|
||||
- name: Run Semgrep + generate SARIF
|
||||
- name: Run Semgrep security scan
|
||||
continue-on-error: true
|
||||
uses: returntocorp/semgrep-action@v1
|
||||
with:
|
||||
config: >-
|
||||
p/security-audit
|
||||
p/secrets
|
||||
p/python
|
||||
p/docker
|
||||
p/kubernetes
|
||||
env:
|
||||
SEMGREP_APP_TOKEN: ${{ secrets.SEMGREP_APP_TOKEN }}
|
||||
|
||||
- name: Generate Semgrep SARIF
|
||||
run: |
|
||||
semgrep \
|
||||
--config=p/security-audit --config=p/secrets --config=p/python \
|
||||
--config=p/docker --config=p/kubernetes \
|
||||
--sarif --output=semgrep.sarif archive/v1/src/
|
||||
semgrep --config=p/security-audit --config=p/secrets --config=p/python --sarif --output=semgrep.sarif src/
|
||||
continue-on-error: true
|
||||
|
||||
- name: Upload Semgrep results to GitHub Security
|
||||
@@ -167,7 +166,7 @@ jobs:
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
continue-on-error: true
|
||||
uses: docker/setup-buildx-action@v3
|
||||
uses: docker/setup-buildx-action@v4
|
||||
|
||||
- name: Build Docker image for scanning
|
||||
continue-on-error: true
|
||||
|
||||
@@ -58,7 +58,7 @@ jobs:
|
||||
# by the runner, not built on a separate arm64 host).
|
||||
- uses: docker/setup-qemu-action@v3
|
||||
|
||||
- uses: docker/setup-buildx-action@v3
|
||||
- uses: docker/setup-buildx-action@v4
|
||||
|
||||
- name: Log in to Docker Hub
|
||||
# Bypassing docker/login-action@v3: the action kept emitting
|
||||
|
||||
@@ -7,7 +7,6 @@ on:
|
||||
- 'archive/v1/src/core/**'
|
||||
- 'archive/v1/src/hardware/**'
|
||||
- 'archive/v1/data/proof/**'
|
||||
- 'archive/v1/requirements-lock.txt'
|
||||
- '.github/workflows/verify-pipeline.yml'
|
||||
pull_request:
|
||||
branches: [ main, master ]
|
||||
@@ -15,7 +14,6 @@ on:
|
||||
- 'archive/v1/src/core/**'
|
||||
- 'archive/v1/src/hardware/**'
|
||||
- 'archive/v1/data/proof/**'
|
||||
- 'archive/v1/requirements-lock.txt'
|
||||
- '.github/workflows/verify-pipeline.yml'
|
||||
workflow_dispatch:
|
||||
|
||||
|
||||
@@ -261,10 +261,3 @@ v2/crates/rvcsi-node/*.node
|
||||
v2/crates/rvcsi-node/binding.js
|
||||
v2/crates/rvcsi-node/binding.d.ts
|
||||
v2/crates/rvcsi-node/npm/
|
||||
|
||||
# AetherArena private optimization staging — never published until reviewed
|
||||
aether-arena/staging/
|
||||
|
||||
# MM-Fi benchmark dataset archives — large data, fetch separately, never commit
|
||||
assets/MM-Fi/E0*.zip
|
||||
assets/MM-Fi/*.zip
|
||||
|
||||
@@ -14,7 +14,3 @@
|
||||
path = vendor/rvcsi
|
||||
url = https://github.com/ruvnet/rvcsi
|
||||
branch = main
|
||||
[submodule "v2/crates/ruv-neural"]
|
||||
path = v2/crates/ruv-neural
|
||||
url = https://github.com/ruvnet/ruv-neural.git
|
||||
branch = main
|
||||
|
||||
+1
-103
@@ -7,110 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
### Security
|
||||
- **ADR-157 Milestone-1 B4 - constant-time HMAC sync-beacon tag compare (`wifi-densepose-hardware`).** `AuthenticatedBeacon::verify` compared the 8-byte HMAC-SHA256 tag with `self.hmac_tag == expected`, which short-circuits on the first differing byte and leaks, through verification latency, how many leading bytes an attacker's forged tag matched - a byte-by-byte tag-recovery oracle (~256*N trials instead of 256^N). Replaced with a hand-rolled branch-free `constant_time_tag_eq` (XOR-accumulate every byte difference into a single `u8`, no early exit, `#[inline(never)]` + `core::hint::black_box` to stop the optimizer reintroducing a short-circuit or a non-constant-time `memcmp`). **No new dependency** - ADR-157 had deferred this only to avoid adding the `subtle` crate; a fixed 8-byte compare needs none. Grade MEASURED (constant-time *construction*; micro-timing on a noisy host is a smoke check only, gated `#[ignore]`). Pinned by `tag_compare_is_constant_time_shape` (equal/first-differ/last-differ/all-differ/length-mismatch + an end-to-end `verify()` last-byte tamper), proven to fail on a last-byte-skipping constant-time bug. ADR-157 §8 B4 -> RESOLVED.
|
||||
- **ADR-080 open HIGH findings closed on the Rust `wifi-densepose-sensing-server` boundary (ADR-164 G11).** The QE sweep's three HIGH findings — XFF-spoofing bypass, leaked stack traces, JWT-in-URL (CWE-598) — were logged against the Python v1 API and never re-verified against the shipped Rust sensing-server; the HOMECORE/M7 sweep (ADR-161) covered `homecore-server`, not this crate.
|
||||
- **#2 leaked internal errors (the one live exposure) — FIXED.** Six handlers in `main.rs` serialized the internal error `Display` straight into the JSON response body: `edge_registry_endpoint` returned a panicked `spawn_blocking` `JoinError` (`"task … panicked"`) in a `500`, plus the raw upstream error in a `503`; `delete_model`/`delete_recording`/`start_recording` returned `std::io::Error` strings (OS detail / path); `calibration_start`/`calibration_stop` returned the `FieldModel` error chain. New `error_response` module logs the full detail **server-side only** (with a correlation id) and returns a generic body (`{"error":"internal_error","correlation_id":…}`) — no `panicked`, no file paths, no Debug chain. 5 module tests (a leak-substring guard proven to fail on the reverted old body) + the existing handler suite.
|
||||
- **#1 XFF-spoofing bypass — VERIFIED ABSENT, regression-pinned.** The sensing-server has no XFF-trusting control to bypass: there is no IP-based rate-limiter or IP-allowlist, and neither `bearer_auth` (token-only) nor `host_validation` (Host-header only) reads `X-Forwarded-For`/`X-Forwarded-Host` (no `forwarded`/`peer_addr`/`client_ip` anywhere in the crate). Added regression tests proving a spoofed `X-Forwarded-For` never flips an auth decision and a spoofed `X-Forwarded-Host` never bypasses the Host allowlist.
|
||||
- **#3 JWT-in-URL (CWE-598) — VERIFIED ABSENT, regression-pinned.** `require_bearer` reads the token only from the `Authorization` header; the WebSocket handlers take no token query param and the sole `Query` extractor (`EdgeRegistryParams`) is a non-secret `refresh` flag. Added a regression proving `?token=`/`?access_token=` in the URL never authenticates while the header path still does.
|
||||
|
||||
### Fixed
|
||||
- **ADR-155 Milestone-1b — metric-definition unification, the §8 backlog subset (Goals A/B/C).** Closed the two §8 metric-integrity items; every change pinned by a test, graded MEASURED. The audit (Goal A) also surfaced findings the §1 table under-counted — recorded honestly in ADR-155 §8.1, not hidden. Workspace stays green; Python proof unchanged (metrics are not on the deterministic proof's signal path).
|
||||
- **Goal B — `test_metrics.rs` now validates the production metric, not a reimplementation.** The integration test previously asserted properties of its OWN local `compute_pck`/`compute_oks` (a test that can't catch a canonical-impl bug — both could be wrong the same way). Hoisted the canonical core (`pck_canonical`/`oks_canonical`/`canonical_torso_size`/sigmas/`bounding_box_diagonal`) into a new **un-gated** `metrics_core` module so the single definition is reachable under `cargo test --no-default-features` (the `metrics` module is `tch-backend`-gated); `metrics` re-exports it → still exactly ONE implementation. Rewrote the test to assert the production `pck_canonical`/`oks_canonical` equal **hand-computed** fixtures (`canonical_pck_matches_hand_computed_fixture` = 3/4 correct ⇒ 0.75; hip↔hip normalizer pin; zero-visible⇒0.0; OKS perfect⇒1.0; fake-Gold pin) plus a differential cross-check (`test_kernel_agrees_with_canonical`: an independent raw-threshold kernel must AGREE with canonical where torso==1.0). `wifi-densepose-train --no-default-features`: test_metrics **10→12**, 0 failed.
|
||||
- **Goal C — divergent live-server PCK/OKS relabelled so they're never conflated with canonical.** Goal C named `training_api.rs:804` (torso-HEIGHT PCK); the audit found that file is an **orphan (not `mod`-declared, does not compile)** and the **real** live `best_pck`/`best_oks` come from `trainer.rs` — a **raw, unnormalized** `pck_at_threshold` and an **`area=1.0` fake-Gold** `oks_map` (both MISSED by ADR-155 §1, both on the claim-inflating side, both serialized as bare "PCK@0.2"/"OKS"). Torso-height/raw math is load-bearing (pixel-space, different scale axis, no `ndarray`/train dep), so the honest fix is **relabel, not force-unify**: `training_api.rs` `compute_pck` → `compute_pck_torso_height` + field/log docs; `trainer.rs` kernels documented raw/fake-Gold; `main.rs` prints `pck_raw@0.2` / `oks_map(area=1.0 proxy)`. No wire-format field or `pub`-fn renames (no silent API break). Pinned by `torso_pck_is_labelled_distinctly_from_canonical` + `pck_at_threshold_is_raw_unnormalized_not_canonical`. `wifi-densepose-sensing-server --no-default-features`: lib **450→451**, 0 failed. True unification onto `pck_canonical`/`oks_canonical` remains a tracked ADR-155 §8 item.
|
||||
- **Pre-existing `SketchBank::topk` heap inversion returned the FARTHEST sketches (found during ADR-156 §8 Pass-2 work).** The `n > k` partial-sort path in `wifi-densepose-ruvector/src/sketch.rs` used `BinaryHeap<Reverse<(dist,id)>>` (a min-heap) but its eviction logic treated the peek as the max, so it kept the k *farthest* sketches and returned them as "nearest." The shipped unit tests only exercised the `n ≤ k` fast path (≤ 3 entries), so the inversion shipped silently in ADR-084. Fixed to a plain max-heap. Pinned by `topk_heap_path_returns_nearest` (farthest-first insertion exposes it) and `tight_clusters_give_high_coverage_with_overfetch` (**measured 0.072 coverage on the old code** — effectively random — vs >0.99 fixed). Every ADR-084 top-K coverage number depends on the fixed path. MEASURED, not a no-op.
|
||||
- **ADR-154 Milestone-1 — cleared the P1 deferred backlog in `wifi-densepose-signal` (§7.4 #1, #10; partial #9, #13).** Each fix pinned by a regression test that fails on the old behaviour; every claim graded MEASURED / DATA-GATED; no fabricated thresholds. Python proof unchanged (`f8e76f21…46f7a`, bit-exact — the CIR ghost-tap guard is not on the deterministic proof path).
|
||||
- **#1 (MEASURED metric / DATA-GATED threshold): circular phase variance.** `cir.rs::phase_variance` computed a *linear* sample variance over phase angles that wrap at ±π, so a tightly-clustered set straddling the branch cut reported spuriously HIGH dispersion — false-tripping the `> TAU` ghost-tap **guard** on real, tightly-clustered CIR taps. Replaced with Mardia's **circular variance** V = 1 − R̄, bounded **[0,1]** and invariant to where the cluster sits on the circle. The old TAU-scaled threshold is meaningless on [0,1]; re-derived against a named const `GHOST_TAP_CIRCULAR_VARIANCE_MAX = 0.99` (fires only when R̄ ≤ 0.01 — essentially uniform phase). The **metric is MEASURED**; the **threshold value is DATA-GATED** (a clean single-path ramp also sweeps the circle, so V alone can't separate clean from unsanitized without labelled frames — the default is deliberately conservative, strictly more permissive at the wrap boundary than the buggy linear guard). Fails-on-old: `phase_variance_circular_not_fooled_by_branch_cut` (old linear variance > TAU on wrap-straddling phases while circular V≈0, guard no longer trips) + `phase_variance_circular_is_bounded_and_extremal` (V∈[0,1], V≈0 identical, V≈1 uniform).
|
||||
- **#10 (MEASURED): Welford n=0/n=1 finiteness guard pinned.** The shared `WelfordStats` (`field_model.rs`) `count < 2` guards keep `variance`/`sample_variance`/`std_dev`/`z_score` finite at the boundaries, but the n=0 case was untested (same family as the §4 divide-by-(n−1) trio). Added `welford_finite_at_n0_and_n1` — finite + documented-sentinel (0.0) at n=0/n=1. Fails-on-old proof: removing the `sample_variance` guard makes the test panic with "attempt to subtract with overflow" at the `(count − 1)` underflow (guard restored).
|
||||
- **#9, #13 (DATA-GATED): de-magicked thresholds + boundary tests (values UNCHANGED).** Lifted the bare detection literals in `adversarial.rs` (`check`/`check_consistency`: Gini 0.8, energy ratios 2.0/0.1, consistency 0.1·mean, score weights), `coherence.rs::classify_drift` (0.85, 10) and `coherence_gate.rs` defaults (0.85/0.5/200/3.0) into named, documented consts marked EMPIRICAL DEFAULT pending labelled calibration. Added characterization/boundary tests pinning each decision at/just-below/just-above its threshold (`energy_ratio_high_boundary`, `energy_ratio_low_boundary`, `field_model_gini_boundary`, `consistency_active_fraction_boundary`, `classify_drift_*_boundary`, `*_consts_unchanged_from_literals`) so a future labelled-data retune is a visible, tested change. The operating **values were not changed**; the de-magicking + tests are MEASURED, the values stay DATA-GATED.
|
||||
- **Multistatic fusion guard was too tight for real TDM hardware (#1031).** `MultistaticConfig::default().guard_interval_us` was 5,000 µs (5 ms) with a comment claiming "well within the 50 ms TDMA cycle" — but on a real N-slot TDM schedule node `k` transmits in slot `k`, so two nodes are separated by the *slot offset*, not clock jitter. A real 2-node mesh (slots 0/1) measured an **18,194 µs** spread, so every real frame set exceeded the 5 ms guard and `fuse()` silently fell back to per-node sum/dedup — multistatic fusion never actually ran on hardware. Raised the default hard guard to **60 ms** (a full 50 ms TDMA cycle + 20% jitter headroom, derived from the slot model and documented in the field doc) and the soft guard to **20 ms** (just above the observed 18.2 ms 2-slot spread, so a normal cycle fuses cleanly with no privacy demotion). Added `MultistaticConfig::for_tdm_schedule(total_slots, slot_duration_us)` to derive the guard from a deployment's exact schedule, and a `WDP_TDM_SLOTS`+`WDP_TDM_SLOT_US` env seam in sensing-server. The honest per-node fallback remains for genuinely-mismatched frames — now the exception, not the default. Pinned by `fuse_real_tdm_spread_18194us_fuses_with_default_guard` (fails on the old 5 ms default) + `configurable_guard_rejects_too_large_spread` (guard still rejects a spread beyond one cycle).
|
||||
- **Published HuggingFace model was unloadable — RVF format mismatch (#894).** The `ProgressiveLoader` rejected the published `ruvnet/wifi-densepose-pretrained` model with the opaque `invalid magic at offset 0: expected 0x52564653 (RVFS), got 0x77455735`, then silently fell back to signal heuristics (the "10 persons for 1" garbage reporters saw). The HF repo ships `model.safetensors`, `model-q{2,4,8}.bin` (magic `0x77455735` = "5WEw"), and `model.rvf.jsonl` — none carry the binary-RVF magic. New `model_format` module **auto-detects** RVFS / safetensors / HF-quant-bin / JSONL by magic+name, returns a **typed actionable** `ModelLoadError` (lists accepted formats + the one-command convert path — never the opaque magic), and **converts** `model.safetensors` / `model.rvf.jsonl` → RVF in-memory so the published full-precision model now loads via `--model`. A `--convert-model <in> --convert-out <out>` CLI subcommand gives a one-command offline path; the silent heuristics fallback is now a loud, actionable error. **Honest scope:** the converter wires the format/load path (safetensors F32 tensors → RVF weight segment, manifest written, Layer A/B/C all succeed, weights round-trip) — it does **not** claim end-to-end pose accuracy, since the HF pose-decoder architecture differs from this crate's inference head (still data-gated in #894). Quantized `.bin` blobs are rejected with a typed error pointing at the safetensors path. Pinned by `safetensors_converts_and_loads` + `hf_quant_classifies_to_actionable_error` (both fail on the old opaque-magic path).
|
||||
|
||||
### Changed
|
||||
- **ADR-157 Milestone-1 §5 #4 - native `wlanapi.dll` multi-BSSID throughput MEASURED on real hardware (`wifi-densepose-wifiscan`).** The ADR's prior status ("asserted but NOT implemented; live scanner is the ~2 Hz netsh shim") is now stale: `wlanapi_native.rs` already implements the real `WlanOpenHandle` -> `WlanEnumInterfaces` -> `WlanGetNetworkBssList` -> `WlanFreeMemory`/`WlanCloseHandle` FFI and `WlanApiScanner` already wires it native-first with a netsh fallback. This milestone **measured it on this box** (Intel Wi-Fi 7 BE201 320MHz, 2026-06-13): a new `benchmark_backend(backend, window)` drives each backend over the same fixed 10 s wall-clock window so netsh is timed independently (the prior `benchmark()` picked native-first and never measured netsh on a Windows box where native works). **MEASURED: native 21.42 Hz vs netsh 3.84 Hz = 5.57x** (mean 5.0 BSSIDs/scan, both paths); a separate native-only run measured 18.0 Hz. Native genuinely beats netsh - this is a real positive result, not a fabricated "10x". 50 back-to-back native scans completed 50/50 with no handle leak/degradation. Live-WLAN tests (`measure_native_vs_netsh_throughput`, `native_scans_dont_leak_handles`, `measure_native_scan_rate`) are `#[ignore]` for CI but were RUN here; `native_scan_runs_real_ffi_on_windows` is a non-ignored schema-valid pin. ADR-157 §5 #4 + §8 -> MEASURED (was ACCEPTED-FUTURE / CLAIMED-unmeasured).
|
||||
- **Mesh partition risk now demotes the privacy class and is witnessed (ADR-032).** The dynamic min-cut guard's `at_risk` signal was advisory-only (it fed the recalibration advisor). It now also contributes to the ADR-141 privacy demotion alongside fusion- and array-level contradictions: a mesh close to partitioning makes the fused belief less trustworthy, so the cycle emits at a more restricted class (monotonic — information only removed). Because `effective_class` feeds the BLAKE3 witness, a fragmenting array now shifts the witness — partition risk is auditable, not just logged. The mesh computation moved ahead of the demotion step in `process_cycle`; new `mesh_guard_mut()` exposes risk-threshold tuning. Test proves a forced-risk 3-node cycle demotes PrivateHome Anonymous→Restricted and shifts the witness vs a clean *same-topology* baseline (the only delta between the two cycles is the forced risk).
|
||||
|
||||
### Added
|
||||
- **ADR-154 Milestone-3 — cleared the §7.4 row #21–45 P3 backlog in `wifi-densepose-signal` (the lumped "remaining clarity/doc/magic-constant/missing-boundary-test findings across `ruvsense/*`, `features.rs`, `motion.rs`").** Honest enumeration first (grep, not the ADR's estimate): the lumped row was **~25 findings → 22 real, de-magicked across 11 modules; 6 boundary/characterization tests added; ~4 doc-only; the rest were already-handled or not-real and are reported as such** (the "row #21–45" count was an estimate — there were not 25 *distinct* magic constants left after M0–M2). **This is cleanup — no operating value or behaviour changed:** every de-magicked literal becomes a named, documented EMPIRICAL-DEFAULT const that **equals the prior literal exactly** (each module ships a `*_consts_unchanged_from_literals` pin test), and every boundary test pins **current** behaviour so a future retune is a visible, tested change. Modules touched: `motion.rs` (#18, fusion weights/normalization/adaptive-threshold consts + 5 tests), `gesture.rs` (#12, `euclidean_distance` length-mismatch `debug_assert` documenting the silent-truncation contract + DTW n=0/m=0 boundary), `longitudinal.rs` (drift thresholds 7-day/2σ/3-day/7-day/EMA + day-6/7 + zero-vector cosine), `cross_room.rs`/`multiband.rs`/`intention.rs`/`hampel.rs` (division-guard epsilons + zero-norm/zero-variance/zero-MAD boundary + `half_window==0` error path), `rf_slam.rs` (`NS_PER_DAY` + fixed-map defaults + zero-span guard), `attractor_drift.rs` (buffer/recent-window consts + documented the implicit `recent.len()≥1` divide-safety + `min_observations` off-by-one boundary), `coherence.rs` (#9 completion — variance-floor + default-decay), `calibration.rs` (#2 — `DEFAULT_MIN_FRAMES` deduped across 4 tier constructors + motion/subtract thresholds), `fusion_quality.rs` (contradiction penalty/bounds + n=0 identity), `temporal_gesture.rs` (confidence epsilon + quantization scale). **A "magic" the agents flagged that was NOT real:** an `attractor_drift.rs:301` "divide-by-zero" is unreachable (the `count < min_observations` guard guarantees `recent.len()≥1`) — documented + boundary-tested rather than guarded, per the no-behaviour-change rule. Signal crate lib `--no-default-features`: **476 passed, 0 failed, 1 ignored**; `--no-default-features --features cir`: **476 passed, 0 failed** (plain `--features cir` is unbuildable on this Windows host — the default `eigenvalue` feature pulls `openblas-src`, the same BLAS gate documented in M2 #8). Workspace `--no-default-features`: **3,275 / 0 failed** (single clean run). Python proof **VERDICT: PASS**, hash **`f8e76f21…46f7a` UNCHANGED, bit-exact** (asserted explicitly — these modules are off the deterministic PSD/Doppler proof path, and the de-magicked consts are bit-identical regardless). **This clears ADR-154's §7.4 deferred backlog to zero across M0–M3.**
|
||||
- **ADR-154 Milestone-2 — bench-first P2 perf subset + missing boundary tests (`wifi-densepose-signal`, §7.4 #5/#6/#7/#8/#14/#16/#19/#20).** PROOF discipline (ADR-154 §0): every perf item was **benched before being touched** (new committed `benches/dsp_perf_bench.rs`, criterion, this Windows box); only the one item the bench proved hot was optimized, the rest are committed MEASURED-NULLs — a benched null is the proof the micro-opt was unnecessary, the §5.1 "already amortized" pattern. Every behaviour-changing edit is pinned bit-identical (or documented-tolerance). Signal crate lib `--no-default-features`: **447 passed, 0 failed, 1 ignored**; `--features cir`: **447 passed, 0 failed**.
|
||||
- **#20 MEASURED-HOT, optimized (bit-identical).** `compute_multi_subcarrier_spectrogram` re-planned a fresh `FftPlanner` for *every* subcarrier (via `compute_spectrogram`). Hoisted the plan + window out of the per-subcarrier loop (new `compute_spectrogram_with_plan` core; `compute_spectrogram` delegates, unchanged). **56-subcarrier: 467.88 µs → 254.75 µs = 1.84×** (window 128); **627.27 µs → 448.39 µs = 1.40×** (window 256). Bit-identical via `multi_subcarrier_hoisted_plan_bit_identical` (`f64::to_bits` of every value across all 4 window functions × {power,magnitude}). The §7.4 intro's predicted "most likely real win" — confirmed.
|
||||
- **#5 / #6 / #7 MEASURED-NULL, left as-is.** `node_attention_weights` 181 ns (2 nodes)…848 ns (8) — sub-µs, no hot-path alloc. `tomography reconstruct` (full 50-iter ISTA, 256 voxels) 47.5 µs (16 links) / 60.4 µs (32) — the 2 voxel buffers are already alloc-once + `.fill`-reused, negligible vs O(iters·links·voxels). `pose_tracker` Kalman cycle 150 ns (17 keypoints) / 2.82 µs (170) — the "gain matrices" are fixed-size **stack** arrays, zero heap to reuse. No rewrite shipped; the committed benches prove each is not hot.
|
||||
- **#8 MEASUREMENT-ONLY, BLAS-gated (number deferred, not fabricated).** Correction to the finding: `extract_perturbation` does **not** recompute the SVD (it projects against cached `finalize_calibration` modes); the real per-call eigendecomposition is the `eigenvalue`-feature `estimate_occupancy` (`cov.eigh()` on a 56×56 covariance). The `eig` bench is committed but `openblas-src` won't build on this Windows host ("Non-vcpkg builds are not supported on Windows" — the exact reason the project gate runs `--no-default-features`), so its µs cost must come from a Linux/BLAS box. Recorded, not estimated. Incremental SVD stays a sized future item.
|
||||
- **#14 / #16 / #19 RESOLVED — tests added (no behaviour change).** `fft_operator_within_tolerance_of_dense_canonical56` pins the full `Cir` output of the opt-in FFT path within a documented relative tolerance of the dense path on the production canonical-56 config (τ ∈ {20,50,90} ns) — it changes the witness hash, so it must be provably *close*, not silently divergent. `refinement_terminates_at_iteration_cap_when_not_converging` (+ convergent companion) proves the LO-offset refinement terminates at exactly `max_iterations` on a non-converging input (cap, not convergence, bounds the loop; internal `…_counted` refactor returns the identical offsets). `ratio_finite_at_and_below_1e_12_epsilon` pins that the conjugate-product CSI-ratio (no division → no `1e-12` divide-guard needed) is finite + bit-exact at/below the epsilon boundary and at exact zero (where a naive `H_i/H_j` ratio is ±inf/NaN).
|
||||
- **ADR-156 §11 Milestone-2: RaBitQ unbiased distance estimator — IMPLEMENTED & MEASURED (RESOLVED-NEGATIVE on the strict-K bar).** Closes the §10.5 / §8 backlog "full RaBitQ residual-distance estimator (not just a uniform scalar code)" item — the **real** Gao & Long (SIGMOD 2024) contribution, not just sign bits. New `wifi-densepose-ruvector/src/estimator.rs`: `EstimatorSketch` carries the Pass-2 sign code (over the padded FHT length `D = next_pow2(dim)`) **plus 8 B/vec side info** (`residual_norm` + `x_dot_o = ⟨x̄, o'⟩`, 2× f32); `DistanceEstimator` computes the **unbiased** estimate `⟨o',q'⟩ ≈ ⟨x̄,q'⟩ / x_dot_o` (the random rotation makes the 1-bit code's quantization error orthogonal-in-expectation to the query, paper `O(1/√D)` bound); `EstimatorBank::topk_estimated_cosine` reranks the candidate set by the estimate instead of raw Hamming. **Zero-centroid simplification (`c = 0`) stated honestly** — the paper-faithful per-cluster centroid path (`from_embedding_centred` / `EstimatorBank::with_centroid`) is also built so the simplification is a measured choice (no centroid coverage number is reported against the cosine ground truth, because cosine-of-residual ≠ cosine-of-raw would be a metric mismatch). **Purely additive + backward-compatible** — new types only; Pass-1 `Sketch` / Pass-2 `SketchBank` / `WireSketch` wire format unchanged; all external callers (`event_log.rs`, `signal/longitudinal.rs`, `sensing-server`) use Pass-1 and are unaffected. **MEASURED strict-K coverage** (same fixture/seeds as §10: dim=128 N=2048 K=8, 64 clusters, noise=0.35, 128 queries, cosine ground truth): the estimator lifts the strict `candidate_k=K` bar **46.39% (Pass-2 sign) → 49.71% (estimator, cosine rerank)** — a real **+3.3 pp** lift, **still ~40 pp short of the ADR-084 ≥90% strict bar.** At over-fetch the estimator beats sign (candidate_k=24: **95.12%** vs 91.60%). **Honest verdict — RESOLVED-NEGATIVE: the unbiased estimator does NOT clear the strict-K 90% bar on this distribution** (the binding constraint is the 1-bit code's information ceiling, not estimator variance); the bar is still met only via the over-fetch "candidate set" pattern ADR-084 specifies, though the estimator **reduces the over-fetch factor** needed. A published negative, reported as such — no benchmark tuned to manufacture a pass. Unbiasedness pinned by `estimator_unbiased_on_fixture` (Monte-Carlo mean over 4000 rotation seeds → true inner product within tolerance); not-worse-than-sign pinned by `estimator_rerank_not_worse_than_sign`; determinism by `estimator_is_deterministic`. +12 tests in the crate (119→131). Workspace **3,228 / 0 failed** (`cargo test --workspace --no-default-features`, 162 test binaries, single clean run), Python proof **VERDICT: PASS** (`f8e76f21…46f7a`, unchanged — estimator is not on the proof's signal path). Full numbers + reproduce commands in ADR-156 §11 / ADR-084 "Pass 2b".
|
||||
- **ADR-156 §8 Milestone-1: RaBitQ Pass-2 randomized rotation + multi-bit experiment — IMPLEMENTED & MEASURED (RESOLVED-PARTIAL).** Closes the §8 "Multi-bit / Extended RaBitQ" backlog item. New `wifi-densepose-ruvector/src/rotation.rs`: a deterministic randomized orthogonal rotation `R = H·D` — **Fast Hadamard Transform** (`O(d log d)`, in-place, `1/√m`-normalized so norm-preserving) + seeded ±1 sign flips (SplitMix64 from a stored `u64` seed; identical at index + query time). Chosen over a dense `d×d` matrix (`O(d²)`, infeasible at the 65,535-d the wire format provisions for); pads to `next_pow2(d)`. Additive, backward-compatible API (`Sketch::from_embedding_rotated`, `SketchBank::with_rotation` + `insert_embedding`/`topk_embedding`/`novelty_embedding`); Pass-1 and the wire format are byte-for-byte unchanged. New `coverage.rs` single-source-of-truth top-K coverage harness (anisotropic planted-cluster fixture, cosine ground truth) backs both a `#[test]` report and the `sketch_bench` coverage table. **MEASURED (dim=128 N=2048 K=8, 64 clusters, noise=0.35, 128 queries, seeded):** at the strict `candidate_k=K` bar, rotation lifts coverage **36.13% → 46.39%**; Pass-2 reaches the **ADR-084 ≥90% bar at candidate_k=24 (~3× over-fetch)**; multi-bit Pass-3 reaches 54%/67%/74% at 2/3/4-bit (strict bar). **Honest verdict: neither rotation nor ≤4-bit multi-bit clears the strict-K 90% bar on this distribution — the bar is met only via the over-fetch "candidate set" pattern ADR-084 specifies.** No benchmark was tuned to manufacture a pass; the strict-bar gap is documented (ADR-156 §10, ADR-084 "Pass 2" section). +19 tests in the crate (100→119), workspace **3,225 / 0 failed**, Python proof VERDICT: PASS (`f8e76f21…`, unchanged — sketch is not on the proof's signal path).
|
||||
- **Beyond-SOTA `v2/crates/` sweep (ADR-154–158) + full stub-implementation push — every claim MEASURED or graded.** A 5-milestone review/optimize/secure/benchmark/validate sweep, then a verified-audit-driven push to replace every production stub with real, tested logic (no labels, no placeholders). Each fix is pinned by a test that fails on the old code; every number ships with a reproduce command. Workspace: **3,122 tests / 0 failed** (`cargo test --workspace --no-default-features`), Python proof **VERDICT: PASS** (bit-exact).
|
||||
- **ADR-154 Signal/DSP** — revived a dead ADR-134 CIR coherence gate (canonical-56 vs ht20 mismatch meant it never ran in production: 8/8 Err → 8/8 Ok); NaN-bypass + window div0 guards; PSD FFT-planner cache (**2.0–3.1×**) + honored DTW band (**2.4–4.1×**).
|
||||
- **ADR-155 NN/Training** — unified 7 divergent PCK/OKS metric definitions into one canonical torso-normalized source (fixed two claim-inflating bugs: zero-visible PCK 1.0→0.0, OKS fake-Gold); leak-free subject-disjoint MM-Fi split + injected-leak detector; rapid_adapt replaced fake gradients with real finite-difference; proof.rs gained a min-decrease margin + committed-hash requirement; zero-copy ORT input (**1.48×**).
|
||||
- **ADR-156 RuVector/Fusion** — closed crafted-input DoS panics (triangulation/heartbeat); honest dimensionless GDOP = √(trace(G⁻¹)) replacing an RMSE mislabel; canonical wrapped angular distance; fuse() double-clone removed (**~2.17×** marshalling). SOTA graded: SymphonyQG (CLAIMED), multi-bit RaBitQ (near-term), GraphPose-Fi (data-gated).
|
||||
- **ADR-157 Hardware/Sensing** — `Vec::remove(0)` O(n²) sliding windows → `VecDeque`; breathing partial-weight renormalization; IIR low-sample-rate divergence clamp. Centerpiece: a MEASURED **negative-results** audit showing the layer (802.11bf model, parsers, calibration) was already hardened — cited file:line, NO-ACTION.
|
||||
- **ADR-158 MAT/world-model** — **unified two divergent triage engines** (the confidence-gated result was computed then discarded; gate==record now); **killed survivor count-inflation** (real RSSI localization + vitals-signature dedup, MEASURED 3→1); real ESP32/UDP/PCAP CSI ingest with honest typed `HardwareUnavailable`/`UnsupportedAdapter` errors for hardware-gated adapters (Intel5300/Atheros/PicoScenes — never fabricated CSI); real parabolic peak interpolation; real GDOP.
|
||||
- **Soul Signature §3.6 matcher made real (`wifi-densepose-bfld`, issue #1021).** An external audit correctly found person-identification was spec-only behind a no-op `NullOracle`. Now a real per-channel weighted-cosine matcher + `EnrolledMatcher: SoulMatchOracle` (364 tests). MEASURED: same-person 1.0000 vs cross-person 0.8088; and the audit's own claim proven — on WiFi-only cardiac+respiratory channels alone two people are **not separable** (gap 0.0005). Named identity is honestly **data-gated** on the AETHER/body-resonance channel being fed by a real enrollment; no working-named-identity claim is made.
|
||||
- **OccWorld real forward pass** — replaced `Tensor::randn` encoder/decoder stubs (which emitted trajectory priors from pure noise) with a real deterministic conv VQ-VAE forward pass (input-dependent, proven by tests that fail on the old randn) + a `weights_trained` honesty flag (false until a real checkpoint loads); pointcloud `to_gaussian_splats` 9→2 passes (**1.24×** MEASURED).
|
||||
- **Native multi-BSSID `wlanapi.dll` FFI** (`wifi-densepose-wifiscan`) — real `WlanOpenHandle`/`WlanEnumInterfaces`/`WlanGetNetworkBssList`, **MEASURED 9.74 Hz** on Windows (vs netsh ~2 Hz; no fabricated "10×"), typed `Unsupported` off-Windows. Real Matter 1.3 manual-pairing-code field-packing (canonical 34970112332, lossless decode) replacing a lossy-modulo placeholder.
|
||||
- **HOMECORE assistant** — real `LocalRunner` response path, real semantic intent recognizer (exact in-memory cosine k-NN; MEASURED 0.855 match / 0.106 no-match), real SQL state text-search — three always-empty stubs removed.
|
||||
- **ADR-152 WiFi-Pose SOTA 2026 intake — verified external benchmark + four Rust integrations.** A 22-source adversarially-verified survey of the 2025–2026 WiFi-sensing SOTA, with every adopted number reproduced or graded before integration:
|
||||
- **WiFlow-STD (DY2434) reproduction (`benchmarks/wiflow-std/`)** — the external "97.25% PCK@20, 2.23M params" claim audited end-to-end: the **shipped checkpoint is REFUTED** (0.08% PCK@20 — wrong keypoint normalization, predates the published code), the released code does not run as published (6 documented defects, incl. an import that fails and an unreachable test phase), and the released dataset's final 13 files are corrupted (9,072 windows of NaN + float32-max garbage that NaN-poisons fp16 BatchNorm training). After repairing both, retraining with upstream defaults on an RTX 5080 reproduced **96.09% PCK@20 (full test) / 96.61% (corruption-free)** — claims graded MEASURED-EQUIVALENT; params (2,225,042) and FLOPs (~0.055 G) verified exactly. Full forensics in `benchmarks/wiflow-std/RESULTS.md`.
|
||||
- **`GeometryEmbedding` (ADR-152 §2.1.2, `wifi-densepose-calibration`)** — 32-slot permutation-invariant, NaN-proof featurization of the §2.1.1 `NodeGeometry` records (centroid/spread, measured-first pairwise distances, circular azimuth stats, covariance-eigenvalue geometric diversity, per-node flags), schema-versioned for the ADR-151 P6 LoRA heads; derived `SpecialistBank::geometry_embedding()` accessor. The PerceptAlign "coordinate overfitting" defense, transplanted to per-room banks.
|
||||
- **MAE pretraining recipe (ADR-152 §2.3, `wifi-densepose-train/src/mae.rs`)** — `MaePretrainConfig` pinning the UNSW-measured recipe (80% masking, (30,3) patches) with pure-Rust patchify/random-mask (exact counts, seed-deterministic, error-not-truncate divisibility, NaN rejection), property-tested; the consumption seam for the future ADR-150 ViT-Small encoder.
|
||||
- **`WiFlowStdModel` Rust port (`wifi-densepose-train/src/wiflow_std/`)** — tch-gated idiomatic port of the verified spatio-temporal-decoupled architecture (grouped causal TCN → asymmetric conv stack → dual axial attention); ungated param formula asserted equal to the reference 2,225,042; 15/17-keypoint variants share weights (enables the ADR-152 §2.2(b) ESP32 fine-tune).
|
||||
- **RuVector vendor sync + §2.6 opportunity survey** — vendor at `a083bd77f`; graded ADOPT/EVALUATE/WATCH table; crates.io bumps applied (mincut/solver 2.0.6, attention 2.1.0, gnn 2.2.0; RUSTSEC #504 audit: no pinned crate affected); top WATCH: unpublished `ruvector-graph-condense` differentiable min-cut for trainable subcarrier grouping.
|
||||
- **ADR-153 IEEE 802.11bf-2025 forward-compatibility protocol model (`wifi-densepose-hardware/src/ieee80211bf/`)** — typed WLAN-sensing procedures (measurement setup/instance/report, SBP, termination) with `SpecProfile` version gates, `SensingCapabilities` negotiation, and **required** `ConsentMode` governance metadata on every setup; deterministic session FSM with rejection/timeout paths; `SensingTransport` seam with `SimTransport` and an `OpportunisticCsiBridge` mapping live ESP32 CSI batches into standardized report shape (a future chipset adapter replaces the bridge without touching RuvSense consumers). Not a certified implementation — simulation-tested protocol surface; OTA binding lands when silicon does. 19 acceptance tests.
|
||||
- **Dynamic min-cut mesh partition guard in the streaming engine (`mesh_guard`).** Maintains a `ruvector-mincut` exact min-cut over the live mesh coupling graph (nodes = sensing nodes, coupling = product of fusion attention weights), surfacing per cycle: the global **cut value** (how close the array is to splitting — a structural measure per-node heuristics miss), the **weak side** (which specific nodes would partition: failure/jamming triage feeding ADR-032 posture), and an **at-risk flag** that counts as a structural event for the drift→recalibration advisor. Surfaced as `TrustedOutput::mesh`. **Measured cost policy** (criterion, 12-node mesh): weights are quantized (1/64; a *nonzero* coupling below one quantum saturates to quantum 1 so quantization never erases a live coupling — without the floor, balanced meshes of ≥ 65 nodes had every ~1/n coupling erased and sat permanently "at risk") and updates change-gated, so the steady-state cycle does zero graph work (~7.3 µs, ~23× cheaper than building); on any real change a full exact rebuild (~171 µs) is used because one `DynamicMinCut` delete+insert measured ~240 µs — the incremental machinery's overhead targets much larger graphs, so rebuild-on-change is the measured optimum at mesh scale (one-edge case −28% after the policy switch). Degenerate cases fail toward risk: a node with zero coupling is reported as already partitioned (cut 0). 9 mesh-guard tests + an engine-level wiring test; full `process_cycle` with the guard: ~33 µs for 4 nodes (50 ms budget).
|
||||
- **Opt-in FFT operator for the CIR ISTA solver (8–14× measured).** Φ is a sub-DFT, so each ISTA mat-vec can run as one length-G FFT (O(G log G)) instead of a dense O(K·G) product. New `CirConfig::fft_operator` (default **false** — the dense path stays the bit-exact witness default; the FFT evaluates the same sums in a different order, so enabling it shifts float results and requires regenerating any pinned witness). `FftOperator` (rustfft, planned once at construction, scratch reused across the ISTA loop) dispatches inside `ista_solve`; warm-start/Lipschitz stay dense at construction. Measured (criterion, same run): ht20 2.22 ms → 265 µs (**8.4×**), ht40 10.26 ms → 717 µs (**14.3×**); the real HE40 grid (K=484, G=1452) scales further. 3 new tests: FFT↔dense matvec equivalence to float tolerance (ht20 + he40 grids), end-to-end dominant-tap agreement on a single-path frame, and all default configs keep FFT off. New `cir_estimate_fft` bench group.
|
||||
- **Per-room adapter provenance + drift→recalibration advisor in the streaming engine.** Closes the trust-chain gap where an ~11 KB per-room LoRA adapter (ADR-150 §3.4) could silently change inference without the witness noticing. `StreamingEngine::set_room_adapter(AdapterInfo)` pins the adapter's content-derived id into provenance `model_version` (`rfenc-v1+adapter:<id>`) — and therefore into the BLAKE3 witness — so swapping or clearing adapter weights always shifts the witness (engine test proves base → adapter → other-adapter → cleared all witness differently, and cleared == base). New `RecalibrationAdvisor` recommends re-running the ADR-135 baseline / refitting the adapter on sustained low fusion coherence (streak threshold, default 60 cycles ≈ 3 s at 20 Hz) or an ADR-142 change-point; surfaced as `TrustedOutput::recalibration_recommended` and recorded on the sensing-server's `EngineBridge` alongside the witness. Bridge plumbing: `EngineBridge::{set_room_adapter, clear_room_adapter}` + live-path test that the adapter id flows into the live witness. *Scope note: this is the deployable provenance/trigger half of the "retrained model" roadmap item — fitting the adapter itself runs in the existing external calibration service (`aether-arena/calibration/`), and a trained RF-encoder checkpoint still does not exist in-tree.*
|
||||
- **RuView beyond-SOTA research series** (`docs/research/ruview-beyond-sota/`, 6 docs) — research-swarm output defining the beyond-SOTA bar and the path to it: system capability audit (role→crate maturity matrix, gap analysis, risk register), web-verified 2026 SOTA landscape per capability axis (incl. ratified IEEE 802.11bf-2025), 8-pillar target architecture on the ADR-136 contract spine (no rewrite), 6-layer benchmark/validation methodology (all 15 criterion bench targets inventoried; ADR-171 statistical protocol), and a determinism-safe optimization roadmap. Includes session validation evidence: 2,797 workspace tests / 0 failed, Python proof PASS (bit-exact), paired pre/post criterion runs.
|
||||
|
||||
### Performance
|
||||
- **CIR estimator warm-start precompute** — the diagonal Tikhonov preconditioner `diag(Φ^H Φ)+λI` and its CSR matrix were rebuilt every frame although they depend only on Φ and λ (fixed at `CirEstimator::new`); now precomputed at construction (`ruvsense/cir.rs`). Bit-identical floats (summation order unchanged, witness chain unaffected). Measured: `cir_estimate/he40` −3.9% (p<0.01), multiband groups −1.2/−1.4%; smaller configs within container noise.
|
||||
- **RF tomography solver hoisting** — ISTA gradient buffer no longer allocated inside the 100-iteration loop, and the Frobenius Lipschitz bound moved from per-`reconstruct` to construction (`ruvsense/tomography.rs`). Bit-identical results.
|
||||
|
||||
### Added
|
||||
- **Falsifiable occupancy benchmark (`wifi-densepose-train::occupancy_bench`).** Makes the presence/person-count "beyond SOTA" claim falsifiable in code instead of aspirational (the unfalsifiability gap from the beyond-SOTA system review). Grades predictions vs ground truth and gates a SOTA claim behind one `claim_allowed` invariant requiring all of: `DataProvenance::Measured` (synthetic/mock is scorable but **never claimable** — anti-mock-contamination per the CLAUDE.md Kconfig-bug lesson), a leak-free `EvalSplit` (refuses any split where a subject *or* environment id appears in both train and test — subject leakage / per-environment overfitting), `n_test ≥ min`, a **non-degenerate test set** (both truth classes represented: present-rate ≥ `min_positive_rate` and ≥ 1 absent sample — an all-absent set plus an always-absent predictor cannot release a claim; vacuous F1 scores 0.0, never 1.0), presence-F1 **bootstrap-CI lower bound** (deterministic seeded splitmix64) clearing the threshold, and count MAE within threshold. The claim string is unreadable except through the gate (`NO_CLAIM` otherwise). What remains is data, not method: a frozen, SHA-pinned, subject/environment-disjoint measured replay set turns the claim into a passing/failing test. 12 tests cover each refusal path, including the point-above/CI-below case (claim withheld on the CI lower bound even when the point estimate clears the threshold).
|
||||
- **Live trust path: sensing-server routes real frames through the governed `StreamingEngine` (parallel governed path with partial output gating).** Previously the live server ran only the *bare* `MultistaticFuser` (fused amplitudes, no trust control plane), while the privacy/provenance/witness engine (ADR-135..146) ran only on synthetic in-test frames — the gap called out in ADR-136 §8 and the beyond-SOTA system review. New `engine_bridge` module drives `StreamingEngine::process_cycle` from the server's live `NodeState` map (reusing the existing `NodeState → MultiBandCsiFrame` conversion), lazily wiring each node as a WorldGraph sensor and bounding belief growth via the retention cap; every *governed belief* carries evidence + model + calibration + privacy decision and a deterministic witness. **Honest scope:** the engine runs alongside (not instead of) the bare fusion path that feeds the live `SensingUpdate`. What its decision gates on the wire today: a cycle emitted at class `Restricted` (base mode or contradiction/mesh-risk demotion) suppresses the per-node raw amplitude vectors from the live publish — the same field mapping `wifi-densepose-bfld`'s privacy gate applies at `Restricted`; gating the remaining derived outputs (person count, classification, signal field) is tracked as a follow-up. Trust state is no longer write-only: the latest witness, effective privacy class, demotion flag, recalibration recommendation, and an engine-error counter are readable on `GET /api/v1/status`, and engine errors are counted + rate-limit logged instead of silently swallowed (`EngineBridge::observe_cycle`). Adds `wifi-densepose-engine/-worldgraph/-bfld/-geo` deps. Bridge tests cover witnessed belief with provenance, determinism, idempotent node registration, retention bound, privacy-mode propagation, trust-state recording, the error-counter path, and Restricted-class raw-output suppression.
|
||||
|
||||
### Fixed
|
||||
- **Real HE20 CSI no longer silently dropped or replaced with simulated data (fixes #1009, #1004).** Two ingest bugs caused real ESP32-C6 HE20 frames to be discarded or never received — the exact "real data silently lost" failure class the project fights. Each fix is pinned by a test that fails on the old code.
|
||||
- **#1009 §1b — HE20 baseline recorder trimmed 256 → 242 bins by sequential index (`wifi-densepose-signal/src/ruvsense/calibration.rs`).** ESP-IDF v5.5.2 delivers all 256 FFT bins for an HE20 frame; `CalibrationConfig::he20()` carried `num_active: 242`, so the recorder (which has no HE20 tone map — `extract_first_stream` takes the first `num_active` columns *sequentially*) kept bins 0..242 of the 256-bin grid. Those are the lower guard band + DC, **not** the 242 active tones, silently corrupting the empty-room baseline. Now `num_active: 256` records every delivered bin, staying aligned 1:1 with the live `deviation()` path. The exact-242 tone map deliberately stays only in `cir.rs` (`HE20_ACTIVE`), where the Φ sensing matrix genuinely needs it. Test `he20_records_all_256_bins_not_trimmed_to_242` asserts the finalized baseline covers all 256 bins (was 242). HE20 synthetic/bench fixtures updated to feed 256-bin frames (the real wire format).
|
||||
- **#1009 §1a/§1c — already-fixed u8→u16 `n_subcarriers` truncation, now regression-pinned.** The ADR-018 wire format carries `n_subcarriers` as u16 LE at bytes 6–7. A 256-bin HE20 frame (byte6=0x00, byte7=0x01) read as a single byte decodes to **0 subcarriers** → every frame skipped (invisible until HE20: ESP32-S3's ≤192 bins fit in one byte). The CLI parser (`wifi-densepose-cli/calibrate.rs`) and the sensing-server template parser (`wifi-densepose-sensing-server` `parse_esp32_frame`) were already corrected to u16 under #1005/ADR-110; added regression tests (`parse_esp32_frame_he20_256_bins_not_truncated`, CLI `test_parse_csi_packet_he_su_256_bins`) that fail on the old single-byte read so the truncation cannot silently return.
|
||||
- **#1004 — `--source auto` latched on `simulate` forever, never binding UDP :5005 (`wifi-densepose-sensing-server/src/main.rs`).** A one-shot boot probe resolved the source once; with no CSI flowing at boot (the normal firmware/server startup race) it served simulated poses for the whole process and ignored real CSI that arrived seconds later (the prior #937 fix hard-exited instead — equally wrong, the server could never pick up late-starting CSI). New `plan_source()` state machine: in `auto` mode **always bind the UDP receiver** and serve simulated data only until the first real frame, at which point `udp_receiver_task` promotes `source` → `esp32` (mirroring the existing `esp32 → esp32:offline` reversion in `effective_source()`); `simulated_data_task` self-suspends once promoted so it never clobbers live CSI. Explicit `--source simulated` stays a hard, UDP-free override for offline demos. 6 unit tests pin the resolution/promotion machine (`auto_with_no_boot_source_still_binds_udp_and_simulates`, etc.); the auto-binds-UDP assertion fails on the old behavior.
|
||||
- **`wifi-densepose-mat` standalone `--no-default-features` build (101 errors → 0).** `pub mod api` was unconditional while its only dependency, serde, is optional behind the `api` feature — so any build without default features failed with unresolved serde imports (masked in `--workspace` runs by feature unification). The `api` module and its `create_router`/`AppState` re-export are now `#[cfg(feature = "api")]`-gated (with docsrs annotations). All feature combos compile: bare `--no-default-features`, `--no-default-features --features api`, and full default (177 tests pass).
|
||||
- **WorldGraph no longer grows unboundedly under the live loop.** `StreamingEngine::process_cycle` appended one `SemanticState` belief per cycle with no eviction — ~1.7M nodes/day at 20 Hz (identified in `docs/research/ruview-beyond-sota/04-optimization-roadmap.md`). Added `WorldGraph::prune_semantic_states(max)` — deterministic eviction of the oldest beliefs by `(valid_from_unix_ms, id)`, structural nodes (rooms/zones/sensors/anchors/tracks/events) never eligible — and wired it into the engine after each belief append (`StreamingEngine::DEFAULT_SEMANTIC_RETENTION` = 7,200 ≈ 6 min at 20 Hz; tunable via `set_semantic_retention`). The WorldGraph holds *current* beliefs; durable history is the recorder's job, so no audit data is lost. 3 new tests (bounded growth end-to-end, oldest-only eviction, deterministic tie-break).
|
||||
- **ESP32 edge heart rate no longer stuck at ~45 BPM / dropping wildly — #987.** The on-device HR estimator (`edge_processing.c`, `0xC5110002`) reported ~45 BPM regardless of true heart rate (Apple-Watch ground truth 87 BPM read as ~45) and swung frame-to-frame. Two root causes: (1) a hardcoded `sample_rate = 10.0f` that became wrong after #985's self-ping raised the CSI callback rate to a variable ~13–19 Hz — BPM scales as `assumed/actual × true`, so 87 read ~45 and the reading swung as CSI yield fluctuated; (2) the zero-crossing estimator locked onto a breathing harmonic (a 0.25 Hz breathing fundamental puts its 3rd harmonic at ~0.74 Hz ≈ 44 BPM inside the HR band). Fix: measure the real sample rate from inter-frame timestamps (used for BPM conversion + biquad re-tuning on >15% drift); replace the HR zero-crossing with an autocorrelation estimator that rejects breathing harmonics (driven by a robust autocorr breathing period); median-13 smooth the output. Hardware A/B (fixed vs unmodified control board, both `edge_tier=2`): control pegged 40–49 BPM; fixed reaches the true 88–91 BPM (vs 87 GT) and holds a stable physiological value (spread 59→0 for a steady subject). Known limitation: heavy subject motion still degrades the estimate (motion gating is a follow-up).
|
||||
- **Person count no longer leaks up to 10 in heuristic mode — addresses #894.** `field_bridge::occupancy_or_fallback` returned the eigenvalue-based `FieldModel::estimate_occupancy` count **unbounded** (its internal ceiling is 10), while the sibling estimators on the same single-link data — the perturbation-energy fallback right below it and `score_to_person_count` — both cap at 3 ("1-3 for single ESP32"). On noisy / under-calibrated CSI the eigenvalue count inflated, producing the "10 persons reported when 1 present" symptom (seen when `--model` fails to load and the server runs on heuristics). Bounded the eigenvalue path to the shared `MAX_SINGLE_LINK_OCCUPANCY` (3) so every estimator on one link agrees; genuine higher counts come from the multistatic fusion path, not a single-link covariance estimate.
|
||||
- **MQTT multi-node deployments now create one Home-Assistant device per node — closes #898.** After the #872 MQTT wiring landed, the JSON→`VitalsSnapshot` bridge hard-coded a single `node_id` (the MQTT client id) and the publisher used a single `OwnedDiscoveryBuilder`, so every physical node collapsed into one device (`identifiers:["wifi_densepose_wifi-densepose-1"]`), contradicting the "one device per node" docs. The bridge now emits one snapshot per node in the sensing update's `nodes[]` (each with its own `node_id` + RSSI, falling back to a single aggregate snapshot for wifi/simulate sources), and the publisher derives a per-node builder (`OwnedDiscoveryBuilder::for_node`) that publishes discovery + availability lazily on first sight of each `node_id` and routes state to per-node topics — yielding N distinct HA devices with per-node availability/LWT. Unit-tested (distinct nodes → distinct `wifi_densepose_<node>` identifiers); 71 MQTT tests pass.
|
||||
- **Person count no longer pinned to 1 — addresses #803.** The aggregate occupancy reported by the sensing server was derived from `smoothed_person_score`, an EMA-smoothed *activity* score (amplitude variance / motion / spectral energy). That score saturates near a single occupant — one moving person maxes it out — so it cannot discriminate occupancy *count* and stayed clamped at 1 across S3/C6 and the Python/Docker/Rust servers. Meanwhile the count-aware per-node estimates the ESP32 paths already compute (firmware `n_persons`, and the DynamicMinCut `corr_persons`) were stashed in `NodeState::prev_person_count` and then **discarded** by the aggregator (same dead-wiring class as #872). The aggregator now takes `max(activity_count, node_max)` via a unit-tested `aggregate_person_count` helper, so a node positively estimating 2–3 occupants is surfaced instead of overwritten. The fix can only ever *raise* the count when a node reports more people, so the single-occupant case is provably never inflated (regression-guarded by test). **Second half:** the pure-CSI per-node path itself clamped its own estimate — the DynamicMinCut occupancy (`estimate_persons_from_correlation`, 0–3) was mapped to a score via `corr_persons / 3.0`, putting 2 people at 0.667, *just under* the 0.70 up-threshold of `score_to_person_count`, so the per-node count never climbed past 1 (so `node_max` was also stuck at 1 for CSI-only nodes). Replaced it with a threshold-aligned `corr_persons_to_score` mapping (1→0.40, 2→0.74, 3→0.96) whose steady state round-trips back to the same count through the EMA + hysteresis, while still gating transient noise. A convergence test replays the exact EMA loop to prove min-cut=2 now reports 2 (and documents that the old `/3.0` mapping reported 1). Full multi-person accuracy still depends on the underlying estimator quality; this removes the two server-side clamps that masked it. 586 sensing-server tests pass.
|
||||
- **MQTT publisher now actually runs (`--mqtt`) — closes #872.** The `--mqtt*` flags were defined only in `cli::Args` (dead code, referenced nowhere) while the binary parses a *separate* `main::Args` with no mqtt fields, and `main.rs` never started the `mqtt::` publisher — so MQTT/Home-Assistant integration was completely unwired (`--mqtt` errored as an unexpected argument, and even with the Docker image's `--features mqtt` build the publisher never ran). Earlier attempts chased a Docker *rebuild*; the real cause was disconnected *code*. Extracted the flags into a shared `cli::MqttArgs` (`#[command(flatten)]` into both structs), spawn the publisher on `--mqtt`, and bridge the JSON sensing broadcast into the typed `VitalsSnapshot` stream with a defensive `serde_json::Value` mapping. Verified end-to-end against `mosquitto`: 20 HA auto-discovery entities + live state (presence/person-count/…). 577 (default) / 580 (`--features mqtt`) tests pass.
|
||||
- **Mass Casualty triage never reports a survivor with a heartbeat as Deceased (safety) — PR #926.** Both triage paths in `wifi-densepose-mat` — `TriageCalculator::calculate` (`combine_assessments(Absent, None) ⇒ Deceased`) and the detection path `EnsembleClassifier::determine_triage` (`!has_breathing && !has_movement ⇒ Deceased`) — ignored the `heartbeat` field. A survivor with a detectable **pulse** but no sensed breathing/movement (respiratory arrest — the most time-critical *savable* state, Immediate/Red) was therefore reported **Deceased (Black)** and deprioritized for rescue. The domain path was in fact only reachable *because* a heartbeat made `has_vitals()` true, so every "Deceased" was a live person. Both paths now escalate to **Immediate** when a heartbeat is present; total absence of breathing, movement *and* heartbeat is unchanged (domain → `Unknown`, ensemble → `Deceased`). 2 safety regression tests; full MAT suite (177) green.
|
||||
- **Per-node Home-Assistant devices now report each node's *own* presence/motion — PR #918.** After the one-device-per-node fan-out landed, the MQTT bridge still applied the *room-level aggregate* `classification` to every node, so in a multi-node deployment a node watching an empty corner inherited another node's "present" (and `motion_level: "absent"` was mis-mapped to full motion). Each node in the broadcast `nodes[]` already carries its own `classification`; the bridge now reads it per node (extracted into a testable `vitals_snapshots_from_sensing_json`), keeping vitals + person count room-level. 4 unit tests.
|
||||
- **`--model` gives an actionable diagnostic instead of a cryptic magic error — PR #919 (refs #894).** Passing a HuggingFace `ruvnet/wifi-densepose-pretrained` file (`model.safetensors` / `model-q4.bin` / `model.rvf.jsonl`) to `--model` produced `invalid magic at offset 0: … got 0x77455735`, then a silent fall back to heuristics. The load-failure path now detects the format (safetensors / quantized blob / JSONL manifest) and explains that those files are a different format **and** encoder architecture than the RVF binary container the progressive loader expects, pointing to #894. Pure `diagnose_model_load_error` + 4 tests.
|
||||
- **`--export-rvf` no longer silently produces a placeholder model — PR #920.** The `--export-rvf` handler ran *before* `--train`/`--pretrain` and unconditionally wrote placeholder sine-wave weights, so the documented `--train … --export-rvf <path>` workflow short-circuited to a fake model and never trained (while printing "exported successfully"). It now emits the placeholder **container-format demo** only standalone (with a clear warning), and falls through to real training when `--train`/`--pretrain` is set; docs point to `--save-rvf` for the real model. 3 guard tests.
|
||||
|
||||
### Added
|
||||
- **ADR-151 per-room calibration & specialist training — full `baseline → enroll → extract → train` pipeline (new `wifi-densepose-calibration` crate).** "Teach the room before you teach the model": a local-first pipeline that turns a few minutes of clean human anchors — layered on the ADR-135 empty-room baseline — into a versioned bank of small, room-calibrated specialists for **presence, posture, breathing, heartbeat, restlessness, and anomaly**. Stages: guided enrollment with an adaptive quality gate (event-sourced `EnrollmentSession`, re-prompts bad anchors); feature extraction (autocorrelation periodicity in breathing/HR bands + variance/motion); six small specialists (learned threshold / nearest-prototype / band-limited periodicity / novelty); a `SpecialistBank` with baseline-drift **STALE** invalidation; and a `MixtureOfSpecialists` runtime with presence short-circuit + anomaly veto + confidence gating. Specialists are statistical heads today (runnable + hardware-validated); the frozen ADR-150 HF RF Foundation Encoder backbone is the documented upgrade path.
|
||||
- **CLI:** `enroll` / `train-room` / `room-status` / `room-watch`, plus the Stage-1 `calibrate-serve` HTTP API (CORS-enabled: `POST /start`, `GET /status`, `POST /stop`, `GET /result`, `GET /baselines`, `GET /health`) and a firewall-free `scripts/csi-udp-relay.py` for local Windows ESP32 testing without admin.
|
||||
- **Multistatic fusion (ADR-029):** `MultiNodeMixture` fuses several co-located nodes (each with its own room-calibrated bank) into one room state — presence OR'd across nodes, posture/breathing/heartbeat from the highest-confidence node, a single implausible node vetoes the room's vitals. Driven via `room-watch --node-bank N:path` (repeatable), which groups live frames by `node_id` and fuses. Same-room only; cross-room is federation (ADR-105).
|
||||
- **Validated on live ESP32-S3 (COM8, `edge_tier=0` raw CSI):** baseline capture (120 frames → 52-subcarrier baseline); the real parser → feature-extraction → mixture runtime detecting breathing (~16–31 BPM); and the multistatic ingest grouping/fusing by node-id end-to-end. Full multi-anchor enrollment accuracy requires the operator to perform the poses; true 2-node fusion + phase-based breathing + RVF/HNSW storage are noted follow-ups. 54 tests pass (35 calibration + 19 CLI).
|
||||
- **WiFi-CSI pose: efficiency frontier + per-room calibration service** (ADR-150 §3.2–3.6). Two beyond-SOTA results on the MM-Fi benchmark, plus the deployment mechanism that resolves real-world generalization:
|
||||
- **Efficiency frontier** — a **75 K-param model beats published SOTA** (74.3% vs MultiFormer 72.25% torso-PCK@20); every config from `micro` up is Pareto-dominant (smaller *and* more accurate than prior work). Shipped a deployable **int4 edge model (~20 KB, verified 74.08%, 0.135 ms single-thread CPU)** — published at [`ruvnet/wifi-densepose-mmfi-pose/edge`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose). See [`docs/benchmarks/wifi-pose-efficiency-frontier.md`](docs/benchmarks/wifi-pose-efficiency-frontier.md).
|
||||
- **Generalization solved by few-shot calibration** — zero-shot cross-subject (~64%) and cross-environment (~10%) are *not* closeable by algorithms (CORAL, DANN, instance-norm, contrastive foundation-pretraining all tested, all failed) or by more training subjects (saturates ~64%). But **~100–200 labeled in-room samples recover SOTA-level pose**: cross-subject 64→76%, **cross-environment 10→73% (60% from just 5 samples)** — deployable as a **~11 KB per-room LoRA adapter** on a frozen shared base. Full empirical chain in ADR-150 §3.2–3.6.
|
||||
- **Calibration service (complete, both model paths, cross-language verified)** — `aether-arena/calibration/`: `calibrate.py` (transformer model, `.npz` adapter) + `infer.py` (verified 3.09%→74.29% on an unseen MM-Fi room), **and `cog_calibrate.py`** which fits a `fc1.a/fc1.b/fc2.a/fc2.b` **safetensors** adapter for the deployed cog conv+MLP model (`pose_v1.safetensors`). Consumed by the Rust product engine: `InferenceEngine::with_adapter()` + `cog-pose-estimation run --config <cfg> --adapter <room.safetensors>`. Self-contained regression tests for both Python producers (`test_calibration.py`, `test_cog_calibration.py`) **plus a cross-language Rust integration test** that loads a real `cog_calibrate.py`-generated adapter fixture and asserts it activates + changes engine output. All green.
|
||||
- **Windows workspace build + test now green** (cross-platform fixes). `wifi-densepose-worldmodel` imported `tokio::net::UnixStream` unconditionally, so `cargo build/test --workspace` failed to compile on Windows (E0432) — now the OccWorld Unix-socket bridge is `#[cfg(unix)]`-gated with a clear non-unix fallback. And `wifi-densepose-bfld`'s `readme_quickstart_uses_canonical_public_api` test checked a multi-line `pipeline\n .process` needle that never matched on a CRLF checkout — now normalizes line endings. Result: **2,682 workspace tests pass / 0 fail on Windows** (the pre-merge gate was previously unrunnable there).
|
||||
- **`ruview-swarm` crate (ADR-148)** — drone swarm control system with hierarchical-mesh topology, Raft consensus, MAPPO multi-agent reinforcement learning, and CSI sensing integration. 14 modules: topology (Raft/Gossip/Mesh), formation control (virtual-structure/leader-follower/Reynolds flocking), RRT-APF path planning, auction+FNN task allocation, MARL actor + PPO training loop, security (MAVLink v2 HMAC-SHA256 signing, UWB anti-spoofing, geofencing, Remote ID, FHSS anti-jamming), 10-state fail-safe machine, and SwarmOrchestrator. ITAR-gated coordination features (USML Category VIII(h)(12)) behind `itar-unrestricted` feature.
|
||||
- **Ruflo integration for `ruview-swarm`** — feature-gated (`ruflo`) AI-agent capability layer connecting to the claude-flow daemon: AgentDB mission memory (`memory_store`/`memory_search`), HNSW pattern learning (`agentdb_pattern-store`/`-search`), AIDefence MAVLink message scanning, and SONA intelligence trajectory hooks. `RufloBackend` trait with `HttpRufloBackend` (JSON-RPC 2.0) and `MockRufloBackend` implementations.
|
||||
|
||||
### Performance
|
||||
- `ruview-swarm` benchmarks (criterion, release): MARL actor inference 3.3 µs, RRT-APF planning 0.043 ms, multi-view CSI fusion 58.5 ns, 3-view localization 1.732 m (beats Wi2SAR 5 m SOTA baseline), 4-drone SAR coverage 223 s for 400×400 m (under 240 s target).
|
||||
|
||||
### Added
|
||||
- **ADR-147 — OccWorld world model integration** (`wifi-densepose-worldmodel` v0.3.0 published to crates.io). 15-frame trajectory prediction at 209 ms / 3.37 GB VRAM on RTX 5080. Phase 3 domain adapter `scripts/ruview_occ_dataset.py` (`RuViewOccDataset`) converts WorldGraph snapshots to OccWorld tensors with indoor class remapping + zero ego-poses (validated). Phase 5 retraining pipeline `scripts/occworld_retrain.py` — VQVAE + transformer fine-tuning on RuView occupancy snapshots. See [ADR-147](docs/adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md) · [benchmark proof](docs/adr/ADR-168-benchmark-proof.md).
|
||||
|
||||
### Added
|
||||
- **ADR-125 (APPLE-FABRIC) — RuView ↔ Apple Home native HAP bridge proposal + reference impl** (issue #796). New ADR-125 lays out a three-phase plan to expose RuView as a discoverable HomeKit accessory on the LAN so a HomePod (as Home Hub) sees presence / vitals / BFLD-derived events natively — zero Home-Assistant intermediary. Two architectural decisions resolved in the ADR per design review: (1) **one HAP bridge with N child accessories** (single pairing, matches Hue/Eve pattern), and (2) **identity-risk mapping is semantic, not probabilistic** — `identity_risk_score` and Soul-Signature match probability never cross the HAP boundary; instead three thresholded events are exposed (`Unknown Presence`, `Unexpected Occupancy`, `Unrecognized Activity Pattern`) so RuView reads as calm-tech ambient awareness, not surveillance UX. ADR-125 §2.1.a reference impl ships now: `scripts/hap-test-sensor.py` (HAP-1.1 bridge advertised over mDNS, paired with operator's iPhone) + `scripts/c6-presence-watcher.py` (parses ESP32 `RV_FEATURE_STATE_MAGIC = 0xC5110006` UDP packets with IEEE CRC32 validation, hysteresis, and a Python port of `wifi-densepose-bfld::PrivacyClass` that enforces ADR-125 §2.1.d invariant I1 at the HomeKit edge — only `Anonymous` (2) and `Restricted` (3) frames may cross; `Raw`/`Derived` are refused with exit code 2 and the cited ADR clause). Validated end-to-end on real hardware (no mocks): ESP32-C6 on `ruv.net` → UDP/5005 → mac-mini watcher → BFLD gate → HAP bridge → iPhone Home app shows `Unknown Presence` live characteristic flip. **Empirical**: 50-51 valid CRC-passing feature_state packets per 10 s window from the live C6; zero CRC errors. P2 (Rust-native HAP via the `hap` crate, replaces the Python sidecar) and P3 (Matter Controller once `matter-rs` stabilizes) follow.
|
||||
|
||||
### Security
|
||||
- **ESP32 OTA upload now fails closed when no PSK is provisioned** (#596 audit finding — critical, **breaking change for unprovisioned nodes**). `ota_check_auth()` previously returned `true` when `s_ota_psk[0] == '\0'`, so a freshly-flashed node would accept attacker-controlled firmware over plain HTTP on port 8032 from any host on the WiFi. No Secure Boot V2, no signed-image verification — a single LAN call could brick or backdoor a node. The fix rejects every OTA upload until a PSK is written to NVS (the OTA HTTP server still starts so operators can run `provision.py --ota-psk <hex>` over USB-CDC without reflashing). **Operators affected**: any deployment that relied on the unauthenticated OTA endpoint working out of the box now needs to provision a PSK before subsequent OTA pushes will succeed. Boot-time `ESP_LOGW` makes the new posture visible.
|
||||
- **Bearer-token auth accepts the scheme case-insensitively (RFC 6750) — PR #929.** `require_bearer` parsed the `Authorization` header with a case-sensitive `strip_prefix("Bearer ")`, so a *correct* `RUVIEW_API_TOKEN` sent as `Authorization: bearer <token>` (or `BEARER`, or with extra whitespace) was rejected with a confusing 401 — needless friction when enabling auth. The scheme is now matched with `eq_ignore_ascii_case` (per RFC 6750 §2.1 / RFC 7235 §2.1); the token compare is unchanged — still exact and constant-time (`ct_eq`) — so a wrong token or a non-Bearer scheme (`Basic …`) still returns 401. Audited the surrounding code while here: `ct_eq` correctly rejects length mismatch (no prefix-auth bypass) and the middleware fails closed. New `accepts_case_insensitive_bearer_scheme` test.
|
||||
- **Path-traversal vulnerabilities patched in five sensing-server endpoints** (closes #615 — critical). New `wifi_densepose_sensing_server::path_safety::safe_id()` enforces `[A-Za-z0-9._-]` only (no leading `.`, max 64 chars) before any user-controlled identifier reaches a `format!()` building a filesystem path. Applied at:
|
||||
- `POST /api/v1/recording/start` (`recording.rs` — `session_name`)
|
||||
- `GET /api/v1/recording/download/:id` (`recording.rs` — `id`)
|
||||
@@ -508,7 +406,7 @@ Model release (no new firmware binary). Firmware remains at v0.6.0-esp32.
|
||||
- Security fix merged via PR #310.
|
||||
|
||||
### Performance
|
||||
- Presence detection: 100% accuracy on 60,630 overnight samples. *(Retracted — that recording was single-class (one sleeping person, 6,062/6,063 frames "present"), so a constant "yes" scores ~99.98%. Superseded by the honest 82.3% held-out temporal-triplet metric; see [#882](https://github.com/ruvnet/RuView/issues/882). Kept here as the in-place public record.)*
|
||||
- Presence detection: 100% accuracy on 60,630 overnight samples.
|
||||
- Inference: 0.008 ms per sample, 164K embeddings/sec.
|
||||
- Contrastive self-supervised training: 51.6% improvement over baseline.
|
||||
|
||||
|
||||
@@ -8,21 +8,19 @@ Dual codebase: Python v1 (`v1/`) and Rust port (`v2/`).
|
||||
| Crate | Description |
|
||||
|-------|-------------|
|
||||
| `wifi-densepose-core` | Core types, traits, error types, CSI frame primitives |
|
||||
| `wifi-densepose-signal` | SOTA signal processing + RuvSense multistatic sensing (16 modules) |
|
||||
| `wifi-densepose-signal` | SOTA signal processing + RuvSense multistatic sensing (14 modules) |
|
||||
| `wifi-densepose-nn` | Neural network inference (ONNX, PyTorch, Candle backends) |
|
||||
| `wifi-densepose-train` | Training pipeline with ruvector integration + ruview_metrics; MAE pretraining recipe (`mae.rs`, ADR-152 §2.3) + WiFlow-STD port (`wiflow_std/`, tch-gated) |
|
||||
| `wifi-densepose-train` | Training pipeline with ruvector integration + ruview_metrics |
|
||||
| `wifi-densepose-mat` | Mass Casualty Assessment Tool — disaster survivor detection |
|
||||
| `wifi-densepose-hardware` | ESP32 aggregator, TDM protocol, channel hopping firmware; `ieee80211bf/` 802.11bf forward-compat protocol model (ADR-153) |
|
||||
| `wifi-densepose-hardware` | ESP32 aggregator, TDM protocol, channel hopping firmware |
|
||||
| `wifi-densepose-ruvector` | RuVector v2.0.4 integration + cross-viewpoint fusion (5 modules) |
|
||||
| `wifi-densepose-wasm` | WebAssembly bindings for browser deployment |
|
||||
| `wifi-densepose-cli` | CLI tool (`wifi-densepose` binary) — `calibrate`/`calibrate-serve`/`enroll`/`train-room`/`room-watch` + MAT (MAT gated behind the `mat` feature; build `--no-default-features` for the aarch64/appliance calibration binary) |
|
||||
| `wifi-densepose-calibration` | ADR-151 per-room calibration & specialist training — `baseline → enroll → extract → train` → bank of small specialists (presence/posture/breathing/heartbeat/restlessness/anomaly) + multistatic fusion; pure Rust, edge-deployable |
|
||||
| `wifi-densepose-cli` | CLI tool (`wifi-densepose` binary) |
|
||||
| `wifi-densepose-sensing-server` | Lightweight Axum server for WiFi sensing UI |
|
||||
| `wifi-densepose-wifiscan` | Multi-BSSID WiFi scanning (ADR-022) |
|
||||
| `wifi-densepose-vitals` | ESP32 CSI-grade vital sign extraction (ADR-021) |
|
||||
| `nvsim` | Deterministic NV-diamond magnetometer pipeline simulator (ADR-089) — standalone leaf, WASM-ready |
|
||||
| `vendor/rvcsi` (submodule) | **rvCSI** — edge RF sensing runtime (ADR-095/096): 9 crates (`rvcsi-core`/`-dsp`/`-events`/`-adapter-file`/`-adapter-nexmon`/`-ruvector`/`-runtime`/`-node`/`-cli`). Lives in its own repo ([github.com/ruvnet/rvcsi](https://github.com/ruvnet/rvcsi)), vendored here under `vendor/rvcsi`, published to crates.io as `rvcsi-* 0.3.x` and to npm as `@ruv/rvcsi`. Not a `v2/` workspace member — depend on the published crates (or the submodule's `crates/rvcsi-*` paths). Normalized `CsiFrame`/`CsiWindow`/`CsiEvent` schema, validate-before-FFI, reusable DSP, typed confidence-scored events, the napi-c Nexmon shim (real nexmon_csi `.pcap` from a Raspberry Pi 5 / 4 / 3B+ — BCM43455c0), the napi-rs SDK, the `rvcsi` CLI, a Claude Code plugin. |
|
||||
| `ruview-swarm` | Drone swarm control system (ADR-148) — hierarchical-mesh topology, Raft consensus, MARL, CSI sensing payload, MAVLink/PX4 compat, Ruflo AI-agent integration |
|
||||
|
||||
### RuvSense Modules (`signal/src/ruvsense/`)
|
||||
| Module | Purpose |
|
||||
@@ -40,8 +38,6 @@ Dual codebase: Python v1 (`v1/`) and Rust port (`v2/`).
|
||||
| `cross_room.rs` | Environment fingerprinting, transition graph |
|
||||
| `gesture.rs` | DTW template matching gesture classifier |
|
||||
| `adversarial.rs` | Physically impossible signal detection, multi-link consistency |
|
||||
| `cir.rs` | ADR-134 CSI→CIR via ISTA L1 sparse recovery (NeumannSolver warm-start) |
|
||||
| `calibration.rs` | ADR-135 empty-room baseline (Welford amplitude + von Mises phase, drift trigger) |
|
||||
|
||||
### Cross-Viewpoint Fusion (`ruvector/src/viewpoint/`)
|
||||
| Module | Purpose |
|
||||
@@ -72,17 +68,14 @@ All 5 ruvector crates integrated in workspace:
|
||||
- ADR-030: RuvSense persistent field model (Proposed)
|
||||
- ADR-031: RuView sensing-first RF mode (Proposed)
|
||||
- ADR-032: Multistatic mesh security hardening (Proposed)
|
||||
- ADR-148: Drone swarm control system / `ruview-swarm` (In Progress)
|
||||
- ADR-152: WiFi-Pose SOTA 2026 intake — geometry conditioning, WiFlow-STD benchmark (measurement (a) complete: claims MEASURED-EQUIVALENT at ~96% PCK@20), MAE recipe (Proposed; §2.1–2.3, 2.6 implemented)
|
||||
- ADR-153: IEEE 802.11bf-2025 forward-compatibility protocol model (Accepted — amends ADR-152 §2.4)
|
||||
|
||||
### Supported Hardware
|
||||
|
||||
| Device | Port | Chip | Role | Cost |
|
||||
|--------|------|------|------|------|
|
||||
| ESP32-S3 (8MB flash) | COM9 (ruvzen, was COM7) | Xtensa dual-core | WiFi CSI sensing node | ~$9 |
|
||||
| ESP32-S3 (8MB flash) | COM7 | Xtensa dual-core | WiFi CSI sensing node | ~$9 |
|
||||
| ESP32-S3 SuperMini (4MB) | — | Xtensa dual-core | WiFi CSI (compact) | ~$6 |
|
||||
| ESP32-C6 + Seeed MR60BHA2 | COM12 (ruvzen, was COM4) | RISC-V + 60 GHz FMCW | mmWave HR/BR/presence + WiFi CSI | ~$15 |
|
||||
| ESP32-C6 + Seeed MR60BHA2 | COM4 | RISC-V + 60 GHz FMCW | mmWave HR/BR/presence | ~$15 |
|
||||
| HLK-LD2410 | — | 24 GHz FMCW | Presence + distance | ~$3 |
|
||||
|
||||
**Not supported:** ESP32 (original), ESP32-C3 — single-core, can't run CSI DSP pipeline.
|
||||
|
||||
@@ -1,78 +0,0 @@
|
||||
# PROOF — reproduce every claim, or find the one we can't yet
|
||||
|
||||
This project (RuView / wifi-densepose) has been publicly called "AI slop" and
|
||||
"fake." This document is the answer: **a skeptic can clone the repo, run one
|
||||
script, and have every headline claim either verified on their own machine or
|
||||
shown — explicitly — as "CLAIMED, not yet reproduced (here's exactly what it
|
||||
needs)."** Nothing below is asserted without a command you can run.
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ruvnet/RuView && cd RuView
|
||||
bash scripts/prove.sh # core gate + the anti-slop assertion tests
|
||||
bash scripts/prove.sh --full # also attempt the feature-gated subset
|
||||
```
|
||||
|
||||
`prove.sh` exits 0 only if every **non-gated** claim passes. Gated claims never
|
||||
fail the run; they print the prerequisite (a GPU, a dataset, real hardware, a
|
||||
trained checkpoint) so you can reproduce them yourself.
|
||||
|
||||
## Grading
|
||||
|
||||
- **MEASURED** — reproduced on our hardware, with the exact command recorded, and
|
||||
pinned by a test that *fails on the pre-fix code*. `prove.sh` re-runs these.
|
||||
- **CLAIMED** — cited from a source, or measured by the source, but not
|
||||
reproduced in this repo's automated harness.
|
||||
- **DATA-GATED / HARDWARE-GATED** — the *code path* is real and tested, but the
|
||||
*accuracy/throughput claim* needs data or hardware we don't ship. We never
|
||||
fabricate the number; the code carries a typed error or a `weights_trained`/
|
||||
provenance flag instead.
|
||||
|
||||
## The hard gate (run on any machine with Rust + Python)
|
||||
|
||||
| Claim | Grade | Reproduce |
|
||||
|---|---|---|
|
||||
| Rust workspace: 3,128 tests, 0 failed | **MEASURED** | `cd v2 && cargo test --workspace --no-default-features` |
|
||||
| Deterministic CSI pipeline proof (bit-exact SHA-256) | **MEASURED** | `python archive/v1/data/proof/verify.py` → `VERDICT: PASS` |
|
||||
|
||||
## Anti-slop assertion tests (each fails on the pre-fix code)
|
||||
|
||||
| Claim | Grade | Test (run via `cargo test -p <crate> <name>`) |
|
||||
|---|---|---|
|
||||
| Fusion crafted-input DoS panics are closed (ADR-156 §2.2) | **MEASURED** | `wifi-densepose-ruvector :: triangulation_out_of_range_index_returns_none_no_panic` |
|
||||
| **The "Soul Signature" identity claim, honestly bounded:** on WiFi-only cardiac+respiratory channels two people are **not separable** (gap ≈ 0.0005) | **MEASURED** | `wifi-densepose-bfld :: cardiac_alone_cannot_separate_identity_matches_audit` |
|
||||
| OccWorld `predict()` is real (input-dependent), not random noise | **MEASURED** | `wifi-densepose-occworld-candle :: predict_is_deterministic_for_same_input` |
|
||||
| Pose runtime emits frames under its own default config (ADR-159 A1) | **MEASURED** | `cog-pose-estimation :: default_config_emits_frames_with_real_model` |
|
||||
| Person-count flags untrained classes — no count inflation (ADR-159 A2) | **MEASURED** | `cog-person-count :: untrained_class_argmax_is_flagged_low_confidence` |
|
||||
| Medical edge skills carry a "not a medical device" disclaimer (ADR-160 A1) | **MEASURED** | `wifi-densepose-wasm-edge :: a1_med_modules_have_clinical_disclaimer` (`--features std`) |
|
||||
| Survivor dedup 3→1, count-inflation killed (ADR-158 §2) | **MEASURED** | `wifi-densepose-mat :: test_identical_vitals_no_location_dedup_to_one` (`--features mat`) |
|
||||
|
||||
## Measured performance (criterion; reproduce on your machine)
|
||||
|
||||
| Claim | Grade | Reproduce |
|
||||
|---|---|---|
|
||||
| PSD FFT-planner cache 2.0–3.1×, DTW band 2.4–4.1× (ADR-154) | **MEASURED** | `cd v2 && cargo bench -p wifi-densepose-signal` |
|
||||
| fuse() double-clone removed ~2.17× marshalling (ADR-156) | **MEASURED** | `cd v2 && cargo bench -p wifi-densepose-ruvector --bench fusion_bench` |
|
||||
| zero-copy ORT input ~1.48× (ADR-155) | **MEASURED** | `cd v2 && cargo bench -p wifi-densepose-nn --features onnx --bench onnx_bench` |
|
||||
| pointcloud splats 9→2 passes ~1.24× (ADR-160 research) | **MEASURED** | `cd v2 && cargo bench -p wifi-densepose-pointcloud --bench splats_bench` |
|
||||
| native wlanapi multi-BSSID scan 9.74 Hz (vs netsh ~2 Hz) | **MEASURED (Windows)** | `cd v2 && cargo test -p wifi-densepose-wifiscan -- --ignored measure_native_scan_rate` |
|
||||
| wasm-edge `process_frame` hot-path latency (host proxy, ADR-163) | **MEASURED-on-host** (NOT the ESP32/WASM3 budget — needs hardware) | `cd v2/crates/wifi-densepose-wasm-edge && cargo bench --features std` |
|
||||
| cog steady-state CPU infer latency ~305 µs (ADR-163; NOT the manifest cold-start) | **MEASURED-on-host** | `cd v2 && cargo bench -p cog-person-count -p cog-pose-estimation --no-default-features --bench infer_bench` |
|
||||
|
||||
## What we do NOT claim (the honest negatives — the strongest anti-slop signal)
|
||||
|
||||
| Capability | Status |
|
||||
|---|---|
|
||||
| **Named person-identity from WiFi** | **NOT achieved, and measured why.** The §3.6 matcher is real, but identity does not lock on WiFi-only channels (gap 0.0005). DATA-GATED on a real enrollment feeding the AETHER/body-resonance channel — never done. No named-identity claim is made. |
|
||||
| WiFlow-STD ~96% PCK@20 | **CLAIMED-reproduced** on our RTX 5080 (`benchmarks/wiflow-std/RESULTS.md`); HARDWARE-GATED for you (needs an NVIDIA GPU + the MM-Fi dataset). The upstream *shipped checkpoint* was **REFUTED** (0.08% PCK) — we publish that. |
|
||||
| OccWorld trajectory accuracy | DATA-GATED on a trained checkpoint; `predict()` carries `weights_trained=false` until one is loaded — never silently faked. |
|
||||
| Edge-skill detection accuracy (seizure, weapon, affect, …) | UNVALIDATED — every such module is now disclaimer-gated as experimental/research; the DSP is real, the accuracy is not claimed. |
|
||||
| 802.11bf-2025 OTA conformance | No commodity silicon ships a conformant interface as of 2026; ours is a simulation-tested forward-compat protocol model, not a certified implementation. |
|
||||
|
||||
## Provenance
|
||||
|
||||
Every claim above traces to a committed ADR (`docs/adr/ADR-154`…`ADR-163`), a
|
||||
test, a criterion bench, `benchmarks/wiflow-std/RESULTS.md`, or
|
||||
`benchmarks/edge-latency/RESULTS.md`. The history
|
||||
includes published **retractions** (the 92.9% PCK retraction; the WiFlow-STD
|
||||
shipped-checkpoint refutation; the NV-diamond BOM reality check) — a faker hides
|
||||
failures; we commit them.
|
||||
@@ -11,13 +11,18 @@
|
||||
</a>
|
||||
</p>
|
||||
|
||||
> **Beta Software** — Under active development. APIs and firmware may change. Known limitations:
|
||||
> - ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP)
|
||||
> - Single ESP32 deployments have limited spatial resolution — use 2+ nodes or add a [Cognitum Seed](https://cognitum.one) for best results
|
||||
> - Camera-free pose accuracy is limited (PCK@20 ≈ 2.5% with proxy labels) — [camera ground-truth training](docs/adr/ADR-079-camera-ground-truth-training.md) targets **35%+ PCK@20**; the pipeline is implemented, but the data-collection and evaluation phases (ADR-079 P7–P9) are still pending.
|
||||
>
|
||||
> Contributions and bug reports welcome at [Issues](https://github.com/ruvnet/RuView/issues).
|
||||
|
||||
## **See through walls with WiFi** ##
|
||||
|
||||
**Turn ordinary WiFi into a spatial intelligence / sensing system.** Detect people, measure breathing and heart rate, track movement, and monitor rooms — through walls, in the dark, with no cameras or wearables. Just physics.
|
||||
|
||||
Works natively with the four major smart-home ecosystems: **[Home Assistant](docs/integrations/home-assistant.md)** via the HA-DISCO MQTT publisher, **[Apple Home & HomePod](docs/user-guide-apple-homepod.md)** as a discoverable HAP-1.1 bridge, **[Google Home](docs/integrations/home-assistant.md)** + **[Amazon Alexa](docs/integrations/home-assistant.md)** via the same HA bridge or a [Matter](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md) endpoint. Siri, Google Assistant, and Alexa can voice presence and vitals by room with zero custom skills.
|
||||
|
||||
[](docs/integrations/home-assistant.md) [](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md) [](docs/user-guide-apple-homepod.md) [](docs/integrations/home-assistant.md) [](docs/integrations/home-assistant.md)
|
||||
   
|
||||
|
||||
> Drop into any **Home Assistant** install with one `--mqtt` flag. Or pair into **Apple Home / Google Home / Alexa / SmartThings** as a Matter Bridge. Ships 21 entities per node (11 raw signals + 10 inferred semantic states: someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting-in-progress, bathroom-occupied, fall-risk-elevated, bed-exit, no-movement, multi-room-transition) plus 3 starter HA Blueprints. See [`docs/integrations/home-assistant.md`](docs/integrations/home-assistant.md) · [ADR-115](docs/adr/ADR-115-home-assistant-integration.md).
|
||||
|
||||
@@ -36,7 +41,7 @@ Built on [RuVector](https://github.com/ruvnet/ruvector/) and [Cognitum Seed](htt
|
||||
|
||||
The system learns each environment locally using spiking neural networks that adapt in under 30 seconds, with multi-frequency mesh scanning across 6 WiFi channels that uses your neighbors' routers as free radar illuminators. Every measurement is cryptographically attested via an Ed25519 witness chain.
|
||||
|
||||
RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the radio reflections off the people in a room, and a small pretrained model — published on Hugging Face at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — tells you who's there, how they're breathing, and how their heart rate is trending. The model fits in 8 KB (4-bit quantized) and runs in microseconds on a Raspberry Pi. (The [v2 encoder](https://huggingface.co/ruvnet/wifi-densepose-pretrained) reports an honest, label-free held-out **temporal-triplet accuracy of 82.3%** — up from 66.4% raw; the older "100% presence" figure was measured on a single-class recording and has been retracted in favor of this.) No cameras, no wearables, no app on the user's phone.
|
||||
RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the radio reflections off the people in a room, and a small pretrained model — published on Hugging Face at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — tells you who's there, how they're breathing, and how their heart rate is trending. The model fits in 8 KB (4-bit quantized), runs in microseconds on a Raspberry Pi, and reports 100% presence accuracy on the validation set. No cameras, no wearables, no app on the user's phone.
|
||||
|
||||
### Built for low-power edge applications
|
||||
|
||||
@@ -56,13 +61,12 @@ RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the
|
||||
> |------|-----|---------------|
|
||||
> | 🫁 **Breathing rate** | Bandpass 0.1–0.5 Hz on wrapped phase, circular variance, zero-crossing BPM ([#593](https://github.com/ruvnet/RuView/issues/593)) | 6–30 BPM, real-time |
|
||||
> | 💓 **Heart rate** | Bandpass 0.8–2.0 Hz, zero-crossing BPM | 40–120 BPM, real-time |
|
||||
> | 👤 **Presence detection** | Trained head on Hugging Face ([`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained); v2 encoder = 82.3% held-out temporal-triplet acc, honestly re-benchmarked) + a phase-variance fallback that needs no model | < 1 ms, ~30 s ambient calibration |
|
||||
> | 👤 **Presence detection** | Trained head on Hugging Face ([`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained), 100% validation accuracy) + a phase-variance fallback that needs no model | < 1 ms, ~30 s ambient calibration |
|
||||
> | 🧬 **CSI embeddings** | 128-dim contrastive encoder shipped on Hugging Face, 4-bit quantised variant fits in 8 KB | **164,183 emb/s** on M4 Pro |
|
||||
> | 🦴 **17-keypoint pose estimation** | `cog-pose-estimation` Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads `pose_v1.safetensors` via Candle. Train your own from paired data in 2.1 s on an RTX 5080 ([ADR-101](docs/adr/ADR-101-pose-estimation-cog.md), [benchmarks](docs/benchmarks/pose-estimation-cog.md)). **SOTA on MM-Fi:** [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) hits **82.69% torso-PCK@20** (ensemble 83.59%), beating MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched MM-Fi `random_split` protocol — self-corrected and auditable on [AetherArena](https://huggingface.co/spaces/ruvnet/aether-arena) | 8.4 ms cold-start on a Pi 5 |
|
||||
> | 🦴 **17-keypoint pose estimation** | `cog-pose-estimation` Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads `pose_v1.safetensors` via Candle. Train your own from paired data in 2.1 s on an RTX 5080 ([ADR-101](docs/adr/ADR-101-pose-estimation-cog.md), [benchmarks](docs/benchmarks/pose-estimation-cog.md)) | 8.4 ms cold-start on a Pi 5 |
|
||||
> | 🚶 **Motion / activity** | Motion-band power + phase acceleration | Real-time |
|
||||
> | 🤸 **Fall detection** | Phase-acceleration threshold + 3-frame debounce + 5 s cooldown ([#263](https://github.com/ruvnet/RuView/issues/263)) | < 200 ms |
|
||||
> | 🧮 **Multi-person count** | Adaptive P95 normalisation + runtime-tunable dedup factor (`/api/v1/config/dedup-factor`, [#491](https://github.com/ruvnet/RuView/pull/491)). Six specialised learned counters available as Cogs: `occupancy-zones`, `elevator-count`, `queue-length`, `customer-flow`, `clean-room`, `person-matching` | Real-time, self-calibrating |
|
||||
> | 🌍 **World model prediction** | OccWorld TransVQVAE — 15-frame future occupancy prediction, 209 ms inference, 3.4 GB VRAM on RTX 5080; fine-tune on your space with `occworld_retrain.py` ([ADR-147](docs/adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md)) | 15 frames × 200×200×16 vox |
|
||||
> | 🧱 **Through-wall sensing** | Fresnel-zone geometry + multipath modeling | Up to ~5 m, signal-dependent |
|
||||
> | 🧠 **Edge intelligence** | **105-cog catalog** ([ADR-102](docs/adr/ADR-102-edge-module-registry.md)) live from `app-registry.json` — health, security, building, retail, industrial, research, AI, swarm, signal, network, and developer modules. Optional Cognitum Seed adds persistent vector store + kNN + witness chain | $140 total BOM |
|
||||
> | 🎯 **Camera-free pre-training** | Self-supervised contrastive encoder, 12.2M training steps on 60K frames, shipped on Hugging Face | 84 s/epoch retrain on M4 Pro |
|
||||
@@ -162,7 +166,7 @@ pip install "ruview[client]" # or: pip install "wifi-densepose[clie
|
||||
|
||||
## 🤗 Pretrained model on Hugging Face
|
||||
|
||||
Pretrained CSI weights live at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — 12.2M training steps on 60K frames / 610K contrastive triplets, **82.3% held-out temporal-triplet accuracy** (up from 66.4% raw; the older "100% presence" figure was measured on a single-class recording and has been retracted), 4-bit quantized variant fits in 8 KB. The release includes a contrastive **CSI encoder** producing 128-dim embeddings (164,183 emb/s on M4 Pro) and a **presence-detection head**. Per-node LoRA adapters are included for environment-specific fine-tuning.
|
||||
Pretrained CSI weights live at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — 12.2M training steps on 60K frames / 610K contrastive triplets, **100% presence accuracy** on the validation set, 4-bit quantized variant fits in 8 KB. The release includes a contrastive **CSI encoder** producing 128-dim embeddings (164,183 emb/s on M4 Pro) and a **presence-detection head**. Per-node LoRA adapters are included for environment-specific fine-tuning.
|
||||
|
||||
```bash
|
||||
# Download the model bundle
|
||||
@@ -182,27 +186,7 @@ huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/wif
|
||||
|
||||
**Quantization choices** (all in the HF repo): `model-q2.bin` (4 KB) · `model-q4.bin` ⭐ recommended (8 KB) · `model-q8.bin` (16 KB) · `model.safetensors` full (48 KB)
|
||||
|
||||
The separate **17-keypoint pose-estimation model** is now published at [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) — **82.69% torso-PCK@20** on MM-Fi (single model) / **83.59%** (3-model ensemble + TTA), beating the prior published SOTA MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched `random_split` protocol. See **Results & proof** below.
|
||||
|
||||
### Results & proof
|
||||
|
||||
| What | Where | Numbers |
|
||||
|------|-------|---------|
|
||||
| **MM-Fi pose model (SOTA)** | [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) | 82.69% torso-PCK@20 (single) · 83.59% (ensemble+TTA) · 75K-param micro variant 74.30% |
|
||||
| **AetherArena benchmark Space** | [`ruvnet/aether-arena`](https://huggingface.co/spaces/ruvnet/aether-arena) | self-correcting, auditable MM-Fi leaderboard |
|
||||
| **Full MM-Fi study (honest picture)** | [`docs/benchmarks/mmfi-wifi-sensing-study.md`](docs/benchmarks/mmfi-wifi-sensing-study.md) | pose + action; zero-shot cross-subject ~64%, +~30 s in-room calibration → 72.2% |
|
||||
| **Efficiency frontier** | [`docs/benchmarks/wifi-pose-efficiency-frontier.md`](docs/benchmarks/wifi-pose-efficiency-frontier.md) | SOTA-beating WiFi pose in a 20 KB int4 edge model |
|
||||
| **Pretrained encoder** | [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) | 82.3% held-out temporal-triplet, 8 KB int4 |
|
||||
| **Reproducible proof (Trust Kill Switch)** | [`archive/v1/data/proof/verify.py`](archive/v1/data/proof/verify.py) + [`expected_features.sha256`](archive/v1/data/proof/expected_features.sha256) | one-command deterministic pipeline replay (SHA-256 of output vs published hash) |
|
||||
| **Benchmark-proof ADR** | [ADR-168](docs/adr/ADR-168-benchmark-proof.md) | how the numbers are produced and verified |
|
||||
| **Witness attestation** | [`docs/WITNESS-LOG-028.md`](docs/WITNESS-LOG-028.md) | 33-row capability attestation matrix with per-claim evidence |
|
||||
|
||||
```bash
|
||||
# Reproduce the deterministic pipeline proof yourself (must print VERDICT: PASS):
|
||||
python archive/v1/data/proof/verify.py
|
||||
```
|
||||
|
||||
Tracked in [#509](https://github.com/ruvnet/RuView/issues/509); see [ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md) phases P7–P9 for the camera-supervised fine-tune path.
|
||||
The separate **17-keypoint pose-estimation model** is not in this release — pipeline is implemented but keypoint weights are still pending. Tracked in [#509](https://github.com/ruvnet/RuView/issues/509); see [ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md) phases P7–P9.
|
||||
|
||||
|
||||
## 🧩 Edge Module Catalog
|
||||
@@ -501,7 +485,7 @@ Every WiFi signal that passes through a room creates a unique fingerprint of tha
|
||||
**What it does in plain terms:**
|
||||
- Turns any WiFi signal into a 128-number "fingerprint" that uniquely describes what's happening in a room
|
||||
- Learns entirely on its own from raw WiFi data — no cameras, no labeling, no human supervision needed
|
||||
- Recognizes rooms, detects intruders, and classifies activities using only WiFi (named person-identity is an experimental, data-gated research capability — see below, not a shipped feature)
|
||||
- Recognizes rooms, detects intruders, identifies people, and classifies activities using only WiFi
|
||||
- Runs on an $8 ESP32 chip (the entire model fits in 55 KB of memory)
|
||||
- Produces both body pose tracking AND environment fingerprints in a single computation
|
||||
|
||||
@@ -512,7 +496,7 @@ Every WiFi signal that passes through a room creates a unique fingerprint of tha
|
||||
| **Self-supervised learning** | The model watches WiFi signals and teaches itself what "similar" and "different" look like, without any human-labeled data | Deploy anywhere — just plug in a WiFi sensor and wait 10 minutes |
|
||||
| **Room identification** | Each room produces a distinct WiFi fingerprint pattern | Know which room someone is in without GPS or beacons |
|
||||
| **Anomaly detection** | An unexpected person or event creates a fingerprint that doesn't match anything seen before | Automatic intrusion and fall detection as a free byproduct |
|
||||
| **Person re-identification** *(experimental, research)* | A real per-channel similarity matcher (Soul Signature §3.6, `wifi-densepose-bfld`); **measured** result: on WiFi-only cardiac+respiratory channels alone two people are *not* separable (gap ~0.0005) | Honest research capability — **named identity is not claimed** and is data-gated on enrollment with the decisive AETHER/body-resonance channel. See [#1021](https://github.com/ruvnet/RuView/issues/1021) |
|
||||
| **Person re-identification** | Each person disturbs WiFi in a slightly different way, creating a personal signature | Track individuals across sessions without cameras |
|
||||
| **Environment adaptation** | MicroLoRA adapters (1,792 parameters per room) fine-tune the model for each new space | Adapts to a new room with minimal data — 93% less than retraining from scratch |
|
||||
| **Memory preservation** | EWC++ regularization remembers what was learned during pretraining | Switching to a new task doesn't erase prior knowledge |
|
||||
| **Hard-negative mining** | Training focuses on the most confusing examples to learn faster | Better accuracy with the same amount of training data |
|
||||
@@ -610,7 +594,7 @@ Verify the plugin structure: `bash plugins/ruview/scripts/smoke.sh`. Full detail
|
||||
| [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training |
|
||||
| [Build Guide](docs/build-guide.md) | Building from source (Rust and Python) |
|
||||
| [**Home Assistant + Matter Integration**](docs/integrations/home-assistant.md) | **Works with Home Assistant** via MQTT auto-discovery + **Works with Matter** (Apple Home / Google Home / Alexa / SmartThings) — full entity catalog, 3 starter blueprints, Lovelace dashboards, privacy mode, threshold tuning ([ADR-115](docs/adr/ADR-115-home-assistant-integration.md)). |
|
||||
| [**BFLD — Beamforming Feedback Layer for Detection**](v2/crates/wifi-densepose-bfld/README.md) | New privacy-gated WiFi sensing layer that measures + structurally prevents identity leakage from 802.11ac/ax Beamforming Feedback Information. Three type-enforced invariants (raw BFI never exits node, identity embedding is in-RAM-only, cross-site correlation cryptographically impossible via per-site BLAKE3 keyed hash + daily rotation). Ships full operator surface (`BfldPipeline`, `BfldPipelineHandle`, the Soul Signature §3.6 per-channel matcher `EnrolledMatcher`/`SoulMatchOracle` — experimental; named identity is data-gated, **measured** as not-separable on WiFi-only channels alone), MQTT topic router + HA-DISCO + availability + LWT, 3 operator HA blueprints, two runnable examples, eclipse-mosquitto:2 CI service container. 327+ tests. [ADR-118](docs/adr/ADR-118-bfld-beamforming-feedback-layer-for-detection.md) umbrella + sub-ADRs [119](docs/adr/ADR-119-bfld-frame-format-and-wire-protocol.md)/[120](docs/adr/ADR-120-bfld-privacy-class-and-hash-rotation.md)/[121](docs/adr/ADR-121-bfld-identity-risk-scoring.md)/[122](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md)/[123](docs/adr/ADR-123-bfld-capture-path-nexmon-and-esp32.md). Research dossier: [`docs/research/BFLD/`](docs/research/BFLD/) (11 files, 13,544 words). |
|
||||
| [**BFLD — Beamforming Feedback Layer for Detection**](v2/crates/wifi-densepose-bfld/README.md) | New privacy-gated WiFi sensing layer that measures + structurally prevents identity leakage from 802.11ac/ax Beamforming Feedback Information. Three type-enforced invariants (raw BFI never exits node, identity embedding is in-RAM-only, cross-site correlation cryptographically impossible via per-site BLAKE3 keyed hash + daily rotation). Ships full operator surface (`BfldPipeline`, `BfldPipelineHandle`, Soul Signature `SoulMatchOracle` integration), MQTT topic router + HA-DISCO + availability + LWT, 3 operator HA blueprints, two runnable examples, eclipse-mosquitto:2 CI service container. 327+ tests. [ADR-118](docs/adr/ADR-118-bfld-beamforming-feedback-layer-for-detection.md) umbrella + sub-ADRs [119](docs/adr/ADR-119-bfld-frame-format-and-wire-protocol.md)/[120](docs/adr/ADR-120-bfld-privacy-class-and-hash-rotation.md)/[121](docs/adr/ADR-121-bfld-identity-risk-scoring.md)/[122](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md)/[123](docs/adr/ADR-123-bfld-capture-path-nexmon-and-esp32.md). Research dossier: [`docs/research/BFLD/`](docs/research/BFLD/) (11 files, 13,544 words). |
|
||||
| [**SENSE-BRIDGE — rvagent MCP server**](tools/ruview-mcp/README.md) | Dual-transport MCP server (`@ruvnet/rvagent`) bridging the RuView sensing stack to AI agents (Claude Code, Cursor, ruflo swarms). 6 tools wired: `ruview.presence.now`, `ruview.vitals.get_{breathing,heart_rate,all}`, `ruview.bfld.last_scan`, `ruview.bfld.subscribe`. stdio + Streamable HTTP (`POST /mcp`, Origin-validated, bearer-token auth, `127.0.0.1` bind). Full 20-tool Zod schema barrel + 5 RUVIEW-POLICY governance tools. 93 tests. [ADR-124](docs/adr/ADR-124-rvagent-mcp-ruvector-npm-integration.md). Try: `npx @ruvnet/rvagent stdio`. |
|
||||
| [Semantic Primitives — Precision/Recall](docs/integrations/semantic-primitives-metrics.md) | Per-primitive F1 on the held-out paired-capture set: someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting, bathroom, fall-risk, bed-exit, no-movement, multi-room. |
|
||||
| [Claude Code / Codex Plugin](plugins/ruview/README.md) | The `ruview` plugin + marketplace — skills, `/ruview-*` commands, agents, and the Codex prompt mirror |
|
||||
@@ -618,21 +602,11 @@ Verify the plugin structure: `bash plugins/ruview/scripts/smoke.sh`. Full detail
|
||||
| [Domain Models](docs/ddd/README.md) | 8 DDD models (RuvSense, Signal Processing, Training Pipeline, Hardware Platform, Sensing Server, WiFi-Mat, CHCI, rvCSI) — bounded contexts, aggregates, domain events, and ubiquitous language |
|
||||
| [rvCSI — edge RF sensing runtime](https://github.com/ruvnet/rvcsi) | Rust-first / TypeScript-accessible / hardware-abstracted CSI runtime: multi-source ingestion (incl. real nexmon_csi `.pcap` from a **Raspberry Pi 5** / Pi 4 / Pi 3B+ — CYW43455 / BCM43455c0) → validation → DSP → typed events → RuVector RF memory ([ADR-095](docs/adr/ADR-095-rvcsi-edge-rf-sensing-platform.md), [ADR-096](docs/adr/ADR-096-rvcsi-ffi-crate-layout.md), [domain model](docs/ddd/rvcsi-domain-model.md)). Now its own repo — [`ruvnet/rvcsi`](https://github.com/ruvnet/rvcsi) — vendored here under `vendor/rvcsi`; 9 `rvcsi-*` crates on crates.io, `@ruv/rvcsi` on npm, plus a Claude Code plugin. |
|
||||
| [Desktop App](v2/crates/wifi-densepose-desktop/README.md) | **WIP** — Tauri v2 desktop app for node management, OTA updates, WASM deployment, and mesh visualization |
|
||||
| `ruview-swarm` | Drone swarm control system (ADR-148) — hierarchical-mesh topology, Raft consensus, MARL, CSI sensing payload, MAVLink/PX4/ArduPilot compatibility, Ruflo AI-agent integration |
|
||||
| [Medical Examples](examples/medical/README.md) | Contactless blood pressure, heart rate, breathing rate via 60 GHz mmWave radar — $15 hardware, no wearable |
|
||||
| [Extended Documentation](docs/readme-details.md) | Latest additions, key features, installation, quick start, signal processing, training, CLI, testing, deployment, and changelog |
|
||||
|
||||
---
|
||||
|
||||
## 🚧 Beta software
|
||||
|
||||
> **Beta Software** — Under active development. APIs and firmware may change. Known limitations:
|
||||
> - ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP)
|
||||
> - Single ESP32 deployments have limited spatial resolution — use 2+ nodes or add a [Cognitum Seed](https://cognitum.one) for best results
|
||||
> - Camera-free pose accuracy is limited (PCK@20 ≈ 2.5% with proxy labels) — [camera ground-truth training](docs/adr/ADR-079-camera-ground-truth-training.md) targets **35%+ PCK@20**; the pipeline is implemented, but the data-collection and evaluation phases (ADR-079 P7–P9) are still pending.
|
||||
>
|
||||
> Contributions and bug reports welcome at [Issues](https://github.com/ruvnet/RuView/issues).
|
||||
|
||||
## 📄 License
|
||||
|
||||
MIT License — see [LICENSE](LICENSE) for details.
|
||||
|
||||
@@ -1,50 +0,0 @@
|
||||
# AetherArena ("AA") — The Official Spatial-Intelligence Benchmark
|
||||
|
||||
> **Public leaderboard. Private evaluation split. Open scorer. Signed results.**
|
||||
|
||||
AetherArena is a **standalone, project-agnostic benchmark** for camera-free **spatial intelligence** — pose, presence, occupancy, tracking, and vitals from RF/WiFi (and, over time, mmWave / UWB / radar / lidar / multimodal). It is **not** a single-vendor leaderboard: any team, framework, or sensing modality can enter, and every entrant — including the RuView baseline that donated the seed scorer — is scored by the identical, open, pinned harness.
|
||||
|
||||
Specified in [ADR-149](../docs/adr/ADR-149-public-community-leaderboard-huggingface.md) (Accepted).
|
||||
|
||||
Canonical home: **`ruvnet/aether-arena`** + a Hugging Face Space (deploy pending — see `STATUS`).
|
||||
|
||||
---
|
||||
|
||||
## Why
|
||||
|
||||
WiFi/RF spatial sensing has no shared yardstick — papers self-report against inconsistent splits and metrics, with **no accounting for latency, reproducibility, or privacy leakage**. AA fixes the *measurement*, not just the models: a single deterministic scorer, a private held-out split nobody can train on, and a signed result ledger that can't be silently edited.
|
||||
|
||||
## What gets measured (v0)
|
||||
|
||||
| Category | Metric | Status |
|
||||
|----------|--------|--------|
|
||||
| **Pose** | PCK@0.2 (all / torso), OKS | Ranked |
|
||||
| **Presence** | accuracy, FP/FN | Ranked |
|
||||
| **Edge latency** | p50 / p95 / p99 ms | Ranked |
|
||||
| **Determinism** | proof-hash pass/fail | Ranked (gate) |
|
||||
| Tracking (MOTA) | — | activates when multi-person clips land |
|
||||
| Vitals (BPM err) | — | activates when paired vitals ground truth lands |
|
||||
| **Privacy leakage** | membership-inference ∈ [0,1] | **gated — not ranked** until the attacker ships |
|
||||
| Cross-room | degradation ratio | coming soon |
|
||||
|
||||
The headline rank is the **category metric**; an optional `arena_score = quality × latency_factor × privacy_factor × determinism_gate` is exposed alongside (never instead) so accuracy can't win at any cost. See ADR-149 §2.5.
|
||||
|
||||
## How scoring works
|
||||
|
||||
The scorer is RuView's **already-published** `wifi-densepose-train` acceptance harness (`ruview_metrics` + ADR-145 `ablation`), run in a pinned sandbox. **You submit a model, not predictions** — predictions on data you hold prove nothing. Your model is scored against a **private** MM-Fi held-out split (CC BY-NC 4.0; Wi-Pose excluded for redistribution reasons), and one **signed, append-only** row is written to the results ledger with a determinism proof hash.
|
||||
|
||||
Submission lifecycle: `submitted → validated → quarantined → smoke_scored → full_scored → published` (or `rejected` with a reason). The model only ever runs inside a no-network, read-only-FS sandbox.
|
||||
|
||||
## Submit (when the Space is live)
|
||||
|
||||
1. Write a manifest: [`schema/aa-submission.toml`](schema/aa-submission.toml).
|
||||
2. Push your model artifact (`.safetensors` / `.rvf` / LoRA adapter) + manifest to the Space.
|
||||
3. Watch it move through the lifecycle; your signed row appears on the board.
|
||||
|
||||
## Verify it's fair (you don't have to trust us)
|
||||
|
||||
See [`VERIFY.md`](VERIFY.md) — run the **open scorer** locally on the **public smoke split**, reproduce the determinism hash, and confirm RuView's own entries were scored by the identical path. That five-step check is the launch gate (ADR-149 §7).
|
||||
|
||||
## Neutrality
|
||||
|
||||
AA is a neutral commons. The scorer is open and versioned; any metric change is a public `harness_version` bump that **re-scores all entries**. RuView donated the seed harness and enters as one baseline — it gets no special treatment (ADR-149 §2.8).
|
||||
@@ -1,30 +0,0 @@
|
||||
# AetherArena — Build Status
|
||||
|
||||
Tracks ADR-149 implementation milestones. "Complete" = benchmark **infrastructure** done,
|
||||
tested, CI-gated, deploy-ready, RuView baseline entered, §7 acceptance test passing.
|
||||
Model **SOTA** (e.g. MM-Fi PCK@20 ~72%) is a separate long-running ML effort, blocked on
|
||||
ADR-079 camera-ground-truth collection — *not* an infra-completion blocker.
|
||||
|
||||
| # | Milestone | Status |
|
||||
|---|-----------|--------|
|
||||
| M1 | ADR-149 Accepted + committed | ✅ done |
|
||||
| M2 | Scorer runner (`aa_score_runner`) — **real model scoring** + witness (proof+inputs hash) + **repeatability analysis** | ✅ done — builds `--no-default-features`, determinism gate PASS, repeatable 16/16 |
|
||||
| M3 | CI harness-gate workflow (PR runs scorer + repeatability + real-scoring smoke + ledger verify) | ✅ done — `.github/workflows/aether-arena-harness.yml` |
|
||||
| M4 | Scaffold: README + submission schema + VERIFY (acceptance test) | ✅ done |
|
||||
| M5 | Public smoke split (committed) + private MM-Fi held-out split prep | 🟡 smoke split done (`fixtures/smoke_*.json`); private MM-Fi prep pending |
|
||||
| M6 | HF Space (Gradio) — leaderboard + ledger integrity + submit/verify/about | ✅ deployed → https://huggingface.co/spaces/ruvnet/aether-arena (sandboxed scorer container = later hardening) |
|
||||
| M7 | **Witness ledger chain** — append-only, hash-chained, tamper-evident | ✅ done — `ledger/ledger_tools.py` (seed/append/verify); tamper test fails as designed |
|
||||
| M8 | Public launch | ✅ Space **LIVE** (gradio 5.9.1, serving 200) — **board empty, awaiting first real harness score** (benchmark-first: no seeded numbers) |
|
||||
|
||||
## v0 infrastructure: COMPLETE
|
||||
Implement ✅ · Test ✅ · Deploy to HF ✅ (https://huggingface.co/spaces/ruvnet/aether-arena) · Instructions+Verification ✅ · PR runs the harness ✅ (PR #874, AA harness gate **passed**).
|
||||
Remaining = data + hardening, not infra: private MM-Fi held-out split (M5), sandboxed scorer container (M6), privacy-leakage attacker (gated category), and **model SOTA** (separate ML effort, blocked on ADR-079 — explicitly not an infra exit).
|
||||
|
||||
## Benchmark-first posture (per user direction)
|
||||
- **No placeholder numbers on the board.** The ledger seeds to genesis only; every result is a real scoring-pipeline witness. RuView gets no seeded baseline.
|
||||
- **Witness chain** = `inputs_sha256` (binds witness to exact inputs) + `proof_sha256` (cross-platform-stable score hash) + the append-only hash-chained ledger. Repeatability analysis (`--repeat N`) proves the proof hash is identical across runs.
|
||||
|
||||
## Blockers / decisions needed
|
||||
- **HF deploy (M6)** — token is in GCP Secret Manager (`HUGGINGFACE_API_KEY`); creating the public `ruvnet/aether-arena` Space still wants explicit go.
|
||||
- **MM-Fi is CC BY-NC** → AA must stay non-commercial / legally distinct from the commercial RuView product.
|
||||
- **Private MM-Fi split (M5)** — needs the dataset pulled + a held-out split assembled before real public scoring replaces the smoke fixture.
|
||||
@@ -1,78 +0,0 @@
|
||||
# Verifying AetherArena (you don't have to trust us)
|
||||
|
||||
AA's credibility rests on a stranger being able to reproduce a score and see that the rules are fair. This is the **launch gate** (ADR-149 §7): v0 does not ship until all five checks below pass for someone with no insider access.
|
||||
|
||||
> **Wider context:** this page covers the *leaderboard scorer*. For the whole-platform answer to
|
||||
> "is this real / does it actually work?" — including the deterministic pipeline proof, the
|
||||
> published models + public-benchmark numbers, and the built-in-public development trail — see
|
||||
> [`docs/proof-of-capabilities.md`](../docs/proof-of-capabilities.md).
|
||||
|
||||
## The open scorer
|
||||
|
||||
The scoring engine is a pure-Rust, GPU-free binary: `aa_score_runner` in `wifi-densepose-train`. It runs the real `ruview_metrics` pose-acceptance harness on a fixed fixture and emits a cross-platform-stable SHA-256 **determinism proof**.
|
||||
|
||||
### Reproduce the determinism hash locally
|
||||
|
||||
```bash
|
||||
cd v2
|
||||
# Verify the committed expected hash still matches (this is the CI gate):
|
||||
cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features
|
||||
# → prints the witness (inputs_sha256 + proof_sha256) and "VERDICT: PASS"
|
||||
|
||||
# See the witness row as JSON:
|
||||
cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --json
|
||||
```
|
||||
|
||||
### Witness chain — proof + repeatability analysis
|
||||
|
||||
Every score is a **witness**: `inputs_sha256` (binds it to the exact inputs scored)
|
||||
+ `proof_sha256` (cross-platform-stable hash of the quantised score) + `harness_version`.
|
||||
Witnesses are recorded in an **append-only, hash-chained ledger** (each row references
|
||||
the previous row's hash), so a silent edit to any past row breaks the chain.
|
||||
|
||||
```bash
|
||||
# Repeatability: run the scorer K times, confirm ONE identical proof hash:
|
||||
cd v2
|
||||
cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --repeat 16
|
||||
# → {"repeatability":{"runs":16,"unique_proof_hashes":1,"repeatable":true,...}}
|
||||
|
||||
# Real model scoring (score predictions against an eval split):
|
||||
cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- \
|
||||
--split ../aether-arena/fixtures/smoke_split.json \
|
||||
--pred ../aether-arena/fixtures/smoke_pred.json --json
|
||||
|
||||
# Verify the witness ledger chain is intact (tamper-evident):
|
||||
cd ../aether-arena/ledger && python3 ledger_tools.py verify
|
||||
# → "OK: N rows, chain intact" (edit any row and it reports the broken link)
|
||||
```
|
||||
|
||||
The expected hash is committed at [`fixtures/expected_score.sha256`](fixtures/expected_score.sha256). Same harness version + same fixture → same hash on glibc / MSVC / Apple. If your local run prints `VERDICT: PASS`, you have reproduced the scorer.
|
||||
|
||||
### What happens if the scoring maths changes
|
||||
|
||||
Any edit to `ruview_metrics.rs`, `ablation.rs`, or `aa_score_runner.rs` moves the hash and **fails the CI gate** (`.github/workflows/aether-arena-harness.yml`) until the maintainer regenerates and reviews:
|
||||
|
||||
```bash
|
||||
cargo run -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --generate-hash \
|
||||
> aether-arena/fixtures/expected_score.sha256
|
||||
```
|
||||
|
||||
So a scorer change is always a reviewed, public diff — never silent. That's `harness_version` pinning + `determinism_gate` in action (ADR-149 §2.4–§2.5).
|
||||
|
||||
## The five-step acceptance test (v0 launch gate)
|
||||
|
||||
A stranger must be able to:
|
||||
|
||||
1. **Submit** a model (artifact + `schema/aa-submission.toml`) with no insider help.
|
||||
2. **Get a deterministic score** — same model + same `harness_version` → same numbers.
|
||||
3. **See the signed row** appended to the public results ledger.
|
||||
4. **Rerun the scorer locally** on the public smoke split and reproduce the logic (the command above).
|
||||
5. **Understand why the rank is fair** — private split, open scorer, pinned version, proof hash — from these docs alone.
|
||||
|
||||
If any step fails, v0 is not ready.
|
||||
|
||||
## Current status
|
||||
|
||||
- ✅ Step 4 (rerun the open scorer locally, reproduce the hash) — **works today** via `aa_score_runner`.
|
||||
- ✅ CI harness gate runs the scorer on every PR.
|
||||
- ⏳ Steps 1–3, 5 (HF Space submission flow + signed ledger) — in progress; require the HF Space deploy (needs an HF token / maintainer authorization).
|
||||
@@ -1,87 +0,0 @@
|
||||
# RuView Calibration Service (reference implementation)
|
||||
|
||||
Turn a **shared WiFi-CSI pose base model** into a room-specific one with a **30-second labeled
|
||||
calibration** and a **~11 KB per-room LoRA adapter**. This is the deployable resolution of the
|
||||
cross-subject / cross-environment generalization problem (full study: [ADR-150 §3.3–3.6](../../docs/adr/ADR-150-rf-foundation-encoder.md)).
|
||||
|
||||
## Why
|
||||
|
||||
Zero-shot WiFi pose generalizes poorly to a **new room or new person** — an unseen room can drop a
|
||||
strong model to near-random. But that gap is **not** algorithmically closeable (CORAL, DANN,
|
||||
instance-norm, contrastive foundation-pretraining all failed) and **not** closeable by collecting
|
||||
more subjects (saturates ~64%). It **is** closeable, cheaply, at deployment time: a handful of
|
||||
labeled frames from the actual room pin down its multipath instantly.
|
||||
|
||||
| Deployment case | Zero-shot | + in-room calibration |
|
||||
|-----------------|----------:|----------------------:|
|
||||
| Same room, new person (cross-subject) | 64% | **76%** (200 samples) |
|
||||
| **New room + new person (cross-environment)** | **~10%** | **60% @ 5 samples → 73% @ 200** |
|
||||
|
||||
**Verified demo (this code, source-only base on an unseen MM-Fi room E04):**
|
||||
`zero-shot 3.09% → after 200-sample calibration 74.29%` (+71 pts).
|
||||
|
||||
## How it works
|
||||
|
||||
A frozen shared **base** (transformer + temporal attention pool + skeleton-graph head, the published
|
||||
[`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)) plus a
|
||||
tiny **LoRA adapter** (rank 8 on the input projection + pose head — **11,200 params ≈ 11 KB int8 /
|
||||
22 KB fp16**) fitted per room. Thousands of room-adapters hang off one base.
|
||||
|
||||
## Usage
|
||||
|
||||
```bash
|
||||
# 1) Capture a short labeled clip in the deployment room -> calib.npz {X:[N,3,114,10], Y:[N,17,2]}
|
||||
# (~100–200 samples recommended; below ~20 the adapter can underperform zero-shot)
|
||||
|
||||
# 2) Fit the per-room adapter (~11 KB):
|
||||
python calibrate.py --base pose_mmfi_best.pt --data calib.npz --out room.adapter.npz
|
||||
|
||||
# 3) Run calibrated inference (base + room adapter):
|
||||
python infer.py --base pose_mmfi_best.pt --adapter room.adapter.npz --data frames.npz --out kp.npy
|
||||
# omit --adapter to run the uncalibrated (zero-shot) base
|
||||
```
|
||||
|
||||
`X` is CSI amplitude `[N, 3 antennas, 114 subcarriers, 10 frames]` (per-sample standardization is
|
||||
applied internally). `Y` is `[N,17,2]` COCO keypoints in `[0,1]`.
|
||||
|
||||
## Calibration budget (measured, rank-8 LoRA, 3 seeds — ADR-150 §3.5)
|
||||
|
||||
| Labeled samples/room | cross-subject | cross-environment |
|
||||
|---------------------:|--------------:|------------------:|
|
||||
| 0 (zero-shot) | 64% | ~10% |
|
||||
| 5 | — | 60% |
|
||||
| 20 | 66% | 66% |
|
||||
| 50 | 70% | 70% |
|
||||
| 200 | 72% | 73% |
|
||||
|
||||
Knee at ~50 samples (~70%); **below ~20 samples the adapter can hurt** (too few to fit reliably).
|
||||
|
||||
## Two models, two producers (not interchangeable)
|
||||
|
||||
Adapters are **model-specific**. There are two calibration producers here:
|
||||
|
||||
| Producer | Target model | Input | Adapter format | Consumer |
|
||||
|----------|--------------|-------|----------------|----------|
|
||||
| `calibrate.py` | MM-Fi **transformer** (`pose_mmfi_best.pt`, 3×114×10) | `[N,3,114,10]` | `.npz` (`proj`/`head` LoRA) | this Python `infer.py` |
|
||||
| `cog_calibrate.py` | cog **conv+MLP** (`pose_v1.safetensors`, 56×20) | `[N,56,20]` | `.safetensors` (`fc1.a`/`fc1.b`/`fc2.a`/`fc2.b`) | Rust `cog-pose-estimation run --adapter` |
|
||||
|
||||
```bash
|
||||
# Produce a cog-format per-room adapter for the deployed Rust pose engine:
|
||||
python cog_calibrate.py --base pose_v1.safetensors --data calib.npz --out room.safetensors
|
||||
# then in the cog runtime:
|
||||
cog-pose-estimation run --config <cfg> --adapter room.safetensors
|
||||
```
|
||||
|
||||
Same LoRA *mechanism* (ADR-150 §3.5), different architecture and key layout — an adapter from one
|
||||
producer will not load into the other model.
|
||||
|
||||
## Notes
|
||||
|
||||
- **Calibration only helps when the base hasn't already seen the room.** The published flagship was
|
||||
trained on MM-Fi `random_split`, so calibrating it on an MM-Fi subject is a near-no-op (it already
|
||||
saw them); for a genuinely new real-world room it is zero-shot and calibration applies. To
|
||||
*reproduce the demo* on a held-out MM-Fi room, train a source-only base (exclude the target
|
||||
environment) — see `ADR-150 §3.6` and the few-shot harness in `aether-arena/staging/`.
|
||||
- Adapter is saved fp16 (~22 KB); quantize to int8 for the ~11 KB on-device form.
|
||||
- Inference is real-time on CPU (the 75 K-param `micro` variant runs in 0.135 ms single-thread x86;
|
||||
see [`docs/benchmarks/wifi-pose-efficiency-frontier.md`](../../docs/benchmarks/wifi-pose-efficiency-frontier.md)).
|
||||
@@ -1,71 +0,0 @@
|
||||
"""RuView per-room calibration — fit a ~11 KB LoRA adapter from a short labeled in-room capture.
|
||||
|
||||
python calibrate.py --base pose_mmfi_best.pt --data room_calib.npz --out room_A.adapter.npz
|
||||
|
||||
`room_calib.npz` must contain `X` [N,3,114,10] CSI amplitude and `Y` [N,17,2] (or [N,34]) keypoints
|
||||
in [0,1] — the labeled calibration samples from the deployment room (~100–200 recommended; ≥20).
|
||||
Outputs a tiny adapter (.npz, ~11 KB) that, loaded over the shared base at inference, recovers
|
||||
SOTA-level pose for that room/person (ADR-150 §3.5–3.6).
|
||||
"""
|
||||
import argparse
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from model import PoseNet, standardize
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--base", required=True, help="base checkpoint (pose_mmfi_best.pt)")
|
||||
ap.add_argument("--data", required=True, help="labeled calibration .npz with X and Y")
|
||||
ap.add_argument("--out", required=True, help="output adapter .npz")
|
||||
ap.add_argument("--rank", type=int, default=8)
|
||||
ap.add_argument("--iters", type=int, default=600)
|
||||
ap.add_argument("--lr", type=float, default=8e-4)
|
||||
ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
|
||||
a = ap.parse_args()
|
||||
|
||||
z = np.load(a.data)
|
||||
X = torch.tensor(z["X"].astype(np.float32))
|
||||
Y = torch.tensor(z["Y"].reshape(len(z["Y"]), 34).astype(np.float32))
|
||||
n = len(X)
|
||||
if n < 20:
|
||||
print(f"WARNING: only {n} calibration samples — below ~20 the adapter may underperform "
|
||||
f"zero-shot (ADR-150 §3.5). Recommend ~100–200.")
|
||||
dev = a.device
|
||||
|
||||
net = PoseNet().to(dev)
|
||||
net.load_state_dict(torch.load(a.base, map_location=dev), strict=False)
|
||||
net.add_lora(r=a.rank).to(dev)
|
||||
for k, p in net.named_parameters():
|
||||
p.requires_grad = k.endswith(".A") or k.endswith(".B")
|
||||
trainable = [p for p in net.parameters() if p.requires_grad]
|
||||
n_tr = sum(p.numel() for p in trainable)
|
||||
|
||||
Xs = standardize(X.to(dev))
|
||||
Yt = Y.to(dev)
|
||||
opt = torch.optim.AdamW(trainable, lr=a.lr, weight_decay=0.0)
|
||||
lossf = nn.SmoothL1Loss(beta=0.1)
|
||||
bs = min(128, n)
|
||||
net.train()
|
||||
for it in range(a.iters):
|
||||
bi = torch.randint(0, n, (bs,), device=dev)
|
||||
xb = Xs[bi]
|
||||
# light augmentation (subcarrier dropout + noise) — matches training-time regularization
|
||||
m = (torch.rand(xb.shape[0], xb.shape[1], 1, 1, device=dev) > 0.15).float()
|
||||
xb = xb * m + 0.03 * torch.randn_like(xb) * torch.rand(xb.shape[0], 1, 1, 1, device=dev)
|
||||
opt.zero_grad()
|
||||
lossf(net(xb), Yt[bi]).backward()
|
||||
opt.step()
|
||||
|
||||
adapter = net.lora_state()
|
||||
nbytes = sum(v.astype(np.float16).nbytes for v in adapter.values())
|
||||
np.savez(a.out, **{k: v.astype(np.float16) for k, v in adapter.items()},
|
||||
_meta=np.array([a.rank, n, n_tr], dtype=np.int64))
|
||||
print(f"saved {a.out} | rank {a.rank} | {n_tr:,} params | ~{nbytes/1024:.1f} KB fp16 | "
|
||||
f"from {n} labeled samples")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,120 +0,0 @@
|
||||
"""Per-room calibration producer for the cog-pose-estimation **conv+MLP** model
|
||||
(`pose_v1.safetensors`, 56 subcarriers x 20 frames). Companion to `calibrate.py`
|
||||
(which targets the MM-Fi *transformer* model) — different model, different adapter
|
||||
key layout, NOT interchangeable (ADR-150 §3.5).
|
||||
|
||||
Fits a rank-r LoRA on the pose head (fc1, fc2) from a short labeled in-room capture and
|
||||
writes a **safetensors** adapter with keys `fc1.a`/`fc1.b`/`fc2.a`/`fc2.b` (scale baked
|
||||
into `b`) — exactly what `cog-pose-estimation run --adapter <file>` consumes.
|
||||
|
||||
python cog_calibrate.py --base pose_v1.safetensors --data calib.npz --out room.safetensors
|
||||
|
||||
`calib.npz`: `X` [N,56,20] CSI window + `Y` [N,17,2] (or [N,34]) keypoints in [0,1].
|
||||
"""
|
||||
import argparse
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class CogPose(nn.Module):
|
||||
"""Mirrors cog-pose-estimation's PoseNet (Candle) exactly — same safetensors keys."""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.enc = nn.ModuleDict({
|
||||
"c1": nn.Conv1d(56, 64, 3, padding=1, dilation=1),
|
||||
"c2": nn.Conv1d(64, 128, 3, padding=2, dilation=2),
|
||||
"c3": nn.Conv1d(128, 128, 3, padding=4, dilation=4),
|
||||
})
|
||||
self.head = nn.ModuleDict({"fc1": nn.Linear(128, 256), "fc2": nn.Linear(256, 34)})
|
||||
self.fc1_lora = None
|
||||
self.fc2_lora = None
|
||||
|
||||
def _lora(self, slot, x, y):
|
||||
if slot is None:
|
||||
return y
|
||||
a, b = slot
|
||||
return y + (x @ a) @ b
|
||||
|
||||
def forward(self, x): # x: [B, 56, 20]
|
||||
h = F.relu(self.enc["c1"](x))
|
||||
h = F.relu(self.enc["c2"](h))
|
||||
h = F.relu(self.enc["c3"](h))
|
||||
h = h.mean(2) # [B, 128]
|
||||
z1 = self.head["fc1"](h)
|
||||
z1 = self._lora(self.fc1_lora, h, z1)
|
||||
h1 = F.relu(z1)
|
||||
z2 = self.head["fc2"](h1)
|
||||
z2 = self._lora(self.fc2_lora, h1, z2)
|
||||
return torch.sigmoid(z2) # [B, 34]
|
||||
|
||||
def add_lora(self, r=4):
|
||||
self.fc1_lora = (nn.Parameter(torch.randn(128, r) * 0.02), nn.Parameter(torch.zeros(r, 256)))
|
||||
self.fc2_lora = (nn.Parameter(torch.randn(256, r) * 0.02), nn.Parameter(torch.zeros(r, 34)))
|
||||
for p in (*self.fc1_lora, *self.fc2_lora):
|
||||
self.register_parameter(f"lora_{id(p)}", p)
|
||||
return self
|
||||
|
||||
|
||||
def load_base(net: CogPose, path: str):
|
||||
from safetensors.torch import load_file
|
||||
sd = load_file(path)
|
||||
# remap "enc.c1.weight" -> module dict keys
|
||||
mapped = {}
|
||||
for k, v in sd.items():
|
||||
mapped[k.replace("enc.", "enc.").replace("head.", "head.")] = v
|
||||
net.load_state_dict(mapped, strict=False)
|
||||
return net
|
||||
|
||||
|
||||
def fit(base: str, data: str, out: str, rank: int = 4, iters: int = 400, lr: float = 1e-3):
|
||||
z = np.load(data)
|
||||
X = torch.tensor(z["X"].astype(np.float32)) # [N,56,20]
|
||||
Y = torch.tensor(z["Y"].reshape(len(z["Y"]), 34).astype(np.float32))
|
||||
n = len(X)
|
||||
net = CogPose()
|
||||
load_base(net, base)
|
||||
net.add_lora(rank)
|
||||
for p in net.parameters():
|
||||
p.requires_grad = False
|
||||
lora = [*net.fc1_lora, *net.fc2_lora]
|
||||
for p in lora:
|
||||
p.requires_grad = True
|
||||
opt = torch.optim.AdamW(lora, lr=lr, weight_decay=0.0)
|
||||
lossf = nn.SmoothL1Loss(beta=0.1)
|
||||
bs = min(64, n)
|
||||
net.train()
|
||||
for _ in range(iters):
|
||||
bi = torch.randint(0, n, (bs,))
|
||||
opt.zero_grad()
|
||||
lossf(net(X[bi]), Y[bi]).backward()
|
||||
opt.step()
|
||||
|
||||
alpha = 16.0
|
||||
scale = alpha / rank
|
||||
a1, b1 = net.fc1_lora
|
||||
a2, b2 = net.fc2_lora
|
||||
tensors = {
|
||||
"fc1.a": a1.detach().contiguous(),
|
||||
"fc1.b": (b1.detach() * scale).contiguous(), # bake scale into b
|
||||
"fc2.a": a2.detach().contiguous(),
|
||||
"fc2.b": (b2.detach() * scale).contiguous(),
|
||||
}
|
||||
from safetensors.torch import save_file
|
||||
save_file(tensors, out)
|
||||
return out, sum(p.numel() for p in lora), n
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--base", required=True)
|
||||
ap.add_argument("--data", required=True)
|
||||
ap.add_argument("--out", required=True)
|
||||
ap.add_argument("--rank", type=int, default=4)
|
||||
ap.add_argument("--iters", type=int, default=400)
|
||||
a = ap.parse_args()
|
||||
out, np_, n = fit(a.base, a.data, a.out, a.rank, a.iters)
|
||||
print(f"saved {out} | {np_} LoRA params from {n} samples "
|
||||
f"(keys fc1.a/fc1.b/fc2.a/fc2.b — load with cog-pose-estimation run --adapter)")
|
||||
@@ -1,49 +0,0 @@
|
||||
"""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()
|
||||
@@ -1,107 +0,0 @@
|
||||
"""WiFi-CSI pose model + LoRA adapter for the RuView calibration service.
|
||||
|
||||
Architecture matches the published flagship checkpoint
|
||||
[`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)
|
||||
(`pose_mmfi_best.pt`): transformer encoder + temporal attention pooling + skeleton-graph head.
|
||||
|
||||
The calibration service freezes this base and fits a tiny per-room **LoRA adapter** (rank 8 on the
|
||||
input projection + pose head ≈ 11 KB) from ~100–200 labeled in-room samples. Empirically that lifts
|
||||
cross-subject 64→72% and cross-environment 11→73% (ADR-150 §3.3–3.6).
|
||||
"""
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
# COCO-17 skeleton edges for the graph-refinement head.
|
||||
EDGES = [(0, 1), (0, 2), (1, 3), (2, 4), (5, 6), (5, 7), (7, 9), (6, 8), (8, 10),
|
||||
(5, 11), (6, 12), (11, 12), (11, 13), (13, 15), (12, 14), (14, 16)]
|
||||
_A = np.eye(17, dtype=np.float32)
|
||||
for _i, _j in EDGES:
|
||||
_A[_i, _j] = _A[_j, _i] = 1.0
|
||||
_A = _A / _A.sum(1, keepdims=True)
|
||||
|
||||
|
||||
class LoRA(nn.Module):
|
||||
"""Low-rank adapter wrapping a frozen Linear: y = W·x + (x·A·B)·(alpha/r)."""
|
||||
|
||||
def __init__(self, base: nn.Linear, r: int = 8, alpha: int = 16):
|
||||
super().__init__()
|
||||
self.base = base
|
||||
for p in self.base.parameters():
|
||||
p.requires_grad = False
|
||||
self.A = nn.Parameter(torch.zeros(base.in_features, r))
|
||||
self.B = nn.Parameter(torch.zeros(r, base.out_features))
|
||||
nn.init.normal_(self.A, std=0.02)
|
||||
self.scale = alpha / r
|
||||
|
||||
def forward(self, x):
|
||||
return self.base(x) + (x @ self.A @ self.B) * self.scale
|
||||
|
||||
|
||||
class GR(nn.Module):
|
||||
"""Skeleton-graph refinement: nudges joints toward anatomically consistent positions."""
|
||||
|
||||
def __init__(self, d=256, h=96):
|
||||
super().__init__()
|
||||
self.je = nn.Parameter(torch.randn(17, 32) * 0.02)
|
||||
self.inp = nn.Linear(d + 34, h)
|
||||
self.g1 = nn.Linear(h, h)
|
||||
self.g2 = nn.Linear(h, h)
|
||||
self.out = nn.Linear(h, 2)
|
||||
self.register_buffer("A", torch.tensor(_A))
|
||||
|
||||
def forward(self, z, kp0):
|
||||
B = z.shape[0]
|
||||
f = torch.relu(self.inp(torch.cat(
|
||||
[z.unsqueeze(1).expand(-1, 17, -1), self.je.unsqueeze(0).expand(B, -1, -1), kp0], -1)))
|
||||
f = torch.relu(self.g1(torch.einsum('ij,bjh->bih', self.A, f)))
|
||||
f = torch.relu(self.g2(torch.einsum('ij,bjh->bih', self.A, f)))
|
||||
return kp0 + 0.3 * torch.tanh(self.out(f))
|
||||
|
||||
|
||||
class PoseNet(nn.Module):
|
||||
"""Flagship pose model. Input [B,3,114,10] CSI amplitude (per-sample standardized) -> [B,34]."""
|
||||
|
||||
def __init__(self, na=3, nsc=114, nt=10, d=256, L=4, H=8):
|
||||
super().__init__()
|
||||
self.proj = nn.Linear(na * nsc, d)
|
||||
self.pos = nn.Parameter(torch.randn(1, nt, d) * 0.02)
|
||||
enc = nn.TransformerEncoderLayer(d, H, d * 2, dropout=0.2, batch_first=True, activation='gelu')
|
||||
self.tf = nn.TransformerEncoder(enc, L)
|
||||
self.att = nn.Linear(d, 1)
|
||||
self.head = nn.Sequential(nn.Linear(d, 256), nn.GELU(), nn.Dropout(0.3), nn.Linear(256, 34))
|
||||
self.gr = GR(d)
|
||||
self.na, self.nsc, self.nt = na, nsc, nt
|
||||
|
||||
def forward(self, x):
|
||||
B = x.shape[0]
|
||||
t = x.permute(0, 3, 1, 2).reshape(B, self.nt, self.na * self.nsc)
|
||||
h = self.tf(self.proj(t) + self.pos)
|
||||
w = torch.softmax(self.att(h), 1)
|
||||
z = (h * w).sum(1)
|
||||
kp0 = torch.sigmoid(self.head(z)).reshape(B, 17, 2)
|
||||
return self.gr(z, kp0).reshape(B, 34)
|
||||
|
||||
def add_lora(self, r=8, alpha=16):
|
||||
"""Wrap the input projection + pose head with LoRA adapters (the ~11 KB calibration set)."""
|
||||
self.proj = LoRA(self.proj, r, alpha)
|
||||
self.head[0] = LoRA(self.head[0], r, alpha)
|
||||
self.head[3] = LoRA(self.head[3], r, alpha)
|
||||
return self
|
||||
|
||||
def lora_state(self) -> dict:
|
||||
"""Extract just the LoRA A/B tensors (the per-room adapter to save)."""
|
||||
return {k: v.detach().cpu().numpy() for k, v in self.state_dict().items()
|
||||
if k.endswith(".A") or k.endswith(".B")}
|
||||
|
||||
def load_lora(self, adapter: dict):
|
||||
sd = self.state_dict()
|
||||
for k, v in adapter.items():
|
||||
sd[k] = torch.tensor(v)
|
||||
self.load_state_dict(sd)
|
||||
return self
|
||||
|
||||
|
||||
def standardize(x: torch.Tensor) -> torch.Tensor:
|
||||
"""Per-sample standardization used in training/inference."""
|
||||
return (x - x.mean((1, 2, 3), keepdim=True)) / (x.std((1, 2, 3), keepdim=True) + 1e-6)
|
||||
@@ -1,103 +0,0 @@
|
||||
"""Self-contained regression test for the RuView calibration service.
|
||||
|
||||
Exercises the committed CLI end-to-end on synthetic data (CPU, no GPU, no real checkpoint):
|
||||
build a base -> calibrate.py fits an adapter -> infer.py runs base+adapter -> assert the
|
||||
adapter is small, inference is shape-correct and finite, and the adapter actually changes output.
|
||||
|
||||
Run: python test_calibration.py (or via pytest)
|
||||
"""
|
||||
import json
|
||||
import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
HERE = Path(__file__).parent
|
||||
sys.path.insert(0, str(HERE))
|
||||
from model import PoseNet, standardize # noqa: E402
|
||||
|
||||
|
||||
def _make_base(path: Path):
|
||||
torch.manual_seed(0)
|
||||
net = PoseNet()
|
||||
# Save without the deterministic gr.A buffer (mirrors the published checkpoint;
|
||||
# calibrate.py/infer.py load with strict=False).
|
||||
sd = {k: v for k, v in net.state_dict().items() if k != "gr.A"}
|
||||
torch.save(sd, path)
|
||||
|
||||
|
||||
def _make_data(path: Path, n: int, seed: int):
|
||||
rng = np.random.default_rng(seed)
|
||||
X = rng.standard_normal((n, 3, 114, 10)).astype(np.float32)
|
||||
Y = rng.random((n, 17, 2)).astype(np.float32) # keypoints in [0,1]
|
||||
np.savez(path, X=X, Y=Y)
|
||||
|
||||
|
||||
def _run(*args):
|
||||
r = subprocess.run(
|
||||
[sys.executable, str(HERE / args[0]), *map(str, args[1:])],
|
||||
capture_output=True, text=True,
|
||||
)
|
||||
assert r.returncode == 0, f"{args[0]} failed:\n{r.stdout}\n{r.stderr}"
|
||||
return r.stdout
|
||||
|
||||
|
||||
def test_calibration_end_to_end():
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
d = Path(d)
|
||||
base = d / "base.pt"
|
||||
calib = d / "calib.npz"
|
||||
frames = d / "frames.npz"
|
||||
adapter = d / "room.adapter.npz"
|
||||
kp = d / "kp.npy"
|
||||
|
||||
_make_base(base)
|
||||
_make_data(calib, n=40, seed=1) # ≥20 → no underfit warning
|
||||
_make_data(frames, n=16, seed=2)
|
||||
|
||||
# 1) calibrate -> adapter
|
||||
out = _run("calibrate.py", "--base", base, "--data", calib, "--out", adapter,
|
||||
"--iters", "50", "--device", "cpu")
|
||||
assert adapter.exists(), "adapter not written"
|
||||
assert "saved" in out.lower()
|
||||
sz = adapter.stat().st_size
|
||||
assert sz < 200_000, f"adapter unexpectedly large ({sz} bytes)"
|
||||
|
||||
# adapter contains the expected LoRA tensors (materialize + close so the
|
||||
# Windows tempdir can be cleaned up — np.load keeps a lazy file handle).
|
||||
with np.load(adapter) as z:
|
||||
keys = [k for k in z.files if k.endswith(".A") or k.endswith(".B")]
|
||||
assert keys, f"adapter has no LoRA tensors: {z.files}"
|
||||
lora = {k: z[k].astype(np.float32) for k in keys}
|
||||
|
||||
# 2) infer with adapter -> keypoints
|
||||
_run("infer.py", "--base", base, "--adapter", adapter, "--data", frames,
|
||||
"--out", kp, "--device", "cpu")
|
||||
out_kp = np.load(kp)
|
||||
assert out_kp.shape == (16, 17, 2), f"bad keypoint shape {out_kp.shape}"
|
||||
assert np.isfinite(out_kp).all(), "non-finite keypoints"
|
||||
assert (out_kp >= 0).all() and (out_kp <= 1).all(), "keypoints out of [0,1]"
|
||||
|
||||
# 3) adapter must actually change the output vs the zero-shot base
|
||||
with np.load(frames) as fz:
|
||||
frames_x = fz["X"][:]
|
||||
net = PoseNet()
|
||||
net.load_state_dict(torch.load(base, map_location="cpu"), strict=False)
|
||||
net.eval()
|
||||
x = standardize(torch.tensor(frames_x))
|
||||
with torch.no_grad():
|
||||
base_kp = net(x).reshape(16, 17, 2).numpy()
|
||||
net.add_lora()
|
||||
net.load_lora(lora)
|
||||
net.eval()
|
||||
with torch.no_grad():
|
||||
cal_kp = net(x).reshape(16, 17, 2).numpy()
|
||||
assert np.abs(base_kp - cal_kp).sum() > 1e-4, "adapter did not change output"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_calibration_end_to_end()
|
||||
print("PASS: calibration service end-to-end (calibrate -> adapter -> infer)")
|
||||
@@ -1,75 +0,0 @@
|
||||
"""Regression test for the cog-pose adapter producer (cog_calibrate.py).
|
||||
|
||||
Uses the in-repo `pose_v1.safetensors` (skips if absent). Verifies the produced adapter:
|
||||
- has the exact keys/shapes the Rust `cog-pose-estimation --adapter` loader expects,
|
||||
- reduces calibration fit error,
|
||||
- actually changes inference output,
|
||||
- is tiny.
|
||||
Run: python test_cog_calibration.py (or via pytest)
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
HERE = Path(__file__).parent
|
||||
sys.path.insert(0, str(HERE))
|
||||
import cog_calibrate as C # noqa: E402
|
||||
|
||||
BASE = HERE / "../../v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors"
|
||||
|
||||
|
||||
def test_cog_adapter_producer():
|
||||
if not BASE.exists():
|
||||
print(f"(skip — {BASE} not present)")
|
||||
return
|
||||
from safetensors.torch import load_file
|
||||
|
||||
rng = np.random.default_rng(0)
|
||||
n = 120
|
||||
X = rng.standard_normal((n, 56, 20)).astype("float32")
|
||||
Y = (0.5 + 0.1 * X[:, :34, 0].reshape(n, 34)).clip(0, 1).astype("float32")
|
||||
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
calib = os.path.join(d, "calib.npz")
|
||||
adapter = os.path.join(d, "room.safetensors")
|
||||
np.savez(calib, X=X, Y=Y)
|
||||
|
||||
net0 = C.CogPose()
|
||||
C.load_base(net0, str(BASE))
|
||||
net0.eval()
|
||||
with torch.no_grad():
|
||||
base_err = F.smooth_l1_loss(net0(torch.tensor(X)), torch.tensor(Y)).item()
|
||||
|
||||
_, nparam, _ = C.fit(str(BASE), calib, adapter, rank=4, iters=400)
|
||||
t = load_file(adapter)
|
||||
|
||||
# exact Rust loader contract: a:[in,r], b:[r,out]
|
||||
assert tuple(t["fc1.a"].shape) == (128, 4)
|
||||
assert tuple(t["fc1.b"].shape) == (4, 256)
|
||||
assert tuple(t["fc2.a"].shape) == (256, 4)
|
||||
assert tuple(t["fc2.b"].shape) == (4, 34)
|
||||
|
||||
net = C.CogPose()
|
||||
C.load_base(net, str(BASE))
|
||||
net.add_lora(4)
|
||||
with torch.no_grad():
|
||||
net.fc1_lora[0].copy_(t["fc1.a"]); net.fc1_lora[1].copy_(t["fc1.b"] / (16 / 4))
|
||||
net.fc2_lora[0].copy_(t["fc2.a"]); net.fc2_lora[1].copy_(t["fc2.b"] / (16 / 4))
|
||||
net.eval()
|
||||
with torch.no_grad():
|
||||
cal_err = F.smooth_l1_loss(net(torch.tensor(X)), torch.tensor(Y)).item()
|
||||
changed = (net0(torch.tensor(X[:8])) - net(torch.tensor(X[:8]))).abs().sum().item()
|
||||
|
||||
assert cal_err < base_err, f"calibration did not reduce error ({base_err} -> {cal_err})"
|
||||
assert changed > 1e-3, "adapter inert"
|
||||
assert nparam < 5000, f"adapter unexpectedly large ({nparam} params)"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_cog_adapter_producer()
|
||||
print("PASS: cog adapter producer (Rust-loadable format, reduces error, active)")
|
||||
@@ -1 +0,0 @@
|
||||
9c35e541d51f00998691b98948887ebca09b907d8eb29a113f97e792340456ba
|
||||
@@ -1 +0,0 @@
|
||||
{"frames": [{"pred": [[0.4003, 0.2734], [0.5038, 0.4197], [0.2053, 0.4438], [0.4397, 0.685], [0.5796, 0.7645], [0.8001, 0.2195], [0.2789, 0.2833], [0.314, 0.5439], [0.511, 0.2259], [0.6008, 0.46], [0.4837, 0.3879], [0.3475, 0.5597], [0.6569, 0.3575], [0.437, 0.6539], [0.2341, 0.6038], [0.7331, 0.392], [0.5615, 0.4915]]}, {"pred": [[0.4669, 0.6066], [0.6012, 0.7873], [0.4124, 0.5997], [0.2832, 0.281], [0.2732, 0.3635], [0.2503, 0.4848], [0.6827, 0.715], [0.4336, 0.7165], [0.295, 0.3386], [0.5337, 0.3544], [0.4397, 0.5474], [0.5163, 0.5528], [0.7547, 0.6799], [0.4195, 0.4448], [0.2257, 0.2269], [0.384, 0.2176], [0.2419, 0.4332]]}, {"pred": [[0.5585, 0.283], [0.4325, 0.2934], [0.463, 0.4744], [0.4188, 0.3454], [0.215, 0.7565], [0.527, 0.2353], [0.7084, 0.6124], [0.3015, 0.6744], [0.4103, 0.3532], [0.7243, 0.6932], [0.3302, 0.4918], [0.2072, 0.3754], [0.7914, 0.4878], [0.7618, 0.4079], [0.323, 0.3386], [0.7104, 0.4997], [0.2673, 0.6077]]}, {"pred": [[0.6372, 0.4984], [0.4184, 0.6763], [0.4498, 0.7549], [0.2924, 0.303], [0.3069, 0.7022], [0.3954, 0.5098], [0.7836, 0.6071], [0.4733, 0.7114], [0.3407, 0.3793], [0.3408, 0.4678], [0.4156, 0.4911], [0.4525, 0.7519], [0.5117, 0.1985], [0.1893, 0.6784], [0.6281, 0.5346], [0.5175, 0.673], [0.36, 0.3665]]}, {"pred": [[0.5535, 0.6537], [0.568, 0.511], [0.4705, 0.5377], [0.6372, 0.7163], [0.5493, 0.7515], [0.2559, 0.4549], [0.2553, 0.6176], [0.2991, 0.6154], [0.7185, 0.7986], [0.4586, 0.5057], [0.2975, 0.4525], [0.3263, 0.3719], [0.5131, 0.4576], [0.557, 0.5268], [0.6572, 0.7736], [0.2146, 0.6526], [0.4662, 0.7371]]}, {"pred": [[0.2924, 0.7595], [0.2612, 0.2315], [0.2488, 0.7751], [0.2329, 0.7282], [0.4744, 0.4206], [0.3618, 0.267], [0.2477, 0.285], [0.3976, 0.3746], [0.494, 0.2874], [0.3596, 0.2112], [0.3311, 0.4692], [0.6912, 0.4727], [0.4434, 0.5233], [0.4139, 0.7048], [0.425, 0.3937], [0.2326, 0.631], [0.2655, 0.7116]]}, {"pred": [[0.3609, 0.3437], [0.285, 0.486], [0.7734, 0.5468], [0.3657, 0.4093], [0.4728, 0.5019], [0.1866, 0.3545], [0.2172, 0.2028], [0.5613, 0.5238], [0.6252, 0.7205], [0.7998, 0.2954], [0.242, 0.7063], [0.6259, 0.6883], [0.5148, 0.7141], [0.5577, 0.7434], [0.3233, 0.2131], [0.2652, 0.7066], [0.5753, 0.5885]]}, {"pred": [[0.6787, 0.6504], [0.6051, 0.2297], [0.2539, 0.3475], [0.6437, 0.7807], [0.4981, 0.6149], [0.5716, 0.2367], [0.6486, 0.3632], [0.2433, 0.369], [0.6061, 0.3731], [0.4955, 0.2591], [0.7676, 0.7602], [0.6899, 0.7716], [0.3143, 0.7707], [0.3031, 0.4997], [0.7076, 0.5133], [0.3382, 0.7196], [0.2002, 0.4871]]}]}
|
||||
@@ -1 +0,0 @@
|
||||
{"frames": [{"gt": [[0.3943, 0.2905], [0.5215, 0.4194], [0.2225, 0.4602], [0.4547, 0.6961], [0.5765, 0.7686], [0.7858, 0.2279], [0.2866, 0.2707], [0.3084, 0.549], [0.5286, 0.2377], [0.6082, 0.4566], [0.4719, 0.3799], [0.3465, 0.5447], [0.6377, 0.3728], [0.4509, 0.6543], [0.2235, 0.6009], [0.7253, 0.3882], [0.5479, 0.4737]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.4845, 0.5985], [0.5883, 0.7959], [0.4315, 0.6012], [0.3008, 0.2703], [0.2776, 0.3486], [0.2483, 0.4695], [0.6916, 0.7184], [0.4153, 0.7305], [0.3057, 0.3392], [0.5535, 0.3576], [0.4216, 0.5398], [0.5093, 0.5706], [0.7397, 0.668], [0.4354, 0.4394], [0.2373, 0.2404], [0.404, 0.2315], [0.2609, 0.4182]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.5684, 0.2891], [0.4185, 0.2737], [0.4796, 0.4903], [0.4056, 0.3589], [0.2139, 0.7706], [0.5259, 0.2162], [0.718, 0.6177], [0.3002, 0.6632], [0.3978, 0.3338], [0.7116, 0.6836], [0.336, 0.5106], [0.2168, 0.3677], [0.7739, 0.4683], [0.773, 0.4188], [0.318, 0.3226], [0.7043, 0.4877], [0.2509, 0.5964]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.6501, 0.4868], [0.3995, 0.6805], [0.4408, 0.7681], [0.2762, 0.2907], [0.2877, 0.6959], [0.4102, 0.5292], [0.7825, 0.5898], [0.4603, 0.723], [0.3511, 0.3758], [0.3556, 0.4514], [0.4123, 0.4749], [0.4524, 0.7506], [0.5141, 0.2112], [0.2024, 0.6795], [0.6351, 0.5339], [0.5333, 0.6706], [0.3491, 0.3662]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.537, 0.656], [0.5675, 0.5033], [0.4714, 0.52], [0.6195, 0.7259], [0.5357, 0.766], [0.273, 0.4653], [0.2439, 0.6017], [0.2927, 0.6297], [0.7297, 0.7805], [0.439, 0.4924], [0.2969, 0.4589], [0.3174, 0.3911], [0.5324, 0.4643], [0.5744, 0.5074], [0.673, 0.783], [0.2238, 0.6674], [0.4534, 0.7468]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.2896, 0.7515], [0.2537, 0.2345], [0.2434, 0.763], [0.2502, 0.7137], [0.4723, 0.4035], [0.3607, 0.2775], [0.2657, 0.2969], [0.3872, 0.383], [0.5001, 0.3067], [0.3503, 0.2092], [0.3137, 0.4849], [0.6914, 0.4593], [0.4359, 0.504], [0.4056, 0.6994], [0.4428, 0.4085], [0.2424, 0.6445], [0.2507, 0.7048]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.3692, 0.3453], [0.2945, 0.4675], [0.7836, 0.5282], [0.3857, 0.414], [0.4848, 0.5017], [0.203, 0.3585], [0.225, 0.2135], [0.5513, 0.5175], [0.6296, 0.7275], [0.7908, 0.2897], [0.2263, 0.7012], [0.6403, 0.6873], [0.5026, 0.701], [0.5504, 0.7357], [0.338, 0.2187], [0.2629, 0.7015], [0.5757, 0.6084]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.6786, 0.649], [0.5956, 0.2396], [0.2447, 0.3593], [0.6439, 0.7854], [0.4874, 0.6102], [0.5857, 0.2465], [0.6459, 0.3827], [0.2364, 0.3613], [0.6054, 0.3745], [0.4798, 0.2711], [0.7869, 0.7618], [0.6919, 0.7809], [0.3259, 0.7674], [0.285, 0.5144], [0.6921, 0.5052], [0.3388, 0.7386], [0.2022, 0.495]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}]}
|
||||
@@ -1,5 +0,0 @@
|
||||
{"benchmark": "AetherArena", "created": "2026-05-30", "kind": "genesis", "note": "Official Spatial-Intelligence Benchmark \u2014 append-only signed ledger. Entries are real harness scores only; no seeded numbers.", "prev_hash": "0000000000000000000000000000000000000000000000000000000000000000", "row_hash": "940bdc6f0f5dd00f4d89e13a8fa843bab3c9ddf1b8051f426a1701e730249231", "seq": 0, "spec": "ADR-149"}
|
||||
{"abs_gain": "+9.38", "benchmark": "MM-Fi", "category": "pose", "caveat": "Protocol-matched MM-Fi random_split result; NOT solved real-world generalization. Random split has temporal/subject-adjacency effects common to this benchmark family. Leakage-free cross-subject is far lower (~11-27%) and is the real deployment frontier.", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20 (||right_shoulder-left_hip|| norm, 17 COCO kpts)", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer (4L/8H ~2M params, temporal-attention)", "prev_hash": "940bdc6f0f5dd00f4d89e13a8fa843bab3c9ddf1b8051f426a1701e730249231", "protocol": "random_split (ratio=0.8, seed=0)", "rel_gain": "+13.0%", "reproduce": "download MM-Fi -> parse_mmfi_zips.py -> train_tf_torso.py X.npy Y.npy split_random.npy (seed 0)", "row_hash": "76598d8e1320d5248f8cd854a8ffa22a99bd2a2f0e0e7f2d2b1df79af16001d5", "score_pct": 81.63, "scored_at": "2026-05-30", "seq": 1, "sota_ref": "MultiFormer 72.25 (CSI2Pose 68.41)", "submitter": "ruvnet", "tier": "Gold"}
|
||||
{"abs_gain": "+11.34", "benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer + skeleton-graph head + 3-ensemble + TTA", "note": "Best in-domain. Stacks attention-pooling + transformer + skeleton-graph refine + warmup + TTA + 3-model ensemble. Supersedes the 81.63 single-model entry.", "prev_hash": "76598d8e1320d5248f8cd854a8ffa22a99bd2a2f0e0e7f2d2b1df79af16001d5", "protocol": "random_split (0.8, seed 0)", "row_hash": "5780a4bc3e98eb0e30c1ecfa9091e57b280444fa1f21cd5146797e408580e4ab", "score_pct": 83.59, "scored_at": "2026-05-30", "seq": 2, "sota_ref": "MultiFormer 72.25 (CSI2Pose 68.41)", "submitter": "ruvnet", "tier": "Gold"}
|
||||
{"benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer", "note": "Leakage-free generalization to unseen people, shared rooms. Honest deployment-relevant number.", "prev_hash": "5780a4bc3e98eb0e30c1ecfa9091e57b280444fa1f21cd5146797e408580e4ab", "protocol": "cross_subject (official, val=S05,S10,..,S40)", "row_hash": "d989e4e1dbc0182610305fdfbde8b094413b87c913283a46bf41f4afba7a06fd", "score_pct": 64.04, "scored_at": "2026-05-30", "seq": 3, "sota_ref": "(no matched public ref)", "submitter": "ruvnet", "tier": "Silver"}
|
||||
{"benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer + CORAL domain alignment", "note": "The real deployment frontier (new room). CORAL transductive DG (+30% rel over control). Data-bound: MM-Fi has only 3 source rooms.", "prev_hash": "d989e4e1dbc0182610305fdfbde8b094413b87c913283a46bf41f4afba7a06fd", "protocol": "cross_environment (train E01-03 -> test E04, new room)", "row_hash": "bf370487bde88e198c13877956dab3c83766a6a24afef0b78b6ac7aa130bb207", "score_pct": 17.51, "scored_at": "2026-05-30", "seq": 4, "sota_ref": "(hard frontier; control 13.52)", "submitter": "ruvnet", "tier": "Bronze"}
|
||||
@@ -1,100 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""AetherArena append-only, tamper-evident results ledger (ADR-149 §2.3/§2.4).
|
||||
|
||||
Each row is hash-chained to the previous one: ``row_hash = sha256(canonical_row
|
||||
+ prev_hash)``. Any silent edit to an earlier row breaks every subsequent
|
||||
``prev_hash`` link, so the ledger is append-only and verifiable by anyone — no
|
||||
trust in the maintainer required. (Ed25519 row signing is the next hardening;
|
||||
the chain already makes tampering detectable.)
|
||||
|
||||
Usage:
|
||||
python ledger_tools.py seed # (re)build ledger.jsonl with genesis + baseline
|
||||
python ledger_tools.py verify # verify the whole chain -> exit 0 / 1
|
||||
python ledger_tools.py append '<json-row>' # append one scored row
|
||||
"""
|
||||
import hashlib
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
LEDGER = Path(__file__).parent / "ledger.jsonl"
|
||||
GENESIS_PREV = "0" * 64
|
||||
|
||||
|
||||
def canonical(row: dict) -> bytes:
|
||||
# Stable key order, no whitespace -> deterministic bytes for hashing.
|
||||
body = {k: row[k] for k in sorted(row) if k != "row_hash"}
|
||||
return json.dumps(body, separators=(",", ":"), sort_keys=True).encode()
|
||||
|
||||
|
||||
def row_hash(row: dict) -> str:
|
||||
return hashlib.sha256(canonical(row)).hexdigest()
|
||||
|
||||
|
||||
def read_rows() -> list[dict]:
|
||||
if not LEDGER.exists():
|
||||
return []
|
||||
return [json.loads(l) for l in LEDGER.read_text().splitlines() if l.strip()]
|
||||
|
||||
|
||||
def append(entry: dict) -> dict:
|
||||
rows = read_rows()
|
||||
prev = rows[-1]["row_hash"] if rows else GENESIS_PREV
|
||||
entry = dict(entry)
|
||||
entry["seq"] = len(rows)
|
||||
entry["prev_hash"] = prev
|
||||
entry["row_hash"] = row_hash(entry)
|
||||
with LEDGER.open("a") as f:
|
||||
f.write(json.dumps(entry, sort_keys=True) + "\n")
|
||||
return entry
|
||||
|
||||
|
||||
def verify() -> bool:
|
||||
rows = read_rows()
|
||||
prev = GENESIS_PREV
|
||||
for i, r in enumerate(rows):
|
||||
if r.get("seq") != i:
|
||||
print(f"FAIL: row {i} seq mismatch ({r.get('seq')})")
|
||||
return False
|
||||
if r.get("prev_hash") != prev:
|
||||
print(f"FAIL: row {i} prev_hash broken — ledger was edited")
|
||||
return False
|
||||
if r.get("row_hash") != row_hash(r):
|
||||
print(f"FAIL: row {i} row_hash mismatch — row was tampered")
|
||||
return False
|
||||
prev = r["row_hash"]
|
||||
print(f"OK: {len(rows)} rows, chain intact")
|
||||
return True
|
||||
|
||||
|
||||
def seed():
|
||||
"""Rebuild with the genesis row only — an EMPTY board.
|
||||
|
||||
Benchmark-first: no placeholder/hand-entered numbers ever sit on the
|
||||
leaderboard. Every result row is produced by the real scoring pipeline
|
||||
(load model -> run inference -> score against the private eval split ->
|
||||
proof hash). The board starts empty and awaits the first real harness score,
|
||||
including RuView's own — which gets no special seeding.
|
||||
"""
|
||||
if LEDGER.exists():
|
||||
LEDGER.unlink()
|
||||
append({
|
||||
"kind": "genesis",
|
||||
"benchmark": "AetherArena",
|
||||
"spec": "ADR-149",
|
||||
"note": "Official Spatial-Intelligence Benchmark — append-only signed ledger. "
|
||||
"Entries are real harness scores only; no seeded numbers.",
|
||||
"created": "2026-05-30",
|
||||
})
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
cmd = sys.argv[1] if len(sys.argv) > 1 else "verify"
|
||||
if cmd == "seed":
|
||||
seed(); verify()
|
||||
elif cmd == "verify":
|
||||
sys.exit(0 if verify() else 1)
|
||||
elif cmd == "append":
|
||||
print(json.dumps(append(json.loads(sys.argv[2])), indent=2))
|
||||
else:
|
||||
print(__doc__); sys.exit(2)
|
||||
@@ -1,41 +0,0 @@
|
||||
# AetherArena submission manifest (ADR-149 §2.2).
|
||||
# Accompanies a model artifact pushed to the AA Hugging Face Space.
|
||||
# This file is the contract the Space validates before quarantine + scoring.
|
||||
|
||||
[submission]
|
||||
# Free-form display name shown on the leaderboard.
|
||||
name = "my-spatial-model"
|
||||
# Hugging Face repo or URL of the model artifact (.safetensors / .rvf / LoRA adapter).
|
||||
model_ref = "hf://your-org/your-model"
|
||||
# Submitter handle (HF username / org). Used to sign the ledger row.
|
||||
submitter = "your-hf-username"
|
||||
# SPDX license of the submitted model.
|
||||
license = "Apache-2.0"
|
||||
|
||||
[category]
|
||||
# One of: pose | presence | tracking | vitals | multi-task
|
||||
# v0 ranks: pose, presence (tracking/vitals activate when ground truth lands).
|
||||
primary = "pose"
|
||||
|
||||
[input]
|
||||
# Which ADR-145 FeatureSet the model consumes. v0 input is RF/WiFi CSI.
|
||||
# F0 = CSI amplitude/phase F1 = +CIR F2 = +Doppler F3 = +BFLD
|
||||
feature_set = "F0"
|
||||
# Tensor I/O contract so the scorer can feed the model correctly.
|
||||
input_shape = [114, 2] # subcarriers × {amp, phase} (example)
|
||||
output_shape = [17, 2] # 17 keypoints × {x, y} normalised [0,1]
|
||||
# Normalisation expected on the input ("none" | "zscore" | "minmax").
|
||||
normalization = "zscore"
|
||||
|
||||
[runtime]
|
||||
# Inference entrypoint inside the artifact (framework-specific).
|
||||
framework = "candle" # candle | onnx | torch
|
||||
# Optional: target the edge-latency category with a declared device class.
|
||||
device_class = "cpu" # cpu | pi5 | gpu
|
||||
|
||||
# Notes:
|
||||
# - You submit a MODEL, never predictions on data you hold.
|
||||
# - Scoring runs against a PRIVATE MM-Fi held-out split in a no-network,
|
||||
# read-only sandbox. You cannot see the eval data.
|
||||
# - The resulting score is a signed, append-only ledger row carrying a
|
||||
# determinism proof hash and the pinned harness_version.
|
||||
@@ -1,37 +0,0 @@
|
||||
---
|
||||
title: AetherArena — Spatial-Intelligence Benchmark
|
||||
emoji: 📡
|
||||
colorFrom: indigo
|
||||
colorTo: purple
|
||||
sdk: gradio
|
||||
sdk_version: 5.9.1
|
||||
python_version: "3.12"
|
||||
app_file: app.py
|
||||
pinned: true
|
||||
license: cc-by-nc-4.0
|
||||
tags:
|
||||
- benchmark
|
||||
- leaderboard
|
||||
- wifi-sensing
|
||||
- spatial-intelligence
|
||||
- pose-estimation
|
||||
---
|
||||
|
||||
# AetherArena ("AA") — The Official Spatial-Intelligence Benchmark
|
||||
|
||||
> Public leaderboard. Private evaluation split. Open scorer. Signed results.
|
||||
|
||||
The field's standard yardstick for camera-free **spatial intelligence** (pose, presence,
|
||||
occupancy, tracking, vitals) from RF/WiFi and, over time, mmWave / UWB / multimodal.
|
||||
|
||||
- **Project-agnostic** — any team, framework, or modality enters; RuView donated the seed
|
||||
scorer and is scored like everyone else.
|
||||
- **Benchmark-first** — the board starts empty; every row is a real scoring-pipeline
|
||||
**witness** (`inputs_sha256` + `proof_sha256` + `harness_version`) in an append-only,
|
||||
hash-chained, tamper-evident ledger.
|
||||
- **Reproducible** — the scorer is open; reproduce any proof hash + repeatability locally.
|
||||
|
||||
Spec: [ADR-149](https://github.com/ruvnet/RuView/blob/main/docs/adr/ADR-149-public-community-leaderboard-huggingface.md).
|
||||
Source + open scorer: https://github.com/ruvnet/RuView/tree/main/aether-arena
|
||||
|
||||
Non-commercial (CC BY-NC 4.0): the v0 eval split derives from MM-Fi (CC BY-NC); AA is operated non-commercially.
|
||||
@@ -1,161 +0,0 @@
|
||||
"""AetherArena ("AA") — The Official Spatial-Intelligence Benchmark.
|
||||
|
||||
Hugging Face Space (Gradio) — the public face of the benchmark (ADR-149).
|
||||
This Space is the presentation + submission layer; the heavy scoring runs in the
|
||||
pinned RuView harness (CI / scorer container), and results land in the append-only,
|
||||
hash-chained **witness ledger** shown here.
|
||||
|
||||
Benchmark-first: the board starts EMPTY. No seeded or hand-entered numbers — every
|
||||
row is a real scoring-pipeline witness (inputs_sha256 + proof_sha256 + harness_version).
|
||||
"""
|
||||
import hashlib
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import gradio as gr
|
||||
|
||||
LEDGER = Path(__file__).parent / "ledger.jsonl"
|
||||
GENESIS_PREV = "0" * 64
|
||||
|
||||
|
||||
def _rows():
|
||||
if not LEDGER.exists():
|
||||
return []
|
||||
return [json.loads(l) for l in LEDGER.read_text().splitlines() if l.strip()]
|
||||
|
||||
|
||||
def _canon(row: dict) -> bytes:
|
||||
body = {k: row[k] for k in sorted(row) if k != "row_hash"}
|
||||
return json.dumps(body, separators=(",", ":"), sort_keys=True).encode()
|
||||
|
||||
|
||||
def verify_chain():
|
||||
rows, prev = _rows(), GENESIS_PREV
|
||||
for i, r in enumerate(rows):
|
||||
if r.get("prev_hash") != prev or r.get("row_hash") != hashlib.sha256(_canon(r)).hexdigest():
|
||||
return f"❌ Ledger chain BROKEN at row {i} — tampering detected."
|
||||
prev = r["row_hash"]
|
||||
return f"✅ Witness ledger chain intact — {len(rows)} row(s), append-only."
|
||||
|
||||
|
||||
def leaderboard(category: str):
|
||||
results = [r for r in _rows() if r.get("kind") == "result" and (category == "all" or r.get("category") == category)]
|
||||
if not results:
|
||||
return [["— no entries yet —", "", "", "", "", ""]]
|
||||
results.sort(key=lambda r: r.get("score_pct") or 0, reverse=True)
|
||||
return [[
|
||||
r.get("submitter", "?"),
|
||||
r.get("model_ref", "?"),
|
||||
f"{r.get('benchmark','?')} / {r.get('protocol','?')}",
|
||||
r.get("metric", "?"),
|
||||
f"{r.get('score_pct', 0):.2f}%",
|
||||
f"{r.get('tier','?')} (vs {r.get('sota_ref','?')})",
|
||||
] for r in results]
|
||||
|
||||
|
||||
FOUR_PART = "### Public leaderboard. Private evaluation split. Open scorer. Signed results."
|
||||
|
||||
ABOUT = """
|
||||
**AetherArena** is the official, project-agnostic **Spatial-Intelligence Benchmark** —
|
||||
camera-free pose, presence, occupancy, tracking, and vitals from RF/WiFi (and, over
|
||||
time, mmWave / UWB / radar / multimodal). It is **not** a single-vendor board: any
|
||||
team, framework, or modality enters, and every entrant — including the RuView baseline
|
||||
that donated the seed scorer — is scored by the identical, open, pinned harness.
|
||||
|
||||
The scorer reuses RuView's released `wifi-densepose-train` acceptance harness
|
||||
(`ruview_metrics` + ablation). You submit a **model, not predictions**; it is scored
|
||||
against a **private** MM-Fi held-out split; one **witness** row (inputs hash + proof
|
||||
hash + harness version) is appended to a **hash-chained, tamper-evident ledger**.
|
||||
|
||||
**For industry:** a vendor-neutral, auditable way to compare RF-sensing models on equal
|
||||
footing — the same standardized splits, the same metric definition, the same signed,
|
||||
reproducible ledger. No more "trust our number on our split." Vendors, labs, and startups
|
||||
all submit through one pipeline and are scored identically.
|
||||
|
||||
**Generalization Track (roadmap):** the headline isn't a single in-domain number — it's a
|
||||
battery of honest tracks: MM-Fi `random_split` (in-domain), `cross_subject` (unseen people),
|
||||
cross-room, cross-device, and confidence-calibration (ECE). Cross-subject is the real
|
||||
deployment frontier and is treated as the flagship hard benchmark.
|
||||
|
||||
Spec: ADR-149. v0 ranks **pose, presence, edge-latency, determinism**. Tracking &
|
||||
vitals activate when their ground truth lands; **privacy-leakage** is gated until the
|
||||
membership-inference attacker ships. Source + the open scorer:
|
||||
https://github.com/ruvnet/RuView/tree/main/aether-arena
|
||||
"""
|
||||
|
||||
SUBMIT = """
|
||||
### Submit a model
|
||||
|
||||
1. Write a manifest — [`schema/aa-submission.toml`](https://github.com/ruvnet/RuView/blob/main/aether-arena/schema/aa-submission.toml):
|
||||
declare your model ref, category, the ADR-145 feature set (F0 CSI … F3 BFLD), and the tensor I/O contract.
|
||||
2. Provide your model artifact (`.safetensors` / `.rvf` / LoRA adapter).
|
||||
3. It moves through `submitted → validated → quarantined → smoke_scored → full_scored → published`,
|
||||
scored in a no-network, read-only sandbox against the private split.
|
||||
4. Your signed witness row appears on the leaderboard.
|
||||
|
||||
**You submit a model, never predictions** — predictions on data you hold prove nothing.
|
||||
"""
|
||||
|
||||
VERIFY = """
|
||||
### Verify it's fair (you don't have to trust us)
|
||||
|
||||
The scorer is open and reproducible. Reproduce the determinism proof + repeatability locally:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ruvnet/RuView && cd RuView/v2
|
||||
# determinism gate (same as CI):
|
||||
cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features
|
||||
# repeatability — N runs, one identical proof hash:
|
||||
cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --repeat 16
|
||||
# verify the append-only witness ledger chain:
|
||||
cd ../aether-arena/ledger && python3 ledger_tools.py verify
|
||||
```
|
||||
|
||||
A stranger must be able to: submit → get a deterministic score → see the signed row →
|
||||
rerun the scorer locally → understand why the rank is fair. That is the launch gate (ADR-149 §7).
|
||||
"""
|
||||
|
||||
with gr.Blocks(title="AetherArena — Spatial-Intelligence Benchmark") as demo:
|
||||
gr.Markdown("# 📡 AetherArena (AA)\n## The Official, Vendor-Neutral Benchmark for WiFi / RF Spatial Sensing")
|
||||
gr.Markdown(FOUR_PART)
|
||||
gr.Markdown(
|
||||
"**An open industry benchmark — for everyone, not any one vendor.** Submit any model, any framework, "
|
||||
"any modality. Every entrant — academic, startup, or incumbent — is scored *identically*: standardized "
|
||||
"protocols (MM-Fi `random_split` / `cross_subject`), matched metrics (torso-PCK@20, the published "
|
||||
"definition), and an auditable, hash-chained **witness ledger** anyone can verify and reproduce.\n\n"
|
||||
"**Why it exists:** WiFi/RF-sensing results are reported with inconsistent splits, metrics, and no "
|
||||
"auditability — so numbers aren't comparable. AetherArena fixes the *measurement*: one protocol, one "
|
||||
"metric, one signed ledger, one-command reproduction. The benchmark is the product; the leaderboard is "
|
||||
"just the scoreboard. (Reference implementation seeded by RuView, ADR-149.)"
|
||||
)
|
||||
chain = gr.Markdown(verify_chain())
|
||||
|
||||
with gr.Tab("🏆 Leaderboard"):
|
||||
gr.Markdown(
|
||||
"### Current standings — MM-Fi WiFi-CSI 2D pose, torso-PCK@20\n"
|
||||
"Ranked, protocol- & metric-matched results. Each row carries its own caveats in the ledger "
|
||||
"(e.g. `random_split` has temporal-adjacency leakage that inflates *all* methods equally — the "
|
||||
"leakage-free `cross_subject` track is the real deployment frontier). **Submit yours — top the board.**"
|
||||
)
|
||||
cat = gr.Dropdown(["all", "pose", "presence"], value="all", label="Category")
|
||||
tbl = gr.Dataframe(
|
||||
headers=["Submitter", "Model", "Benchmark / Protocol", "Metric", "Score", "Tier (vs prior SOTA)"],
|
||||
value=leaderboard("all"), interactive=False, wrap=True,
|
||||
)
|
||||
cat.change(leaderboard, cat, tbl)
|
||||
gr.Markdown(
|
||||
"*Vendor-neutral & benchmark-first: every row is a real, metric- and protocol-matched result — "
|
||||
"no seeded or vendor-favored numbers. Integrity is enforced, not promised: the current top entry's "
|
||||
"score was self-corrected down from an inflated metric (91.86% bbox → 81.63% torso) before it could "
|
||||
"be published. The same scorer and ledger apply to every submitter.*"
|
||||
)
|
||||
|
||||
with gr.Tab("📤 Submit"):
|
||||
gr.Markdown(SUBMIT)
|
||||
with gr.Tab("🔬 Verify"):
|
||||
gr.Markdown(VERIFY)
|
||||
with gr.Tab("ℹ️ About"):
|
||||
gr.Markdown(ABOUT)
|
||||
|
||||
if __name__ == "__main__":
|
||||
demo.launch(server_name="0.0.0.0", server_port=7860)
|
||||
@@ -1,5 +0,0 @@
|
||||
{"benchmark": "AetherArena", "created": "2026-05-30", "kind": "genesis", "note": "Official Spatial-Intelligence Benchmark \u2014 append-only signed ledger. Entries are real harness scores only; no seeded numbers.", "prev_hash": "0000000000000000000000000000000000000000000000000000000000000000", "row_hash": "940bdc6f0f5dd00f4d89e13a8fa843bab3c9ddf1b8051f426a1701e730249231", "seq": 0, "spec": "ADR-149"}
|
||||
{"abs_gain": "+9.38", "benchmark": "MM-Fi", "category": "pose", "caveat": "Protocol-matched MM-Fi random_split result; NOT solved real-world generalization. Random split has temporal/subject-adjacency effects common to this benchmark family. Leakage-free cross-subject is far lower (~11-27%) and is the real deployment frontier.", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20 (||right_shoulder-left_hip|| norm, 17 COCO kpts)", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer (4L/8H ~2M params, temporal-attention)", "prev_hash": "940bdc6f0f5dd00f4d89e13a8fa843bab3c9ddf1b8051f426a1701e730249231", "protocol": "random_split (ratio=0.8, seed=0)", "rel_gain": "+13.0%", "reproduce": "download MM-Fi -> parse_mmfi_zips.py -> train_tf_torso.py X.npy Y.npy split_random.npy (seed 0)", "row_hash": "76598d8e1320d5248f8cd854a8ffa22a99bd2a2f0e0e7f2d2b1df79af16001d5", "score_pct": 81.63, "scored_at": "2026-05-30", "seq": 1, "sota_ref": "MultiFormer 72.25 (CSI2Pose 68.41)", "submitter": "ruvnet", "tier": "Gold"}
|
||||
{"abs_gain": "+11.34", "benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer + skeleton-graph head + 3-ensemble + TTA", "note": "Best in-domain. Stacks attention-pooling + transformer + skeleton-graph refine + warmup + TTA + 3-model ensemble. Supersedes the 81.63 single-model entry.", "prev_hash": "76598d8e1320d5248f8cd854a8ffa22a99bd2a2f0e0e7f2d2b1df79af16001d5", "protocol": "random_split (0.8, seed 0)", "row_hash": "5780a4bc3e98eb0e30c1ecfa9091e57b280444fa1f21cd5146797e408580e4ab", "score_pct": 83.59, "scored_at": "2026-05-30", "seq": 2, "sota_ref": "MultiFormer 72.25 (CSI2Pose 68.41)", "submitter": "ruvnet", "tier": "Gold"}
|
||||
{"benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer", "note": "Leakage-free generalization to unseen people, shared rooms. Honest deployment-relevant number.", "prev_hash": "5780a4bc3e98eb0e30c1ecfa9091e57b280444fa1f21cd5146797e408580e4ab", "protocol": "cross_subject (official, val=S05,S10,..,S40)", "row_hash": "d989e4e1dbc0182610305fdfbde8b094413b87c913283a46bf41f4afba7a06fd", "score_pct": 64.04, "scored_at": "2026-05-30", "seq": 3, "sota_ref": "(no matched public ref)", "submitter": "ruvnet", "tier": "Silver"}
|
||||
{"benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer + CORAL domain alignment", "note": "The real deployment frontier (new room). CORAL transductive DG (+30% rel over control). Data-bound: MM-Fi has only 3 source rooms.", "prev_hash": "d989e4e1dbc0182610305fdfbde8b094413b87c913283a46bf41f4afba7a06fd", "protocol": "cross_environment (train E01-03 -> test E04, new room)", "row_hash": "bf370487bde88e198c13877956dab3c83766a6a24afef0b78b6ac7aa130bb207", "score_pct": 17.51, "scored_at": "2026-05-30", "seq": 4, "sota_ref": "(hard frontier; control 13.52)", "submitter": "ruvnet", "tier": "Bronze"}
|
||||
@@ -1 +0,0 @@
|
||||
gradio==5.9.1
|
||||
@@ -1,130 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
CIR Verification Helper (ADR-134)
|
||||
|
||||
Optional Python comparator — invokes the Rust cir_proof_runner binary and
|
||||
checks its output against expected_cir_features.sha256.
|
||||
|
||||
Usage:
|
||||
python cir_verify_helper.py # verify against stored hash
|
||||
python cir_verify_helper.py --generate # regenerate hash via Rust binary
|
||||
|
||||
This script is a thin wrapper; all cryptographic work is done in the Rust
|
||||
binary. It exists to integrate the CIR proof step into the Python verify.py
|
||||
flow if needed.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
REPO_ROOT = os.path.abspath(os.path.join(SCRIPT_DIR, "..", "..", "..", ".."))
|
||||
|
||||
|
||||
def find_binary() -> str:
|
||||
"""Locate the cir_proof_runner binary."""
|
||||
candidates = [
|
||||
os.path.join(REPO_ROOT, "v2", "target", "release", "cir_proof_runner"),
|
||||
os.path.join(REPO_ROOT, "v2", "target", "release", "cir_proof_runner.exe"),
|
||||
os.path.join(REPO_ROOT, "v2", "target", "debug", "cir_proof_runner"),
|
||||
os.path.join(REPO_ROOT, "v2", "target", "debug", "cir_proof_runner.exe"),
|
||||
]
|
||||
for path in candidates:
|
||||
if os.path.isfile(path):
|
||||
return path
|
||||
return ""
|
||||
|
||||
|
||||
def build_binary() -> bool:
|
||||
"""Build the release binary via cargo."""
|
||||
print("Building cir_proof_runner (release)...")
|
||||
result = subprocess.run(
|
||||
[
|
||||
"cargo", "build",
|
||||
"-p", "wifi-densepose-signal",
|
||||
"--bin", "cir_proof_runner",
|
||||
"--release",
|
||||
"--no-default-features",
|
||||
],
|
||||
cwd=os.path.join(REPO_ROOT, "v2"),
|
||||
capture_output=True,
|
||||
text=True,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
print("Build failed:", result.stderr[-2000:])
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def run_generate(binary: str) -> str:
|
||||
"""Run the binary with --generate-hash; return the hex hash."""
|
||||
result = subprocess.run(
|
||||
[binary, "--generate-hash"],
|
||||
cwd=REPO_ROOT,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
print("Error running binary:", result.stderr)
|
||||
return ""
|
||||
return result.stdout.strip()
|
||||
|
||||
|
||||
def run_verify(binary: str) -> bool:
|
||||
"""Run the binary in verify mode; return True on PASS."""
|
||||
result = subprocess.run(
|
||||
[binary],
|
||||
cwd=REPO_ROOT,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
)
|
||||
print(result.stdout.strip())
|
||||
if result.stderr.strip():
|
||||
print(result.stderr.strip(), file=sys.stderr)
|
||||
return result.returncode == 0
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(description="CIR verification helper (ADR-134)")
|
||||
parser.add_argument(
|
||||
"--generate",
|
||||
action="store_true",
|
||||
help="Regenerate expected_cir_features.sha256 via Rust binary",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--build",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Build the binary before running (default: use cached binary)",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
binary = find_binary()
|
||||
|
||||
if args.build or not binary:
|
||||
if not build_binary():
|
||||
sys.exit(1)
|
||||
binary = find_binary()
|
||||
|
||||
if not binary:
|
||||
print("ERROR: cir_proof_runner binary not found. Run with --build.")
|
||||
sys.exit(1)
|
||||
|
||||
if args.generate:
|
||||
hash_val = run_generate(binary)
|
||||
if not hash_val:
|
||||
sys.exit(1)
|
||||
hash_file = os.path.join(SCRIPT_DIR, "expected_cir_features.sha256")
|
||||
with open(hash_file, "w") as f:
|
||||
f.write(hash_val + "\n")
|
||||
print(f"Wrote CIR hash to {hash_file}")
|
||||
print(f"Hash: {hash_val}")
|
||||
else:
|
||||
ok = run_verify(binary)
|
||||
sys.exit(0 if ok else 1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1 +0,0 @@
|
||||
d6bce07ecb1648e6936561df44bf4a3bfc17bb0ba5f692646b2301d105b52f67
|
||||
@@ -1 +0,0 @@
|
||||
304d54690af468dc6cbf0f2a1332f109cf187d5e2eab454efd8554cebc45bdeb
|
||||
@@ -1 +1 @@
|
||||
f8e76f21a0f9852b70b6d9dd5318239f6b20cbcb4cdd995863263cecdc446f7a
|
||||
667eb054c44ac510342665bf9c93d608868a8ead948ae8774b2796ebce6f8fe7
|
||||
Binary file not shown.
+16
-148
@@ -185,14 +185,7 @@ def frame_to_csi_data(frame, signal_meta):
|
||||
# observed pipeline-amplified ULP drift and is still far below any meaningful
|
||||
# signal change (CSI phase precision is ~1e-3 rad; PSD bins differ by orders
|
||||
# of magnitude). Round to this precision, then hash.
|
||||
#
|
||||
# NOTE: 6 decimals collapses the divergence *across Linux microarchitectures*
|
||||
# but NOT Windows-vs-Linux, where the pocketfft/BLAS difference exceeds 1e-6 on
|
||||
# a few elements that then straddle the 6th-decimal rounding boundary. The
|
||||
# precision is overridable via PROOF_HASH_DECIMALS so it can be coarsened to a
|
||||
# value that is boundary-stable across *all* platforms (Windows + Linux + macOS)
|
||||
# while staying far below any signal-meaningful change.
|
||||
HASH_QUANTIZATION_DECIMALS = int(os.environ.get("PROOF_HASH_DECIMALS", "6"))
|
||||
HASH_QUANTIZATION_DECIMALS = 6
|
||||
|
||||
|
||||
def features_to_bytes(features):
|
||||
@@ -212,20 +205,13 @@ def features_to_bytes(features):
|
||||
"""
|
||||
parts = []
|
||||
|
||||
# Serialize each feature array in declaration order.
|
||||
# doppler_shift is INTENTIONALLY excluded: it is peak-normalized
|
||||
# (`spectrum / max(spectrum)` in csi_processor._extract_doppler_features),
|
||||
# and when the raw spectrum has near-tied peaks the argmax flips under
|
||||
# cross-microarchitecture FP reordering, renormalizing the whole array
|
||||
# (O(1) divergence — not absorbable by any tolerance). The remaining five
|
||||
# features, including the FFT-based PSD, reproduce deterministically and
|
||||
# provide the proof. (The underlying doppler instability is a production
|
||||
# reproducibility bug tracked separately.)
|
||||
# Serialize each feature array in declaration order
|
||||
for array in [
|
||||
features.amplitude_mean,
|
||||
features.amplitude_variance,
|
||||
features.phase_difference,
|
||||
features.correlation_matrix,
|
||||
features.doppler_shift,
|
||||
features.power_spectral_density,
|
||||
]:
|
||||
flat = np.asarray(array, dtype=np.float64).ravel()
|
||||
@@ -239,45 +225,6 @@ def features_to_bytes(features):
|
||||
return b"".join(parts)
|
||||
|
||||
|
||||
# ── Cross-platform tolerance gate (issue #560 follow-up) ─────────────────────
|
||||
# The SHA-256 of fixed-decimal-rounded features is bit-exact only WITHIN one
|
||||
# CPU microarchitecture. The pocketfft / BLAS kernels in the manylinux
|
||||
# numpy/scipy wheels reorder floating-point reductions differently across
|
||||
# microarchs (e.g. a GitHub Azure runner vs a developer box vs another Linux
|
||||
# host), and the resulting ~1e-6 *relative* drift lands on large-magnitude PSD
|
||||
# bins as an absolute difference too large for ANY fixed-decimal grid to absorb
|
||||
# (empirically the hash diverges across microarchs even at 2 decimals). So:
|
||||
# • the hash is the strong, bit-exact, SAME-platform proof, and
|
||||
# • a relative tolerance against a committed reference vector is the
|
||||
# platform-INDEPENDENT proof.
|
||||
# A run PASSES if either matches. Tolerances sit ~100x over the observed
|
||||
# microarch drift and ~10x under any signal-meaningful change (CSI phase
|
||||
# precision ~1e-3 rad), so real pipeline regressions still fail.
|
||||
TOLERANCE_RTOL = 1e-4
|
||||
TOLERANCE_ATOL = 1e-6
|
||||
REFERENCE_VECTOR_FILENAME = "expected_features_reference.npz"
|
||||
|
||||
|
||||
def features_to_vector(features):
|
||||
"""Concatenate a frame's feature arrays as raw float64 (no rounding).
|
||||
|
||||
Mirrors ``features_to_bytes`` ordering but keeps full precision, for the
|
||||
tolerance-based cross-platform comparison.
|
||||
"""
|
||||
# doppler_shift excluded — see features_to_bytes for the rationale
|
||||
# (peak-normalization argmax instability across CPU microarchitectures).
|
||||
arrays = [
|
||||
features.amplitude_mean,
|
||||
features.amplitude_variance,
|
||||
features.phase_difference,
|
||||
features.correlation_matrix,
|
||||
features.power_spectral_density,
|
||||
]
|
||||
return np.concatenate(
|
||||
[np.asarray(a, dtype=np.float64).ravel() for a in arrays]
|
||||
)
|
||||
|
||||
|
||||
def compute_pipeline_hash(data_path, verbose=False):
|
||||
"""Run the full pipeline and compute the SHA-256 hash of all features.
|
||||
|
||||
@@ -320,7 +267,6 @@ def compute_pipeline_hash(data_path, verbose=False):
|
||||
features_count = 0
|
||||
total_feature_bytes = 0
|
||||
last_features = None
|
||||
feature_vectors = []
|
||||
doppler_nonzero_count = 0
|
||||
doppler_shape = None
|
||||
psd_shape = None
|
||||
@@ -337,7 +283,6 @@ def compute_pipeline_hash(data_path, verbose=False):
|
||||
if features is not None:
|
||||
feature_bytes = features_to_bytes(features)
|
||||
hasher.update(feature_bytes)
|
||||
feature_vectors.append(features_to_vector(features))
|
||||
features_count += 1
|
||||
total_feature_bytes += len(feature_bytes)
|
||||
last_features = features
|
||||
@@ -406,11 +351,7 @@ def compute_pipeline_hash(data_path, verbose=False):
|
||||
"psd_shape": psd_shape,
|
||||
}
|
||||
|
||||
reference_vector = (
|
||||
np.concatenate(feature_vectors) if feature_vectors else np.array([], dtype=np.float64)
|
||||
)
|
||||
|
||||
return hasher.hexdigest(), reference_vector, stats
|
||||
return hasher.hexdigest(), stats
|
||||
|
||||
|
||||
def audit_codebase(base_dir=None):
|
||||
@@ -526,7 +467,7 @@ def main():
|
||||
print(" This runs the SAME CSIProcessor.preprocess_csi_data() and")
|
||||
print(" CSIProcessor.extract_features() used in production.")
|
||||
print()
|
||||
computed_hash, computed_vector, stats = compute_pipeline_hash(data_path, verbose=args.verbose)
|
||||
computed_hash, stats = compute_pipeline_hash(data_path, verbose=args.verbose)
|
||||
|
||||
# ---------------------------------------------------------------
|
||||
# Step 3: Hash comparison
|
||||
@@ -538,11 +479,8 @@ def main():
|
||||
with open(hash_path, "w") as f:
|
||||
f.write(computed_hash + "\n")
|
||||
print(f" Wrote expected hash to {hash_path}")
|
||||
ref_path = os.path.join(SCRIPT_DIR, REFERENCE_VECTOR_FILENAME)
|
||||
np.savez_compressed(ref_path, features=computed_vector)
|
||||
print(f" Wrote reference vector ({computed_vector.size} values) to {ref_path}")
|
||||
print()
|
||||
print(" HASH + REFERENCE GENERATED -- run without --generate-hash to verify.")
|
||||
print(" HASH GENERATED -- run without --generate-hash to verify.")
|
||||
print("=" * 72)
|
||||
return
|
||||
|
||||
@@ -561,70 +499,13 @@ def main():
|
||||
|
||||
print(f" Expected: {expected_hash}")
|
||||
|
||||
hash_match = computed_hash == expected_hash
|
||||
|
||||
# Cross-platform fallback: if the bit-exact hash differs (different CPU
|
||||
# microarchitecture reorders the pocketfft/BLAS reductions), accept the run
|
||||
# when the raw feature vector matches the committed reference within a
|
||||
# relative tolerance — platform-independent where the hash is not (#560).
|
||||
tolerance_match = False
|
||||
max_abs_dev = None
|
||||
max_rel_dev = None
|
||||
ref_path = os.path.join(SCRIPT_DIR, REFERENCE_VECTOR_FILENAME)
|
||||
if not hash_match and os.path.exists(ref_path):
|
||||
ref_vec = np.load(ref_path)["features"]
|
||||
if ref_vec.shape == computed_vector.shape:
|
||||
tolerance_match = bool(
|
||||
np.allclose(
|
||||
computed_vector, ref_vec, rtol=TOLERANCE_RTOL, atol=TOLERANCE_ATOL
|
||||
)
|
||||
)
|
||||
diff = np.abs(computed_vector - ref_vec)
|
||||
max_abs_dev = float(np.max(diff)) if diff.size else 0.0
|
||||
max_rel_dev = (
|
||||
float(np.max(diff / np.maximum(np.abs(ref_vec), 1e-12)))
|
||||
if diff.size
|
||||
else 0.0
|
||||
)
|
||||
|
||||
if hash_match:
|
||||
match_status = "MATCH (bit-exact)"
|
||||
elif tolerance_match:
|
||||
match_status = f"TOLERANCE MATCH (max rel dev {max_rel_dev:.2e})"
|
||||
if computed_hash == expected_hash:
|
||||
match_status = "MATCH"
|
||||
else:
|
||||
match_status = "MISMATCH"
|
||||
print(f" Status: {match_status}")
|
||||
print()
|
||||
|
||||
if not hash_match and max_abs_dev is not None:
|
||||
block_sizes = [56, 56, 55, 9, 128] # per-frame feature layout (doppler excluded)
|
||||
block_names = ["amp_mean", "amp_var", "phase_diff", "corr", "psd"]
|
||||
frame_len = sum(block_sizes)
|
||||
tol = TOLERANCE_ATOL + TOLERANCE_RTOL * np.abs(ref_vec)
|
||||
outside = diff > tol
|
||||
n_out = int(outside.sum())
|
||||
print(
|
||||
f" DIVERGENCE: {n_out}/{computed_vector.size} outside tol "
|
||||
f"({100.0 * n_out / computed_vector.size:.4f}%) "
|
||||
f"max|d|={max_abs_dev:.3e} maxrel={max_rel_dev:.3e}"
|
||||
)
|
||||
if n_out:
|
||||
wf = np.where(outside)[0] % frame_len
|
||||
bounds = np.cumsum([0] + block_sizes)
|
||||
parts = []
|
||||
for bi, name in enumerate(block_names):
|
||||
c = int(((wf >= bounds[bi]) & (wf < bounds[bi + 1])).sum())
|
||||
if c:
|
||||
parts.append(f"{name}={c}")
|
||||
print(f" by feature: {', '.join(parts)}")
|
||||
for w in np.argsort(diff)[::-1][:4]:
|
||||
b = int(np.searchsorted(bounds, int(w) % frame_len, side="right")) - 1
|
||||
print(
|
||||
f" worst idx {int(w)} ({block_names[b]}): "
|
||||
f"ref={ref_vec[int(w)]:.6g} got={computed_vector[int(w)]:.6g}"
|
||||
)
|
||||
print()
|
||||
|
||||
# ---------------------------------------------------------------
|
||||
# Step 4: Audit (if requested or always in full mode)
|
||||
# ---------------------------------------------------------------
|
||||
@@ -647,22 +528,14 @@ def main():
|
||||
# Final verdict
|
||||
# ---------------------------------------------------------------
|
||||
print("=" * 72)
|
||||
if hash_match or tolerance_match:
|
||||
if computed_hash == expected_hash:
|
||||
print(" VERDICT: PASS")
|
||||
print()
|
||||
if hash_match:
|
||||
print(" The pipeline produced a SHA-256 hash that matches the published")
|
||||
print(" expected hash (bit-exact). This proves:")
|
||||
else:
|
||||
print(" The bit-exact hash differs (CPU-microarchitecture FP reordering),")
|
||||
print(" but the raw feature vector matches the published reference within")
|
||||
print(
|
||||
f" rtol={TOLERANCE_RTOL:g} / atol={TOLERANCE_ATOL:g} "
|
||||
f"(max rel dev {max_rel_dev:.2e}). This proves:"
|
||||
)
|
||||
print(" The pipeline produced a SHA-256 hash that matches the published")
|
||||
print(" expected hash. This proves:")
|
||||
print(" 1. The SAME signal processing code ran on the reference signal")
|
||||
print(" 2. The output is DETERMINISTIC (same input -> same output)")
|
||||
print(" 3. No randomness was introduced")
|
||||
print(" 3. No randomness was introduced (hash would differ)")
|
||||
print(" 4. The code path includes: noise removal, Hamming windowing,")
|
||||
print(" amplitude normalization, FFT-based Doppler extraction,")
|
||||
print(" and power spectral density computation")
|
||||
@@ -673,19 +546,14 @@ def main():
|
||||
else:
|
||||
print(" VERDICT: FAIL")
|
||||
print()
|
||||
print(" The pipeline output does NOT match the expected hash OR the")
|
||||
print(" reference feature vector within tolerance.")
|
||||
if max_rel_dev is not None:
|
||||
print(
|
||||
f" max abs dev: {max_abs_dev:.3e} max rel dev: {max_rel_dev:.3e}"
|
||||
f" (rtol={TOLERANCE_RTOL:g}, atol={TOLERANCE_ATOL:g})"
|
||||
)
|
||||
print(" The pipeline output does NOT match the expected hash.")
|
||||
print()
|
||||
print(" Possible causes:")
|
||||
print(" - Numpy/scipy version mismatch (check requirements)")
|
||||
print(" - Code change in CSI processor that alters numerical output")
|
||||
print(" - A real (non-microarch) numerical regression")
|
||||
print(" - Platform floating-point differences (unlikely for IEEE 754)")
|
||||
print()
|
||||
print(" To update after an intentional change:")
|
||||
print(" To update the expected hash after intentional changes:")
|
||||
print(" python verify.py --generate-hash")
|
||||
print("=" * 72)
|
||||
sys.exit(1)
|
||||
|
||||
@@ -6,14 +6,8 @@
|
||||
#
|
||||
# To update: change versions, run `python v1/data/proof/verify.py --generate-hash`,
|
||||
# then commit the new expected_features.sha256.
|
||||
#
|
||||
# numpy/scipy track the versions the *published* expected hash
|
||||
# (expected_features.sha256 = ca58956c…) was generated with — modern numpy 2.x,
|
||||
# i.e. what a fresh `pip install numpy` and the proof-of-capabilities.md skeptic
|
||||
# path produce today. The old 1.26.4 pin no longer matched that hash and made
|
||||
# the determinism gate fail against its own published proof.
|
||||
|
||||
numpy==2.4.2
|
||||
scipy==1.17.1
|
||||
numpy==1.26.4
|
||||
scipy==1.14.1
|
||||
pydantic==2.10.4
|
||||
pydantic-settings==2.7.1
|
||||
|
||||
@@ -26,12 +26,7 @@ class Settings(BaseSettings):
|
||||
workers: int = Field(default=1, description="Number of worker processes")
|
||||
|
||||
# Security settings
|
||||
secret_key: str = Field(
|
||||
default="dev-not-secret-CHANGE-IN-PROD",
|
||||
description="Secret key for JWT tokens (production deployments "
|
||||
"MUST override via SECRET_KEY env or .env; the dev "
|
||||
"default is rejected by validate_production_config)",
|
||||
)
|
||||
secret_key: str = Field(..., description="Secret key for JWT tokens")
|
||||
jwt_algorithm: str = Field(default="HS256", description="JWT algorithm")
|
||||
jwt_expire_hours: int = Field(default=24, description="JWT token expiration in hours")
|
||||
allowed_hosts: List[str] = Field(default=["*"], description="Allowed hosts")
|
||||
@@ -163,14 +158,7 @@ class Settings(BaseSettings):
|
||||
model_config = SettingsConfigDict(
|
||||
env_file=".env",
|
||||
env_file_encoding="utf-8",
|
||||
case_sensitive=False,
|
||||
# Tolerate `.env` keys that this Settings model doesn't declare
|
||||
# (e.g., NPM_TOKEN, DOCKER_HUB_TOKEN, PYPI_TOKEN used by other
|
||||
# tooling). Without `extra="ignore"` pydantic-settings 2.x
|
||||
# raises `ValidationError: Extra inputs are not permitted` and
|
||||
# leaks the offending values into the error message — a real
|
||||
# security concern for secret tokens. See verify.py / `./verify`.
|
||||
extra="ignore",
|
||||
case_sensitive=False
|
||||
)
|
||||
|
||||
@field_validator("environment")
|
||||
|
||||
@@ -221,15 +221,11 @@ class ESP32BinaryParser:
|
||||
|
||||
snr = float(rssi - noise_floor)
|
||||
frequency = float(freq_mhz) * 1e6
|
||||
bandwidth = 20e6 # default; could infer from n_subcarriers
|
||||
|
||||
# Bandwidth inference (issue #1005): HE-LTF uses a 4x denser tone
|
||||
# grid than HT-LTF on the same channel width — an HE-SU frame with
|
||||
# 256 bins (242 active HE20 tones) is a *20 MHz* capture, not 160.
|
||||
if ppdu_byte in (1, 2, 3): # HE-SU / HE-MU / HE-TB
|
||||
bandwidth = 40e6 if (flags_byte & 0x01) or n_subcarriers > 256 else 20e6
|
||||
elif n_subcarriers <= 64: # ESP32 HT20 delivers the full 64-bin FFT
|
||||
if n_subcarriers <= 56:
|
||||
bandwidth = 20e6
|
||||
elif n_subcarriers <= 128:
|
||||
elif n_subcarriers <= 114:
|
||||
bandwidth = 40e6
|
||||
elif n_subcarriers <= 242:
|
||||
bandwidth = 80e6
|
||||
|
||||
@@ -107,25 +107,16 @@ class PoseService:
|
||||
async def _initialize_models(self):
|
||||
"""Initialize neural network models."""
|
||||
try:
|
||||
# Initialize DensePose model. DensePoseHead requires a config
|
||||
# dict — input_channels matches the modality translator's output
|
||||
# (256), with the standard DensePose 24 body parts and 2 (U,V)
|
||||
# coordinates. (Previously called with no args → TypeError at
|
||||
# startup, which broke the API service.)
|
||||
densepose_config = {
|
||||
'input_channels': 256,
|
||||
'num_body_parts': 24,
|
||||
'num_uv_coordinates': 2,
|
||||
}
|
||||
# Initialize DensePose model
|
||||
if self.settings.pose_model_path:
|
||||
self.densepose_model = DensePoseHead(densepose_config)
|
||||
self.densepose_model = DensePoseHead()
|
||||
# Load model weights if path is provided
|
||||
# model_state = torch.load(self.settings.pose_model_path)
|
||||
# self.densepose_model.load_state_dict(model_state)
|
||||
self.logger.info("DensePose model loaded")
|
||||
else:
|
||||
self.logger.warning("No pose model path provided, using default model")
|
||||
self.densepose_model = DensePoseHead(densepose_config)
|
||||
self.densepose_model = DensePoseHead()
|
||||
|
||||
# Initialize modality translation
|
||||
config = {
|
||||
|
||||
@@ -1,137 +0,0 @@
|
||||
# Edge-Latency Benchmark Results — ADR-163
|
||||
|
||||
Converting **CLAIMED** edge latency budgets into **MEASURED-on-host** numbers,
|
||||
closing the measurement debt flagged by Milestones 5/6 (ADR-159 / ADR-160).
|
||||
Benches + docs only — **no production-code behavior changed**.
|
||||
|
||||
## The honest caveat, up front (read before citing any number)
|
||||
|
||||
Two distinct gaps separate every number below from the figure it is converting:
|
||||
|
||||
1. **Host ≠ ESP32.** The wasm-edge skill modules document budgets *"on ESP32-S3
|
||||
WASM3"* (e.g. `exo_time_crystal`: "H (<10 ms)"). These benches run **native
|
||||
x86_64 on a development laptop**, not the Xtensa/WASM3 target. A native host
|
||||
median is an **upper bound on the algorithm's work**, not the ESP32 number.
|
||||
WASM3 interpretation on a ~240 MHz Xtensa core is typically 1–2 orders of
|
||||
magnitude slower than native `-O` host code, so a host median far under the
|
||||
budget **does NOT prove the ESP32 meets it.** *The ESP32 figure is NOT
|
||||
reproduced here — it needs hardware.*
|
||||
|
||||
2. **Bench ≠ the doc-claimed measurement.** For the cogs, the manifest cites a
|
||||
**cold-start** number (`cold_start_ms_avg`, weight-load included); these
|
||||
benches measure **steady-state** per-frame `infer` (warm, weights resident).
|
||||
Different measurements; we report both, labelled.
|
||||
|
||||
Grades (per `benchmarks/wiflow-std/RESULTS.md` / ADR-152 vocabulary):
|
||||
- **MEASURED-on-host** — reproduced in this repo on the machine below, exact
|
||||
command recorded. NOT the ESP32 / NOT the cold-start figure.
|
||||
- **CLAIMED (ESP32)** — the doc budget; UNMEASURED on hardware here.
|
||||
|
||||
## Machine
|
||||
|
||||
| | |
|
||||
|---|---|
|
||||
| Host | `ruvzen` (Windows 11, this dev box) |
|
||||
| CPU | Intel Core Ultra 9 285H |
|
||||
| Toolchain | `cargo 1.91.1`, `--release` (opt-level per crate profile) |
|
||||
| Bench harness | criterion 0.5 (`time: [low **median** high]` reported below) |
|
||||
| Date | 2026-06-12 |
|
||||
|
||||
Run-to-run spread on this box is non-trivial (criterion's low/high bracket the
|
||||
median by a few %); the medians below are single-session captures with the smoke
|
||||
settings `--warm-up-time 1 --measurement-time 2` (wasm-edge) / `3` (cogs). Re-run
|
||||
for your own machine — the absolute numbers are host-specific.
|
||||
|
||||
---
|
||||
|
||||
## T1 — wasm-edge `process_frame` hot paths (ADR-160 deferred item → DONE host)
|
||||
|
||||
The crate is **excluded from the v2 workspace**; bench from the crate dir.
|
||||
|
||||
```bash
|
||||
cd v2/crates/wifi-densepose-wasm-edge
|
||||
cargo bench --features std -- --warm-up-time 1 --measurement-time 2
|
||||
# med_seizure_detect is medical-experimental-gated:
|
||||
cargo bench --features std,medical-experimental -- --warm-up-time 1 --measurement-time 2 med_seizure
|
||||
```
|
||||
|
||||
| Hot path (M6-audit-named) | Bench id | Host median | Grade | Doc budget (CLAIMED, ESP32) |
|
||||
|---|---|---|---|---|
|
||||
| `exo_time_crystal` 256-pt × 128-lag autocorrelation (full buffer) | `exo_time_crystal::process_frame[autocorr_256x128]` | **17.3 µs** | MEASURED-on-host | "H (<10 ms) on ESP32-S3 WASM3" — **NOT reproduced here (needs hardware)** |
|
||||
| `exo_ghost_hunter` empty-room periodicity + hidden-breathing | `exo_ghost_hunter::process_frame[empty_room_periodicity]` | **1.44 µs** | MEASURED-on-host | research/exotic; no firm ESP32 figure — host proxy only |
|
||||
| `sec_weapon_detect` per-subcarrier Welford (MAX_SC=32) | `sec_weapon_detect::process_frame[per_sc_welford]` | **0.42 µs** (420 ns) | MEASURED-on-host | research-grade; calibration-gated — host proxy only |
|
||||
| `med_seizure_detect` clonic-phase rhythm path (steady-state frame) | `med_seizure_detect::process_frame[clonic_rhythm]` | **0.10 µs** (105 ns) | MEASURED-on-host (feature-gated) | doc budget "S (<5 ms) on ESP32"; **NOT reproduced here** |
|
||||
|
||||
Reading these honestly:
|
||||
|
||||
- `exo_time_crystal` at **17.3 µs host** is the only one whose host cost is even
|
||||
in the same *thousandths* of its 10 ms ESP32 budget — it does the most work
|
||||
(~32K MACs/frame). 17.3 µs native says the algorithm is cheap; it says
|
||||
**nothing** about whether WASM3-on-Xtensa lands under 10 ms. A naïve
|
||||
host→ESP32 extrapolation (assume 100× interpreter+clock penalty) would put it
|
||||
near ~1.7 ms, comfortably under — **but that is an extrapolation, not a
|
||||
measurement**, and is recorded here only to show the host number is not
|
||||
obviously in tension with the budget. ESP32 figure: **UNMEASURED**.
|
||||
- `med_seizure_detect`'s 105 ns is the **steady-state** per-frame cost; the
|
||||
expensive clonic autocorrelation only fires when the state machine is in the
|
||||
clonic phase, so this is a lower-bound on the heavy path, not the worst case.
|
||||
It is still a real, committed host datapoint.
|
||||
- The pre-existing `tests/budget_compliance.rs` already asserts the L/S/H
|
||||
wall-clock tiers (25 passing tests); these criterion benches add the
|
||||
regression-grade, reproducible median that ADR-160 deferred.
|
||||
|
||||
---
|
||||
|
||||
## T2 — cog steady-state inference latency (ADR-159/160 deferred item → DONE)
|
||||
|
||||
Cog crates are normal workspace members; bench from `v2/`. Real weights
|
||||
(`count_v1.safetensors` / `pose_v1.safetensors`) ship in-repo under each cog's
|
||||
`cog/artifacts/`, so the bench measures the **real Candle CPU forward**, not the
|
||||
stub (the bench `assert!`s `backend().starts_with("candle-")`).
|
||||
|
||||
```bash
|
||||
cd v2
|
||||
cargo bench -p cog-person-count --no-default-features --bench infer_bench -- --warm-up-time 1 --measurement-time 3
|
||||
cargo bench -p cog-pose-estimation --no-default-features --bench infer_bench -- --warm-up-time 1 --measurement-time 3
|
||||
```
|
||||
|
||||
| Cog | Bench id | Host median (steady-state infer, CPU) | Grade | Manifest cold-start (CLAIMED, different measurement + machine) |
|
||||
|---|---|---|---|---|
|
||||
| cog-person-count | `cog_person_count::infer[cpu_real_weights_steady_state]` | **305 µs** (idle box) | MEASURED-on-host | — (person-count manifest carries comparable provenance) |
|
||||
| cog-pose-estimation | `cog_pose_estimation::infer[cpu_real_weights_steady_state]` | **305 µs** (idle box) | MEASURED-on-host | `cold_start_ms_avg: 5.4` (30 invocations, **ruvultra/RTX 5080 host**, candle 0.9 cpu) — **cold-start, NOT steady-state; NOT this machine** |
|
||||
|
||||
> Spread caveat (observed, honest): both medians above were captured with the box
|
||||
> otherwise idle. A re-run of the validate-form command *while a second cargo job
|
||||
> was loading the same cores* gave 385 µs (person-count) / 973 µs (pose) —
|
||||
> the criterion low/high bracket widens to ~0.34–1.18 ms under contention. The
|
||||
> 305 µs figures are the idle-box datapoints; the absolute number is host- and
|
||||
> load-dependent (the ~10× pose swing is core contention, not a code change).
|
||||
|
||||
Reading these honestly:
|
||||
|
||||
- **Steady-state ≠ cold-start.** The pose manifest's `5.4 ms` folds in one-time
|
||||
weight load / mmap / first-forward allocation. This bench warms the engine
|
||||
first and times only the recurring per-frame forward, on a *different
|
||||
machine*. The two numbers are not comparable and we do not claim this bench
|
||||
reproduces the 5.4 ms manifest figure.
|
||||
- Both cogs share the same conv encoder; person-count adds a count head +
|
||||
confidence head, pose adds a 256-wide MLP head. The host steady-state cost is
|
||||
dominated by the three dilated Conv1d layers (56→64→128→128) shared by both —
|
||||
which is why both land at ~305 µs.
|
||||
- **Empirical confirmation of the steady-state/cold-start gap:** pose
|
||||
steady-state (305 µs host) is ~18× *under* the manifest's 5.4 ms cold-start.
|
||||
Even accounting for the different machine, this is the expected shape — the
|
||||
bulk of cold-start is one-time setup, not the forward pass — and it is exactly
|
||||
why conflating the two would be dishonest.
|
||||
|
||||
---
|
||||
|
||||
## Status vs the deferred items
|
||||
|
||||
| Deferred item | Was | Now |
|
||||
|---|---|---|
|
||||
| ADR-160 "Criterion benches for `process_frame` budget claims" | ACCEPTED-FUTURE | **DONE (host)**; ESP32-on-hardware still **PENDING** (needs the wasm32 target + a flashed ESP32-S3) |
|
||||
| ADR-159/160 cog inference latency (`cold_start_ms_avg` uncommitted-benched) | CLAIMED | **MEASURED-on-host (steady-state)**; cold-start-on-ruvultra remains the manifest's separate claim |
|
||||
|
||||
Nothing here changes runtime behavior — these are benches + this results file
|
||||
only. No crate needs republishing.
|
||||
@@ -1,132 +0,0 @@
|
||||
# Edge-Skill Synthetic-Ground-Truth Validation — RESULTS
|
||||
|
||||
**Crate:** `v2/crates/wifi-densepose-wasm-edge` (workspace-EXCLUDED — build from its own dir)
|
||||
**Branch:** `feat/edge-skills-synthetic-validation`
|
||||
**ADR:** [ADR-160](../../docs/adr/ADR-160-edge-skill-library-honest-labeling.md)
|
||||
**Date:** 2026-06-13
|
||||
**Harness:** `tests/synthetic_validation.rs`
|
||||
|
||||
> **HONESTY BOUNDARY — read first.** Everything below is **synthetic-ground-truth
|
||||
> validation**: a signal is *planted* with a known answer, the **real** detector
|
||||
> is run, and detection accuracy / precision / recall / rate-error is **measured**.
|
||||
> This is **NOT field accuracy.** A skill that recovers a planted sinusoid here is
|
||||
> proven to do the math it claims on a *constructed* signal; it is **NOT** proven
|
||||
> to work on real CSI in a real room. Skills whose detection target cannot be
|
||||
> honestly planted (clinical, weapon, affect, sleep-stage, sign-language) are
|
||||
> **NOT** given a number — they are listed under **DATA-GATED** with the real
|
||||
> data each would require.
|
||||
|
||||
## Reproduce
|
||||
|
||||
```bash
|
||||
cd v2/crates/wifi-densepose-wasm-edge # workspace-excluded; build here
|
||||
cargo test --features std --test synthetic_validation -- --nocapture
|
||||
# also runs under the medical tier (med_* skills stay DATA-GATED, not validated):
|
||||
cargo test --features std,medical-experimental --test synthetic_validation -- --nocapture
|
||||
```
|
||||
|
||||
Each `MEASURED-on-synthetic | …` line printed by the harness is the source of the
|
||||
table below. Numbers are deterministic (no RNG; pseudo-noise uses a fixed LCG seed).
|
||||
|
||||
---
|
||||
|
||||
## MEASURED-on-synthetic (constructible skills)
|
||||
|
||||
| Skill | What was planted (ground truth) | Result | Grade |
|
||||
|-------|----------------------------------|--------|-------|
|
||||
| **vital_trend** | BPM held N≥6 calls at each threshold band (brady/tachy-pnea <12 / >25, brady/tachy-cardia <50 / >120, apnea breathing<1.0 for ≥20) vs normal | **acc 1.000, prec 1.000, recall 1.000** (TP5 FP0 TN5 FN0) | MEASURED |
|
||||
| **exo_time_crystal** | period-2 coordinated motion vs pseudo-noise + flat | **acc 1.000** (TP1 FP0 TN2 FN0) | MEASURED † |
|
||||
| **exo_ghost_hunter** (hidden breathing) | phase sinusoid at lag-8 (breathing band 5–15) in an empty room vs flat phase | **acc 1.000**; planted score **1.000**, flat **0.000** | MEASURED |
|
||||
| **occupancy** | 220-frame flat-amplitude calibration, then strong per-zone amplitude variance vs flat | **acc 1.000** (TP1 FP0 TN1 FN0) | MEASURED |
|
||||
| **intrusion** | calibrate→arm (330 quiet frames), then per-subcarrier Δphase>1.5 + Δamp≫3σ vs quiet | **acc 1.000** (TP1 FP0 TN1 FN0) | MEASURED |
|
||||
| **exo_rain_detect** | empty room, 60-frame baseline, then broadband variance (8/8 groups, ratio≫2.5) for ≥10 frames vs stable-low | **acc 1.000** (TP1 FP0 TN1 FN0) | MEASURED |
|
||||
| **sig_flash_attention** | sustained high phase+amplitude in each of the 8 subcarrier groups; assert reported attention peak == planted group | **peak-localization 8/8 = 1.000** | MEASURED |
|
||||
| **spt_spiking_tracker** | sparse (2-subcarrier) large phase-delta in each of the 4 zones; assert tracked zone == planted zone | **zone-localization 4/4 = 1.000** | MEASURED ‡ |
|
||||
| **sig_optimal_transport** | sustained large frame-to-frame amplitude-distribution change vs stationary | **acc 1.000** (TP1 FP0 TN1 FN0) | MEASURED |
|
||||
| **sig_mincut_person_match** | 2 persons with distinct stable per-region variance signatures over 40 frames | **person ids assigned, 0 id-swaps / 40 frames** | MEASURED |
|
||||
| **lrn_dtw_gesture_learn** | stillness → 3 identical gesture rehearsals → enrollment | **template enrolled (templates=1)** | MEASURED (enroll) §|
|
||||
| **sig_sparse_recovery** | 30 clean frames to init, then 8/32 (25%) nulled subcarriers | **dropout-detect + recovery-trigger = PASS** | MEASURED (trigger) ¶|
|
||||
|
||||
### Caveats on individual results
|
||||
|
||||
† **exo_time_crystal — honest discriminative limit.** A *pure* periodic signal
|
||||
already has autocorrelation peaks at lag L **and** 2L (natural harmonics), so this
|
||||
"period-doubling" detector cannot separate a true period-2 sub-harmonic from a
|
||||
plain periodic signal — an earlier plant using a clean sine produced a *false
|
||||
positive* (recorded during development). The construct it **can** discriminate
|
||||
with known ground truth is **periodic-coordination vs aperiodic** (noise/flat),
|
||||
which is what is measured (1.000). The original "sub-harmonic vs clean period"
|
||||
claim is **NOT** validatable with this algorithm.
|
||||
|
||||
‡ **spt_spiking_tracker — plant must be sparse.** With weights init'd home=1.0 /
|
||||
cross=0.25, firing all 8 inputs in a zone (8×0.25=2.0 > threshold 1.0) overdrives
|
||||
*every* output neuron and the tracker collapses to zone 0 (measured 1/4 during
|
||||
development). Firing only 2 inputs (home 2.0 fires, cross 0.5 silent) yields clean
|
||||
4/4 zone localization. The validatable claim is *single-zone* localization.
|
||||
|
||||
§ **lrn_dtw_gesture_learn — enrollment validated; replay-match NOT.** The
|
||||
deterministic, constructible part (stillness → 3 identical rehearsals → a template
|
||||
is enrolled) is MEASURED. The DTW *replay match* (731) did **not** fire on the
|
||||
identical replay in this run (`match_same=false`) — replay-recognition accuracy is
|
||||
**reported, not asserted**, and is not claimed as validated.
|
||||
|
||||
¶ **sig_sparse_recovery — trigger validated; recovery accuracy is NEGATIVE.**
|
||||
The dropout-detection + ISTA-recovery *trigger* pipeline fires correctly on >10%
|
||||
planted nulls (asserted). But the **measured recovery accuracy is NOT a win**:
|
||||
recovered RMSE **1.0045** vs unrecovered-null RMSE **0.9830** (**−2.2%**, i.e.
|
||||
slightly *worse* than leaving the nulls at zero) on a neighbor-correlated signal.
|
||||
The tridiagonal correlation model's fixed point does not equal the planted truth.
|
||||
**The recovery's reconstruction quality is therefore NOT validated as effective on
|
||||
synthetic data** — only its detection/trigger path is. Reported honestly; no
|
||||
positive number claimed.
|
||||
|
||||
---
|
||||
|
||||
## DATA-GATED — NOT validatable on synthetic data
|
||||
|
||||
Planting a "seizure-like" / "weapon-like" / "happy-like" synthetic signal and
|
||||
claiming the detector "works" validates **nothing real** and is exactly the
|
||||
AI-slop this project fights. These skills run real DSP (per ADR-160, 0 stubs) and
|
||||
keep their ADR-160 disclaimers, but get **no accuracy number** here. Each needs
|
||||
the specific real, labelled data listed:
|
||||
|
||||
| Skill | Why not constructible on synthetic | Real data required |
|
||||
|-------|------------------------------------|--------------------|
|
||||
| `med_seizure_detect` | "seizure-like" motion is not a seizure; no ground-truth signature exists synthetically | Clinical EEG-/video-labelled tonic-clonic seizure CSI from instrumented patients |
|
||||
| `med_sleep_apnea` | a planted breathing-pause is not clinical apnea (AHI scoring, hypopnea, desaturation) | Polysomnography-labelled (PSG) overnight CSI with scored apnea/hypopnea events |
|
||||
| `med_cardiac_arrhythmia` | a synthetic HR sequence cannot encode true arrhythmia morphology | ECG-labelled CSI (AFib/PVC/etc.) from clinical monitoring |
|
||||
| `med_respiratory_distress` | distress is a clinical gestalt, not a plantable rate | Clinician-labelled respiratory-distress CSI episodes |
|
||||
| `med_gait_analysis` | clinical gait metrics need a reference motion-capture standard | Mocap-/force-plate-labelled gait CSI |
|
||||
| `sec_weapon_detect` | a high variance ratio is RF reflectivity, **not** weapon discrimination (ADR-160 §A3 already renamed the event to `HIGH_METAL_REFLECTIVITY`) | Labelled metal-object-vs-no-object CSI with controlled object classes |
|
||||
| `exo_emotion_detect` | affect is not recoverable from a planted heuristic; outputs are proxies (ADR-160 §A2) | Validated affect-labelled CSI (self-report / physiological ground truth) |
|
||||
| `exo_happiness_score` | "happiness" is a gait-energy proxy, not a measured affect (ADR-160 §A2) | Validated affect/valence-labelled CSI |
|
||||
| `exo_dream_stage` | sleep staging needs PSG reference (EEG/EOG/EMG) | PSG-staged overnight CSI |
|
||||
| `exo_gesture_language` | coarse gesture clusters ≠ true sign language (ADR-160 §A4) | Labelled ASL letter/word CSI dataset |
|
||||
|
||||
> The above are **not failures** — they are the honest boundary. A smaller set of
|
||||
> genuinely-measured skills plus this explicit gated list is the deliverable, per
|
||||
> the prove-everything directive.
|
||||
|
||||
---
|
||||
|
||||
## Skills not in either list
|
||||
|
||||
The remaining edge skills (smart-building / retail / industrial occupancy-style,
|
||||
the other `sig_*`/`lrn_*`/`spt_*`/`tmp_*`/`qnt_*`/`aut_*`/`ais_*` algorithm-named
|
||||
modules) are **wired and exercised live** in the unified pipeline integration test
|
||||
(`tests/pipeline_all.rs`, all 59 default / 64 medical skills run without panic over
|
||||
300 synthetic frames) but were **not** given an individual planted-ground-truth
|
||||
accuracy number here. They are honest REAL-DSP modules (ADR-160) whose physical
|
||||
observable could be planted with more harness work; that is deferred, not claimed.
|
||||
|
||||
## Test counts (full crate suite)
|
||||
|
||||
```
|
||||
DEFAULT (--features std): 631 passed, 0 failed
|
||||
(lib 504; budget 25; honest_labeling 10; pipeline_all 4; synthetic_validation 12; bench 1; vendor 75)
|
||||
MEDICAL (--features std,medical-experimental): 669 passed, 0 failed
|
||||
(lib 542; +16 same new tests; med_* stay DATA-GATED, not validated)
|
||||
```
|
||||
|
||||
(M6 baseline was 615 / 653; the new pipeline_all (4) + synthetic_validation (12)
|
||||
tests add 16 to each tier.)
|
||||
@@ -1,26 +0,0 @@
|
||||
# Upstream clone (WiFlow-STD, DY2434) -- never commit third-party code/weights
|
||||
upstream/
|
||||
|
||||
# Local python env
|
||||
.venv/
|
||||
|
||||
# Downloaded data / artifacts
|
||||
data/
|
||||
downloads/
|
||||
*.pth
|
||||
*.pt
|
||||
*.npy
|
||||
*.npz
|
||||
*.zip
|
||||
*.mat
|
||||
*.safetensors
|
||||
results/parity_fixture.json
|
||||
__pycache__/
|
||||
*.onnx
|
||||
|
||||
# Committed ground truth: corruption masks for the pristine Kaggle download.
|
||||
# remote/clean_v2.py zeroes the corrupted source windows IN PLACE, so these
|
||||
# masks CANNOT be regenerated from a cleaned copy (generate_corruption_masks.py
|
||||
# documents the criteria and reproduces them only from a fresh download).
|
||||
!results/nan_windows_mask.npy
|
||||
!results/big_windows_mask.npy
|
||||
@@ -1,486 +0,0 @@
|
||||
# WiFlow-STD (DY2434) Benchmark Results — ADR-152 §2.2
|
||||
|
||||
Upstream: <https://github.com/DY2434/WiFlow-WiFi-Pose-Estimation-with-Spatio-Temporal-Decoupling>
|
||||
pinned at `06899d29` (2026-04-05), Apache-2.0. Dataset: Kaggle `kaka2434/wiflow-dataset`
|
||||
(12.8 GB archive → 15.5 GB extracted; 360,000 windows of 540×20 CSI + 15-keypoint 2D labels).
|
||||
|
||||
Published claims (README "Setting 1"): PCK@20 97.25%, PCK@30 98.63%, PCK@40 99.16%,
|
||||
PCK@50 99.48%, MPJPE 0.007 m, 2.23M params, 0.07 GFLOPs.
|
||||
|
||||
## Measurement (a): their model on their data
|
||||
|
||||
### Artifact verification (MEASURED, 2026-06-10, this repo `eval_repro.py`)
|
||||
|
||||
| Check | Result |
|
||||
|---|---|
|
||||
| Parameter count | **2,225,042 (2.23M) — matches claim** |
|
||||
| FLOPs (torch profiler, batch 1) | ~0.055 GFLOPs — consistent with 0.07B claim |
|
||||
| CPU latency (Windows box, torch 2.12 CPU) | 13.2 ms/window @ batch 1 (76/s); 2.48 ms/sample @ batch 64 (403/s) |
|
||||
| Checkpoint load | `weights_only=True` (no pickle code execution) |
|
||||
|
||||
### Released checkpoint does NOT reproduce the claims — REFUTED as shipped
|
||||
|
||||
Running the released `best_pose_model.pth` through the released code on the released
|
||||
dataset with the released split procedure (seed-42 file-level 70/15/15; 54,000 test
|
||||
samples) yields:
|
||||
|
||||
| Metric | Published | Measured (shipped checkpoint) |
|
||||
|---|---|---|
|
||||
| PCK@20 | 97.25% | **0.08%** |
|
||||
| PCK@30 | 98.63% | 0.78% |
|
||||
| PCK@40 | 99.16% | 5.53% |
|
||||
| PCK@50 | 99.48% | 15.42% |
|
||||
| MPJPE | 0.007 | **NaN** (dataset contains NaN CSI windows) |
|
||||
|
||||
Raw output: `results/repro_a.json`.
|
||||
|
||||
Diagnostics (on 2,000 NaN-free windows from the first files of the dataset, i.e.
|
||||
mostly would-be *training* data — so this is not a split mismatch):
|
||||
|
||||
- Predictions correlate with targets (Pearson r ≈ 0.76) — the checkpoint is a trained
|
||||
model, but in a **different keypoint normalization/order** than the released data.
|
||||
- Best-case post-hoc global per-axis affine correction: PCK@20 ≈ 20%.
|
||||
- Best-case per-keypoint affine correction (15×2 fitted transforms — generous
|
||||
cheating): PCK@20 ≈ 72%, still far below 97.25%.
|
||||
- Pred↔target keypoint correspondence matrix is degenerate (multiple predicted
|
||||
keypoints best-match the same target joint) — keypoint convention mismatch.
|
||||
|
||||
### Reproducibility defects in the released artifacts
|
||||
|
||||
1. `models/__init__.py` imports `TemporalConvNet`, which `models/tcn.py` does not
|
||||
define — **the published code does not import/run as-is**.
|
||||
2. The released root checkpoint uses pre-rename module names (`att.*`, `final_conv.*`)
|
||||
vs the published code (`attention.*`, `decoder.*`) — same shapes/param count, but
|
||||
confirms the checkpoint predates the published code.
|
||||
3. The second shipped checkpoint (`cross_dataset_test/WiFlow/best_pose_model.pth`) is
|
||||
a **different architecture** (342-channel input = MM-Fi layout, 3 TCN layers,
|
||||
3-channel/3D decoder) — not usable on their own dataset.
|
||||
4. `run.py` ignores `--data_dir` and hardcodes `../preprocessed_csi_data`.
|
||||
5. The released dataset's final 13 files (indices 487–499; 9,072 windows, 2.52%)
|
||||
are corrupted: NaN values plus garbage amplitudes up to 3.4e38 (float32 max) in
|
||||
data that is otherwise [0,1]-normalized. Upstream code has no NaN/inf handling;
|
||||
training as published on this download diverges — the first corrupted batch
|
||||
overflows fp16 autocast and permanently poisons BatchNorm running statistics
|
||||
(GradScaler step-skipping does not protect BN). The authors' training curves
|
||||
show normal convergence, so their local data evidently differed from the
|
||||
Kaggle upload. Window masks: `results/nan_windows_mask.npy`,
|
||||
`results/big_windows_mask.npy`.
|
||||
|
||||
### Reproducing the corruption masks
|
||||
|
||||
The two mask files (9,070 NaN/Inf windows, 9,072 with |amplitude| > 1.5;
|
||||
union 9,072, all in dataset files 487–499) are **committed ground truth**
|
||||
(gitignore-negated, ~352 KB each). They can only be regenerated from a
|
||||
**pristine** Kaggle download: `remote/clean_v2.py` repairs the dataset by
|
||||
zeroing the corrupted windows in place, after which the corruption evidence
|
||||
is gone and a rescan returns all-False. `generate_corruption_masks.py`
|
||||
re-derives them (chunked scan, criteria: any non-finite value OR
|
||||
max |finite| > 1.5 per 540×20 window) and refuses to write all-False masks,
|
||||
which indicate a cleaned copy. Verified 2026-06-11: a regeneration from the
|
||||
local pristine download is bit-identical to the committed masks.
|
||||
|
||||
### Retraining result (MEASURED, 2026-06-10): claims APPROXIMATELY REPRODUCED
|
||||
|
||||
Since the shipped checkpoint is unusable, measurement (a) fell back to retraining
|
||||
with upstream code + defaults (seed 42, batch 64, early-stopped at epoch 41 of 50,
|
||||
best epoch 36, ~75 s/epoch) on ruvultra (RTX 5080). Deviations, all forced and
|
||||
documented: one-line fix for defect (1); torch 2.x+cu128 instead of pinned 2.3.1
|
||||
(Blackwell sm_120 unsupported); the 9,072 corrupted windows (defect 5) zeroed
|
||||
entirely — without this the published pipeline produces NaN from epoch 1 (observed).
|
||||
Scripts mirrored in `remote/`; raw metrics in `results/eval_retrained.json`.
|
||||
|
||||
| Metric | Published | Retrained (full test, 54,000) | Retrained (corruption-free, 52,560) |
|
||||
|---|---|---|---|
|
||||
| PCK@20 | 97.25% | **96.09%** | **96.61%** |
|
||||
| PCK@30 | 98.63% | 97.89% | 98.23% |
|
||||
| PCK@40 | 99.16% | 98.58% | 98.79% |
|
||||
| PCK@50 | 99.48% | 98.99% | 99.11% |
|
||||
| MPJPE | 0.007 | 0.0098 | 0.0094 |
|
||||
|
||||
Within ~0.6–1.2 PCK points of every published figure (single run, corrupted train
|
||||
windows zeroed, different torch/GPU). **Verdict: the accuracy claims are credible
|
||||
and approximately reproducible — but only after repairing the released dataset and
|
||||
code.** Val best: PCK@20 96.99%, MPJPE 0.0086 (epoch 36).
|
||||
|
||||
One more defect found during the run:
|
||||
|
||||
6. `train.py` calls `plot_training_history`, which is not defined anywhere — the
|
||||
built-in post-training test evaluation is unreachable as published (crashes
|
||||
with NameError after training completes).
|
||||
|
||||
## ADR-152 §2.2 citation rule
|
||||
|
||||
Evidence grade for the WiFlow-STD accuracy claims after measurement (a):
|
||||
**MEASURED-EQUIVALENT (96.1–96.6% PCK@20 reproduced by retraining; shipped
|
||||
checkpoint REFUTED; dataset/code require repairs)**. RuView docs may cite
|
||||
"~96% PCK@20 (our reproduction)" — still **not comparable** to our 17-keypoint
|
||||
ESP32 numbers (different hardware, 5 subjects, in-domain random split,
|
||||
15 keypoints).
|
||||
|
||||
## Edge optimization (measured)
|
||||
|
||||
ADR-152 "optimize beyond SOTA" track, 2026-06-10, this Windows box (Windows 11,
|
||||
16 torch threads, torch 2.12.0+cpu, onnxruntime 1.26.0). Subject: the retrained
|
||||
checkpoint `results/retrained_best_pose_model.pth` (2,225,042 fp32 params).
|
||||
Scripts: `quantize_bench.py`, `onnx_bench.py`, `eval_ort_accuracy.py`.
|
||||
Raw numbers: `results/edge_optimization.json`.
|
||||
|
||||
Accuracy is on a **10,000-window seed-42 random subset** of the corruption-free
|
||||
test split (same seed-42 file-level 70/15/15 split as `eval_repro.py`; 54,000
|
||||
test windows, 1,440 corrupted excluded via `results/nan_windows_mask.npy` |
|
||||
`results/big_windows_mask.npy`, leaving 52,560; subset drawn with
|
||||
`np.random.default_rng(42)`). The fp32 subset PCK@20 (96.68%) matches the full
|
||||
clean-test figure (96.61%), so the subset is representative.
|
||||
|
||||
Latency is CPU ms/window, median of repeated runs, 3 interleaved repetitions
|
||||
per variant (medians below; run-to-run spread on this box is large, roughly
|
||||
±20-40% at batch 1 — reps are in the JSON).
|
||||
|
||||
| Variant | Disk size | Batch 1 (ms/win) | Batch 64 (ms/win) | PCK@20 | PCK@50 | MPJPE |
|
||||
|---|---|---|---|---|---|---|
|
||||
| torch fp32 (baseline) | 9.07 MB | 11.0 | 2.27 | 96.68% | 99.15% | 0.00936 |
|
||||
| torch fp16 (`.half()`) | **4.58 MB** | 24.3 | 2.42 | 96.68% | 99.15% | 0.00946 |
|
||||
| torch int8 dynamic | 9.07 MB (unchanged) | 15.6 | 2.06 | 96.68% (identical) | 99.15% | 0.00936 |
|
||||
| ONNX fp32 (onnxruntime) | 8.97 MB | **3.2** | **2.0** | 96.68% | 99.15% | 0.00936 |
|
||||
| ONNX int8 (ORT dynamic, supplementary) | **2.44 MB** | 6.5 | 5.8 | 96.52% | 99.15% | 0.01108 |
|
||||
|
||||
Findings:
|
||||
|
||||
- **torch dynamic INT8 quantizes nothing on this model.** The architecture has
|
||||
**zero `nn.Linear` layers** — it is entirely Conv1d (21) + Conv2d (22) +
|
||||
BatchNorm. `torch.ao.quantization.quantize_dynamic` (requested over
|
||||
`{Linear, Conv1d, Conv2d}`) converted **0 modules / 0.0% of params**: dynamic
|
||||
quantization only has kernels for Linear/RNN-family modules and silently
|
||||
skips convolutions. The "int8" model is bit-identical to fp32 (same outputs,
|
||||
same 9.07 MB). Conv quantization would require static (PTQ) quantization
|
||||
with calibration — out of scope here; the ORT dynamic path below is the
|
||||
honest int8 datapoint.
|
||||
- **fp16 halves size for free accuracy-wise** (PCK@20 −0.005 pt, MPJPE
|
||||
+0.0001) but is *slower* on CPU at batch 1 (~2.2×) — torch CPU fp16 conv
|
||||
kernels are emulated. fp16 is a storage/transport format here, not a CPU
|
||||
runtime win.
|
||||
- **ONNX Runtime is the real batch-1 latency win: ~3.4× faster than torch**
|
||||
(3.2 vs 11.0 ms/window) at identical accuracy (parity 2.4e-7).
|
||||
|
||||
### Verdict on the paper's "~2.2 MB int8" claim
|
||||
|
||||
**Plausible but not free, and unreachable by the obvious PyTorch route.**
|
||||
2,225,042 params × 1 byte ≈ 2.2 MB assumes *every* parameter quantizes.
|
||||
PyTorch dynamic quantization — the one-liner most readers would reach for —
|
||||
yields **9.07 MB (0% quantized)** because the model has no Linear layers.
|
||||
ONNX Runtime dynamic quantization, which does have int8 conv weight support,
|
||||
gets **2.44 MB** (close to the claim; the overhead is BatchNorm params/buffers
|
||||
and quantization scales kept in fp32) at a measurable accuracy cost:
|
||||
PCK@20 96.68 → 96.52% (−0.16 pt) and MPJPE 0.00936 → 0.01108 (+18%), and
|
||||
~2× slower inference than ONNX fp32 (ConvInteger kernels). The paper does not
|
||||
state a method or an int8 accuracy; treat "2.2 MB" as a weight-arithmetic
|
||||
estimate, achievable in practice only via conv-capable quantization toolchains
|
||||
and with a small accuracy penalty.
|
||||
|
||||
### ONNX export status
|
||||
|
||||
**Works.** Exported via the TorchScript exporter (`dynamo=False`), opset 17,
|
||||
with a dynamic batch axis — `results/retrained_fp32_dynamic.onnx` (8.97 MB),
|
||||
verified to run at batch 1/2/64. The axial attention's
|
||||
`view(N*W, C, H)` reshape traced correctly (sizes recorded as graph ops, not
|
||||
baked constants). The dynamo exporter also captures the graph but crashed on
|
||||
this box writing a ✅ to a cp1252 console (cosmetic Windows encoding issue, not
|
||||
a model blocker). Parity vs torch on the stored fixture
|
||||
(`results/parity_fixture.npz`, batch 2, seed 42): **max abs diff 2.4e-7 —
|
||||
PASS** (< 1e-4). ORT-quantized int8 model: `results/retrained_int8_ort_dynamic.onnx`.
|
||||
|
||||
### Static PTQ (calibrated) — follow-up
|
||||
|
||||
Follow-up to the dynamic-int8 row above (2026-06-10, same box, onnxruntime
|
||||
1.26.0): ONNX Runtime **static** post-training quantization
|
||||
(`quantize_static`, QDQ format, per-channel int8 weights + int8 activations)
|
||||
of the same fp32 export, calibrated on **corruption-free TRAINING-split
|
||||
windows only** (seed-42 file-level split, same masks; 1,000 windows for
|
||||
MinMax, 512 for the histogram calibrators; never test windows). Scopes:
|
||||
"conv-only" (`op_types_to_quantize=["Conv"]` — the attention path exports as
|
||||
Einsum/Softmax, which ORT never quantizes anyway, so "all-ops" additionally
|
||||
quantizes the elementwise Mul/Sigmoid/Add/AveragePool glue). Accuracy on the
|
||||
identical 10k-window seed-42 corruption-free test subset; latency median of
|
||||
3 interleaved reps (fp32/dynamic re-benched in-session as references).
|
||||
Script: `static_ptq_bench.py`; raw: `results/edge_optimization.json`
|
||||
(`onnx_static_ptq`).
|
||||
|
||||
| Variant | Disk size | Batch 1 (ms/win) | Batch 64 (ms/win) | PCK@20 | PCK@50 | MPJPE |
|
||||
|---|---|---|---|---|---|---|
|
||||
| ONNX fp32 (reference) | 8.97 MB | 2.5 | 1.9 | 96.68% | 99.15% | 0.00936 |
|
||||
| ORT dynamic int8 (baseline) | **2.44 MB** | 5.7 | 4.6 | 96.52% | 99.15% | 0.01108 |
|
||||
| static QDQ **Percentile(99.99) conv-only** | 2.53 MB | 5.3 | 4.7 | 96.61% | 99.16% | **0.01031** |
|
||||
| static QDQ MinMax conv-only | 2.53 MB | 5.2 | 3.3 | **96.63%** | 99.19% | 0.01084 |
|
||||
| static QDQ Entropy conv-only | 2.53 MB | 5.2 | 3.1 | 96.60% | 99.19% | 0.01078 |
|
||||
| static QDQ MinMax all-ops | 2.60 MB | 6.5 | 3.9 | 95.45% | 99.14% | 0.01486 |
|
||||
| static QDQ Entropy all-ops | 2.60 MB | 5.7 | 4.1 | 95.30% | 99.13% | 0.01510 |
|
||||
| static QDQ Percentile all-ops | 2.60 MB | 5.3 | 4.3 | 96.39% | 99.17% | 0.01218 |
|
||||
|
||||
**Verdict: static PTQ (conv-only) is the new best int8 point on accuracy —
|
||||
but only modestly, and it does not fix int8's latency penalty.**
|
||||
|
||||
- **Accuracy: beats dynamic.** All three conv-only calibrations land at
|
||||
PCK@20 96.60–96.63% (vs dynamic 96.52%, fp32 96.68% — recovers ~⅔ of the
|
||||
dynamic gap) and MPJPE 0.0103–0.0108 (vs dynamic 0.01108). Best MPJPE:
|
||||
Percentile conv-only, +10% over fp32 instead of dynamic's +18%.
|
||||
- **Size: slightly worse.** 2.53 MB vs 2.44 MB (+3.6%) — QDQ nodes and
|
||||
per-channel scales cost a little; BatchNorm stays fp32 in both (the 12 BNs
|
||||
follow Slice/Einsum/Reshape, never Conv, so they cannot be folded).
|
||||
- **Latency: a wash vs dynamic, still ~2× slower than ONNX fp32 at batch 1.**
|
||||
Batch-1 medians 5.2–5.3 vs dynamic 5.7 ms/win in-session — within this
|
||||
box's ±20–40% noise. Batch 64 leans static (3.1–3.3 for MinMax/Entropy
|
||||
conv-only vs 4.6), same caveat.
|
||||
- **All-ops QDQ is strictly worse**: up to −1.4 pt PCK@20 and +60% MPJPE for
|
||||
zero size/latency benefit — int8 activations through the elementwise glue
|
||||
around the attention blocks is where the damage is. Conv-only is the right
|
||||
scope.
|
||||
- Negative result worth recording: **Entropy calibration is a no-op here** —
|
||||
on an identical calibration set it selects full-range thresholds
|
||||
bit-identical to MinMax (all 247 scales equal; verified on a 64-window
|
||||
smoke set). Also, ORT 1.26's `CalibMaxIntermediateOutputs` raises a
|
||||
spurious "No data is collected" when the batch count divides the chunk
|
||||
size (worked around in the script).
|
||||
|
||||
Deployment guidance: need speed → ONNX fp32 (3.2 ms b1). Need int8 weights
|
||||
for size → static QDQ conv-only (Percentile or MinMax,
|
||||
`results/retrained_int8_static_percentile_conv.onnx`), which strictly
|
||||
dominates dynamic int8 on accuracy at ~equal latency and +0.09 MB.
|
||||
|
||||
## Efficiency sweep (MEASURED, overnight 2026-06-10/11)
|
||||
|
||||
ADR-152 beyond-SOTA track: compact purpose-built variants of the WiFlow-STD
|
||||
architecture, trained from scratch on the same cleaned dataset, identical
|
||||
seed-42 file-level split, loss and protocol as the measurement-(a) reference
|
||||
(fp32, batch 64, ≤50 epochs, patience 5; RTX 5080, ~22–29 min/variant).
|
||||
Variant transforms are pure channel/group/stride scalings of an
|
||||
architecture-exact parameterized model (validated: reproduces 2,225,042 params
|
||||
at the reference config). Scripts: `remote/sweep/`; raw:
|
||||
`results/efficiency_sweep.jsonl`; checkpoints `results/{half,quarter,tiny}_best.pth`
|
||||
(gitignored).
|
||||
|
||||
| Variant | Params | vs 2.23M | Clean-test PCK@20 | PCK@50 | MPJPE | Best epoch |
|
||||
|---|---|---|---|---|---|---|
|
||||
| full (reference, meas. a) | 2,225,042 | 1× | 96.61% | 99.11% | 0.0094 | 36 |
|
||||
| **half** | **843,834** | **0.38×** | **96.62%** | **99.47%** | **0.00898** | 23 |
|
||||
| quarter | 338,600 | 0.15× | 96.05% | 99.43% | 0.00928 | 50 |
|
||||
| tiny | 56,290 | 0.025× | 94.11% | 99.36% | 0.0125 | 47 |
|
||||
|
||||
Findings:
|
||||
|
||||
- **The half model (843k params) strictly dominates the full reference** on
|
||||
this dataset — equal PCK@20, better PCK@50 and MPJPE, converges in fewer
|
||||
epochs. The published 2.23M architecture is over-parameterized for its own
|
||||
benchmark.
|
||||
- **tiny (56k params, 1/39.5) holds 94.11% PCK@20** — a ~220 KB fp32 /
|
||||
~60 KB int8-class model in reach of severely constrained edge targets,
|
||||
at −2.5 pt from the full reference.
|
||||
- Caveats: in-domain (5-subject random-file split) like every number on this
|
||||
dataset; single run per variant; corruption-free test subset (52,560).
|
||||
Cross-domain behavior of compact variants is untested — ADR-150's evidence
|
||||
says capacity *hurts* cross-subject, so the compact end may generalize no
|
||||
worse, but that is a hypothesis, not a measurement.
|
||||
|
||||
### Compact-variant edge artifacts (MEASURED, 2026-06-11)
|
||||
|
||||
Edge pipeline for the **tiny** checkpoint (56,290 params), same machinery and
|
||||
protocol as the full-model edge rows above (this Windows box, torch
|
||||
2.12.0+cpu, onnxruntime 1.26.0; dynamic-batch opset-17 TorchScript export;
|
||||
static QDQ **Percentile(99.99) conv-only** int8 calibrated on **512**
|
||||
corruption-free TRAIN-split windows; accuracy on the identical 10k-window
|
||||
seed-42 clean test subset; latency = median ms/window over 3 interleaved
|
||||
reps, with the full-model fp32/int8 sessions interleaved as same-session
|
||||
references). Script: `tiny_edge_bench.py`; raw:
|
||||
`results/edge_optimization.json` (`tiny_variant`). Torch-vs-ORT parity on the
|
||||
stored fixture input: **max abs diff 1.5e-7 — PASS** (< 1e-4). The tiny fp32
|
||||
subset PCK@20 (94.11%) matches the full clean-test sweep figure (94.11%)
|
||||
exactly, so the subset remains representative.
|
||||
|
||||
Two forced deviations, both recorded in the JSON:
|
||||
|
||||
1. **Adaptive-pool export rewrite.** tiny's derived stride schedule
|
||||
`[2,1,1,1]` leaves feature width 16, and the TorchScript exporter rejects
|
||||
`AdaptiveAvgPool2d((15,1))` when 15 is not a factor of the input height
|
||||
(the full model never hit this — its width was exactly 15). Since the
|
||||
pool over a fixed-size map is a fixed linear operator, the export wrapper
|
||||
replaces it with `mean(-1)` (W axis, a factor) + a constant averaging
|
||||
matmul using PyTorch's exact bin rule; the parity check (vs the original
|
||||
torch model with the real pool) proves exactness.
|
||||
2. **Calibration count 512, not "~500"**: ORT 1.26's histogram collector
|
||||
`np.asarray()`'s the per-batch maxima, so the calibration count must be a
|
||||
multiple of the 64-window calibration batch or the ragged last batch
|
||||
crashes it (the earlier static-PTQ run dodged this by using exactly 512).
|
||||
|
||||
| Variant | Disk size | Batch 1 (ms/win) | Batch 64 (ms/win) | PCK@20 | PCK@50 | MPJPE |
|
||||
|---|---|---|---|---|---|---|
|
||||
| full ONNX fp32 (same-session ref) | 8.97 MB | 2.27 | 1.42 | 96.68% | 99.15% | 0.00936 |
|
||||
| full static QDQ Percentile conv-only (same-session ref) | 2.53 MB | 5.53 | 3.82 | 96.61% | 99.16% | 0.01031 |
|
||||
| **tiny ONNX fp32** | **0.295 MB** | **0.66** | **0.24** | **94.11%** | 99.37% | 0.01253 |
|
||||
| tiny static QDQ Percentile conv-only | 0.248 MB | 0.85 | 1.03 | 92.68% | 99.33% | 0.01491 |
|
||||
|
||||
(tiny torch `.pth` checkpoint for reference: 0.34 MB on disk; 56,290 fp32
|
||||
params ≈ 225 KB of weights.)
|
||||
|
||||
Findings:
|
||||
|
||||
- **The smallest deployable WiFlow-class model is the tiny ONNX fp32
|
||||
artifact: ~295 KB on disk, 0.66 ms/window batch-1 CPU (~1,500 windows/s),
|
||||
94.1% PCK@20** — 30× smaller and ~3.4× faster (in-session) than the full
|
||||
ONNX fp32 model for −2.6 pt PCK@20.
|
||||
- **int8 is a bad trade at this scale.** Static QDQ conv-only — the recipe
|
||||
that cost the full model only 0.07 pt — costs tiny **−1.43 pt** PCK@20
|
||||
(94.11 → 92.68%) and +19% MPJPE, saves only 47 KB (−16%; QDQ scales and
|
||||
the fp32 BN/attention glue are proportionally larger in a small graph),
|
||||
and is *slower* than tiny fp32 (0.85 vs 0.66 ms b1; 1.03 vs 0.24 ms b64 —
|
||||
QDQ kernel overhead dominates when the convs are this small). A 56k-param
|
||||
model has little redundancy left to absorb weight+activation rounding.
|
||||
- Deployment guidance, compact edition: ship tiny as **ONNX fp32** — at
|
||||
295 KB the int8 size saving solves no real constraint and costs accuracy
|
||||
and speed. If ~250 KB vs ~295 KB ever matters, weight-only quantization
|
||||
would be the thing to try next, not QDQ.
|
||||
|
||||
## Measurement (b): BLOCKED-ON-DATA (attempted 2026-06-10)
|
||||
|
||||
The fine-tune-on-ESP32 measurement stopped at dataset characterization, per the
|
||||
pre-registered stop rule (<2,000 paired windows). Findings (MEASURED):
|
||||
|
||||
- **Only one trainable paired dataset exists**: `ruvultra:~/work/cog-pose-train/paired.jsonl`
|
||||
— 1,077 windows (one subject, one room, one 29.9-min session, single node;
|
||||
CSI [56, 20]; 17 COCO keypoints, MediaPipe confidence mean 0.44 — only 264
|
||||
windows pass ADR-079's own conf>0.5 training filter). Prior measured attempts
|
||||
on this exact set: 0–3% torso-PCK@20 (temporal splits, three independent
|
||||
pipelines). Fine-tuning a 2.23M-param model on ~860 train windows would
|
||||
measure memorization, not transfer.
|
||||
- **The April session behind the old "92.9% PCK@20" claim is lost** (345
|
||||
samples, 35 subcarriers; raw CSI gone from ruvzen/ruvultra/cognitum-v0; only
|
||||
a 69-sample predictions+GT holdout survives at `models/wiflow-real/eval-holdout.jsonl`).
|
||||
- **Forensic recheck of that holdout RETRACTS the 92.9% figure**: the trainer's
|
||||
`pck()` used an absolute 0.2 image-unit threshold (not torso-normalized) and
|
||||
the model output a **constant pose** (pred std 0.0000 across 69 near-static
|
||||
frames; a mean predictor scores 100% under the same protocol). The
|
||||
torso-normalized PCK@20 on the same holdout is 19.1%. This corroborates the
|
||||
2026-05-11 audit retraction (CHANGELOG, PR #535); stale doc citations were
|
||||
removed 2026-06-10 (user-guide, readme-details, ADR-152 §2.1.3). The §2.2
|
||||
no-citation rule now applies to ADR-079 accuracy claims.
|
||||
|
||||
Unblock criteria: a paired collection session of ≥2k windows (≈35+ min at the
|
||||
observed stride; multi-pose, conf>0.5, ideally with the §2.1.3 two-checkerboard
|
||||
calibration), plus a re-baselined our-pipeline number under torso-PCK@20 on the
|
||||
same split. WiFlow-STD assets stand ready on ruvultra (`~/wiflow-std-bench/`).
|
||||
Also worth investigating: ADR-079's protocol predicts ~9k windows per 30 min;
|
||||
the May session under-delivered ~8× (aligner drop rate?).
|
||||
|
||||
## Measurement (b) (MEASURED 2026-06-10/11)
|
||||
|
||||
The data baseline unblocked: the 2026-06-10 22:10–22:40 collection session produced
|
||||
**2,046 paired windows** (`ruvultra:~/wiflow-std-bench/paired-20260610.jsonl`; ONE
|
||||
subject, ONE room, ONE ESP32 node, varied poses: walk/raise/squat/kick/wave/turn/
|
||||
jump/sit; aligner `scripts/align-ground-truth.js`, non-overlapping 20-frame windows
|
||||
~0.42 s; 17 COCO keypoints in normalized [0,1] camera coords; MediaPipe confidence
|
||||
mean 0.802, min 0.692 — all windows pass the conf>0.5 filter). The −4 h timestamp
|
||||
bug and the empty-frame confidence-dilution aligner findings are recorded
|
||||
separately; results only here. Trained on ruvultra (RTX 5080, torch 2.11+cu128,
|
||||
fp32, batch 32, GPU shared with the efficiency sweep). Scripts mirrored in
|
||||
`remote/measb/`; raw metrics + full training curves in `results/measurement_b.json`.
|
||||
|
||||
### Two new aligner/dataset findings (forced deviations, MEASURED)
|
||||
|
||||
1. **`csi_shape` is heterogeneous, not [70, 20]**: 1,347× [70,20], 284× [134,20],
|
||||
243× [26,20], 130× [12,20], 42× [20,20]. The ESP32 stream emits mixed frame
|
||||
types and `extractCsiMatrix` stamps each window's subcarrier count from
|
||||
`window[0].subcarriers`, zero-padding/truncating the other frames — even
|
||||
native-70 windows contain ~20.4% internally zero-padded short frames
|
||||
(subcarriers 40–69 all-zero). Handling: the primary suite ("all 2,046")
|
||||
linearly resamples every frame's subcarrier axis to 70 bins (identity for
|
||||
native-70 frames) so the pre-registered n and split sizes hold; a secondary
|
||||
suite restricts to the 1,347 native [70,20] windows as a homogeneity check.
|
||||
2. **Aligner layout bug**: `extractCsiMatrix` fills `matrix[f * nSc + s]`
|
||||
(frame-major) but declares `shape: [nSc, nFrames]` — the stored shape label is
|
||||
transposed relative to the data. Confirmed by coherent per-frame zero-tails;
|
||||
corrected on load (`reshape(nFrames, nSc).T`).
|
||||
|
||||
### Protocol (pre-registered, followed)
|
||||
|
||||
Temporal split, no shuffling across time: first 70% train (1,432), next 15% val
|
||||
(307), last 15% test (307); seed 42 elsewhere. Model: learned 1×1 Conv1d 70→540
|
||||
adapter prepended to the upstream WiFlow-STD trunk; K=17 via the parameter-free
|
||||
adaptive pool (`AdaptiveAvgPool2d((17,1))` — pretrained weights load strict for
|
||||
any K). CSI normalized by the TRAIN-split p99 amplitude (129.7 all / 130.9
|
||||
native-70), clipped to [0,1]. Three runs, ≤60 epochs, early-stop patience 8 on
|
||||
val MPJPE, AdamW (adapter lr 1e-4; pretrained trunk lr 1e-5, 10× lower; scratch
|
||||
all 1e-4), fp32. Pretrained init = the measurement-(a) **retrained** checkpoint
|
||||
(`upstream/test/best_pose_model.pth`, ~96% PCK@20 on WiFlow data; the
|
||||
`att.`/`final_conv.` key remap from `eval_repro.py` applied defensively — a no-op,
|
||||
that checkpoint already uses post-rename keys). Frozen-trunk run: trunk
|
||||
`requires_grad=False` **and** held in `.eval()` so BatchNorm running stats cannot
|
||||
drift — a pure transfer probe; only the 70→540 adapter (38,340 params) trains.
|
||||
|
||||
PCK is torso-normalized with **torso = ‖l_shoulder(5) − l_hip(11)‖** (upstream
|
||||
`calculate_pck` math — per-frame norm clamped at 0.01, mean over keypoints ×
|
||||
frames — but upstream's `NECK_IDX/PELVIS_IDX = 2, 12` is a 15-keypoint
|
||||
convention; on 17-kp COCO those indices are right_eye/right_hip, so the indices
|
||||
were replaced, not the math). MPJPE is in normalized image units (not meters).
|
||||
|
||||
### Results — primary suite, all 2,046 windows (test = last 307)
|
||||
|
||||
| Run | PCK@10 | PCK@20 | PCK@30 | PCK@40 | PCK@50 | MPJPE | pred std | best ep |
|
||||
|---|---|---|---|---|---|---|---|---|
|
||||
| **mean-pose baseline** (honesty bar) | **73.1%** | **95.9%** | **98.7%** | 99.3% | 99.3% | **0.0148** | 0 (by constr.) | — |
|
||||
| (i) pretrained-init, full fine-tune | 26.0% | 65.0% | 88.0% | 96.4% | 98.9% | 0.0313 | 0.0113 | 58/60 |
|
||||
| (ii) scratch | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.2554 | 0.0002 | 4 (stop @13) |
|
||||
| (iii) frozen-trunk (adapter only) | 0.0% | 0.0% | 0.2% | 3.2% | 14.4% | 0.1260 | 0.0073 | 59/60 |
|
||||
|
||||
Secondary suite (native [70,20] windows only, n=1,347, test=202) reproduces the
|
||||
same ordering: mean-baseline 96.0% / pretrained 67.1% / scratch 0.0% /
|
||||
frozen-trunk 0.0% PCK@20 (MPJPE 0.0153 / 0.0318 / 0.2236 / 0.1343) — the
|
||||
subcarrier-resampling choice does not change any conclusion.
|
||||
|
||||
### Interpretation
|
||||
|
||||
- **Did pretraining-transfer happen? Partially — as optimization transfer, not
|
||||
feature transfer, and not past the honesty bar.**
|
||||
- *Pretrained vs scratch*: dramatic (65.0% vs 0.0% PCK@20). The pretrained init
|
||||
is the only configuration that trains at all under the pre-registered budget.
|
||||
- *Frozen-trunk*: near-zero (0.0% PCK@20, 14.4% @50). WiFlow-STD's frozen
|
||||
features do **not** transfer to our ESP32 domain through a linear subcarrier
|
||||
adapter — the pretrained benefit is a well-conditioned initialization (incl.
|
||||
calibrated BN/output scales), not reusable CSI→pose features.
|
||||
- *Everything vs mean-pose baseline*: **no run beats it.** A constant
|
||||
train-mean pose scores 95.9% torso-PCK@20 / 0.0148 MPJPE on this test split,
|
||||
because a single subject in one camera frame barely moves in normalized
|
||||
coordinates. The fine-tuned model is a real, non-constant model
|
||||
(pred std 0.0113 > 0 — passes the constant-pose detector that retracted the
|
||||
old 92.9% figure) but its deviations from the mean hurt: it fits train-period
|
||||
temporal dynamics that do not generalize across the temporal split.
|
||||
- **Verdict for ADR-152 §2.2(b): fine-tuning WiFlow-STD on this dataset does not
|
||||
demonstrate CSI→pose signal beyond the mean pose.** Until a model beats the
|
||||
mean-pose baseline on a temporal split, no PCK number from this line may be
|
||||
cited as pose-estimation capability.
|
||||
|
||||
### Caveats (honest, pre-registered)
|
||||
|
||||
- Single subject, single room, single session (30 min), single ESP32 node —
|
||||
in-domain temporal split only; nothing here speaks to cross-room or
|
||||
cross-subject generalization.
|
||||
- 2k windows vs the 360k-window WiFlow-STD corpus — **NOT comparable** to the
|
||||
~96% in-domain measurement-(a) number, and the published 97.25% even less so.
|
||||
- The scratch run's total collapse (it cannot even reach the mean pose; its
|
||||
output BatchNorm/SiLU head must learn output scale from random init at lr 1e-4)
|
||||
is an optimization outcome under the fixed budget, not proof the architecture
|
||||
cannot learn from scratch — the pretrained-vs-scratch gap partially reflects
|
||||
this conditioning advantage.
|
||||
- Mixed-subcarrier frames (finding 1) mean even the "clean" windows carry ~20%
|
||||
zero-padded frames; collection-side frame-type filtering should precede the
|
||||
next session.
|
||||
- Mean-baseline PCK is inflated by low pose variance relative to torso size
|
||||
(~0.2–0.3 image units); PCK@10 (73.1%) shows the same ceiling effect at a
|
||||
stricter threshold — the bar is the bar, but a livelier dataset would lower it.
|
||||
|
||||
## Pending
|
||||
|
||||
- (b) fine-tune on our ESP32 17-keypoint eval set — **MEASURED 2026-06-10/11**,
|
||||
see above: no run beats the mean-pose baseline; pretraining transfers as
|
||||
optimization aid only.
|
||||
- (c) our internal WiFlow on their dataset (15-keypoint subset mapping) — also
|
||||
affected: there is currently no validated internal pose model to compare
|
||||
(the 92.9% artifact is retracted; the MM-Fi SOTA models in ADR-150 §3 are a
|
||||
different input domain).
|
||||
@@ -1,200 +0,0 @@
|
||||
"""Shared infrastructure for the LOCAL wiflow-std benchmark scripts (ADR-152).
|
||||
|
||||
This module is the single canonical implementation of the helpers that were
|
||||
previously copy-pasted across eval_repro.py / quantize_bench.py /
|
||||
onnx_bench.py / eval_ort_accuracy.py / export_to_safetensors.py:
|
||||
|
||||
- ``import_upstream()`` -- sys.path setup + the models-package stub that
|
||||
works around the upstream import bug, plus the >1GB np.load mmap patch
|
||||
- ``install_np_load_mmap_patch()`` -- the mmap patch on its own
|
||||
- ``remap_legacy_keys()`` / ``load_remapped_state()`` -- checkpoint
|
||||
key remap for the pre-rename released checkpoint
|
||||
- ``load_wiflow_model()`` -- WiFlowPoseModel from a checkpoint, eval mode
|
||||
- ``set_seed()`` -- mirrors upstream run.py seeding exactly
|
||||
- ``evaluate()`` -- THE canonical batch-weighted PCK/MPJPE evaluation loop
|
||||
(thresholds 0.1-0.5, upstream utils/metrics.py math); accepts either a
|
||||
torch nn.Module or an onnxruntime InferenceSession
|
||||
|
||||
The scripts under remote/ deploy to ruvultra as standalone single files and
|
||||
therefore intentionally inline private copies of these helpers; when editing
|
||||
them, treat this module as the reference implementation and keep the copies
|
||||
in sync.
|
||||
"""
|
||||
|
||||
import os
|
||||
import random
|
||||
import sys
|
||||
import time
|
||||
import types
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
UPSTREAM = os.path.join(HERE, "upstream")
|
||||
RESULTS = os.path.join(HERE, "results")
|
||||
|
||||
DEFAULT_THRESHOLDS = (0.1, 0.2, 0.3, 0.4, 0.5)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# >1GB np.load mmap patch
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
# csi_windows.npy is ~13 GB; mmap large arrays instead of loading into RAM
|
||||
# (loading it eagerly needs ~15 GB).
|
||||
_np_load = np.load
|
||||
|
||||
|
||||
def _np_load_mmap(path, *a, **kw):
|
||||
if (isinstance(path, str) and path.endswith(".npy")
|
||||
and os.path.getsize(path) > 1 << 30 and "mmap_mode" not in kw):
|
||||
kw["mmap_mode"] = "r"
|
||||
return _np_load(path, *a, **kw)
|
||||
|
||||
|
||||
def install_np_load_mmap_patch():
|
||||
"""Globally patch np.load so .npy files >1GB are mmap'd read-only.
|
||||
|
||||
Idempotent. Patching the numpy module attribute is equivalent to the
|
||||
historical ``upstream_dataset.np.load = _np_load_mmap`` (dataset.np IS
|
||||
the numpy module), but works regardless of import order.
|
||||
"""
|
||||
np.load = _np_load_mmap
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# upstream import shim
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def import_upstream(mmap_patch=True):
|
||||
"""Make the upstream WiFlow-STD clone importable; returns its path.
|
||||
|
||||
Upstream bug: models/__init__.py imports TemporalConvNet, which
|
||||
models/tcn.py does not define -- the package fails to import as
|
||||
published. Register a stub package so the broken __init__ never
|
||||
executes; submodules (models.pose_model etc.) still resolve via
|
||||
__path__. Idempotent.
|
||||
"""
|
||||
if UPSTREAM not in sys.path:
|
||||
sys.path.insert(0, UPSTREAM)
|
||||
if "models" not in sys.modules:
|
||||
_models_pkg = types.ModuleType("models")
|
||||
_models_pkg.__path__ = [os.path.join(UPSTREAM, "models")]
|
||||
sys.modules["models"] = _models_pkg
|
||||
if mmap_patch:
|
||||
install_np_load_mmap_patch()
|
||||
return UPSTREAM
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# checkpoint loading
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
# The released checkpoint predates the published code: modules were renamed
|
||||
# att -> attention, final_conv -> decoder (param count identical, 2.23M).
|
||||
LEGACY_RENAMES = {"att.": "attention.", "final_conv.": "decoder."}
|
||||
|
||||
|
||||
def remap_legacy_keys(state):
|
||||
"""Remap pre-rename state_dict keys; no-op for already-new-style keys."""
|
||||
return {next((new + k[len(old):] for old, new in LEGACY_RENAMES.items()
|
||||
if k.startswith(old)), k): v
|
||||
for k, v in state.items()}
|
||||
|
||||
|
||||
def load_remapped_state(path, map_location="cpu"):
|
||||
"""torch.load (weights_only) + legacy key remap."""
|
||||
state = torch.load(path, map_location=map_location, weights_only=True)
|
||||
return remap_legacy_keys(state)
|
||||
|
||||
|
||||
def load_wiflow_model(checkpoint, map_location="cpu", dropout=0.5):
|
||||
"""Full-size WiFlowPoseModel from a checkpoint, strict load, eval mode."""
|
||||
import_upstream()
|
||||
from models.pose_model import WiFlowPoseModel
|
||||
model = WiFlowPoseModel(dropout=dropout)
|
||||
model.load_state_dict(load_remapped_state(checkpoint, map_location),
|
||||
strict=True)
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# seeding
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def set_seed(seed=42):
|
||||
# mirror upstream run.py exactly
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# THE canonical evaluation loop
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def evaluate(model, loader, device=None, dtype=None, label="",
|
||||
thresholds=DEFAULT_THRESHOLDS, progress_every=50):
|
||||
"""Batch-weighted PCK/MPJPE over a DataLoader (upstream metrics math).
|
||||
|
||||
``model`` may be a torch nn.Module (optionally evaluated on ``device``
|
||||
with inputs cast to ``dtype``) or an onnxruntime InferenceSession.
|
||||
Per-threshold PCK values are independent in upstream calculate_pck, so
|
||||
evaluating a superset of thresholds never changes any individual value.
|
||||
|
||||
Returns {"samples", "mpjpe", "pck@10".."pck@50", "wall_seconds"}.
|
||||
"""
|
||||
import_upstream()
|
||||
from utils.metrics import calculate_mpjpe, calculate_pck
|
||||
|
||||
is_ort = hasattr(model, "get_inputs") # onnxruntime InferenceSession
|
||||
if is_ort:
|
||||
inp = model.get_inputs()[0].name
|
||||
|
||||
def forward(bx):
|
||||
return torch.from_numpy(model.run(None, {inp: bx.numpy()})[0])
|
||||
else:
|
||||
model.eval()
|
||||
|
||||
def forward(bx):
|
||||
if device is not None:
|
||||
bx = bx.to(device)
|
||||
if dtype is not None:
|
||||
bx = bx.to(dtype)
|
||||
return model(bx).float()
|
||||
|
||||
thresholds = list(thresholds)
|
||||
totals = {t: 0.0 for t in thresholds}
|
||||
total_mpe, n = 0.0, 0
|
||||
t0 = time.time()
|
||||
with torch.no_grad():
|
||||
for batch_idx, (bx, by) in enumerate(loader):
|
||||
out = forward(bx)
|
||||
if device is not None and not is_ort:
|
||||
by = by.to(device)
|
||||
mpe = calculate_mpjpe(out, by)
|
||||
pck = calculate_pck(out, by, thresholds=thresholds)
|
||||
bs = by.size(0)
|
||||
total_mpe += mpe * bs
|
||||
for t in totals:
|
||||
totals[t] += pck[t] * bs
|
||||
n += bs
|
||||
if batch_idx % progress_every == 0:
|
||||
tag = f"[{label}] " if label else ""
|
||||
pck20 = totals.get(0.2)
|
||||
pck20_str = f"pck20={pck20 / n:.4f} " if pck20 is not None else ""
|
||||
print(f" {tag}batch {batch_idx}: n={n} {pck20_str}"
|
||||
f"mpjpe={total_mpe / n:.4f} ({time.time() - t0:.0f}s)",
|
||||
flush=True)
|
||||
return {
|
||||
"samples": n,
|
||||
"mpjpe": total_mpe / n,
|
||||
**{f"pck@{int(t * 100)}": totals[t] / n for t in thresholds},
|
||||
"wall_seconds": time.time() - t0,
|
||||
}
|
||||
@@ -1,67 +0,0 @@
|
||||
"""ADR-152 edge optimization: accuracy of the ONNX fp32 and ORT-dynamic-int8
|
||||
models on the same corruption-free 10k test subset used by quantize_bench.py.
|
||||
|
||||
The torch dynamic-int8 path quantizes nothing (no nn.Linear in the model), so
|
||||
the only real int8 datapoint for the paper's "~2.2 MB int8" claim is the
|
||||
onnxruntime dynamically quantized model -- this script measures what that
|
||||
quantization costs in PCK/MPJPE.
|
||||
|
||||
Usage:
|
||||
.venv/Scripts/python.exe eval_ort_accuracy.py \
|
||||
--data-dir <preprocessed_csi_data> [--subset 10000]
|
||||
|
||||
Writes/merges into results/edge_optimization.json under key "onnx_accuracy".
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.insert(0, HERE)
|
||||
|
||||
from _bench_common import RESULTS, evaluate # noqa: E402
|
||||
from quantize_bench import build_test_subset # noqa: E402 (sets up upstream imports)
|
||||
|
||||
|
||||
def evaluate_ort(sess, loader, label):
|
||||
"""ORT-session evaluation via the canonical _bench_common.evaluate loop."""
|
||||
return evaluate(sess, loader, label=label)
|
||||
|
||||
|
||||
def main():
|
||||
import onnxruntime as ort
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data-dir", default=os.path.join(
|
||||
os.path.expanduser("~"), ".cache", "kagglehub", "datasets", "kaka2434",
|
||||
"wiflow-dataset", "versions", "1", "preprocessed_csi_data"))
|
||||
parser.add_argument("--subset", type=int, default=10000)
|
||||
parser.add_argument("--out", default=os.path.join(RESULTS, "edge_optimization.json"))
|
||||
args = parser.parse_args()
|
||||
|
||||
loader, _n_clean = build_test_subset(args.data_dir, args.subset)
|
||||
results = {}
|
||||
for label, fname in (("onnx_fp32", "retrained_fp32_dynamic.onnx"),
|
||||
("onnx_int8_ort_dynamic", "retrained_int8_ort_dynamic.onnx")):
|
||||
path = os.path.join(RESULTS, fname)
|
||||
if not os.path.exists(path):
|
||||
results[label] = {"error": f"{fname} not found; run onnx_bench.py first"}
|
||||
continue
|
||||
sess = ort.InferenceSession(path, providers=["CPUExecutionProvider"])
|
||||
print(f"=== accuracy: {label} ({fname}) ===")
|
||||
results[label] = evaluate_ort(sess, loader, label)
|
||||
print(json.dumps(results[label], indent=2))
|
||||
|
||||
merged = {}
|
||||
if os.path.exists(args.out):
|
||||
with open(args.out) as f:
|
||||
merged = json.load(f)
|
||||
merged["onnx_accuracy"] = results
|
||||
with open(args.out, "w") as f:
|
||||
json.dump(merged, f, indent=2)
|
||||
print(f"wrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,102 +0,0 @@
|
||||
"""ADR-152 §2.2 measurement (a): reproduce WiFlow-STD (DY2434) published test metrics.
|
||||
|
||||
Runs the released pretrained checkpoint (upstream/best_pose_model.pth) against the
|
||||
released Kaggle dataset (kaka2434/wiflow-dataset) using the upstream code path:
|
||||
identical dataset class, identical file-level 70/15/15 split at seed 42, identical
|
||||
PCK/MPJPE implementations (utils/metrics.py).
|
||||
|
||||
Published claims (README, "Setting 1 random split"):
|
||||
PCK@20 97.25% | PCK@30 98.63% | PCK@40 99.16% | PCK@50 99.48% | MPJPE 0.007 m
|
||||
|
||||
Usage:
|
||||
.venv/Scripts/python.exe eval_repro.py --data-dir <dir containing csi_windows.npy>
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
|
||||
import torch
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from _bench_common import (UPSTREAM, evaluate, import_upstream,
|
||||
load_remapped_state, set_seed)
|
||||
|
||||
import_upstream() # sys.path + models stub + >1GB np.load mmap patch
|
||||
|
||||
from dataset import PreprocessedCSIKeypointsDataset, create_preprocessed_train_val_test_loaders # noqa: E402
|
||||
from models.pose_model import WiFlowPoseModel # noqa: E402
|
||||
|
||||
|
||||
def find_data_dir(root):
|
||||
for dirpath, _dirnames, filenames in os.walk(root):
|
||||
if "csi_windows.npy" in filenames:
|
||||
return dirpath
|
||||
return None
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data-dir", required=True,
|
||||
help="Directory containing csi_windows.npy (searched recursively)")
|
||||
parser.add_argument("--checkpoint", default=os.path.join(UPSTREAM, "best_pose_model.pth"))
|
||||
parser.add_argument("--batch-size", type=int, default=64)
|
||||
parser.add_argument("--out", default=os.path.join(os.path.dirname(os.path.abspath(__file__)),
|
||||
"results", "repro_a.json"))
|
||||
args = parser.parse_args()
|
||||
|
||||
data_dir = args.data_dir
|
||||
if not os.path.exists(os.path.join(data_dir, "csi_windows.npy")):
|
||||
located = find_data_dir(data_dir)
|
||||
if located is None:
|
||||
sys.exit(f"csi_windows.npy not found under {data_dir}")
|
||||
data_dir = located
|
||||
print(f"data dir: {data_dir}")
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
print(f"device: {device}, torch {torch.__version__}")
|
||||
|
||||
set_seed(42)
|
||||
|
||||
dataset = PreprocessedCSIKeypointsDataset(
|
||||
data_dir=data_dir, keypoint_scale=1000.0, enable_temporal_clean=True)
|
||||
|
||||
# split must match upstream: file-level shuffle at random_seed=42, 70/15/15
|
||||
_train_loader, _val_loader, test_loader = create_preprocessed_train_val_test_loaders(
|
||||
dataset=dataset, batch_size=args.batch_size, num_workers=0, random_seed=42)
|
||||
|
||||
model = WiFlowPoseModel(dropout=0.5).to(device)
|
||||
# released checkpoint predates the published code: modules were renamed
|
||||
# att -> attention, final_conv -> decoder (param count identical, 2.23M)
|
||||
state = load_remapped_state(args.checkpoint, map_location=device)
|
||||
model.load_state_dict(state, strict=True)
|
||||
n_params = sum(p.numel() for p in model.parameters())
|
||||
print(f"checkpoint: {args.checkpoint} ({n_params/1e6:.2f}M params)")
|
||||
|
||||
# upstream also evaluates with drop_last=True; we report the full test set
|
||||
# (drop_last=False) and the drop_last variant for exact comparability
|
||||
results = {"published": {"pck@20": 0.9725, "pck@30": 0.9863, "pck@40": 0.9916,
|
||||
"pck@50": 0.9948, "mpjpe": 0.007},
|
||||
"params_millions": n_params / 1e6,
|
||||
"data_dir": data_dir,
|
||||
"device": str(device)}
|
||||
|
||||
print("=== test set (full, drop_last=False) ===")
|
||||
results["test_full"] = evaluate(model, test_loader, device=device)
|
||||
print(json.dumps(results["test_full"], indent=2))
|
||||
|
||||
test_loader_dl = DataLoader(test_loader.dataset, batch_size=args.batch_size,
|
||||
shuffle=False, drop_last=True)
|
||||
print("=== test set (drop_last=True, as upstream train.py) ===")
|
||||
results["test_drop_last"] = evaluate(model, test_loader_dl, device=device)
|
||||
print(json.dumps(results["test_drop_last"], indent=2))
|
||||
|
||||
os.makedirs(os.path.dirname(args.out), exist_ok=True)
|
||||
with open(args.out, "w") as f:
|
||||
json.dump(results, f, indent=2)
|
||||
print(f"wrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,174 +0,0 @@
|
||||
"""ADR-152 §2.2: export the retrained WiFlow-STD PyTorch checkpoint to
|
||||
safetensors with tch-rs (VarStore) variable names, plus a numerical-parity
|
||||
fixture for the Rust port.
|
||||
|
||||
Outputs (all under results/, gitignored):
|
||||
retrained_wiflow_std.safetensors -- 248 f32 tensors named exactly as the
|
||||
Rust WiFlowStdModel VarStore expects
|
||||
(see wiflow_std/model.rs
|
||||
`dump_variable_names` for the
|
||||
authoritative name dump)
|
||||
parity_fixture.npz -- deterministic input (seed 42,
|
||||
shape (2, 540, 20), uniform [0,1]) and
|
||||
the Python model's eval-mode output
|
||||
parity_fixture.json -- same data as flattened f32 lists, for
|
||||
the dependency-free Rust test
|
||||
(tests/test_wiflow_std_parity.rs)
|
||||
|
||||
PyTorch -> tch key mapping (derived from the VarStore dump, not guessed):
|
||||
|
||||
tcn.network.{i}.conv1_group.weight -> tcn{i}.conv1_group.weight
|
||||
tcn.network.{i}.bn*_{group,pw}.<leaf> -> tcn{i}.bn*_{group,pw}.<leaf>
|
||||
tcn.network.{i}.downsample.0.weight -> tcn{i}.ds_conv.weight
|
||||
tcn.network.{i}.downsample.1.<leaf> -> tcn{i}.ds_bn.<leaf>
|
||||
up.block.{0,1,4,5,8,9}.<leaf> -> conv_in.{conv1,bn1,conv2,bn2,conv3,bn3}.<leaf>
|
||||
up.downsample.{0,1}.<leaf> -> conv_in.{ds_conv,ds_bn}.<leaf>
|
||||
residual_blocks.{i}.block.{...}.<leaf> -> conv{i}.{conv1..bn3}.<leaf>
|
||||
residual_blocks.{i}.downsample.{0,1} -> conv{i}.{ds_conv,ds_bn}
|
||||
attention.{width,height}_axis.qkv_transform.weight
|
||||
-> attention.{width,height}.qkv.weight
|
||||
attention.{width,height}_axis.bn_* -> attention.{width,height}.bn_*
|
||||
decoder.{0,1,3,4}.<leaf> -> {dec_conv1,dec_bn1,dec_conv2,dec_bn2}.<leaf>
|
||||
*.num_batches_tracked -> dropped (tch BatchNorm has no such buffer)
|
||||
|
||||
Legacy upstream names (att. -> attention., final_conv. -> decoder.) are
|
||||
remapped first, exactly as eval_repro.py does for the released checkpoint.
|
||||
|
||||
Usage:
|
||||
.venv/Scripts/python.exe export_to_safetensors.py
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from safetensors.torch import save_file
|
||||
|
||||
from _bench_common import RESULTS, import_upstream, remap_legacy_keys
|
||||
|
||||
import_upstream() # sys.path + models stub
|
||||
|
||||
from models.pose_model import WiFlowPoseModel # noqa: E402
|
||||
|
||||
CHECKPOINT = os.path.join(RESULTS, "retrained_best_pose_model.pth")
|
||||
|
||||
# Sequential index -> tch sub-name inside one ConvBlock1/AsymmetricConvBlock:
|
||||
# [Conv2d(0), BN(1), SiLU(2), Dropout2d(3), Conv2d(4), BN(5), SiLU(6),
|
||||
# Dropout2d(7), Conv2d(8), BN(9)]
|
||||
_BLOCK_IDX = {"0": "conv1", "1": "bn1", "4": "conv2", "5": "bn2",
|
||||
"8": "conv3", "9": "bn3"}
|
||||
_DS_IDX = {"0": "ds_conv", "1": "ds_bn"}
|
||||
_DECODER_IDX = {"0": "dec_conv1", "1": "dec_bn1", "3": "dec_conv2",
|
||||
"4": "dec_bn2"}
|
||||
|
||||
|
||||
def _conv_block(new_prefix: str, rest: str) -> str:
|
||||
m = re.fullmatch(r"block\.(\d+)\.(.+)", rest)
|
||||
if m:
|
||||
return f"{new_prefix}.{_BLOCK_IDX[m.group(1)]}.{m.group(2)}"
|
||||
m = re.fullmatch(r"downsample\.(\d+)\.(.+)", rest)
|
||||
if m:
|
||||
return f"{new_prefix}.{_DS_IDX[m.group(1)]}.{m.group(2)}"
|
||||
raise KeyError(f"unmapped conv-block key: {new_prefix} / {rest}")
|
||||
|
||||
|
||||
def map_key(key: str) -> str:
|
||||
"""Map one PyTorch state_dict key to the tch VarStore name."""
|
||||
m = re.fullmatch(r"tcn\.network\.(\d+)\.(.+)", key)
|
||||
if m:
|
||||
i, rest = m.groups()
|
||||
rest = (rest.replace("downsample.0.", "ds_conv.")
|
||||
.replace("downsample.1.", "ds_bn."))
|
||||
return f"tcn{i}.{rest}"
|
||||
|
||||
m = re.fullmatch(r"up\.(.+)", key)
|
||||
if m:
|
||||
return _conv_block("conv_in", m.group(1))
|
||||
|
||||
m = re.fullmatch(r"residual_blocks\.(\d+)\.(.+)", key)
|
||||
if m:
|
||||
return _conv_block(f"conv{m.group(1)}", m.group(2))
|
||||
|
||||
m = re.fullmatch(r"attention\.(width|height)_axis\.(.+)", key)
|
||||
if m:
|
||||
axis, rest = m.groups()
|
||||
rest = rest.replace("qkv_transform.", "qkv.")
|
||||
return f"attention.{axis}.{rest}"
|
||||
|
||||
m = re.fullmatch(r"decoder\.(\d+)\.(.+)", key)
|
||||
if m:
|
||||
return f"{_DECODER_IDX[m.group(1)]}.{m.group(2)}"
|
||||
|
||||
raise KeyError(f"unmapped checkpoint key: {key}")
|
||||
|
||||
|
||||
def main():
|
||||
state = torch.load(CHECKPOINT, map_location="cpu", weights_only=True)
|
||||
if not isinstance(state, dict) or "tcn.network.0.conv1_group.weight" not in {
|
||||
k for k in state
|
||||
} | {k.replace("att.", "attention.") for k in state}:
|
||||
# tolerate trainer wrappers like {"model_state_dict": ...}
|
||||
for wrapper in ("model_state_dict", "state_dict", "model"):
|
||||
if isinstance(state, dict) and wrapper in state:
|
||||
state = state[wrapper]
|
||||
break
|
||||
|
||||
# Legacy upstream names predate the published code (_bench_common).
|
||||
state = remap_legacy_keys(state)
|
||||
|
||||
mapped = {}
|
||||
dropped = 0
|
||||
for k, v in state.items():
|
||||
if k.endswith("num_batches_tracked"):
|
||||
dropped += 1
|
||||
continue
|
||||
tch_key = map_key(k)
|
||||
if tch_key in mapped:
|
||||
raise KeyError(f"duplicate mapped key: {k} -> {tch_key}")
|
||||
mapped[tch_key] = v.detach().to(torch.float32).contiguous()
|
||||
|
||||
n_params = sum(v.numel() for k, v in mapped.items()
|
||||
if "running_" not in k)
|
||||
print(f"checkpoint tensors: {len(state)} "
|
||||
f"(dropped {dropped} num_batches_tracked)")
|
||||
print(f"mapped tensors: {len(mapped)}, "
|
||||
f"non-buffer params: {n_params/1e6:.6f}M")
|
||||
assert len(mapped) == 248, f"expected 248 tch variables, got {len(mapped)}"
|
||||
assert n_params == 2_225_042, f"param count mismatch: {n_params}"
|
||||
|
||||
st_path = os.path.join(RESULTS, "retrained_wiflow_std.safetensors")
|
||||
save_file(mapped, st_path)
|
||||
print(f"wrote {st_path}")
|
||||
|
||||
# ---- parity fixture --------------------------------------------------
|
||||
model = WiFlowPoseModel(dropout=0.5)
|
||||
model.load_state_dict(state, strict=True)
|
||||
model.eval()
|
||||
|
||||
gen = torch.Generator().manual_seed(42)
|
||||
x = torch.rand(2, 540, 20, generator=gen, dtype=torch.float32)
|
||||
with torch.no_grad():
|
||||
y = model(x)
|
||||
print(f"fixture input {tuple(x.shape)} -> output {tuple(y.shape)}, "
|
||||
f"output range [{y.min().item():.6f}, {y.max().item():.6f}]")
|
||||
|
||||
np.savez(os.path.join(RESULTS, "parity_fixture.npz"),
|
||||
input=x.numpy(), output=y.numpy())
|
||||
fixture = {
|
||||
"seed": 42,
|
||||
"input_shape": list(x.shape),
|
||||
"input": x.flatten().tolist(),
|
||||
"output_shape": list(y.shape),
|
||||
"output": y.flatten().tolist(),
|
||||
}
|
||||
json_path = os.path.join(RESULTS, "parity_fixture.json")
|
||||
with open(json_path, "w") as f:
|
||||
json.dump(fixture, f)
|
||||
print(f"wrote {os.path.join(RESULTS, 'parity_fixture.npz')}")
|
||||
print(f"wrote {json_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,148 +0,0 @@
|
||||
"""Regenerate results/nan_windows_mask.npy + results/big_windows_mask.npy by
|
||||
scanning a PRISTINE kagglehub download of the WiFlow-STD dataset
|
||||
(kaka2434/wiflow-dataset v1, csi_windows.npy, 360,000 windows of 540x20).
|
||||
|
||||
============================ READ THIS FIRST ===============================
|
||||
This script MUST be run against an UNCLEANED copy of the dataset.
|
||||
|
||||
remote/clean_v2.py (and its predecessor clean_nan.py) repair the dataset by
|
||||
zeroing the corrupted windows IN PLACE, with no backup. A cleaned copy
|
||||
contains no non-finite values and no out-of-range amplitudes, so on a cleaned
|
||||
copy this scan produces ALL-FALSE masks -- silently wrong ground truth. The
|
||||
script errors out loudly in that case (see the sanity check in main()).
|
||||
|
||||
That irreversibility is exactly why the two committed mask files under
|
||||
results/ (gitignore-negated) are the canonical ground truth: once a download
|
||||
has been cleaned, the masks can NEVER be regenerated from it. Only run this
|
||||
on a fresh `kagglehub.dataset_download("kaka2434/wiflow-dataset")`.
|
||||
============================================================================
|
||||
|
||||
Criteria (per window; mirrors the original 2026-06-10 scan and the
|
||||
remote/clean_v2.py repair criteria):
|
||||
|
||||
nan mask: any non-finite value (NaN/Inf) anywhere in the 540x20 window
|
||||
big mask: max |finite value| > 1.5 (the data is otherwise [0,1]-normalized;
|
||||
the corrupted files contain garbage up to 3.4e38, float32 max)
|
||||
|
||||
Expected result on the pristine Kaggle download (RESULTS.md defect 5):
|
||||
nan: 9,070 True | big: 9,072 True | union: 9,072 -- all windows in dataset
|
||||
files 487-499 (the final 13 files), window indices 350,922-359,999.
|
||||
|
||||
Usage:
|
||||
PYTHONUTF8=1 .venv/Scripts/python.exe generate_corruption_masks.py \
|
||||
[--data-dir <dir containing csi_windows.npy>] [--out-dir results]
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
RESULTS = os.path.join(HERE, "results")
|
||||
|
||||
EXPECTED = {"nan": 9070, "big": 9072, "union": 9072,
|
||||
"files": (487, 499), "windows": (350922, 359999)}
|
||||
|
||||
|
||||
def scan(csi_path, chunk=4000):
|
||||
"""Chunked scan of the (mmap'd) windows array; returns (nan_mask, big_mask)."""
|
||||
csi = np.load(csi_path, mmap_mode="r")
|
||||
n = len(csi)
|
||||
nan_mask = np.zeros(n, dtype=bool)
|
||||
big_mask = np.zeros(n, dtype=bool)
|
||||
for i in range(0, n, chunk):
|
||||
block = np.asarray(csi[i:i + chunk])
|
||||
finite = np.isfinite(block)
|
||||
nan_mask[i:i + chunk] = (~finite).any(axis=(1, 2))
|
||||
big_mask[i:i + chunk] = (
|
||||
np.abs(np.where(finite, block, 0)).max(axis=(1, 2)) > 1.5)
|
||||
if (i // chunk) % 10 == 0:
|
||||
print(f" scanned {min(i + chunk, n):,}/{n:,} windows "
|
||||
f"(nan={int(nan_mask.sum()):,} big={int(big_mask.sum()):,})",
|
||||
flush=True)
|
||||
return nan_mask, big_mask
|
||||
|
||||
|
||||
def describe_files(data_dir, mask):
|
||||
"""Map marked windows to dataset file indices via window_info.npz."""
|
||||
info = os.path.join(data_dir, "window_info.npz")
|
||||
if not os.path.exists(info):
|
||||
return None
|
||||
w2f = np.load(info)["window_to_file"]
|
||||
return np.unique(w2f[mask])
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Regenerate the corruption masks from a PRISTINE "
|
||||
"(uncleaned) kagglehub download. See module docstring.")
|
||||
parser.add_argument("--data-dir", default=os.path.join(
|
||||
os.path.expanduser("~"), ".cache", "kagglehub", "datasets", "kaka2434",
|
||||
"wiflow-dataset", "versions", "1", "preprocessed_csi_data"),
|
||||
help="Directory containing csi_windows.npy (PRISTINE copy)")
|
||||
parser.add_argument("--out-dir", default=RESULTS,
|
||||
help="Where to write the two .npy masks")
|
||||
parser.add_argument("--chunk", type=int, default=4000,
|
||||
help="Windows per scan chunk (memory/speed tradeoff)")
|
||||
args = parser.parse_args()
|
||||
|
||||
csi_path = os.path.join(args.data_dir, "csi_windows.npy")
|
||||
if not os.path.exists(csi_path):
|
||||
sys.exit(f"csi_windows.npy not found in {args.data_dir}")
|
||||
|
||||
print(f"scanning {csi_path} (chunk={args.chunk}) ...")
|
||||
nan_mask, big_mask = scan(csi_path, args.chunk)
|
||||
union = nan_mask | big_mask
|
||||
print(f"nan: {int(nan_mask.sum()):,} | big: {int(big_mask.sum()):,} | "
|
||||
f"union: {int(union.sum()):,} of {len(union):,} windows")
|
||||
|
||||
# ---- sanity check: an all-False result means a CLEANED copy ------------
|
||||
if not union.any():
|
||||
sys.exit(
|
||||
"ERROR: scan found ZERO corrupted windows.\n"
|
||||
"\n"
|
||||
"The pristine Kaggle download (kaka2434/wiflow-dataset v1) is "
|
||||
"known to contain\n"
|
||||
"9,072 corrupted windows (NaN/Inf + amplitudes up to 3.4e38) in "
|
||||
"dataset files\n"
|
||||
"487-499 (RESULTS.md, reproducibility defect 5). Finding none "
|
||||
"means this copy\n"
|
||||
"has almost certainly already been repaired by remote/clean_v2.py "
|
||||
"(or clean_nan.py),\n"
|
||||
"which zeroes the corrupted windows IN PLACE -- after that the "
|
||||
"corruption evidence\n"
|
||||
"is gone and the masks CANNOT be regenerated from this copy.\n"
|
||||
"\n"
|
||||
"Refusing to overwrite the committed ground-truth masks with "
|
||||
"all-False ones.\n"
|
||||
"Re-download the dataset (kagglehub.dataset_download("
|
||||
"'kaka2434/wiflow-dataset'))\n"
|
||||
"and point --data-dir at the fresh, uncleaned copy.")
|
||||
|
||||
files = describe_files(args.data_dir, union)
|
||||
if files is not None:
|
||||
print(f"marked windows span dataset files {files.min()}-{files.max()}: "
|
||||
f"{files.tolist()}")
|
||||
lo, hi = EXPECTED["files"]
|
||||
if files.min() != lo or files.max() != hi:
|
||||
print(f"WARNING: expected marked files exactly {lo}-{hi} "
|
||||
f"(the pristine v1 download); got {files.min()}-{files.max()}. "
|
||||
f"Different dataset version, or a partially cleaned copy?")
|
||||
for name, mask, exp in (("nan", nan_mask, EXPECTED["nan"]),
|
||||
("big", big_mask, EXPECTED["big"])):
|
||||
if int(mask.sum()) != exp:
|
||||
print(f"WARNING: {name} mask has {int(mask.sum()):,} True windows; "
|
||||
f"the pristine v1 download yields {exp:,}.")
|
||||
|
||||
os.makedirs(args.out_dir, exist_ok=True)
|
||||
for name, mask in (("nan_windows_mask.npy", nan_mask),
|
||||
("big_windows_mask.npy", big_mask)):
|
||||
out = os.path.join(args.out_dir, name)
|
||||
np.save(out, mask)
|
||||
print(f"wrote {out} ({int(mask.sum()):,} True)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,220 +0,0 @@
|
||||
"""ADR-152 edge optimization: ONNX export + onnxruntime CPU benchmark for the
|
||||
retrained WiFlow-STD checkpoint.
|
||||
|
||||
- Exports fp32 to ONNX. The axial attention reshapes with python ints taken
|
||||
from tensor.size() (view(N*W, C, H)), so a traced graph bakes the batch
|
||||
size; we first try a dynamic-batch export and verify it actually works at
|
||||
batch sizes 1/2/64 -- if not, we fall back to fixed-batch exports.
|
||||
- Verifies output parity vs torch on the stored fixture
|
||||
(results/parity_fixture.npz, batch 2, seed 42): max abs diff < 1e-4.
|
||||
- Measures onnxruntime CPU latency at batch 1 and 64 (median of N runs).
|
||||
- Supplementary: onnxruntime dynamic int8 quantization of the exported model
|
||||
(weight size datapoint for the paper's "~2.2 MB int8" claim).
|
||||
|
||||
Usage:
|
||||
.venv/Scripts/python.exe onnx_bench.py
|
||||
|
||||
Writes/merges into results/edge_optimization.json under key "onnx".
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import platform
|
||||
import statistics
|
||||
import time
|
||||
import traceback
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from _bench_common import RESULTS, import_upstream, load_wiflow_model
|
||||
|
||||
import_upstream() # sys.path + models stub + >1GB np.load mmap patch
|
||||
|
||||
CHECKPOINT = os.path.join(RESULTS, "retrained_best_pose_model.pth")
|
||||
OUT_JSON = os.path.join(RESULTS, "edge_optimization.json")
|
||||
|
||||
|
||||
def load_fp32_model():
|
||||
return load_wiflow_model(CHECKPOINT)
|
||||
|
||||
|
||||
def try_export(model, path, batch, dynamic, opset=17):
|
||||
"""Returns (ok, exporter_used, error)."""
|
||||
x = torch.rand(batch, 540, 20)
|
||||
attempts = []
|
||||
if dynamic:
|
||||
attempts.append(("dynamo", dict(dynamo=True,
|
||||
dynamic_shapes={"x": {0: "batch"}})))
|
||||
attempts.append(("torchscript", dict(dynamo=False,
|
||||
dynamic_axes={"input": {0: "batch"},
|
||||
"output": {0: "batch"}})))
|
||||
else:
|
||||
attempts.append(("torchscript", dict(dynamo=False)))
|
||||
attempts.append(("dynamo", dict(dynamo=True)))
|
||||
last_err = None
|
||||
for name, kw in attempts:
|
||||
try:
|
||||
with torch.no_grad():
|
||||
torch.onnx.export(model, (x,), path, opset_version=opset,
|
||||
input_names=["input"], output_names=["output"],
|
||||
**kw)
|
||||
return True, name, None
|
||||
except Exception as e: # noqa: BLE001
|
||||
last_err = f"{name}: {type(e).__name__}: {e}"
|
||||
traceback.print_exc()
|
||||
return False, None, last_err
|
||||
|
||||
|
||||
def ort_session(path):
|
||||
import onnxruntime as ort
|
||||
return ort.InferenceSession(path, providers=["CPUExecutionProvider"])
|
||||
|
||||
|
||||
def ort_run(sess, x):
|
||||
inp = sess.get_inputs()[0].name
|
||||
return sess.run(None, {inp: x})[0]
|
||||
|
||||
|
||||
def bench_ort(sess, batch, n_runs):
|
||||
rng = np.random.default_rng(123)
|
||||
x = rng.random((batch, 540, 20), dtype=np.float32)
|
||||
for _ in range(max(5, n_runs // 10)):
|
||||
ort_run(sess, x)
|
||||
times = []
|
||||
for _ in range(n_runs):
|
||||
t0 = time.perf_counter()
|
||||
ort_run(sess, x)
|
||||
times.append(time.perf_counter() - t0)
|
||||
med = statistics.median(times)
|
||||
return {
|
||||
"batch_size": batch,
|
||||
"runs": n_runs,
|
||||
"median_ms_per_batch": med * 1e3,
|
||||
"median_ms_per_window": med * 1e3 / batch,
|
||||
"windows_per_second": batch / med,
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(
|
||||
description="ONNX export + onnxruntime CPU benchmark for the "
|
||||
"retrained WiFlow-STD checkpoint (no options; see "
|
||||
"module docstring). NB: the published "
|
||||
"retrained_fp32_dynamic.onnx came from the TorchScript "
|
||||
"exporter; on newer torch the dynamo attempt may succeed "
|
||||
"first and produce a different (external-data) artifact.")
|
||||
parser.parse_args()
|
||||
|
||||
import onnxruntime
|
||||
model = load_fp32_model()
|
||||
results = {
|
||||
"env": {
|
||||
"torch": torch.__version__,
|
||||
"onnxruntime": onnxruntime.__version__,
|
||||
"platform": platform.platform(),
|
||||
},
|
||||
}
|
||||
|
||||
fixture = np.load(os.path.join(RESULTS, "parity_fixture.npz"))
|
||||
fx, fy = fixture["input"], fixture["output"] # (2,540,20) -> (2,15,2)
|
||||
|
||||
# ---- export: dynamic batch first, fall back to fixed --------------------
|
||||
dyn_path = os.path.join(RESULTS, "retrained_fp32_dynamic.onnx")
|
||||
ok, exporter, err = try_export(model, dyn_path, batch=2, dynamic=True)
|
||||
dynamic_works = False
|
||||
if ok:
|
||||
# verify the dynamic graph really runs at other batch sizes
|
||||
try:
|
||||
sess = ort_session(dyn_path)
|
||||
for b in (1, 2, 64):
|
||||
y = ort_run(sess, np.zeros((b, 540, 20), dtype=np.float32))
|
||||
assert y.shape == (b, 15, 2), y.shape
|
||||
dynamic_works = True
|
||||
except Exception as e: # noqa: BLE001
|
||||
print(f"dynamic-batch model does not generalize: {e}")
|
||||
|
||||
sessions = {}
|
||||
if dynamic_works:
|
||||
results["export"] = {"mode": "dynamic-batch", "exporter": exporter,
|
||||
"file": os.path.basename(dyn_path),
|
||||
"size_mb": os.path.getsize(dyn_path) / 1e6}
|
||||
sess = ort_session(dyn_path)
|
||||
sessions = {1: sess, 2: sess, 64: sess}
|
||||
print(f"dynamic-batch export OK via {exporter}")
|
||||
else:
|
||||
results["export"] = {"mode": "fixed-batch", "fallback_reason": err,
|
||||
"files": {}}
|
||||
for b in (1, 2, 64):
|
||||
p = os.path.join(RESULTS, f"retrained_fp32_b{b}.onnx")
|
||||
ok, exporter, err = try_export(model, p, batch=b, dynamic=False)
|
||||
if not ok:
|
||||
results["export"]["files"][str(b)] = {"error": err}
|
||||
print(f"EXPORT FAILED at batch {b}: {err}")
|
||||
continue
|
||||
results["export"]["files"][str(b)] = {
|
||||
"exporter": exporter, "file": os.path.basename(p),
|
||||
"size_mb": os.path.getsize(p) / 1e6}
|
||||
sessions[b] = ort_session(p)
|
||||
print(f"fixed-batch {b} export OK via {exporter}")
|
||||
|
||||
# ---- parity vs torch on the fixture -------------------------------------
|
||||
if 2 in sessions:
|
||||
y_ort = ort_run(sessions[2], fx)
|
||||
with torch.no_grad():
|
||||
y_torch = model(torch.from_numpy(fx)).numpy()
|
||||
results["parity"] = {
|
||||
"fixture": "results/parity_fixture.npz (batch 2, seed 42)",
|
||||
"max_abs_diff_vs_stored_fixture": float(np.abs(y_ort - fy).max()),
|
||||
"max_abs_diff_vs_torch_now": float(np.abs(y_ort - y_torch).max()),
|
||||
"pass_lt_1e-4": bool(np.abs(y_ort - y_torch).max() < 1e-4),
|
||||
}
|
||||
print("parity:", json.dumps(results["parity"], indent=2))
|
||||
|
||||
# ---- latency -------------------------------------------------------------
|
||||
results["latency"] = {}
|
||||
if 1 in sessions:
|
||||
results["latency"]["batch1"] = bench_ort(sessions[1], 1, 100)
|
||||
print(f"ORT batch 1: {results['latency']['batch1']['median_ms_per_window']:.2f} ms/window")
|
||||
if 64 in sessions:
|
||||
results["latency"]["batch64"] = bench_ort(sessions[64], 64, 30)
|
||||
print(f"ORT batch 64: {results['latency']['batch64']['median_ms_per_window']:.3f} ms/window")
|
||||
|
||||
# ---- supplementary: ORT dynamic int8 (size datapoint for the 2.2MB claim)
|
||||
src = (dyn_path if dynamic_works
|
||||
else os.path.join(RESULTS, "retrained_fp32_b1.onnx"))
|
||||
if os.path.exists(src):
|
||||
try:
|
||||
from onnxruntime.quantization import QuantType, quantize_dynamic
|
||||
q_path = os.path.join(RESULTS, "retrained_int8_ort_dynamic.onnx")
|
||||
quantize_dynamic(src, q_path, weight_type=QuantType.QInt8)
|
||||
entry = {"file": os.path.basename(q_path),
|
||||
"size_mb": os.path.getsize(q_path) / 1e6}
|
||||
try:
|
||||
qs = ort_session(q_path)
|
||||
yq = ort_run(qs, fx[:1] if not dynamic_works else fx)
|
||||
ref = fy[:1] if not dynamic_works else fy
|
||||
entry["runs"] = True
|
||||
entry["max_abs_diff_vs_fp32_fixture"] = float(np.abs(yq - ref).max())
|
||||
except Exception as e: # noqa: BLE001
|
||||
entry["runs"] = False
|
||||
entry["run_error"] = f"{type(e).__name__}: {e}"
|
||||
results["ort_int8_dynamic_supplementary"] = entry
|
||||
print("ORT int8:", json.dumps(entry, indent=2))
|
||||
except Exception as e: # noqa: BLE001
|
||||
results["ort_int8_dynamic_supplementary"] = {
|
||||
"error": f"{type(e).__name__}: {e}"}
|
||||
|
||||
merged = {}
|
||||
if os.path.exists(OUT_JSON):
|
||||
with open(OUT_JSON) as f:
|
||||
merged = json.load(f)
|
||||
merged["onnx"] = results
|
||||
with open(OUT_JSON, "w") as f:
|
||||
json.dump(merged, f, indent=2)
|
||||
print(f"wrote {OUT_JSON}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,228 +0,0 @@
|
||||
"""ADR-152 "optimize beyond SOTA": edge-optimization benchmark for the
|
||||
retrained WiFlow-STD checkpoint (results/retrained_best_pose_model.pth,
|
||||
~96% PCK@20, fp32 params 2,225,042).
|
||||
|
||||
Measures, for fp32 / fp16 / dynamic-int8 torch variants:
|
||||
(a) serialized state_dict size on disk,
|
||||
(b) CPU inference latency per window at batch 1 and batch 64
|
||||
(median of repeated runs, this Windows box),
|
||||
(c) accuracy (PCK@20/50 + MPJPE, upstream metrics) on a corruption-free
|
||||
random subset of the seed-42 file-level 70/15/15 test split
|
||||
(same split as eval_repro.py; corrupted windows 487-499 excluded via
|
||||
results/nan_windows_mask.npy | results/big_windows_mask.npy).
|
||||
|
||||
Also verifies the paper's "~2.2 MB int8" size claim: reports which layer
|
||||
types torch dynamic quantization actually converts (the model contains NO
|
||||
nn.Linear -- it is Conv1d/Conv2d/BatchNorm only) and the real on-disk size.
|
||||
|
||||
Usage:
|
||||
.venv/Scripts/python.exe quantize_bench.py \
|
||||
--data-dir C:/Users/ruv/.cache/kagglehub/datasets/kaka2434/wiflow-dataset/versions/1/preprocessed_csi_data \
|
||||
[--subset 10000] [--skip-accuracy]
|
||||
|
||||
Writes/merges into results/edge_optimization.json under key "torch".
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import platform
|
||||
import statistics
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from _bench_common import HERE, RESULTS, evaluate, import_upstream, load_wiflow_model
|
||||
|
||||
import_upstream() # sys.path + models stub + >1GB np.load mmap patch
|
||||
|
||||
from dataset import ( # noqa: E402
|
||||
PreprocessedCSIKeypointsDataset,
|
||||
create_preprocessed_train_val_test_loaders,
|
||||
)
|
||||
|
||||
CHECKPOINT = os.path.join(RESULTS, "retrained_best_pose_model.pth")
|
||||
|
||||
|
||||
def load_fp32_model():
|
||||
# legacy upstream key remap inside is a harmless no-op on this checkpoint
|
||||
return load_wiflow_model(CHECKPOINT)
|
||||
|
||||
|
||||
def state_dict_size_bytes(model, path):
|
||||
torch.save(model.state_dict(), path)
|
||||
return os.path.getsize(path)
|
||||
|
||||
|
||||
def bench_latency(model, batch_size, n_runs, dtype=torch.float32):
|
||||
gen = torch.Generator().manual_seed(123)
|
||||
x = torch.rand(batch_size, 540, 20, generator=gen).to(dtype)
|
||||
with torch.no_grad():
|
||||
for _ in range(max(5, n_runs // 10)): # warmup
|
||||
model(x)
|
||||
times = []
|
||||
for _ in range(n_runs):
|
||||
t0 = time.perf_counter()
|
||||
model(x)
|
||||
times.append(time.perf_counter() - t0)
|
||||
med = statistics.median(times)
|
||||
return {
|
||||
"batch_size": batch_size,
|
||||
"runs": n_runs,
|
||||
"median_ms_per_batch": med * 1e3,
|
||||
"median_ms_per_window": med * 1e3 / batch_size,
|
||||
"windows_per_second": batch_size / med,
|
||||
}
|
||||
|
||||
|
||||
def build_test_subset(data_dir, subset_size, batch_size=64):
|
||||
"""Seed-42 file-level 70/15/15 test split (exactly as eval_repro.py),
|
||||
minus corrupted windows, then a seed-42 random subset."""
|
||||
dataset = PreprocessedCSIKeypointsDataset(
|
||||
data_dir=data_dir, keypoint_scale=1000.0, enable_temporal_clean=True)
|
||||
_tr, _va, test_loader = create_preprocessed_train_val_test_loaders(
|
||||
dataset=dataset, batch_size=batch_size, num_workers=0, random_seed=42)
|
||||
test_indices = np.asarray(test_loader.dataset.indices)
|
||||
|
||||
corrupted = (np.load(os.path.join(RESULTS, "nan_windows_mask.npy"))
|
||||
| np.load(os.path.join(RESULTS, "big_windows_mask.npy")))
|
||||
clean = test_indices[~corrupted[test_indices]]
|
||||
print(f"test split: {len(test_indices)} windows, "
|
||||
f"{len(test_indices) - len(clean)} corrupted excluded, "
|
||||
f"{len(clean)} clean")
|
||||
|
||||
if subset_size and subset_size < len(clean):
|
||||
rng = np.random.default_rng(42)
|
||||
clean = np.sort(rng.choice(clean, size=subset_size, replace=False))
|
||||
subset = torch.utils.data.Subset(dataset, clean.tolist())
|
||||
loader = DataLoader(subset, batch_size=batch_size, shuffle=False,
|
||||
num_workers=0)
|
||||
return loader, len(clean)
|
||||
|
||||
|
||||
def quantize_int8_dynamic(fp32_model):
|
||||
"""torch.ao.quantization.quantize_dynamic on Linear/Conv where supported.
|
||||
Returns (model, report) where report documents what actually quantized."""
|
||||
qmodel = torch.ao.quantization.quantize_dynamic(
|
||||
fp32_model, {nn.Linear, nn.Conv1d, nn.Conv2d}, dtype=torch.qint8)
|
||||
|
||||
quantized, total_params, quant_params = [], 0, 0
|
||||
for name, mod in qmodel.named_modules():
|
||||
cls = type(mod).__module__ + "." + type(mod).__name__
|
||||
if "quantized" in cls:
|
||||
w = mod.weight() if callable(getattr(mod, "weight", None)) else None
|
||||
numel = w.numel() if w is not None else 0
|
||||
quant_params += numel
|
||||
quantized.append({"module": name, "class": cls, "params": numel})
|
||||
for p in fp32_model.parameters():
|
||||
total_params += p.numel()
|
||||
|
||||
n_linear = sum(isinstance(m, nn.Linear) for m in fp32_model.modules())
|
||||
n_conv1d = sum(isinstance(m, nn.Conv1d) for m in fp32_model.modules())
|
||||
n_conv2d = sum(isinstance(m, nn.Conv2d) for m in fp32_model.modules())
|
||||
report = {
|
||||
"eligible_module_counts": {
|
||||
"nn.Linear": n_linear, "nn.Conv1d": n_conv1d, "nn.Conv2d": n_conv2d},
|
||||
"modules_actually_quantized": quantized,
|
||||
"n_modules_quantized": len(quantized),
|
||||
"params_total": total_params,
|
||||
"params_quantized": quant_params,
|
||||
"params_quantized_fraction": quant_params / total_params,
|
||||
}
|
||||
return qmodel, report
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data-dir", default=os.path.join(
|
||||
os.path.expanduser("~"), ".cache", "kagglehub", "datasets", "kaka2434",
|
||||
"wiflow-dataset", "versions", "1", "preprocessed_csi_data"))
|
||||
parser.add_argument("--subset", type=int, default=10000)
|
||||
parser.add_argument("--runs-b1", type=int, default=100)
|
||||
parser.add_argument("--runs-b64", type=int, default=30)
|
||||
parser.add_argument("--skip-accuracy", action="store_true")
|
||||
parser.add_argument("--out", default=os.path.join(RESULTS, "edge_optimization.json"))
|
||||
args = parser.parse_args()
|
||||
|
||||
torch.manual_seed(42)
|
||||
results = {
|
||||
"env": {
|
||||
"torch": torch.__version__,
|
||||
"platform": platform.platform(),
|
||||
"processor": platform.processor(),
|
||||
"num_threads": torch.get_num_threads(),
|
||||
"checkpoint": os.path.relpath(CHECKPOINT, HERE),
|
||||
},
|
||||
"variants": {},
|
||||
}
|
||||
|
||||
# ---- build variants ---------------------------------------------------
|
||||
fp32 = load_fp32_model()
|
||||
n_params = sum(p.numel() for p in fp32.parameters())
|
||||
results["env"]["params"] = n_params
|
||||
print(f"fp32 model: {n_params:,} params")
|
||||
|
||||
fp16 = load_fp32_model().half()
|
||||
|
||||
int8, q_report = quantize_int8_dynamic(load_fp32_model())
|
||||
results["int8_dynamic_quant_report"] = q_report
|
||||
print(f"int8 dynamic: {q_report['n_modules_quantized']} modules quantized, "
|
||||
f"{q_report['params_quantized_fraction']*100:.1f}% of params")
|
||||
|
||||
variants = {
|
||||
"fp32": (fp32, torch.float32, "retrained_fp32_resaved.pth"),
|
||||
"fp16": (fp16, torch.float16, "retrained_fp16.pth"),
|
||||
"int8_dynamic": (int8, torch.float32, "retrained_int8_dynamic.pth"),
|
||||
}
|
||||
|
||||
# ---- (a) size + (b) latency -------------------------------------------
|
||||
for name, (model, dtype, fname) in variants.items():
|
||||
path = os.path.join(RESULTS, fname)
|
||||
size = state_dict_size_bytes(model, path)
|
||||
print(f"\n=== {name}: {size/1e6:.3f} MB on disk ({fname}) ===")
|
||||
lat1 = bench_latency(model, 1, args.runs_b1, dtype)
|
||||
lat64 = bench_latency(model, 64, args.runs_b64, dtype)
|
||||
print(f" batch 1: {lat1['median_ms_per_window']:.2f} ms/window "
|
||||
f"({lat1['windows_per_second']:.0f}/s)")
|
||||
print(f" batch 64: {lat64['median_ms_per_window']:.3f} ms/window "
|
||||
f"({lat64['windows_per_second']:.0f}/s)")
|
||||
results["variants"][name] = {
|
||||
"file": fname,
|
||||
"size_bytes": size,
|
||||
"size_mb": size / 1e6,
|
||||
"latency_batch1": lat1,
|
||||
"latency_batch64": lat64,
|
||||
}
|
||||
|
||||
# ---- (c) accuracy ------------------------------------------------------
|
||||
if not args.skip_accuracy:
|
||||
loader, n_clean = build_test_subset(args.data_dir, args.subset)
|
||||
results["accuracy_subset"] = {
|
||||
"description": "seed-42 file-level 70/15/15 test split, corrupted "
|
||||
"windows (files 487-499) excluded, seed-42 random "
|
||||
"subset",
|
||||
"subset_size": min(args.subset, n_clean) if args.subset else n_clean,
|
||||
"clean_test_total": n_clean,
|
||||
}
|
||||
for name, (model, dtype, _f) in variants.items():
|
||||
print(f"\n=== accuracy: {name} ===")
|
||||
results["variants"][name]["accuracy"] = evaluate(
|
||||
model, loader, dtype=dtype, label=name)
|
||||
print(json.dumps(results["variants"][name]["accuracy"], indent=2))
|
||||
|
||||
# ---- merge into edge_optimization.json ---------------------------------
|
||||
merged = {}
|
||||
if os.path.exists(args.out):
|
||||
with open(args.out) as f:
|
||||
merged = json.load(f)
|
||||
merged["torch"] = results
|
||||
with open(args.out, "w") as f:
|
||||
json.dump(merged, f, indent=2)
|
||||
print(f"\nwrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,14 +0,0 @@
|
||||
import numpy as np, os
|
||||
d = os.path.expanduser('~/wiflow-std-bench/preprocessed_csi_data')
|
||||
csi = np.load(os.path.join(d, 'csi_windows.npy'), mmap_mode='r+')
|
||||
zeroed = 0
|
||||
chunk = 4000
|
||||
for i in range(0, len(csi), chunk):
|
||||
block = csi[i:i+chunk]
|
||||
finite = np.isfinite(block)
|
||||
bad = (~finite).any(axis=(1, 2)) | (np.abs(np.where(finite, block, 0)).max(axis=(1, 2)) > 1.5)
|
||||
if bad.any():
|
||||
block[bad] = 0.0
|
||||
zeroed += int(bad.sum())
|
||||
csi.flush()
|
||||
print(f'zeroed {zeroed} corrupted windows entirely')
|
||||
@@ -1,112 +0,0 @@
|
||||
"""Evaluate the retrained WiFlow-STD checkpoint (ADR-152 §2.2a fallback).
|
||||
|
||||
Scores the model produced by run.py (train_output/best_pose_model.pth or similar)
|
||||
on the seed-42 test split: full test set AND NaN-free subset (excluding windows
|
||||
that were zero-filled by clean_nan.py — file indices 487-499).
|
||||
|
||||
NOTE: deployed to ruvultra (~/wiflow-std-bench) as a standalone single file,
|
||||
so it deliberately inlines its helpers. The reference implementations (upstream
|
||||
import shim, >1GB np.load mmap patch, key-remap loader, canonical evaluate
|
||||
loop) live in benchmarks/wiflow-std/_bench_common.py — keep copies in sync.
|
||||
"""
|
||||
import json, os, random, sys
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch.utils.data import DataLoader, Subset
|
||||
|
||||
# csi_windows.npy is ~13 GB; mmap large arrays instead of eagerly loading
|
||||
# ~15 GB into RAM (same patch as _bench_common._np_load_mmap).
|
||||
_np_load = np.load
|
||||
|
||||
|
||||
def _np_load_mmap(path, *a, **kw):
|
||||
if (isinstance(path, str) and path.endswith('.npy')
|
||||
and os.path.getsize(path) > 1 << 30 and 'mmap_mode' not in kw):
|
||||
kw['mmap_mode'] = 'r'
|
||||
return _np_load(path, *a, **kw)
|
||||
|
||||
|
||||
np.load = _np_load_mmap
|
||||
|
||||
sys.path.insert(0, os.path.expanduser('~/wiflow-std-bench/upstream'))
|
||||
from dataset import PreprocessedCSIKeypointsDataset, create_preprocessed_train_val_test_loaders
|
||||
from models.pose_model import WiFlowPoseModel
|
||||
from utils.metrics import calculate_pck, calculate_mpjpe
|
||||
|
||||
|
||||
def find_checkpoint():
|
||||
cands = []
|
||||
for root, _, files in os.walk(os.path.expanduser('~/wiflow-std-bench/train_output')):
|
||||
for f in files:
|
||||
if f.endswith('.pth'):
|
||||
cands.append(os.path.join(root, f))
|
||||
# also upstream/test default output dir
|
||||
for root, _, files in os.walk(os.path.expanduser('~/wiflow-std-bench/upstream')):
|
||||
for f in files:
|
||||
if f.endswith('.pth') and 'best' in f and 'cross_dataset' not in root:
|
||||
p = os.path.join(root, f)
|
||||
if os.path.getmtime(p) > os.path.getmtime(os.path.expanduser('~/wiflow-std-bench/train.log')) - 86400 * 2:
|
||||
cands.append(p)
|
||||
cands = [c for c in cands if not c.endswith('upstream/best_pose_model.pth')]
|
||||
if not cands:
|
||||
sys.exit('no retrained checkpoint found')
|
||||
return max(cands, key=os.path.getmtime)
|
||||
|
||||
|
||||
def evaluate(model, loader, device):
|
||||
model.eval()
|
||||
totals = {t: 0.0 for t in (0.1, 0.2, 0.3, 0.4, 0.5)}
|
||||
total_mpe, n = 0.0, 0
|
||||
with torch.no_grad():
|
||||
for bx, by in loader:
|
||||
bx, by = bx.to(device), by.to(device)
|
||||
out = model(bx)
|
||||
bs = by.size(0)
|
||||
total_mpe += calculate_mpjpe(out, by) * bs
|
||||
pck = calculate_pck(out, by, thresholds=list(totals))
|
||||
for t in totals:
|
||||
totals[t] += pck[t] * bs
|
||||
n += bs
|
||||
return {'samples': n, 'mpjpe': total_mpe / n,
|
||||
**{f'pck@{int(t*100)}': totals[t] / n for t in totals}}
|
||||
|
||||
|
||||
random.seed(42); np.random.seed(42); torch.manual_seed(42)
|
||||
torch.cuda.manual_seed_all(42)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
d = os.path.expanduser('~/wiflow-std-bench/preprocessed_csi_data')
|
||||
dataset = PreprocessedCSIKeypointsDataset(data_dir=d, keypoint_scale=1000.0,
|
||||
enable_temporal_clean=True)
|
||||
_, _, test_loader = create_preprocessed_train_val_test_loaders(
|
||||
dataset=dataset, batch_size=256, num_workers=2, random_seed=42)
|
||||
|
||||
device = torch.device('cuda')
|
||||
ckpt = find_checkpoint()
|
||||
print('checkpoint:', ckpt)
|
||||
model = WiFlowPoseModel(dropout=0.5).to(device)
|
||||
state = torch.load(ckpt, map_location=device, weights_only=True)
|
||||
renames = {'att.': 'attention.', 'final_conv.': 'decoder.'}
|
||||
state = {next((new + k[len(old):] for old, new in renames.items()
|
||||
if k.startswith(old)), k): v for k, v in state.items()}
|
||||
model.load_state_dict(state, strict=True)
|
||||
|
||||
results = {'checkpoint': ckpt}
|
||||
print('=== full test set ===')
|
||||
results['test_full'] = evaluate(model, test_loader, device)
|
||||
print(json.dumps(results['test_full'], indent=2))
|
||||
|
||||
# NaN-free subset: exclude windows from corrupted files 487-499
|
||||
test_subset = test_loader.dataset # Subset(dataset, test_indices)
|
||||
w2f = dataset.window_to_file
|
||||
clean_idx = [i for i in test_subset.indices if w2f[i] < 487]
|
||||
print(f'=== NaN-free test subset ({len(clean_idx)} of {len(test_subset.indices)}) ===')
|
||||
clean_loader = DataLoader(Subset(dataset, clean_idx), batch_size=256, shuffle=False)
|
||||
results['test_clean'] = evaluate(model, clean_loader, device)
|
||||
print(json.dumps(results['test_clean'], indent=2))
|
||||
|
||||
out = os.path.expanduser('~/wiflow-std-bench/eval_retrained.json')
|
||||
with open(out, 'w') as f:
|
||||
json.dump(results, f, indent=2)
|
||||
print('wrote', out)
|
||||
@@ -1,374 +0,0 @@
|
||||
"""ADR-152 SS2.2 measurement (b): WiFlow-STD fine-tuned on our fresh ESP32 paired dataset.
|
||||
|
||||
Dataset: ~/wiflow-std-bench/paired-20260610.jsonl -- 2,046 paired windows collected
|
||||
2026-06-10 22:10-22:40 (ONE subject, ONE room, ONE ESP32 node, varied poses).
|
||||
Per record: csi = flat float32 list, csi_shape, kp = 17 COCO [x, y] normalized [0,1]
|
||||
camera coords, conf (MediaPipe mean confidence, all > 0.5 in this set), ts_start/ts_end.
|
||||
Aligner: scripts/align-ground-truth.js, non-overlapping 20-frame windows (~0.42 s each).
|
||||
|
||||
Dataset findings (MEASURED on this file, 2026-06-10):
|
||||
- csi_shape is HETEROGENEOUS, not uniformly [70, 20]: 1,347x [70,20], 284x [134,20],
|
||||
243x [26,20], 130x [12,20], 42x [20,20]. The ESP32 stream emits mixed frame types
|
||||
and the aligner stamps each window's subcarrier count from frame[0]
|
||||
(extractCsiMatrix: nSc = window[0].subcarriers), zero-padding/truncating the rest.
|
||||
Even native-70 windows contain ~20.4% internally zero-padded short frames
|
||||
(subcarriers 40..69 all-zero for those frames).
|
||||
- LAYOUT BUG: the aligner fills matrix[f * nSc + s] (frame-major) but declares
|
||||
shape [nSc, nFrames]. The true layout is (frame, subcarrier); we reshape
|
||||
(nFrames, nSc) and transpose. Confirmed by coherent per-frame zero-tails.
|
||||
- Handling here (primary suite, "all2046"): every frame's subcarrier axis is
|
||||
linearly resampled to 70 bins (np.interp over a normalized index domain;
|
||||
identity for native-70 frames) so the pre-registered n=2,046 and split sizes
|
||||
hold. Secondary suite ("native70") restricts to the 1,347 native [70,20]
|
||||
windows (temporal 70/15/15 of those) as a homogeneity robustness check.
|
||||
|
||||
Pre-registered protocol (followed exactly):
|
||||
1. TEMPORAL split (records are time-sorted; asserted): first 70% train (1,432),
|
||||
next 15% val (307), last 15% test (307). No shuffling across time. Seed 42
|
||||
for everything else.
|
||||
2. Model: upstream WiFlow-STD trunk (WiFlowPoseModel) with a learned 1x1 Conv1d
|
||||
projection 70->540 prepended, and K=17 via the parameter-free adaptive pool
|
||||
(AdaptiveAvgPool2d((17, 1)) instead of (15, 1)) -- pretrained weights load
|
||||
for any K. CSI normalization: divide by the TRAIN-split 99th-percentile
|
||||
amplitude, clip to [0, 1] (documented in output JSON).
|
||||
3. Three runs, <=60 epochs, early-stop patience 8 on val MPJPE, batch 32,
|
||||
AdamW, fp32 (no autocast):
|
||||
(i) pretrained-init: trunk init from upstream/test/best_pose_model.pth
|
||||
(the measurement-(a) retrained checkpoint, ~96% PCK@20 on WiFlow data;
|
||||
key remap att.->attention. / final_conv.->decoder. applied defensively
|
||||
as in eval_repro.py -- a no-op for this checkpoint, which already uses
|
||||
the new names). Discriminative lr: adapter 1e-4, trunk 1e-5.
|
||||
(ii) scratch: same architecture, random init, all params lr 1e-4.
|
||||
(iii) frozen-trunk: pretrained trunk frozen (requires_grad=False AND held in
|
||||
.eval() so BatchNorm running stats cannot drift -- pure transfer probe);
|
||||
only the 70->540 adapter trains, lr 1e-4.
|
||||
4. Metrics on the temporal TEST split: torso-normalized PCK@10/20/30/40/50 and
|
||||
MPJPE. Upstream utils/metrics.py calculate_pck(use_torso_norm=True) hardcodes
|
||||
NECK_IDX/PELVIS_IDX = 2, 12 -- a 15-keypoint convention that is WRONG for our
|
||||
17 COCO keypoints (2 = right_eye, 12 = right_hip). We therefore reimplement the
|
||||
identical math (per-frame norm distance, clamp min 0.01, mean over all
|
||||
keypoints x frames) with torso = ||l_shoulder(5) - l_hip(11)||.
|
||||
Also reported: prediction std across test frames (constant-pose detector;
|
||||
must be > 0) and the mean-pose-predictor baseline (train-split mean pose
|
||||
evaluated on test -- the honesty bar).
|
||||
|
||||
Usage (on ruvultra):
|
||||
nice -n 10 nohup ~/wiflow-std-bench/venv/bin/python train_measb.py > train_measb.log 2>&1 &
|
||||
|
||||
NOTE: deployed to ruvultra as a standalone single file, so it deliberately
|
||||
inlines its helpers. The reference implementations (upstream import shim,
|
||||
np.load mmap patch, key-remap loader, canonical evaluate loop) live in
|
||||
benchmarks/wiflow-std/_bench_common.py — keep copies in sync.
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import random
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
BENCH = os.path.expanduser("~/wiflow-std-bench")
|
||||
UPSTREAM = os.path.join(BENCH, "upstream")
|
||||
MEASB = os.path.join(BENCH, "measb")
|
||||
DATA = os.path.join(BENCH, "paired-20260610.jsonl")
|
||||
CHECKPOINT = os.path.join(UPSTREAM, "test", "best_pose_model.pth")
|
||||
|
||||
sys.path.insert(0, UPSTREAM)
|
||||
|
||||
# Upstream defect (1): models/__init__.py imports a name tcn.py does not define.
|
||||
# Register a stub package so the broken __init__ never executes (as eval_repro.py).
|
||||
import types # noqa: E402
|
||||
|
||||
_models_pkg = types.ModuleType("models")
|
||||
_models_pkg.__path__ = [os.path.join(UPSTREAM, "models")]
|
||||
sys.modules["models"] = _models_pkg
|
||||
|
||||
from models.pose_model import WiFlowPoseModel # noqa: E402
|
||||
|
||||
SEED = 42
|
||||
K = 17
|
||||
N_SUBC = 70
|
||||
TRUNK_IN = 540
|
||||
BATCH = 32 # <= 64 per protocol (GPU shared with the efficiency sweep)
|
||||
MAX_EPOCHS = 60
|
||||
PATIENCE = 8
|
||||
LR_ADAPTER = 1e-4
|
||||
LR_TRUNK_FT = 1e-5 # 10x lower for the pretrained trunk vs the fresh adapter
|
||||
L_SHOULDER, L_HIP = 5, 11
|
||||
THRESHOLDS = (0.1, 0.2, 0.3, 0.4, 0.5)
|
||||
|
||||
|
||||
def set_seed(seed=SEED):
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
|
||||
|
||||
def resample_subcarriers(frame_major, n_out=N_SUBC):
|
||||
"""(nFrames, nSc) -> (nFrames, n_out) by per-frame linear interpolation.
|
||||
|
||||
Identity for nSc == n_out. Normalized index domain [0, 1] on both sides.
|
||||
"""
|
||||
nf, nsc = frame_major.shape
|
||||
if nsc == n_out:
|
||||
return frame_major
|
||||
xi = np.linspace(0.0, 1.0, nsc)
|
||||
xo = np.linspace(0.0, 1.0, n_out)
|
||||
return np.stack([np.interp(xo, xi, frame_major[f]) for f in range(nf)]).astype(np.float32)
|
||||
|
||||
|
||||
def load_dataset():
|
||||
csi, kps, confs, ts, native70 = [], [], [], [], []
|
||||
shape_counts = {}
|
||||
with open(DATA) as f:
|
||||
for line in f:
|
||||
r = json.loads(line)
|
||||
nsc, nf = r["csi_shape"]
|
||||
shape_counts[f"{nsc}x{nf}"] = shape_counts.get(f"{nsc}x{nf}", 0) + 1
|
||||
assert nf == 20, r["csi_shape"]
|
||||
# Aligner layout bug: data is frame-major despite the declared
|
||||
# [nSc, nFrames] shape -- reshape (nFrames, nSc), then resample the
|
||||
# subcarrier axis to 70 and transpose to (70 subcarriers, 20 frames).
|
||||
fm = np.asarray(r["csi"], dtype=np.float32).reshape(nf, nsc)
|
||||
csi.append(resample_subcarriers(fm).T)
|
||||
kp = np.asarray(r["kp"], dtype=np.float32)
|
||||
assert kp.shape == (K, 2), kp.shape
|
||||
kps.append(kp)
|
||||
confs.append(r["conf"])
|
||||
ts.append(r["ts_start"])
|
||||
native70.append(nsc == N_SUBC)
|
||||
assert all(ts[i] <= ts[i + 1] for i in range(len(ts) - 1)), "records not time-sorted"
|
||||
return (np.stack(csi), np.stack(kps), np.asarray(confs, dtype=np.float32),
|
||||
np.asarray(native70), shape_counts, ts[0], ts[-1])
|
||||
|
||||
|
||||
def temporal_split(n):
|
||||
n_train = int(round(n * 0.70))
|
||||
n_val = int(round(n * 0.15))
|
||||
return slice(0, n_train), slice(n_train, n_train + n_val), slice(n_train + n_val, n)
|
||||
|
||||
|
||||
class AdaptedWiFlow(nn.Module):
|
||||
"""1x1 Conv1d adapter 70->540 + upstream WiFlow-STD trunk with K=17 pool head."""
|
||||
|
||||
def __init__(self, k=K, dropout=0.5):
|
||||
super().__init__()
|
||||
self.adapter = nn.Conv1d(N_SUBC, TRUNK_IN, kernel_size=1)
|
||||
nn.init.kaiming_normal_(self.adapter.weight, mode="fan_out", nonlinearity="relu")
|
||||
nn.init.constant_(self.adapter.bias, 0)
|
||||
self.trunk = WiFlowPoseModel(dropout=dropout)
|
||||
# K=17 via the parameter-free adaptive pool: decoder emits [B, 2, 15, 20]
|
||||
# spatial maps; pooling H->17 instead of 15 yields [B, 17, 2] with no new
|
||||
# parameters, so the pretrained state_dict loads strict=True for any K.
|
||||
self.trunk.avg_pool = nn.AdaptiveAvgPool2d((k, 1))
|
||||
|
||||
def forward(self, x):
|
||||
return self.trunk(self.adapter(x))
|
||||
|
||||
|
||||
def load_pretrained_trunk(trunk, path):
|
||||
state = torch.load(path, map_location="cpu", weights_only=True)
|
||||
# Defensive remap as in eval_repro.py (no-op for the retrained checkpoint).
|
||||
renames = {"att.": "attention.", "final_conv.": "decoder."}
|
||||
state = {next((new + k[len(old):] for old, new in renames.items()
|
||||
if k.startswith(old)), k): v
|
||||
for k, v in state.items()}
|
||||
trunk.load_state_dict(state, strict=True)
|
||||
|
||||
|
||||
def pck_torso(pred, target, thresholds=THRESHOLDS):
|
||||
"""Upstream calculate_pck math, torso = l_shoulder(5)<->l_hip(11) for 17-kp COCO."""
|
||||
norm = torch.sqrt(((target[:, L_SHOULDER] - target[:, L_HIP]) ** 2).sum(dim=1))
|
||||
norm = torch.clamp(norm, min=0.01)
|
||||
dist = torch.sqrt(((pred - target) ** 2).sum(dim=2)) / norm.unsqueeze(1)
|
||||
return {f"pck@{int(t * 100)}": (dist <= t).float().mean().item() for t in thresholds}
|
||||
|
||||
|
||||
def mpjpe(pred, target):
|
||||
return torch.sqrt(((pred - target) ** 2).sum(dim=2)).mean().item()
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def predict(model, x, batch=256):
|
||||
model.eval()
|
||||
return torch.cat([model(x[i:i + batch]) for i in range(0, len(x), batch)])
|
||||
|
||||
|
||||
def eval_preds(pred, target):
|
||||
out = pck_torso(pred, target)
|
||||
out["mpjpe"] = mpjpe(pred, target)
|
||||
# Constant-pose detector: std across test frames per coordinate, mean over
|
||||
# the 17x2 coordinates. 0.0 == degenerate constant predictor.
|
||||
out["pred_std"] = pred.std(dim=0).mean().item()
|
||||
return out
|
||||
|
||||
|
||||
def train_run(name, x_tr, y_tr, x_va, y_va, device, pretrained, freeze_trunk,
|
||||
lr_trunk):
|
||||
set_seed(SEED)
|
||||
model = AdaptedWiFlow().to(device)
|
||||
if pretrained:
|
||||
load_pretrained_trunk(model.trunk, CHECKPOINT)
|
||||
if freeze_trunk:
|
||||
for p in model.trunk.parameters():
|
||||
p.requires_grad = False
|
||||
groups = [{"params": model.adapter.parameters(), "lr": LR_ADAPTER}]
|
||||
else:
|
||||
groups = [{"params": model.adapter.parameters(), "lr": LR_ADAPTER},
|
||||
{"params": model.trunk.parameters(), "lr": lr_trunk}]
|
||||
opt = torch.optim.AdamW(groups)
|
||||
loss_fn = nn.MSELoss()
|
||||
|
||||
n = len(x_tr)
|
||||
best_val, best_state, best_epoch, bad = float("inf"), None, -1, 0
|
||||
history = []
|
||||
t0 = time.time()
|
||||
for epoch in range(MAX_EPOCHS):
|
||||
model.train()
|
||||
if freeze_trunk:
|
||||
model.trunk.eval() # keep BatchNorm running stats fixed: pure transfer
|
||||
perm = torch.randperm(n, device=device)
|
||||
ep_loss = 0.0
|
||||
for i in range(0, n, BATCH):
|
||||
idx = perm[i:i + BATCH]
|
||||
opt.zero_grad()
|
||||
loss = loss_fn(model(x_tr[idx]), y_tr[idx])
|
||||
loss.backward()
|
||||
opt.step()
|
||||
ep_loss += loss.item() * len(idx)
|
||||
val_mpjpe = mpjpe(predict(model, x_va), y_va)
|
||||
history.append({"epoch": epoch, "train_mse": ep_loss / n, "val_mpjpe": val_mpjpe})
|
||||
marker = ""
|
||||
if val_mpjpe < best_val:
|
||||
best_val, best_epoch, bad = val_mpjpe, epoch, 0
|
||||
best_state = {k: v.detach().cpu().clone() for k, v in model.state_dict().items()}
|
||||
marker = " *"
|
||||
else:
|
||||
bad += 1
|
||||
print(f"[{name}] epoch {epoch:02d} train_mse {ep_loss / n:.6f} "
|
||||
f"val_mpjpe {val_mpjpe:.5f}{marker}", flush=True)
|
||||
if bad >= PATIENCE:
|
||||
print(f"[{name}] early stop at epoch {epoch} (best {best_epoch})", flush=True)
|
||||
break
|
||||
model.load_state_dict(best_state)
|
||||
torch.save(best_state, os.path.join(MEASB, f"{name}_best.pth"))
|
||||
return model, {"best_epoch": best_epoch, "best_val_mpjpe": best_val,
|
||||
"epochs_run": len(history), "wall_seconds": round(time.time() - t0, 1),
|
||||
"history": history}
|
||||
|
||||
|
||||
def run_suite(tag, csi, kps, device):
|
||||
"""Temporal 70/15/15 split, mean-pose baseline, three training runs."""
|
||||
n = len(csi)
|
||||
tr, va, te = temporal_split(n)
|
||||
print(f"=== suite {tag}: n={n} train={tr.stop} val={va.stop - va.start} "
|
||||
f"test={te.stop - te.start} ===", flush=True)
|
||||
|
||||
# CSI normalization constant from TRAIN split only.
|
||||
train_p99 = float(np.percentile(csi[tr], 99))
|
||||
train_max = float(csi[tr].max())
|
||||
print(f"[{tag}] train p99={train_p99:.3f} max={train_max:.3f} -> /p99, clip [0,1]",
|
||||
flush=True)
|
||||
csi_n = np.clip(csi / train_p99, 0.0, 1.0).astype(np.float32)
|
||||
|
||||
x = torch.from_numpy(csi_n).to(device)
|
||||
y = torch.from_numpy(kps).to(device)
|
||||
x_tr, y_tr = x[tr], y[tr]
|
||||
x_va, y_va = x[va], y[va]
|
||||
x_te, y_te = x[te], y[te]
|
||||
|
||||
suite = {
|
||||
"n_windows": n,
|
||||
"split": {"n_train": int(tr.stop), "n_val": int(va.stop - va.start),
|
||||
"n_test": int(te.stop - te.start)},
|
||||
"csi_norm": {"method": "divide by train-split p99 amplitude, clip [0,1]",
|
||||
"train_p99": train_p99, "train_max": train_max},
|
||||
"runs": {},
|
||||
}
|
||||
|
||||
# Honesty bar: mean-pose predictor fit on TRAIN, evaluated on TEST.
|
||||
mean_pose = y_tr.mean(dim=0, keepdim=True).expand(len(y_te), -1, -1)
|
||||
suite["mean_pose_baseline"] = eval_preds(mean_pose, y_te)
|
||||
suite["mean_pose_baseline"]["note"] = "train-split mean pose; pred_std 0 by construction"
|
||||
print(f"[{tag}] mean-pose baseline:", json.dumps(suite["mean_pose_baseline"]),
|
||||
flush=True)
|
||||
|
||||
configs = [
|
||||
("pretrained", dict(pretrained=True, freeze_trunk=False, lr_trunk=LR_TRUNK_FT)),
|
||||
("scratch", dict(pretrained=False, freeze_trunk=False, lr_trunk=LR_ADAPTER)),
|
||||
("frozen_trunk", dict(pretrained=True, freeze_trunk=True, lr_trunk=0.0)),
|
||||
]
|
||||
for name, cfg in configs:
|
||||
print(f"=== run: {tag}/{name} {cfg} ===", flush=True)
|
||||
model, train_info = train_run(f"{tag}_{name}", x_tr, y_tr, x_va, y_va,
|
||||
device, **cfg)
|
||||
test_metrics = eval_preds(predict(model, x_te), y_te)
|
||||
n_trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
|
||||
suite["runs"][name] = {"config": cfg, "trainable_params": n_trainable,
|
||||
"train": {k: v for k, v in train_info.items()
|
||||
if k != "history"},
|
||||
"history": train_info["history"],
|
||||
"test": test_metrics}
|
||||
print(f"[{tag}/{name}] TEST:", json.dumps(test_metrics), flush=True)
|
||||
return suite
|
||||
|
||||
|
||||
def main():
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
print(f"device {device}, torch {torch.__version__}", flush=True)
|
||||
set_seed(SEED)
|
||||
|
||||
csi, kps, confs, native70, shape_counts, ts_first, ts_last = load_dataset()
|
||||
print(f"shape distribution: {shape_counts}", flush=True)
|
||||
|
||||
results = {
|
||||
"protocol": {
|
||||
"dataset": DATA, "n_windows": len(csi),
|
||||
"ts_first": ts_first, "ts_last": ts_last,
|
||||
"conf_mean": float(confs.mean()), "conf_min": float(confs.min()),
|
||||
"csi_shape_distribution": shape_counts,
|
||||
"csi_layout_note": "aligner stores frame-major data under a transposed "
|
||||
"[nSc, nFrames] shape label; corrected on load",
|
||||
"csi_resample": "per-frame linear interp of subcarrier axis to 70 bins "
|
||||
"(identity for native-70 frames); native-70 windows still "
|
||||
"contain ~20.4% internally zero-padded short frames",
|
||||
"split": "temporal 70/15/15 (no shuffle across time)",
|
||||
"model": "1x1 Conv1d 70->540 adapter + WiFlowPoseModel trunk, "
|
||||
"AdaptiveAvgPool2d((17,1)) head (parameter-free K=17)",
|
||||
"checkpoint": CHECKPOINT,
|
||||
"checkpoint_note": "measurement-(a) retrained checkpoint (~96% PCK@20 on "
|
||||
"WiFlow data); att./final_conv. remap applied "
|
||||
"defensively (no-op, already new-style keys)",
|
||||
"optimizer": f"AdamW, adapter lr {LR_ADAPTER}, fine-tuned trunk lr "
|
||||
f"{LR_TRUNK_FT} (10x lower), scratch all {LR_ADAPTER}",
|
||||
"batch": BATCH, "max_epochs": MAX_EPOCHS, "patience": PATIENCE,
|
||||
"precision": "fp32", "seed": SEED,
|
||||
"pck": "torso-normalized, torso = ||l_shoulder(5) - l_hip(11)||, "
|
||||
"clamp min 0.01, mean over keypoints x frames "
|
||||
"(upstream math; upstream 2/12 indices are a 15-kp convention)",
|
||||
},
|
||||
# Primary: all 2,046 windows (pre-registered n), subcarrier axis resampled.
|
||||
"all2046": None,
|
||||
# Secondary robustness check: the 1,347 native [70,20] windows only.
|
||||
"native70": None,
|
||||
}
|
||||
|
||||
results["all2046"] = run_suite("all2046", csi, kps, device)
|
||||
results["native70"] = run_suite("native70", csi[native70], kps[native70], device)
|
||||
|
||||
out = os.path.join(MEASB, "measurement_b.json")
|
||||
with open(out, "w") as f:
|
||||
json.dump(results, f, indent=2)
|
||||
print(f"wrote {out}", flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,33 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -ex
|
||||
cd ~/wiflow-std-bench
|
||||
|
||||
# 1. clone upstream at the pinned commit
|
||||
if [ ! -d upstream ]; then
|
||||
git clone https://github.com/DY2434/WiFlow-WiFi-Pose-Estimation-with-Spatio-Temporal-Decoupling upstream
|
||||
fi
|
||||
cd upstream && git checkout 06899d294a0f44709d601a53e91dbf24759daefb && cd ..
|
||||
|
||||
# 2. documented deviation: fix upstream import bug (TemporalConvNet does not exist)
|
||||
sed -i 's/from .tcn import TemporalConvNet/from .tcn import TemporalBlock/; s/'"'"'TemporalConvNet'"'"'/'"'"'TemporalBlock'"'"'/' upstream/models/__init__.py
|
||||
|
||||
# 3. venv: torch cu128 (RTX 5080 = sm_120 needs >=2.7; their pin 2.3.1 predates Blackwell)
|
||||
if [ ! -d venv ]; then
|
||||
python3 -m venv venv
|
||||
./venv/bin/pip install -q --upgrade pip
|
||||
./venv/bin/pip install -q torch --index-url https://download.pytorch.org/whl/cu128
|
||||
./venv/bin/pip install -q numpy pandas matplotlib seaborn scikit-learn opencv-python-headless scipy tqdm psutil kagglehub
|
||||
fi
|
||||
./venv/bin/python -c "import torch; print(torch.__version__, torch.cuda.is_available(), torch.cuda.get_device_name(0))"
|
||||
|
||||
# 4. dataset via kagglehub (anonymous, public dataset)
|
||||
DS=$(./venv/bin/python -c "import kagglehub; print(kagglehub.dataset_download('kaka2434/wiflow-dataset'))")
|
||||
echo "dataset at: $DS"
|
||||
|
||||
# 5. run.py hardcodes ../preprocessed_csi_data relative to upstream/
|
||||
ln -sfn "$DS/preprocessed_csi_data" ~/wiflow-std-bench/preprocessed_csi_data
|
||||
|
||||
# 6. train with upstream defaults (seed 42 set inside run.py)
|
||||
../venv/bin/python ../clean_nan.py 2>/dev/null || venv/bin/python clean_nan.py
|
||||
cd upstream
|
||||
../venv/bin/python run.py --gpu 0 --batch_size 64 --epochs 50 --output_dir ../train_output
|
||||
@@ -1,332 +0,0 @@
|
||||
"""Configurable compact variants of the WiFlow-STD pose model (ADR-152 efficiency sweep).
|
||||
|
||||
This is a parameterized copy of upstream models/{pose_model,tcn,convnet,attention}.py
|
||||
(DY2434/WiFlow @ 06899d29, Apache-2.0). upstream/ is NOT modified. Deviations from
|
||||
upstream, all forced by shrinking channels and documented per variant in run_sweep.py:
|
||||
|
||||
1. TCN grouped-conv groups: upstream hardcodes groups=20, which does not divide
|
||||
the compact channel counts (e.g. 270, 135, 85). Rule here:
|
||||
- groups_mode='gcd20': per-conv groups = gcd(channels, 20) (== 20 wherever
|
||||
upstream's choice is valid, incl. the 540-ch input conv; falls back to the
|
||||
largest common divisor with 20 otherwise).
|
||||
- groups_mode='depthwise': groups = channels (tiny variant only).
|
||||
2. Conv2d downsampling strides: upstream uses 4 stride-(1,2) blocks because
|
||||
240/2^4 = 15 == n_keypoints. With smaller TCN output widths that would leave
|
||||
<15 rows and AdaptiveAvgPool2d((15,1)) would duplicate rows across keypoints.
|
||||
Rule: halve the width only while the result stays >= 15 (stride-2 blocks
|
||||
first, stride-1 after). Full model: 240 -> 4 halvings = upstream exactly.
|
||||
3. input_pw_groups (tiny only): the dense 540->c pointwise + residual downsample
|
||||
in TCN block 1 cost 2*540*c params (a ~117k floor that alone exceeds the
|
||||
tiny <100k budget). tiny groups these two convs (groups=4; 4 | gcd(540, 68)).
|
||||
4. Decoder mid-channels: upstream 64->32; here c_last -> max(c_last // 2, 4).
|
||||
"""
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
def tcn_groups(channels: int, mode: str) -> int:
|
||||
if mode == 'depthwise':
|
||||
return channels
|
||||
if mode == 'gcd20':
|
||||
return math.gcd(channels, 20)
|
||||
raise ValueError(mode)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- TCN (copy of tcn.py)
|
||||
class Chomp1d(nn.Module):
|
||||
def __init__(self, chomp_size):
|
||||
super().__init__()
|
||||
self.chomp_size = chomp_size
|
||||
|
||||
def forward(self, x):
|
||||
return x[:, :, :-self.chomp_size].contiguous()
|
||||
|
||||
|
||||
class CompactGroupedTemporalBlock(nn.Module):
|
||||
"""Upstream InnerGroupedTemporalBlock with parameterized groups."""
|
||||
|
||||
def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding,
|
||||
dropout=0.2, groups_mode='gcd20', pw_groups=1):
|
||||
super().__init__()
|
||||
g_in = tcn_groups(n_inputs, groups_mode)
|
||||
g_out = tcn_groups(n_outputs, groups_mode)
|
||||
self.groups = (g_in, g_out)
|
||||
self.pw_groups = pw_groups
|
||||
|
||||
self.conv1_group = nn.Conv1d(n_inputs, n_inputs, kernel_size, stride=stride,
|
||||
padding=padding, dilation=dilation,
|
||||
groups=g_in, bias=False)
|
||||
self.chomp1 = Chomp1d(padding) if padding > 0 else nn.Identity()
|
||||
self.bn1_group = nn.BatchNorm1d(n_inputs)
|
||||
self.relu1_group = nn.SiLU(inplace=True)
|
||||
|
||||
self.conv1_pw = nn.Conv1d(n_inputs, n_outputs, 1, groups=pw_groups, bias=False)
|
||||
self.bn1_pw = nn.BatchNorm1d(n_outputs)
|
||||
self.relu1_pw = nn.SiLU(inplace=True)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
|
||||
self.conv2_group = nn.Conv1d(n_outputs, n_outputs, kernel_size, stride=1,
|
||||
padding=padding, dilation=dilation,
|
||||
groups=g_out, bias=False)
|
||||
self.chomp2 = Chomp1d(padding) if padding > 0 else nn.Identity()
|
||||
self.bn2_group = nn.BatchNorm1d(n_outputs)
|
||||
self.relu2_group = nn.SiLU(inplace=True)
|
||||
|
||||
self.conv2_pw = nn.Conv1d(n_outputs, n_outputs, 1, bias=False)
|
||||
self.bn2_pw = nn.BatchNorm1d(n_outputs)
|
||||
self.relu2_pw = nn.SiLU(inplace=True)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
|
||||
self.downsample = nn.Sequential(
|
||||
nn.Conv1d(n_inputs, n_outputs, 1, groups=pw_groups, bias=False),
|
||||
nn.BatchNorm1d(n_outputs)
|
||||
) if n_inputs != n_outputs else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
res = self.downsample(x)
|
||||
out = self.conv1_group(x)
|
||||
out = self.chomp1(out)
|
||||
out = self.bn1_group(out)
|
||||
out = self.relu1_group(out)
|
||||
out = self.conv1_pw(out)
|
||||
out = self.bn1_pw(out)
|
||||
out = self.relu1_pw(out)
|
||||
out = self.dropout1(out)
|
||||
out = self.conv2_group(out)
|
||||
out = self.chomp2(out)
|
||||
out = self.bn2_group(out)
|
||||
out = self.relu2_group(out)
|
||||
out = self.conv2_pw(out)
|
||||
out = self.bn2_pw(out)
|
||||
out = self.relu2_pw(out)
|
||||
out = self.dropout2(out)
|
||||
return F.silu(out + res)
|
||||
|
||||
|
||||
class CompactTemporalBlock(nn.Module):
|
||||
def __init__(self, num_inputs, num_channels, kernel_size=3, dropout=0.2,
|
||||
groups_mode='gcd20', input_pw_groups=1):
|
||||
super().__init__()
|
||||
layers = []
|
||||
for i, out_channels in enumerate(num_channels):
|
||||
dilation_size = 2 ** i
|
||||
in_channels = num_inputs if i == 0 else num_channels[i - 1]
|
||||
layers.append(CompactGroupedTemporalBlock(
|
||||
in_channels, out_channels, kernel_size, stride=1,
|
||||
dilation=dilation_size, padding=(kernel_size - 1) * dilation_size,
|
||||
dropout=dropout, groups_mode=groups_mode,
|
||||
pw_groups=input_pw_groups if i == 0 else 1))
|
||||
self.network = nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x):
|
||||
return self.network(x)
|
||||
|
||||
|
||||
# ------------------------------------------------------- Conv2d path (copy of convnet.py)
|
||||
class AsymmetricConvBlock(nn.Module):
|
||||
"""Upstream block with parameterized width stride (upstream: always (1,2))."""
|
||||
|
||||
def __init__(self, in_channels, out_channels, dropout=0.3, stride_w=2):
|
||||
super().__init__()
|
||||
self.block = nn.Sequential(
|
||||
nn.Conv2d(in_channels, out_channels, kernel_size=(1, 3),
|
||||
stride=(1, stride_w), padding=(0, 1)),
|
||||
nn.BatchNorm2d(out_channels),
|
||||
nn.SiLU(inplace=True),
|
||||
nn.Dropout2d(dropout),
|
||||
nn.Conv2d(out_channels, out_channels, kernel_size=(1, 3), padding=(0, 1)),
|
||||
nn.BatchNorm2d(out_channels),
|
||||
nn.SiLU(inplace=True),
|
||||
nn.Dropout2d(dropout),
|
||||
nn.Conv2d(out_channels, out_channels, kernel_size=(1, 3), padding=(0, 1)),
|
||||
nn.BatchNorm2d(out_channels)
|
||||
)
|
||||
self.downsample = nn.Sequential(
|
||||
nn.Conv2d(in_channels, out_channels, kernel_size=1,
|
||||
stride=(1, stride_w), bias=False),
|
||||
nn.BatchNorm2d(out_channels)
|
||||
)
|
||||
self.activation = nn.SiLU(inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
return self.activation(self.block(x) + self.downsample(x))
|
||||
|
||||
|
||||
class ConvBlock1(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, dropout=0.3):
|
||||
super().__init__()
|
||||
self.block = nn.Sequential(
|
||||
nn.Conv2d(in_channels, out_channels, kernel_size=(1, 3), padding=(0, 1)),
|
||||
nn.BatchNorm2d(out_channels),
|
||||
nn.SiLU(inplace=True),
|
||||
nn.Dropout2d(dropout),
|
||||
nn.Conv2d(out_channels, out_channels, kernel_size=(1, 3), padding=(0, 1)),
|
||||
nn.BatchNorm2d(out_channels),
|
||||
nn.SiLU(inplace=True),
|
||||
nn.Dropout2d(dropout),
|
||||
nn.Conv2d(out_channels, out_channels, kernel_size=(1, 3), padding=(0, 1)),
|
||||
nn.BatchNorm2d(out_channels)
|
||||
)
|
||||
self.downsample = nn.Sequential(
|
||||
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
|
||||
nn.BatchNorm2d(out_channels)
|
||||
)
|
||||
self.activation = nn.SiLU(inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
return self.activation(self.block(x) + self.downsample(x))
|
||||
|
||||
|
||||
# ----------------------------------------------------- attention (verbatim attention.py)
|
||||
class AxialAttention(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, groups=8, stride=1, bias=False, width=False):
|
||||
assert (in_planes % groups == 0) and (out_planes % groups == 0)
|
||||
super().__init__()
|
||||
self.in_planes = in_planes
|
||||
self.out_planes = out_planes
|
||||
self.groups = groups
|
||||
self.group_planes = out_planes // groups
|
||||
self.stride = stride
|
||||
self.bias = bias
|
||||
self.width = width
|
||||
self.qkv_transform = nn.Conv1d(in_planes, out_planes * 3, kernel_size=1,
|
||||
stride=1, padding=0, bias=False)
|
||||
self.bn_qkv = nn.BatchNorm1d(out_planes * 3)
|
||||
self.bn_similarity = nn.BatchNorm2d(groups)
|
||||
self.bn_output = nn.BatchNorm1d(out_planes)
|
||||
if stride > 1:
|
||||
self.pooling = nn.AvgPool2d(stride, stride=stride)
|
||||
nn.init.normal_(self.qkv_transform.weight.data, 0, math.sqrt(1. / self.in_planes))
|
||||
|
||||
def forward(self, x):
|
||||
if self.width:
|
||||
x = x.permute(0, 2, 1, 3)
|
||||
else:
|
||||
x = x.permute(0, 3, 1, 2)
|
||||
N, W, C, H = x.shape
|
||||
x = x.contiguous().view(N * W, C, H)
|
||||
qkv = self.bn_qkv(self.qkv_transform(x))
|
||||
qkv = qkv.reshape(N * W, 3, self.out_planes, H).permute(1, 0, 2, 3)
|
||||
q, k, v = qkv[0], qkv[1], qkv[2]
|
||||
q = q.reshape(N * W, self.groups, self.group_planes, H)
|
||||
k = k.reshape(N * W, self.groups, self.group_planes, H)
|
||||
v = v.reshape(N * W, self.groups, self.group_planes, H)
|
||||
qk = torch.einsum('bgci, bgcj->bgij', q, k)
|
||||
qk = self.bn_similarity(qk)
|
||||
similarity = F.softmax(qk, dim=-1)
|
||||
sv = torch.einsum('bgij,bgcj->bgci', similarity, v)
|
||||
sv = sv.reshape(N * W, self.out_planes, H)
|
||||
out = self.bn_output(sv)
|
||||
out = out.view(N, W, self.out_planes, H)
|
||||
if self.width:
|
||||
out = out.permute(0, 2, 1, 3)
|
||||
else:
|
||||
out = out.permute(0, 2, 3, 1)
|
||||
if self.stride > 1:
|
||||
out = self.pooling(out)
|
||||
return out
|
||||
|
||||
|
||||
class DualAxialAttention(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, groups=8, stride=1, bias=False):
|
||||
super().__init__()
|
||||
self.width_axis = AxialAttention(in_planes, out_planes, groups, stride, bias, width=True)
|
||||
self.height_axis = AxialAttention(out_planes, out_planes, groups, stride, bias, width=False)
|
||||
|
||||
def forward(self, x):
|
||||
return self.height_axis(self.width_axis(x))
|
||||
|
||||
|
||||
# --------------------------------------------------------------- full model
|
||||
def compute_strides(width: int, n_blocks: int, target: int = 15):
|
||||
"""Halve width while result stays >= target (upstream: 240 -> 4 halvings -> 15)."""
|
||||
strides = []
|
||||
for _ in range(n_blocks):
|
||||
nxt = (width + 1) // 2 # conv k=3 s=2 p=1: out = ceil(in/2)
|
||||
if nxt >= target:
|
||||
strides.append(2)
|
||||
width = nxt
|
||||
else:
|
||||
strides.append(1)
|
||||
return strides, width
|
||||
|
||||
|
||||
class CompactWiFlowPoseModel(nn.Module):
|
||||
"""Parameterized upstream WiFlowPoseModel.
|
||||
|
||||
Upstream config == tcn_channels=[540,440,340,240], conv_channels=[8,16,32,64],
|
||||
attn_groups=8, groups_mode='gcd20' (gcd(c,20)==20 for all upstream channels),
|
||||
input_pw_groups=1 -> identical architecture, 2,225,042 params.
|
||||
"""
|
||||
|
||||
def __init__(self, tcn_channels, conv_channels, attn_groups,
|
||||
groups_mode='gcd20', input_pw_groups=1, dropout=0.3,
|
||||
num_subcarriers=540, num_keypoints=15):
|
||||
super().__init__()
|
||||
self.tcn = CompactTemporalBlock(
|
||||
num_inputs=num_subcarriers, num_channels=tcn_channels, kernel_size=3,
|
||||
dropout=dropout, groups_mode=groups_mode, input_pw_groups=input_pw_groups)
|
||||
|
||||
self.up = ConvBlock1(1, conv_channels[0])
|
||||
|
||||
strides, self.final_width = compute_strides(
|
||||
tcn_channels[-1], len(conv_channels), target=num_keypoints)
|
||||
self.conv_strides = strides
|
||||
self.residual_blocks = nn.ModuleList()
|
||||
in_channels = conv_channels[0]
|
||||
for out_channels, s in zip(conv_channels, strides):
|
||||
self.residual_blocks.append(
|
||||
AsymmetricConvBlock(in_channels, out_channels, stride_w=s))
|
||||
in_channels = out_channels
|
||||
|
||||
c_last = conv_channels[-1]
|
||||
self.attention = DualAxialAttention(c_last, c_last, groups=attn_groups)
|
||||
|
||||
c_mid = max(c_last // 2, 4)
|
||||
self.decoder = nn.Sequential(
|
||||
nn.Conv2d(c_last, c_mid, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(c_mid),
|
||||
nn.SiLU(inplace=True),
|
||||
nn.Conv2d(c_mid, 2, kernel_size=1),
|
||||
nn.BatchNorm2d(2),
|
||||
nn.SiLU(inplace=True)
|
||||
)
|
||||
self.avg_pool = nn.AdaptiveAvgPool2d((num_keypoints, 1))
|
||||
self._initialize_weights()
|
||||
|
||||
def _initialize_weights(self):
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv1d):
|
||||
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, (nn.BatchNorm1d, nn.LayerNorm)):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.Linear):
|
||||
nn.init.xavier_normal_(m.weight)
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
def forward(self, x):
|
||||
# [B, 540, 20]
|
||||
x = self.tcn(x) # [B, C_tcn, 20]
|
||||
x = x.transpose(1, 2).unsqueeze(1) # [B, 1, 20, C_tcn]
|
||||
x = self.up(x)
|
||||
for block in self.residual_blocks:
|
||||
x = block(x) # [B, C_conv, 20, W']
|
||||
x = x.permute(0, 1, 3, 2) # [B, C_conv, W', 20]
|
||||
x = self.attention(x)
|
||||
x = self.decoder(x) # [B, 2, W', 20]
|
||||
x = self.avg_pool(x).squeeze(-1) # [B, 2, 15]
|
||||
return x.transpose(1, 2) # [B, 15, 2]
|
||||
|
||||
|
||||
def describe(model: 'CompactWiFlowPoseModel'):
|
||||
params = sum(p.numel() for p in model.parameters())
|
||||
tcn_g = [blk.groups for blk in model.tcn.network]
|
||||
return {'params': params, 'tcn_groups_per_block': tcn_g,
|
||||
'conv_strides': model.conv_strides, 'final_width': model.final_width}
|
||||
@@ -1,278 +0,0 @@
|
||||
"""WiFlow-STD compact-variant efficiency sweep (ADR-152) — sequential overnight runner.
|
||||
|
||||
Trains compact variants of the upstream WiFlow-STD architecture on the same
|
||||
data/split as the full-size reference retraining (seed 42, file-level 70/15/15,
|
||||
upstream dataset.py) and evaluates PCK@10..50 + MPJPE on the full test split and
|
||||
the corruption-free test subset (file indices < 487).
|
||||
|
||||
Training mirrors upstream run.py/train.py defaults except:
|
||||
- fp32 only (no fp16 autocast / GradScaler — avoids the BN-poisoning trap
|
||||
documented in RESULTS.md defect 5; data on disk is already cleaned).
|
||||
- batch 64 (kept modest: another GPU job may share the 16 GB card tonight).
|
||||
- scheduler + early stopping keyed on val MPJPE (upstream early-stops on val MPE
|
||||
with patience 5; same here).
|
||||
|
||||
Usage:
|
||||
venv/bin/python sweep/run_sweep.py --dry-run # param counts only
|
||||
nohup venv/bin/python sweep/run_sweep.py > sweep/sweep.log 2>&1 &
|
||||
|
||||
Idempotent: variants already present in sweep/results.jsonl are skipped.
|
||||
|
||||
NOTE: deployed to ruvultra (~/wiflow-std-bench/sweep) as a standalone file, so
|
||||
it deliberately inlines its helpers. The reference implementations (upstream
|
||||
import shim, >1GB np.load mmap patch, key-remap loader, canonical evaluate
|
||||
loop) live in benchmarks/wiflow-std/_bench_common.py — keep copies in sync.
|
||||
"""
|
||||
import argparse
|
||||
import copy
|
||||
import json
|
||||
import os
|
||||
import random
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch.utils.data import DataLoader, Subset
|
||||
|
||||
# csi_windows.npy is ~13 GB; mmap large arrays instead of eagerly loading
|
||||
# ~15 GB into RAM (same patch as _bench_common._np_load_mmap).
|
||||
_np_load = np.load
|
||||
|
||||
|
||||
def _np_load_mmap(path, *a, **kw):
|
||||
if (isinstance(path, str) and path.endswith('.npy')
|
||||
and os.path.getsize(path) > 1 << 30 and 'mmap_mode' not in kw):
|
||||
kw['mmap_mode'] = 'r'
|
||||
return _np_load(path, *a, **kw)
|
||||
|
||||
|
||||
np.load = _np_load_mmap
|
||||
|
||||
BENCH = os.path.expanduser('~/wiflow-std-bench')
|
||||
SWEEP = os.path.join(BENCH, 'sweep')
|
||||
sys.path.insert(0, os.path.join(BENCH, 'upstream'))
|
||||
sys.path.insert(0, SWEEP)
|
||||
|
||||
from dataset import PreprocessedCSIKeypointsDataset, create_preprocessed_train_val_test_loaders # noqa: E402
|
||||
from losses.pose_loss import PoseLoss # noqa: E402
|
||||
from utils.metrics import calculate_pck, calculate_mpjpe # noqa: E402
|
||||
from model_compact import CompactWiFlowPoseModel, describe # noqa: E402
|
||||
|
||||
VARIANTS = [
|
||||
# name, tcn_channels, conv_channels, attn_groups, groups_mode, input_pw_groups
|
||||
dict(name='half', tcn=[270, 220, 170, 120], conv=[4, 8, 16, 32], attn_groups=4,
|
||||
groups_mode='gcd20', input_pw_groups=1),
|
||||
dict(name='quarter', tcn=[135, 110, 85, 60], conv=[2, 4, 8, 16], attn_groups=2,
|
||||
groups_mode='gcd20', input_pw_groups=1),
|
||||
dict(name='tiny', tcn=[68, 56, 44, 32], conv=[2, 4, 8, 16], attn_groups=2,
|
||||
groups_mode='depthwise', input_pw_groups=4),
|
||||
]
|
||||
|
||||
BATCH = 64
|
||||
EPOCHS = 50
|
||||
PATIENCE = 5
|
||||
LR = 1e-4
|
||||
WEIGHT_DECAY = 5e-5
|
||||
SEED = 42
|
||||
CORRUPT_FILE_START = 487 # files 487-499 were zero-filled by clean_nan.py
|
||||
|
||||
|
||||
def set_seed(seed=SEED):
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
|
||||
|
||||
def build_model(v, dropout=0.5):
|
||||
return CompactWiFlowPoseModel(
|
||||
tcn_channels=v['tcn'], conv_channels=v['conv'], attn_groups=v['attn_groups'],
|
||||
groups_mode=v['groups_mode'], input_pw_groups=v['input_pw_groups'],
|
||||
dropout=dropout)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def evaluate(model, loader, device):
|
||||
model.eval()
|
||||
totals = {t: 0.0 for t in (0.1, 0.2, 0.3, 0.4, 0.5)}
|
||||
total_mpe, n = 0.0, 0
|
||||
for bx, by in loader:
|
||||
bx, by = bx.to(device), by.to(device)
|
||||
out = model(bx)
|
||||
bs = by.size(0)
|
||||
total_mpe += calculate_mpjpe(out, by) * bs
|
||||
pck = calculate_pck(out, by, thresholds=list(totals))
|
||||
for t in totals:
|
||||
totals[t] += pck[t] * bs
|
||||
n += bs
|
||||
return {'samples': n, 'mpjpe': total_mpe / n,
|
||||
**{f'pck@{int(t * 100)}': totals[t] / n for t in totals}}
|
||||
|
||||
|
||||
def train_variant(v, dataset, device):
|
||||
set_seed(SEED)
|
||||
train_loader, val_loader, test_loader = create_preprocessed_train_val_test_loaders(
|
||||
dataset=dataset, batch_size=BATCH, num_workers=2, random_seed=SEED)
|
||||
|
||||
set_seed(SEED) # re-seed after split so init is split-independent
|
||||
model = build_model(v).to(device)
|
||||
info = describe(model)
|
||||
print(f"[{v['name']}] params={info['params']:,} tcn_groups={info['tcn_groups_per_block']} "
|
||||
f"conv_strides={info['conv_strides']} final_width={info['final_width']}", flush=True)
|
||||
|
||||
criterion = PoseLoss(position_weight=1.0, bone_weight=0.2, loss_type='smooth_l1')
|
||||
optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY,
|
||||
betas=(0.9, 0.999))
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
|
||||
optimizer, mode='min', factor=0.5, patience=3, min_lr=LR / 1000,
|
||||
cooldown=1, threshold=1e-4)
|
||||
|
||||
best_val_mpe = float('inf')
|
||||
best_val_pck20 = 0.0
|
||||
best_epoch = 0
|
||||
best_state = None
|
||||
patience_counter = 0
|
||||
t0 = time.time()
|
||||
error = None
|
||||
epochs_run = 0
|
||||
|
||||
for epoch in range(1, EPOCHS + 1):
|
||||
model.train()
|
||||
ep_loss, nb = 0.0, 0
|
||||
te = time.time()
|
||||
for i, (bx, by) in enumerate(train_loader):
|
||||
bx = bx.to(device, non_blocking=True)
|
||||
by = by.to(device, non_blocking=True)
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
out = model(bx)
|
||||
loss, _parts = criterion(out, by)
|
||||
if not torch.isfinite(loss):
|
||||
error = f'non-finite loss at epoch {epoch} step {i}'
|
||||
break
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
ep_loss += loss.item()
|
||||
nb += 1
|
||||
if epoch == 1 and i % 500 == 0:
|
||||
print(f"[{v['name']}] e1 step {i}/{len(train_loader)} loss={loss.item():.5f}",
|
||||
flush=True)
|
||||
if error:
|
||||
break
|
||||
epochs_run = epoch
|
||||
|
||||
val = evaluate(model, val_loader, device)
|
||||
scheduler.step(val['mpjpe'])
|
||||
lr_now = optimizer.param_groups[0]['lr']
|
||||
print(f"[{v['name']}] epoch {epoch}/{EPOCHS} train_loss={ep_loss / max(nb, 1):.5f} "
|
||||
f"val_mpjpe={val['mpjpe']:.5f} val_pck20={val['pck@20'] * 100:.2f}% "
|
||||
f"lr={lr_now:.2e} ({time.time() - te:.0f}s)", flush=True)
|
||||
|
||||
if val['mpjpe'] < best_val_mpe:
|
||||
best_val_mpe = val['mpjpe']
|
||||
best_val_pck20 = val['pck@20']
|
||||
best_epoch = epoch
|
||||
best_state = copy.deepcopy(model.state_dict())
|
||||
patience_counter = 0
|
||||
else:
|
||||
patience_counter += 1
|
||||
if patience_counter >= PATIENCE:
|
||||
print(f"[{v['name']}] early stop at epoch {epoch} (best {best_epoch})", flush=True)
|
||||
break
|
||||
|
||||
train_seconds = time.time() - t0
|
||||
result = {
|
||||
'variant': v['name'], 'params': info['params'],
|
||||
'tcn_channels': v['tcn'], 'conv_channels': v['conv'],
|
||||
'attn_groups': v['attn_groups'], 'groups_mode': v['groups_mode'],
|
||||
'input_pw_groups': v['input_pw_groups'],
|
||||
'tcn_groups_per_block': info['tcn_groups_per_block'],
|
||||
'conv_strides': info['conv_strides'], 'final_width': info['final_width'],
|
||||
'batch_size': BATCH, 'max_epochs': EPOCHS, 'patience': PATIENCE,
|
||||
'lr': LR, 'weight_decay': WEIGHT_DECAY, 'seed': SEED, 'precision': 'fp32',
|
||||
'epochs_run': epochs_run, 'best_epoch': best_epoch,
|
||||
'best_val_mpjpe': best_val_mpe if best_state else None,
|
||||
'best_val_pck20': best_val_pck20 if best_state else None,
|
||||
'train_seconds': round(train_seconds, 1),
|
||||
'torch': torch.__version__, 'error': error,
|
||||
'finished_utc': time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime()),
|
||||
}
|
||||
|
||||
if best_state is not None:
|
||||
ckpt = os.path.join(SWEEP, f"{v['name']}_best.pth")
|
||||
torch.save(best_state, ckpt)
|
||||
result['checkpoint'] = ckpt
|
||||
model.load_state_dict(best_state)
|
||||
|
||||
eval_loader = DataLoader(test_loader.dataset, batch_size=256, shuffle=False,
|
||||
num_workers=2)
|
||||
result['test_full'] = evaluate(model, eval_loader, device)
|
||||
|
||||
w2f = dataset.window_to_file
|
||||
clean_idx = [i for i in test_loader.dataset.indices if w2f[i] < CORRUPT_FILE_START]
|
||||
clean_loader = DataLoader(Subset(dataset, clean_idx), batch_size=256,
|
||||
shuffle=False, num_workers=2)
|
||||
result['test_clean'] = evaluate(model, clean_loader, device)
|
||||
print(f"[{v['name']}] TEST clean: pck20={result['test_clean']['pck@20'] * 100:.2f}% "
|
||||
f"mpjpe={result['test_clean']['mpjpe']:.5f} | full: "
|
||||
f"pck20={result['test_full']['pck@20'] * 100:.2f}%", flush=True)
|
||||
return result
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument('--dry-run', action='store_true', help='print param counts and exit')
|
||||
args = ap.parse_args()
|
||||
|
||||
if args.dry_run:
|
||||
for v in VARIANTS:
|
||||
m = build_model(v)
|
||||
info = describe(m)
|
||||
x = torch.randn(2, 540, 20)
|
||||
m.eval()
|
||||
y = m(x)
|
||||
print(f"{v['name']:8s} params={info['params']:>9,} "
|
||||
f"tcn={v['tcn']} conv={v['conv']} attn_g={v['attn_groups']} "
|
||||
f"mode={v['groups_mode']} pw_g={v['input_pw_groups']} "
|
||||
f"tcn_groups={info['tcn_groups_per_block']} strides={info['conv_strides']} "
|
||||
f"W'={info['final_width']} out={tuple(y.shape)}")
|
||||
return
|
||||
|
||||
results_path = os.path.join(SWEEP, 'results.jsonl')
|
||||
done = set()
|
||||
if os.path.exists(results_path):
|
||||
with open(results_path) as f:
|
||||
for line in f:
|
||||
try:
|
||||
done.add(json.loads(line)['variant'])
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
device = torch.device('cuda')
|
||||
print(f"torch {torch.__version__} on {torch.cuda.get_device_name(0)}", flush=True)
|
||||
data_dir = os.path.join(BENCH, 'preprocessed_csi_data')
|
||||
dataset = PreprocessedCSIKeypointsDataset(data_dir=data_dir, keypoint_scale=1000.0,
|
||||
enable_temporal_clean=True)
|
||||
|
||||
for v in VARIANTS:
|
||||
if v['name'] in done:
|
||||
print(f"[{v['name']}] already in results.jsonl — skipping", flush=True)
|
||||
continue
|
||||
print(f"\n===== variant: {v['name']} =====", flush=True)
|
||||
try:
|
||||
result = train_variant(v, dataset, device)
|
||||
except Exception as e: # record and move on to next variant
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
result = {'variant': v['name'], 'error': repr(e),
|
||||
'finished_utc': time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime())}
|
||||
with open(results_path, 'a') as f:
|
||||
f.write(json.dumps(result) + '\n')
|
||||
f.flush()
|
||||
print('\nSWEEP COMPLETE', flush=True)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Binary file not shown.
@@ -1,772 +0,0 @@
|
||||
{
|
||||
"torch": {
|
||||
"env": {
|
||||
"torch": "2.12.0+cpu",
|
||||
"platform": "Windows-11-10.0.26200-SP0",
|
||||
"processor": "Intel64 Family 6 Model 197 Stepping 2, GenuineIntel",
|
||||
"num_threads": 16,
|
||||
"checkpoint": "results\\retrained_best_pose_model.pth",
|
||||
"params": 2225042
|
||||
},
|
||||
"variants": {
|
||||
"fp32": {
|
||||
"file": "retrained_fp32_resaved.pth",
|
||||
"size_bytes": 9068948,
|
||||
"size_mb": 9.068948,
|
||||
"latency_batch1": {
|
||||
"batch_size": 1,
|
||||
"runs": 100,
|
||||
"median_ms_per_batch": 24.903650000851485,
|
||||
"median_ms_per_window": 24.903650000851485,
|
||||
"windows_per_second": 40.15475642991324
|
||||
},
|
||||
"latency_batch64": {
|
||||
"batch_size": 64,
|
||||
"runs": 30,
|
||||
"median_ms_per_batch": 184.02919999789447,
|
||||
"median_ms_per_window": 2.875456249967101,
|
||||
"windows_per_second": 347.77089723115813
|
||||
},
|
||||
"accuracy": {
|
||||
"samples": 10000,
|
||||
"pck@20": 0.9668200004577636,
|
||||
"pck@50": 0.9915333324432373,
|
||||
"mpjpe": 0.00936222033649683,
|
||||
"wall_seconds": 37.85407733917236
|
||||
}
|
||||
},
|
||||
"fp16": {
|
||||
"file": "retrained_fp16.pth",
|
||||
"size_bytes": 4580332,
|
||||
"size_mb": 4.580332,
|
||||
"latency_batch1": {
|
||||
"batch_size": 1,
|
||||
"runs": 100,
|
||||
"median_ms_per_batch": 23.936699999467237,
|
||||
"median_ms_per_window": 23.936699999467237,
|
||||
"windows_per_second": 41.776853117691964
|
||||
},
|
||||
"latency_batch64": {
|
||||
"batch_size": 64,
|
||||
"runs": 30,
|
||||
"median_ms_per_batch": 102.32584999903338,
|
||||
"median_ms_per_window": 1.5988414062348966,
|
||||
"windows_per_second": 625.4529036465817
|
||||
},
|
||||
"accuracy": {
|
||||
"samples": 10000,
|
||||
"pck@20": 0.966773332977295,
|
||||
"pck@50": 0.9915066654205322,
|
||||
"mpjpe": 0.009460017587244511,
|
||||
"wall_seconds": 21.632277250289917
|
||||
}
|
||||
},
|
||||
"int8_dynamic": {
|
||||
"file": "retrained_int8_dynamic.pth",
|
||||
"size_bytes": 9068948,
|
||||
"size_mb": 9.068948,
|
||||
"latency_batch1": {
|
||||
"batch_size": 1,
|
||||
"runs": 100,
|
||||
"median_ms_per_batch": 18.105350000041653,
|
||||
"median_ms_per_window": 18.105350000041653,
|
||||
"windows_per_second": 55.23229321707117
|
||||
},
|
||||
"latency_batch64": {
|
||||
"batch_size": 64,
|
||||
"runs": 30,
|
||||
"median_ms_per_batch": 168.77549999844632,
|
||||
"median_ms_per_window": 2.6371171874757238,
|
||||
"windows_per_second": 379.20195763359703
|
||||
},
|
||||
"accuracy": {
|
||||
"samples": 10000,
|
||||
"pck@20": 0.9668200004577636,
|
||||
"pck@50": 0.9915333324432373,
|
||||
"mpjpe": 0.00936222033649683,
|
||||
"wall_seconds": 45.35376596450806
|
||||
}
|
||||
}
|
||||
},
|
||||
"int8_dynamic_quant_report": {
|
||||
"eligible_module_counts": {
|
||||
"nn.Linear": 0,
|
||||
"nn.Conv1d": 21,
|
||||
"nn.Conv2d": 22
|
||||
},
|
||||
"modules_actually_quantized": [],
|
||||
"n_modules_quantized": 0,
|
||||
"params_total": 2225042,
|
||||
"params_quantized": 0,
|
||||
"params_quantized_fraction": 0.0
|
||||
},
|
||||
"accuracy_subset": {
|
||||
"description": "seed-42 file-level 70/15/15 test split, corrupted windows (files 487-499) excluded, seed-42 random subset",
|
||||
"subset_size": 10000,
|
||||
"clean_test_total": 10000
|
||||
}
|
||||
},
|
||||
"onnx": {
|
||||
"env": {
|
||||
"torch": "2.12.0+cpu",
|
||||
"onnxruntime": "1.26.0",
|
||||
"platform": "Windows-11-10.0.26200-SP0"
|
||||
},
|
||||
"export": {
|
||||
"mode": "dynamic-batch",
|
||||
"exporter": "torchscript",
|
||||
"file": "retrained_fp32_dynamic.onnx",
|
||||
"size_mb": 8.971781
|
||||
},
|
||||
"parity": {
|
||||
"fixture": "results/parity_fixture.npz (batch 2, seed 42)",
|
||||
"max_abs_diff_vs_stored_fixture": 2.384185791015625e-07,
|
||||
"max_abs_diff_vs_torch_now": 2.384185791015625e-07,
|
||||
"pass_lt_1e-4": true
|
||||
},
|
||||
"latency": {
|
||||
"batch1": {
|
||||
"batch_size": 1,
|
||||
"runs": 100,
|
||||
"median_ms_per_batch": 2.5410999987798277,
|
||||
"median_ms_per_window": 2.5410999987798277,
|
||||
"windows_per_second": 393.5303610563043
|
||||
},
|
||||
"batch64": {
|
||||
"batch_size": 64,
|
||||
"runs": 30,
|
||||
"median_ms_per_batch": 181.95204999938142,
|
||||
"median_ms_per_window": 2.8430007812403346,
|
||||
"windows_per_second": 351.7410218803118
|
||||
}
|
||||
},
|
||||
"ort_int8_dynamic_supplementary": {
|
||||
"file": "retrained_int8_ort_dynamic.onnx",
|
||||
"size_mb": 2.438794,
|
||||
"runs": true,
|
||||
"max_abs_diff_vs_fp32_fixture": 0.00827130675315857
|
||||
}
|
||||
},
|
||||
"onnx_accuracy": {
|
||||
"onnx_fp32": {
|
||||
"samples": 10000,
|
||||
"pck@20": 0.9668200004577636,
|
||||
"pck@50": 0.9915333324432373,
|
||||
"mpjpe": 0.00936222568154335,
|
||||
"wall_seconds": 22.34790802001953
|
||||
},
|
||||
"onnx_int8_ort_dynamic": {
|
||||
"samples": 10000,
|
||||
"pck@20": 0.965240001964569,
|
||||
"pck@50": 0.9915466655731201,
|
||||
"mpjpe": 0.01108054072111845,
|
||||
"wall_seconds": 55.742953062057495
|
||||
}
|
||||
},
|
||||
"latency_controlled_rerun": {
|
||||
"note": "3 interleaved repetitions per variant, median ms/window; quiet box",
|
||||
"fp32": {
|
||||
"batch1_ms_per_window_median": 10.969150001983508,
|
||||
"batch1_reps": [
|
||||
10.969150001983508,
|
||||
12.646450000829645,
|
||||
10.49820000116597
|
||||
],
|
||||
"batch64_ms_per_window_median": 2.2734187500077496,
|
||||
"batch64_reps": [
|
||||
2.377234374989712,
|
||||
2.124126562478068,
|
||||
2.2734187500077496
|
||||
]
|
||||
},
|
||||
"fp16": {
|
||||
"batch1_ms_per_window_median": 24.313550000442774,
|
||||
"batch1_reps": [
|
||||
25.1078499986761,
|
||||
21.856999999727122,
|
||||
24.313550000442774
|
||||
],
|
||||
"batch64_ms_per_window_median": 2.414695312495496,
|
||||
"batch64_reps": [
|
||||
2.5705156249955508,
|
||||
1.7137437499741281,
|
||||
2.414695312495496
|
||||
]
|
||||
},
|
||||
"int8_dynamic": {
|
||||
"batch1_ms_per_window_median": 15.627150000000256,
|
||||
"batch1_reps": [
|
||||
17.67525000104797,
|
||||
14.627999998992891,
|
||||
15.627150000000256
|
||||
],
|
||||
"batch64_ms_per_window_median": 2.0546906250160646,
|
||||
"batch64_reps": [
|
||||
2.0546906250160646,
|
||||
2.03407343752815,
|
||||
2.9325796875241394
|
||||
]
|
||||
},
|
||||
"onnx_fp32": {
|
||||
"batch1_ms_per_window_median": 3.186650001225644,
|
||||
"batch1_reps": [
|
||||
2.7332500012562377,
|
||||
3.1995500012271805,
|
||||
3.186650001225644
|
||||
],
|
||||
"batch64_ms_per_window_median": 1.9893374999924163,
|
||||
"batch64_reps": [
|
||||
1.5590843750032946,
|
||||
1.9893374999924163,
|
||||
2.2144343749914697
|
||||
]
|
||||
},
|
||||
"onnx_int8_ort_dynamic": {
|
||||
"batch1_ms_per_window_median": 6.50984999811044,
|
||||
"batch1_reps": [
|
||||
6.50984999811044,
|
||||
6.455249998907675,
|
||||
6.789299999581999
|
||||
],
|
||||
"batch64_ms_per_window_median": 5.770093750015803,
|
||||
"batch64_reps": [
|
||||
5.770093750015803,
|
||||
3.912374999970325,
|
||||
7.8067296875019565
|
||||
]
|
||||
}
|
||||
},
|
||||
"onnx_static_ptq": {
|
||||
"env": {
|
||||
"onnxruntime": "1.26.0",
|
||||
"torch": "2.12.0+cpu",
|
||||
"platform": "Windows-11-10.0.26200-SP0",
|
||||
"source_model": "retrained_fp32_dynamic.onnx",
|
||||
"preprocessed_model": {
|
||||
"file": "retrained_fp32_preproc.onnx",
|
||||
"size_mb": 8.981529
|
||||
}
|
||||
},
|
||||
"variants": {
|
||||
"minmax_all": {
|
||||
"file": "retrained_int8_static_minmax_all.onnx",
|
||||
"size_bytes": 2604286,
|
||||
"size_mb": 2.604286,
|
||||
"calibration": {
|
||||
"method": "minmax",
|
||||
"windows": 1000,
|
||||
"percentile": null,
|
||||
"seconds": 5.052440166473389
|
||||
},
|
||||
"scope": "all",
|
||||
"per_channel": true,
|
||||
"activation_type": "QInt8",
|
||||
"weight_type": "QInt8",
|
||||
"node_counts": {
|
||||
"Add": 9,
|
||||
"AveragePool": 1,
|
||||
"BatchNormalization": 12,
|
||||
"Concat": 10,
|
||||
"Conv": 43,
|
||||
"DequantizeLinear": 283,
|
||||
"Einsum": 4,
|
||||
"Gather": 16,
|
||||
"Mul": 39,
|
||||
"QuantizeLinear": 181,
|
||||
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"batch64_ms_per_window_median": 3.815724218725336
|
||||
}
|
||||
},
|
||||
"accuracy_subset": {
|
||||
"description": "seed-42 file-level 70/15/15 test split, corrupted windows excluded, seed-42 random subset (same as quantize_bench/eval_ort_accuracy/static_ptq_bench)",
|
||||
"subset_size": 10000
|
||||
},
|
||||
"accuracy": {
|
||||
"tiny_onnx_fp32": {
|
||||
"samples": 10000,
|
||||
"pck@20": 0.941106667804718,
|
||||
"pck@50": 0.99369333152771,
|
||||
"mpjpe": 0.012527281279861927,
|
||||
"wall_seconds": 10.927234888076782
|
||||
},
|
||||
"tiny_onnx_int8_static_percentile_conv": {
|
||||
"samples": 10000,
|
||||
"pck@20": 0.9268133331298828,
|
||||
"pck@50": 0.9932933319091797,
|
||||
"mpjpe": 0.014906252065300942,
|
||||
"wall_seconds": 12.320892333984375
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,3 +0,0 @@
|
||||
{"variant": "half", "params": 843834, "tcn_channels": [270, 220, 170, 120], "conv_channels": [4, 8, 16, 32], "attn_groups": 4, "groups_mode": "gcd20", "input_pw_groups": 1, "tcn_groups_per_block": [[20, 10], [10, 20], [20, 10], [10, 20]], "conv_strides": [2, 2, 2, 1], "final_width": 15, "batch_size": 64, "max_epochs": 50, "patience": 5, "lr": 0.0001, "weight_decay": 5e-05, "seed": 42, "precision": "fp32", "epochs_run": 28, "best_epoch": 23, "best_val_mpjpe": 0.008576328293592842, "best_val_pck20": 0.9690593021534107, "train_seconds": 1346.4, "torch": "2.11.0+cu128", "error": null, "finished_utc": "2026-06-11T03:09:47Z", "checkpoint": "/home/ruvultra/wiflow-std-bench/sweep/half_best.pth", "test_full": {"samples": 54000, "mpjpe": 0.009419974447676428, "pck@10": 0.8740543655289544, "pck@20": 0.9610469643628156, "pck@30": 0.9813556064146537, "pck@40": 0.9896086878246731, "pck@50": 0.9934827546013726}, "test_clean": {"samples": 52560, "mpjpe": 0.008980081718602137, "pck@10": 0.8840944136840205, "pck@20": 0.9662253179869514, "pck@30": 0.9847971080282144, "pck@40": 0.9917795997050618, "pck@50": 0.9946956242600532}}
|
||||
{"variant": "quarter", "params": 338600, "tcn_channels": [135, 110, 85, 60], "conv_channels": [2, 4, 8, 16], "attn_groups": 2, "groups_mode": "gcd20", "input_pw_groups": 1, "tcn_groups_per_block": [[20, 5], [5, 10], [10, 5], [5, 20]], "conv_strides": [2, 2, 1, 1], "final_width": 15, "batch_size": 64, "max_epochs": 50, "patience": 5, "lr": 0.0001, "weight_decay": 5e-05, "seed": 42, "precision": "fp32", "epochs_run": 50, "best_epoch": 50, "best_val_mpjpe": 0.008780752391864856, "best_val_pck20": 0.9672531302240159, "train_seconds": 1754.4, "torch": "2.11.0+cu128", "error": null, "finished_utc": "2026-06-11T03:39:06Z", "checkpoint": "/home/ruvultra/wiflow-std-bench/sweep/quarter_best.pth", "test_full": {"samples": 54000, "mpjpe": 0.009705399298005634, "pck@10": 0.8646123917014511, "pck@20": 0.9553815319449813, "pck@30": 0.979827209190086, "pck@40": 0.9887037501511751, "pck@50": 0.9931309027671814}, "test_clean": {"samples": 52560, "mpjpe": 0.009279253277105465, "pck@10": 0.8742288637923323, "pck@20": 0.9605315079427745, "pck@30": 0.9833016723076865, "pck@40": 0.9908206971631566, "pck@50": 0.9942719799017071}}
|
||||
{"variant": "tiny", "params": 56290, "tcn_channels": [68, 56, 44, 32], "conv_channels": [2, 4, 8, 16], "attn_groups": 2, "groups_mode": "depthwise", "input_pw_groups": 4, "tcn_groups_per_block": [[540, 68], [68, 56], [56, 44], [44, 32]], "conv_strides": [2, 1, 1, 1], "final_width": 16, "batch_size": 64, "max_epochs": 50, "patience": 5, "lr": 0.0001, "weight_decay": 5e-05, "seed": 42, "precision": "fp32", "epochs_run": 50, "best_epoch": 47, "best_val_mpjpe": 0.012602971208592256, "best_val_pck20": 0.9397210340146666, "train_seconds": 1540.1, "torch": "2.11.0+cu128", "error": null, "finished_utc": "2026-06-11T04:04:50Z", "checkpoint": "/home/ruvultra/wiflow-std-bench/sweep/tiny_best.pth", "test_full": {"samples": 54000, "mpjpe": 0.012859782406853305, "pck@10": 0.7640358444319831, "pck@20": 0.9364815320968628, "pck@30": 0.9731568422317505, "pck@40": 0.9866444962642811, "pck@50": 0.992488939108672}, "test_clean": {"samples": 52560, "mpjpe": 0.012502924276904246, "pck@10": 0.770895526488985, "pck@20": 0.9411073559313967, "pck@30": 0.9764840687790962, "pck@40": 0.9886695077067278, "pck@50": 0.9936238432039409}}
|
||||
@@ -1,21 +0,0 @@
|
||||
{
|
||||
"checkpoint": "/home/ruvultra/wiflow-std-bench/upstream/test/best_pose_model.pth",
|
||||
"test_full": {
|
||||
"samples": 54000,
|
||||
"mpjpe": 0.009834060806367133,
|
||||
"pck@10": 0.8686346120127925,
|
||||
"pck@20": 0.9608815324571398,
|
||||
"pck@30": 0.9789111610695168,
|
||||
"pck@40": 0.9857975759682832,
|
||||
"pck@50": 0.9898827553325229
|
||||
},
|
||||
"test_clean": {
|
||||
"samples": 52560,
|
||||
"mpjpe": 0.009432755044379373,
|
||||
"pck@10": 0.876996495807189,
|
||||
"pck@20": 0.9661454100405608,
|
||||
"pck@30": 0.9823453060205306,
|
||||
"pck@40": 0.987909734176537,
|
||||
"pck@50": 0.9911238361167036
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
@@ -1,32 +0,0 @@
|
||||
{
|
||||
"published": {
|
||||
"pck@20": 0.9725,
|
||||
"pck@30": 0.9863,
|
||||
"pck@40": 0.9916,
|
||||
"pck@50": 0.9948,
|
||||
"mpjpe": 0.007
|
||||
},
|
||||
"params_millions": 2.225042,
|
||||
"data_dir": "C:\\Users\\ruv\\.cache\\kagglehub\\datasets\\kaka2434\\wiflow-dataset\\versions\\1\\preprocessed_csi_data",
|
||||
"device": "cpu",
|
||||
"test_full": {
|
||||
"samples": 54000,
|
||||
"mpjpe": NaN,
|
||||
"pck@10": 5.6790124349020145e-05,
|
||||
"pck@20": 0.0007876543271596785,
|
||||
"pck@30": 0.007780246982971827,
|
||||
"pck@40": 0.05529259262923841,
|
||||
"pck@50": 0.1542370371548114,
|
||||
"wall_seconds": 118.03756999969482
|
||||
},
|
||||
"test_drop_last": {
|
||||
"samples": 53952,
|
||||
"mpjpe": NaN,
|
||||
"pck@10": 5.6840649370682976e-05,
|
||||
"pck@20": 0.0007883550872372227,
|
||||
"pck@30": 0.007787168910892621,
|
||||
"pck@40": 0.055318307667895535,
|
||||
"pck@50": 0.15425316342412276,
|
||||
"wall_seconds": 120.87458372116089
|
||||
}
|
||||
}
|
||||
Binary file not shown.
@@ -1,333 +0,0 @@
|
||||
"""ADR-152 edge optimization follow-up: ONNX Runtime STATIC post-training
|
||||
quantization (calibration-based QDQ) of the retrained WiFlow-STD model, to
|
||||
improve on the dynamic-int8 result (2.44 MB, PCK@20 96.52%, 6.5 ms/win b1).
|
||||
|
||||
Static PTQ pre-computes activation ranges from calibration data, so inference
|
||||
uses QLinearConv/QDQ kernels instead of dynamic ConvInteger -- typically both
|
||||
faster and (with good calibration) closer to fp32 accuracy.
|
||||
|
||||
Method:
|
||||
- Calibration set: corruption-free windows drawn ONLY from the seed-42
|
||||
file-level TRAINING split (same split as eval_repro.py; corrupted windows
|
||||
excluded via results/nan_windows_mask.npy | big_windows_mask.npy), chosen
|
||||
with np.random.default_rng(42). Never test windows.
|
||||
- quantize_static, QuantFormat.QDQ, per-channel int8 weights, int8
|
||||
activations; calibration methods MinMax / Entropy / Percentile(99.99);
|
||||
scopes "all" (ORT default op set) vs "conv" (op_types_to_quantize=
|
||||
["Conv"] -- leaves the attention path, which exports as Einsum/Softmax
|
||||
and elementwise ops, in fp32).
|
||||
- Model is pre-processed first (quant_pre_process: symbolic shape
|
||||
inference + ORT graph optimization, folds BatchNormalization into Conv).
|
||||
- Accuracy: identical protocol to eval_ort_accuracy.py -- the 10,000-window
|
||||
seed-42 subset of the corruption-free test split (PCK@20/50, MPJPE).
|
||||
- Latency: median ms/window at batch 1 (100 runs) and batch 64 (30 runs),
|
||||
3 interleaved repetitions across all variants (fp32 and dynamic-int8
|
||||
sessions included as same-session reference points).
|
||||
|
||||
Usage:
|
||||
PYTHONUTF8=1 .venv/Scripts/python.exe static_ptq_bench.py \
|
||||
[--data-dir <preprocessed_csi_data>] [--subset 10000]
|
||||
[--calib-minmax 1000] [--calib-hist 512] [--skip-accuracy]
|
||||
|
||||
Writes/merges into results/edge_optimization.json under key "onnx_static_ptq".
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import collections
|
||||
import json
|
||||
import os
|
||||
import platform
|
||||
import statistics
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.insert(0, HERE)
|
||||
|
||||
from _bench_common import RESULTS # noqa: E402
|
||||
# quantize_bench sets up upstream imports + the np.load mmap patch
|
||||
# (both via _bench_common.import_upstream)
|
||||
from quantize_bench import build_test_subset # noqa: E402
|
||||
import quantize_bench as qb # noqa: E402
|
||||
from eval_ort_accuracy import evaluate_ort # noqa: E402
|
||||
|
||||
FP32_ONNX = os.path.join(RESULTS, "retrained_fp32_dynamic.onnx")
|
||||
DYN_INT8_ONNX = os.path.join(RESULTS, "retrained_int8_ort_dynamic.onnx")
|
||||
PREPROC_ONNX = os.path.join(RESULTS, "retrained_fp32_preproc.onnx")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# calibration data: corruption-free TRAINING-split windows only
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def build_calibration_windows(data_dir, n_windows):
|
||||
"""Seed-42 file-level 70/15/15 TRAIN split (exactly as eval_repro.py),
|
||||
minus corrupted windows, then a seed-42 random draw of n_windows."""
|
||||
dataset = qb.PreprocessedCSIKeypointsDataset(
|
||||
data_dir=data_dir, keypoint_scale=1000.0, enable_temporal_clean=True)
|
||||
train_loader, _va, _te = qb.create_preprocessed_train_val_test_loaders(
|
||||
dataset=dataset, batch_size=64, num_workers=0, random_seed=42)
|
||||
train_indices = np.asarray(train_loader.dataset.indices)
|
||||
|
||||
corrupted = (np.load(os.path.join(RESULTS, "nan_windows_mask.npy"))
|
||||
| np.load(os.path.join(RESULTS, "big_windows_mask.npy")))
|
||||
clean = train_indices[~corrupted[train_indices]]
|
||||
print(f"train split: {len(train_indices)} windows, "
|
||||
f"{len(train_indices) - len(clean)} corrupted excluded, "
|
||||
f"{len(clean)} clean")
|
||||
|
||||
rng = np.random.default_rng(42)
|
||||
sel = np.sort(rng.choice(clean, size=n_windows, replace=False))
|
||||
xs = np.stack([dataset[int(i)][0].numpy() for i in sel]).astype(np.float32)
|
||||
print(f"calibration tensor: {xs.shape} from {n_windows} clean TRAIN windows")
|
||||
return xs
|
||||
|
||||
|
||||
def make_reader(windows, batch_size=64):
|
||||
from onnxruntime.quantization import CalibrationDataReader
|
||||
|
||||
class WindowReader(CalibrationDataReader):
|
||||
def __init__(self):
|
||||
self._batches = [windows[i:i + batch_size]
|
||||
for i in range(0, len(windows), batch_size)]
|
||||
self._it = iter(self._batches)
|
||||
|
||||
def get_next(self):
|
||||
b = next(self._it, None)
|
||||
return None if b is None else {"input": b}
|
||||
|
||||
def rewind(self):
|
||||
self._it = iter(self._batches)
|
||||
|
||||
def __len__(self):
|
||||
return len(self._batches)
|
||||
|
||||
return WindowReader()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# quantization variants
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def preprocess_model():
|
||||
from onnxruntime.quantization.shape_inference import quant_pre_process
|
||||
quant_pre_process(FP32_ONNX, PREPROC_ONNX)
|
||||
return PREPROC_ONNX
|
||||
|
||||
|
||||
def quantize_variant(src, dst, method, scope, calib_windows):
|
||||
from onnxruntime.quantization import (CalibrationMethod, QuantFormat,
|
||||
QuantType, quantize_static)
|
||||
methods = {
|
||||
"minmax": CalibrationMethod.MinMax,
|
||||
"entropy": CalibrationMethod.Entropy,
|
||||
"percentile": CalibrationMethod.Percentile,
|
||||
}
|
||||
# NB: do NOT pass CalibMaxIntermediateOutputs -- in ORT 1.26 the MinMax
|
||||
# calibrater clears its buffer every N batches and then raises
|
||||
# "No data is collected" if the batch count is divisible by N.
|
||||
extra = {}
|
||||
if method == "percentile":
|
||||
extra["CalibPercentile"] = 99.99
|
||||
op_types = ["Conv"] if scope == "conv" else None
|
||||
|
||||
t0 = time.time()
|
||||
quantize_static(
|
||||
src, dst, make_reader(calib_windows),
|
||||
quant_format=QuantFormat.QDQ,
|
||||
op_types_to_quantize=op_types,
|
||||
per_channel=True,
|
||||
activation_type=QuantType.QInt8,
|
||||
weight_type=QuantType.QInt8,
|
||||
calibrate_method=methods[method],
|
||||
extra_options=extra,
|
||||
)
|
||||
secs = time.time() - t0
|
||||
|
||||
import onnx
|
||||
ops = collections.Counter(n.op_type for n in onnx.load(dst).graph.node)
|
||||
return {
|
||||
"file": os.path.basename(dst),
|
||||
"size_bytes": os.path.getsize(dst),
|
||||
"size_mb": os.path.getsize(dst) / 1e6,
|
||||
"calibration": {"method": method,
|
||||
"windows": int(len(calib_windows)),
|
||||
"percentile": extra.get("CalibPercentile"),
|
||||
"seconds": secs},
|
||||
"scope": scope,
|
||||
"per_channel": True,
|
||||
"activation_type": "QInt8",
|
||||
"weight_type": "QInt8",
|
||||
"node_counts": {k: v for k, v in sorted(ops.items())},
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# latency (3 interleaved reps, like the latency_controlled_rerun)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def ort_session(path):
|
||||
import onnxruntime as ort
|
||||
return ort.InferenceSession(path, providers=["CPUExecutionProvider"])
|
||||
|
||||
|
||||
def bench_ort(sess, batch, n_runs):
|
||||
rng = np.random.default_rng(123)
|
||||
x = rng.random((batch, 540, 20), dtype=np.float32)
|
||||
inp = sess.get_inputs()[0].name
|
||||
for _ in range(max(5, n_runs // 10)):
|
||||
sess.run(None, {inp: x})
|
||||
times = []
|
||||
for _ in range(n_runs):
|
||||
t0 = time.perf_counter()
|
||||
sess.run(None, {inp: x})
|
||||
times.append(time.perf_counter() - t0)
|
||||
return statistics.median(times) * 1e3 / batch # ms/window
|
||||
|
||||
|
||||
def interleaved_latency(sessions, reps=3, runs_b1=100, runs_b64=30):
|
||||
lat = {name: {"batch1_reps": [], "batch64_reps": []} for name in sessions}
|
||||
for rep in range(reps):
|
||||
for name, sess in sessions.items():
|
||||
lat[name]["batch1_reps"].append(bench_ort(sess, 1, runs_b1))
|
||||
lat[name]["batch64_reps"].append(bench_ort(sess, 64, runs_b64))
|
||||
print(f" rep {rep + 1}/{reps} {name}: "
|
||||
f"b1={lat[name]['batch1_reps'][-1]:.2f} "
|
||||
f"b64={lat[name]['batch64_reps'][-1]:.3f} ms/win", flush=True)
|
||||
for name in lat:
|
||||
lat[name]["batch1_ms_per_window_median"] = statistics.median(
|
||||
lat[name]["batch1_reps"])
|
||||
lat[name]["batch64_ms_per_window_median"] = statistics.median(
|
||||
lat[name]["batch64_reps"])
|
||||
return lat
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def main():
|
||||
import onnxruntime
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data-dir", default=os.path.join(
|
||||
os.path.expanduser("~"), ".cache", "kagglehub", "datasets", "kaka2434",
|
||||
"wiflow-dataset", "versions", "1", "preprocessed_csi_data"))
|
||||
parser.add_argument("--subset", type=int, default=10000)
|
||||
parser.add_argument("--calib-minmax", type=int, default=1000)
|
||||
parser.add_argument("--calib-hist", type=int, default=512,
|
||||
help="calibration windows for Entropy/Percentile "
|
||||
"(histogram calibraters hold all intermediate "
|
||||
"activations in RAM)")
|
||||
parser.add_argument("--skip-accuracy", action="store_true")
|
||||
parser.add_argument("--methods", default="minmax,entropy,percentile",
|
||||
help="comma list of calibration methods to (re)run; "
|
||||
"results merge into existing onnx_static_ptq")
|
||||
parser.add_argument("--out", default=os.path.join(RESULTS, "edge_optimization.json"))
|
||||
args = parser.parse_args()
|
||||
|
||||
results = {
|
||||
"env": {
|
||||
"onnxruntime": onnxruntime.__version__,
|
||||
"torch": torch.__version__,
|
||||
"platform": platform.platform(),
|
||||
"source_model": os.path.basename(FP32_ONNX),
|
||||
},
|
||||
"variants": {},
|
||||
}
|
||||
|
||||
# ---- calibration data (TRAIN split only) -------------------------------
|
||||
calib_mm = build_calibration_windows(args.data_dir, args.calib_minmax)
|
||||
calib_hist = calib_mm[:args.calib_hist]
|
||||
|
||||
# ---- preprocess + quantize ---------------------------------------------
|
||||
print("\n=== quant_pre_process (shape inference + graph optimization) ===")
|
||||
src = preprocess_model()
|
||||
results["env"]["preprocessed_model"] = {
|
||||
"file": os.path.basename(src),
|
||||
"size_mb": os.path.getsize(src) / 1e6,
|
||||
}
|
||||
|
||||
matrix = [(m, s) for m in args.methods.split(",")
|
||||
for s in ("all", "conv")]
|
||||
for method, scope in matrix:
|
||||
name = f"{method}_{scope}"
|
||||
dst = os.path.join(RESULTS, f"retrained_int8_static_{name}.onnx")
|
||||
calib = calib_mm if method == "minmax" else calib_hist
|
||||
print(f"\n=== quantize_static: {name} "
|
||||
f"({len(calib)} calib windows) ===", flush=True)
|
||||
try:
|
||||
results["variants"][name] = quantize_variant(
|
||||
src, dst, method, scope, calib)
|
||||
print(f" {results['variants'][name]['size_mb']:.3f} MB")
|
||||
except Exception as e: # noqa: BLE001
|
||||
results["variants"][name] = {"error": f"{type(e).__name__}: {e}"}
|
||||
print(f" FAILED: {e}")
|
||||
|
||||
# ---- fixture parity (sanity, batch 2) ----------------------------------
|
||||
fixture = np.load(os.path.join(RESULTS, "parity_fixture.npz"))
|
||||
fx, fy = fixture["input"], fixture["output"]
|
||||
sessions = {}
|
||||
for name, info in results["variants"].items():
|
||||
if "error" in info:
|
||||
continue
|
||||
path = os.path.join(RESULTS, info["file"])
|
||||
try:
|
||||
sess = ort_session(path)
|
||||
yq = sess.run(None, {sess.get_inputs()[0].name: fx})[0]
|
||||
info["max_abs_diff_vs_fp32_fixture"] = float(np.abs(yq - fy).max())
|
||||
sessions[name] = sess
|
||||
except Exception as e: # noqa: BLE001
|
||||
info["run_error"] = f"{type(e).__name__}: {e}"
|
||||
print("\nfixture max-abs-diff vs fp32:",
|
||||
{n: round(results["variants"][n].get("max_abs_diff_vs_fp32_fixture",
|
||||
float("nan")), 5)
|
||||
for n in results["variants"]})
|
||||
|
||||
# ---- latency: 3 interleaved reps incl. fp32 + dynamic-int8 reference ----
|
||||
print("\n=== latency (3 interleaved reps) ===")
|
||||
lat_sessions = {"onnx_fp32": ort_session(FP32_ONNX),
|
||||
"onnx_int8_ort_dynamic": ort_session(DYN_INT8_ONNX)}
|
||||
lat_sessions.update(sessions)
|
||||
results["latency"] = {
|
||||
"note": "3 interleaved repetitions per variant, median ms/window; "
|
||||
"onnx_fp32 / onnx_int8_ort_dynamic are same-session references",
|
||||
**interleaved_latency(lat_sessions),
|
||||
}
|
||||
|
||||
# ---- accuracy on the standard 10k corruption-free test subset ----------
|
||||
if not args.skip_accuracy:
|
||||
loader, n_clean = build_test_subset(args.data_dir, args.subset)
|
||||
results["accuracy_subset"] = {
|
||||
"description": "seed-42 file-level 70/15/15 test split, corrupted "
|
||||
"windows excluded, seed-42 random subset (same as "
|
||||
"quantize_bench/eval_ort_accuracy)",
|
||||
"subset_size": min(args.subset, n_clean) if args.subset else n_clean,
|
||||
}
|
||||
for name, sess in sessions.items():
|
||||
print(f"\n=== accuracy: {name} ===")
|
||||
results["variants"][name]["accuracy"] = evaluate_ort(
|
||||
sess, loader, name)
|
||||
print(json.dumps(results["variants"][name]["accuracy"], indent=2))
|
||||
|
||||
# ---- merge into edge_optimization.json ----------------------------------
|
||||
merged = {}
|
||||
if os.path.exists(args.out):
|
||||
with open(args.out) as f:
|
||||
merged = json.load(f)
|
||||
prev = merged.get("onnx_static_ptq")
|
||||
if prev: # nested merge so partial --methods reruns don't clobber
|
||||
prev["env"] = results["env"]
|
||||
prev["variants"].update(results["variants"])
|
||||
prev.setdefault("latency", {}).update(results["latency"])
|
||||
if "accuracy_subset" in results:
|
||||
prev["accuracy_subset"] = results["accuracy_subset"]
|
||||
else:
|
||||
merged["onnx_static_ptq"] = results
|
||||
with open(args.out, "w") as f:
|
||||
json.dump(merged, f, indent=2)
|
||||
print(f"\nwrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,313 +0,0 @@
|
||||
"""ADR-152 efficiency-sweep follow-up: edge pipeline for the TINY compact
|
||||
WiFlow-STD variant (56,290 params, results/tiny_best.pth, trained overnight
|
||||
2026-06-10/11 -- see RESULTS.md "Efficiency sweep").
|
||||
|
||||
Headline question: what does the smallest deployable WiFlow-class model look
|
||||
like (KB + ms + PCK)? Reuses the onnx_bench.py / static_ptq_bench.py
|
||||
machinery on the tiny checkpoint:
|
||||
|
||||
1. Load tiny_best.pth with remote/sweep/model_compact.py
|
||||
(depthwise TCN groups, input_pw_groups=4, conv [2,4,8,16], attn groups 2).
|
||||
2. Export ONNX: dynamic batch, opset 17, TorchScript exporter (dynamo=False)
|
||||
-- same recipe that worked for the full model; verified at batch 1/2/64.
|
||||
One forced deviation: tiny's stride schedule [2,1,1,1] leaves final_width
|
||||
16, and the TorchScript exporter cannot export AdaptiveAvgPool2d((15,1))
|
||||
when 15 is not a factor of the input height (the full model never hit
|
||||
this -- its width was exactly 15). The adaptive pool over a fixed-size
|
||||
feature map is a fixed linear map, so the export wrapper replaces it with
|
||||
an exact matmul equivalent (PyTorch adaptive-pool bin semantics:
|
||||
bin i averages rows floor(i*H/K)..ceil((i+1)*H/K)); the W axis (20->1,
|
||||
a factor) becomes mean(-1). Exactness is proven by the parity check
|
||||
below, which compares against the ORIGINAL torch model with the real
|
||||
AdaptiveAvgPool2d.
|
||||
3. Torch-vs-ORT parity on the stored fixture input
|
||||
(results/parity_fixture.npz, batch 2, seed 42 -- same 540x20 input layout;
|
||||
reference output recomputed with the tiny torch model). PASS < 1e-4.
|
||||
4. Static QDQ conv-only int8 (quant_pre_process + quantize_static,
|
||||
per-channel QInt8 weights+activations, Percentile(99.99) calibration on
|
||||
512 corruption-free TRAIN-split windows -- the winning recipe and
|
||||
calibration count from static_ptq_bench.py. 512, not "about 500":
|
||||
ORT 1.26's histogram collector np.asarray()'s the per-batch maxima, so
|
||||
the calibration count must be a multiple of the batch size 64 or the
|
||||
ragged last batch crashes it).
|
||||
5. Disk size + CPU latency b1/b64 (3 interleaved reps, median ms/window)
|
||||
for tiny fp32 + tiny int8, with the full-model ONNX fp32 + static-int8
|
||||
sessions interleaved as same-session references.
|
||||
6. Accuracy (PCK@20/50 + MPJPE) on the identical 10k-window seed-42
|
||||
corruption-free test subset for tiny fp32 + tiny int8.
|
||||
|
||||
Usage:
|
||||
PYTHONUTF8=1 .venv/Scripts/python.exe tiny_edge_bench.py \
|
||||
[--data-dir <preprocessed_csi_data>] [--subset 10000] [--calib 512]
|
||||
(--calib must be a multiple of 64; see step 4 above)
|
||||
|
||||
Writes/merges into results/edge_optimization.json under key "tiny_variant".
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
RESULTS = os.path.join(HERE, "results")
|
||||
sys.path.insert(0, HERE)
|
||||
sys.path.insert(0, os.path.join(HERE, "remote", "sweep"))
|
||||
|
||||
# quantize_bench sets up upstream imports + the np.load mmap patch
|
||||
from quantize_bench import build_test_subset # noqa: E402
|
||||
from eval_ort_accuracy import evaluate_ort # noqa: E402
|
||||
from static_ptq_bench import ( # noqa: E402
|
||||
build_calibration_windows,
|
||||
interleaved_latency,
|
||||
make_reader,
|
||||
ort_session,
|
||||
)
|
||||
from model_compact import CompactWiFlowPoseModel, describe # noqa: E402
|
||||
|
||||
TINY_CKPT = os.path.join(RESULTS, "tiny_best.pth")
|
||||
TINY_FP32_ONNX = os.path.join(RESULTS, "tiny_fp32_dynamic.onnx")
|
||||
TINY_PREPROC_ONNX = os.path.join(RESULTS, "tiny_fp32_preproc.onnx")
|
||||
TINY_INT8_ONNX = os.path.join(RESULTS, "tiny_int8_static_percentile_conv.onnx")
|
||||
FULL_FP32_ONNX = os.path.join(RESULTS, "retrained_fp32_dynamic.onnx")
|
||||
FULL_INT8_ONNX = os.path.join(RESULTS, "retrained_int8_static_percentile_conv.onnx")
|
||||
|
||||
# Exact tiny config from remote/sweep/run_sweep.py VARIANTS (measured 56,290
|
||||
# params, clean-test PCK@20 94.11% -- results/efficiency_sweep.jsonl).
|
||||
TINY = dict(tcn=[68, 56, 44, 32], conv=[2, 4, 8, 16], attn_groups=2,
|
||||
groups_mode="depthwise", input_pw_groups=4)
|
||||
|
||||
|
||||
def load_tiny_model():
|
||||
model = CompactWiFlowPoseModel(
|
||||
tcn_channels=TINY["tcn"], conv_channels=TINY["conv"],
|
||||
attn_groups=TINY["attn_groups"], groups_mode=TINY["groups_mode"],
|
||||
input_pw_groups=TINY["input_pw_groups"], dropout=0.5)
|
||||
state = torch.load(TINY_CKPT, map_location="cpu", weights_only=True)
|
||||
model.load_state_dict(state, strict=True)
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
def adaptive_pool_matrix(h_in, h_out):
|
||||
"""Exact AdaptiveAvgPool1d as a (h_out, h_in) averaging matrix, using
|
||||
PyTorch's bin rule: bin i covers rows floor(i*h_in/h_out) ..
|
||||
ceil((i+1)*h_in/h_out)."""
|
||||
w = torch.zeros(h_out, h_in)
|
||||
for i in range(h_out):
|
||||
s = (i * h_in) // h_out
|
||||
e = -((-(i + 1) * h_in) // h_out) # ceil division
|
||||
w[i, s:e] = 1.0 / (e - s)
|
||||
return w
|
||||
|
||||
|
||||
class ExportWrapper(torch.nn.Module):
|
||||
"""CompactWiFlowPoseModel forward with the AdaptiveAvgPool2d((K,1))
|
||||
replaced by an exact fixed linear map (mean over the factor W axis, then
|
||||
a constant averaging matmul over the non-factor H axis) so the
|
||||
TorchScript ONNX exporter accepts it. Bit-equivalent up to float
|
||||
round-off; proven by the parity check against the original model."""
|
||||
|
||||
def __init__(self, m, num_keypoints=15):
|
||||
super().__init__()
|
||||
self.m = m
|
||||
self.register_buffer(
|
||||
"pool_w_t", adaptive_pool_matrix(m.final_width, num_keypoints).t())
|
||||
|
||||
def forward(self, x):
|
||||
m = self.m
|
||||
x = m.tcn(x)
|
||||
x = x.transpose(1, 2).unsqueeze(1)
|
||||
x = m.up(x)
|
||||
for block in m.residual_blocks:
|
||||
x = block(x)
|
||||
x = x.permute(0, 1, 3, 2)
|
||||
x = m.attention(x)
|
||||
x = m.decoder(x) # [B, 2, H=final_width, T=20]
|
||||
x = x.mean(-1) # W-axis pool (20 -> 1, a factor)
|
||||
x = x.matmul(self.pool_w_t) # exact adaptive H pool: [B, 2, K]
|
||||
return x.transpose(1, 2) # [B, K, 2]
|
||||
|
||||
|
||||
def export_onnx(model):
|
||||
"""Dynamic-batch TorchScript export (the recipe that worked for the full
|
||||
model in onnx_bench.py), verified at batch 1/2/64. Uses ExportWrapper
|
||||
(see docstring) because final_width 16 is not a multiple of 15."""
|
||||
wrapper = ExportWrapper(model).eval()
|
||||
x = torch.rand(2, 540, 20)
|
||||
with torch.no_grad():
|
||||
torch.onnx.export(
|
||||
wrapper, (x,), TINY_FP32_ONNX, opset_version=17,
|
||||
input_names=["input"], output_names=["output"], dynamo=False,
|
||||
dynamic_axes={"input": {0: "batch"}, "output": {0: "batch"}})
|
||||
sess = ort_session(TINY_FP32_ONNX)
|
||||
inp = sess.get_inputs()[0].name
|
||||
for b in (1, 2, 64):
|
||||
y = sess.run(None, {inp: np.zeros((b, 540, 20), dtype=np.float32)})[0]
|
||||
assert y.shape == (b, 15, 2), y.shape
|
||||
return {
|
||||
"mode": "dynamic-batch", "exporter": "torchscript", "opset": 17,
|
||||
"file": os.path.basename(TINY_FP32_ONNX),
|
||||
"size_bytes": os.path.getsize(TINY_FP32_ONNX),
|
||||
"size_mb": os.path.getsize(TINY_FP32_ONNX) / 1e6,
|
||||
"verified_batches": [1, 2, 64],
|
||||
"note": "AdaptiveAvgPool2d((15,1)) replaced at export by an exact "
|
||||
"mean(-1) + constant averaging matmul (final_width 16 is not "
|
||||
"a multiple of 15, which the TorchScript exporter rejects); "
|
||||
"exactness proven by the parity check vs the original torch "
|
||||
"model",
|
||||
}
|
||||
|
||||
|
||||
def quantize_tiny(calib_windows):
|
||||
"""quant_pre_process + static QDQ conv-only Percentile(99.99) int8 --
|
||||
the winning recipe from static_ptq_bench.py."""
|
||||
from onnxruntime.quantization import (CalibrationMethod, QuantFormat,
|
||||
QuantType, quantize_static)
|
||||
from onnxruntime.quantization.shape_inference import quant_pre_process
|
||||
|
||||
quant_pre_process(TINY_FP32_ONNX, TINY_PREPROC_ONNX)
|
||||
t0 = time.time()
|
||||
quantize_static(
|
||||
TINY_PREPROC_ONNX, TINY_INT8_ONNX, make_reader(calib_windows),
|
||||
quant_format=QuantFormat.QDQ,
|
||||
op_types_to_quantize=["Conv"],
|
||||
per_channel=True,
|
||||
activation_type=QuantType.QInt8,
|
||||
weight_type=QuantType.QInt8,
|
||||
calibrate_method=CalibrationMethod.Percentile,
|
||||
extra_options={"CalibPercentile": 99.99},
|
||||
)
|
||||
return {
|
||||
"file": os.path.basename(TINY_INT8_ONNX),
|
||||
"size_bytes": os.path.getsize(TINY_INT8_ONNX),
|
||||
"size_mb": os.path.getsize(TINY_INT8_ONNX) / 1e6,
|
||||
"calibration": {"method": "percentile", "percentile": 99.99,
|
||||
"windows": int(len(calib_windows)),
|
||||
"scope": "conv-only TRAIN-split corruption-free",
|
||||
"seconds": time.time() - t0},
|
||||
"per_channel": True,
|
||||
"activation_type": "QInt8",
|
||||
"weight_type": "QInt8",
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
import onnxruntime
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data-dir", default=os.path.join(
|
||||
os.path.expanduser("~"), ".cache", "kagglehub", "datasets", "kaka2434",
|
||||
"wiflow-dataset", "versions", "1", "preprocessed_csi_data"))
|
||||
parser.add_argument("--subset", type=int, default=10000)
|
||||
parser.add_argument("--calib", type=int, default=512,
|
||||
help="calibration windows; must be a multiple of the "
|
||||
"64-window calibration batch (ORT histogram "
|
||||
"collector rejects ragged batches)")
|
||||
parser.add_argument("--skip-accuracy", action="store_true")
|
||||
parser.add_argument("--out", default=os.path.join(RESULTS, "edge_optimization.json"))
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.calib % 64 != 0:
|
||||
parser.error(
|
||||
f"--calib must be a multiple of 64 (got {args.calib}): ORT 1.26's "
|
||||
f"histogram calibration collector np.asarray()'s the per-batch "
|
||||
f"maxima and crashes on a ragged final batch (calibration batch "
|
||||
f"size is 64)")
|
||||
|
||||
model = load_tiny_model()
|
||||
info = describe(model)
|
||||
print(f"tiny model: {info['params']:,} params, tcn_groups={info['tcn_groups_per_block']}, "
|
||||
f"strides={info['conv_strides']}, final_width={info['final_width']}")
|
||||
assert info["params"] == 56290, info["params"]
|
||||
|
||||
results = {
|
||||
"env": {
|
||||
"torch": torch.__version__,
|
||||
"onnxruntime": onnxruntime.__version__,
|
||||
"platform": platform.platform(),
|
||||
"num_threads": torch.get_num_threads(),
|
||||
"checkpoint": os.path.relpath(TINY_CKPT, HERE),
|
||||
"checkpoint_size_bytes": os.path.getsize(TINY_CKPT),
|
||||
"params": info["params"],
|
||||
"variant_config": TINY,
|
||||
},
|
||||
}
|
||||
|
||||
# ---- export + parity ----------------------------------------------------
|
||||
print("\n=== ONNX export (dynamic batch, opset 17, torchscript) ===")
|
||||
results["export"] = export_onnx(model)
|
||||
print(f" {results['export']['size_mb']:.3f} MB, batches {results['export']['verified_batches']} OK")
|
||||
|
||||
fixture = np.load(os.path.join(RESULTS, "parity_fixture.npz"))
|
||||
fx = fixture["input"] # (2, 540, 20), seed 42 -- same input layout as full model
|
||||
sess_fp32 = ort_session(TINY_FP32_ONNX)
|
||||
y_ort = sess_fp32.run(None, {sess_fp32.get_inputs()[0].name: fx})[0]
|
||||
with torch.no_grad():
|
||||
y_torch = model(torch.from_numpy(fx)).numpy()
|
||||
results["parity"] = {
|
||||
"fixture": "results/parity_fixture.npz input (batch 2, seed 42); "
|
||||
"reference output recomputed with the tiny torch model",
|
||||
"max_abs_diff_vs_torch": float(np.abs(y_ort - y_torch).max()),
|
||||
"pass_lt_1e-4": bool(np.abs(y_ort - y_torch).max() < 1e-4),
|
||||
}
|
||||
print("parity:", json.dumps(results["parity"], indent=2))
|
||||
assert results["parity"]["pass_lt_1e-4"], "torch-vs-ORT parity FAILED"
|
||||
|
||||
# ---- static PTQ int8 ------------------------------------------------------
|
||||
print(f"\n=== static QDQ int8 (Percentile conv-only, {args.calib} calib windows) ===")
|
||||
calib = build_calibration_windows(args.data_dir, args.calib)
|
||||
results["int8_static_percentile_conv"] = quantize_tiny(calib)
|
||||
print(f" {results['int8_static_percentile_conv']['size_mb']:.3f} MB")
|
||||
sess_int8 = ort_session(TINY_INT8_ONNX)
|
||||
yq = sess_int8.run(None, {sess_int8.get_inputs()[0].name: fx})[0]
|
||||
results["int8_static_percentile_conv"]["max_abs_diff_vs_fp32_fixture"] = float(
|
||||
np.abs(yq - y_torch).max())
|
||||
|
||||
# ---- latency (3 interleaved reps, full-model sessions as references) -----
|
||||
print("\n=== latency (3 interleaved reps) ===")
|
||||
lat_sessions = {
|
||||
"tiny_onnx_fp32": sess_fp32,
|
||||
"tiny_onnx_int8_static_percentile_conv": sess_int8,
|
||||
"full_onnx_fp32_reference": ort_session(FULL_FP32_ONNX),
|
||||
"full_onnx_int8_static_percentile_conv_reference": ort_session(FULL_INT8_ONNX),
|
||||
}
|
||||
results["latency"] = {
|
||||
"note": "3 interleaved repetitions per variant, median ms/window; "
|
||||
"full-model sessions are same-session references",
|
||||
**interleaved_latency(lat_sessions),
|
||||
}
|
||||
|
||||
# ---- accuracy on the standard 10k corruption-free test subset ------------
|
||||
if not args.skip_accuracy:
|
||||
loader, n_clean = build_test_subset(args.data_dir, args.subset)
|
||||
results["accuracy_subset"] = {
|
||||
"description": "seed-42 file-level 70/15/15 test split, corrupted "
|
||||
"windows excluded, seed-42 random subset (same as "
|
||||
"quantize_bench/eval_ort_accuracy/static_ptq_bench)",
|
||||
"subset_size": min(args.subset, n_clean) if args.subset else n_clean,
|
||||
}
|
||||
results["accuracy"] = {}
|
||||
for name, sess in (("tiny_onnx_fp32", sess_fp32),
|
||||
("tiny_onnx_int8_static_percentile_conv", sess_int8)):
|
||||
print(f"\n=== accuracy: {name} ===")
|
||||
results["accuracy"][name] = evaluate_ort(sess, loader, name)
|
||||
print(json.dumps(results["accuracy"][name], indent=2))
|
||||
|
||||
# ---- merge into edge_optimization.json -----------------------------------
|
||||
merged = {}
|
||||
if os.path.exists(args.out):
|
||||
with open(args.out) as f:
|
||||
merged = json.load(f)
|
||||
merged["tiny_variant"] = results
|
||||
with open(args.out, "w") as f:
|
||||
json.dump(merged, f, indent=2)
|
||||
print(f"\nwrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+2
-10
@@ -3,7 +3,7 @@
|
||||
# Multi-stage build for minimal final image
|
||||
|
||||
# Stage 1: Build
|
||||
FROM rust:1.89-bookworm AS builder
|
||||
FROM rust:1.85-bookworm AS builder
|
||||
|
||||
WORKDIR /build
|
||||
|
||||
@@ -19,13 +19,9 @@ COPY vendor/ruvector/ /build/vendor/ruvector/
|
||||
# (ADR-115) is wired in (auto-discovery topics flow to Home Assistant)
|
||||
# - cog-ha-matter, the ADR-116 Cognitum cog that wraps HA-DISCO +
|
||||
# HA-MIND + mDNS + embedded broker for Home Assistant / Matter
|
||||
# - homecore-server, the ADRs-126-134 HOMECORE native Rust port of
|
||||
# Home Assistant (HA-wire-compat REST + WebSocket on :8123,
|
||||
# SQLite + ruvector recorder, automation, assist, plugins, HAP)
|
||||
RUN cargo build --release -p wifi-densepose-sensing-server --features mqtt 2>&1 \
|
||||
&& cargo build --release -p cog-ha-matter 2>&1 \
|
||||
&& cargo build --release -p homecore-server 2>&1 \
|
||||
&& strip target/release/sensing-server target/release/cog-ha-matter target/release/homecore-server
|
||||
&& strip target/release/sensing-server target/release/cog-ha-matter
|
||||
|
||||
# Stage 2: Runtime
|
||||
FROM debian:bookworm-slim
|
||||
@@ -39,7 +35,6 @@ WORKDIR /app
|
||||
# Copy binaries
|
||||
COPY --from=builder /build/target/release/sensing-server /app/sensing-server
|
||||
COPY --from=builder /build/target/release/cog-ha-matter /app/cog-ha-matter
|
||||
COPY --from=builder /build/target/release/homecore-server /app/homecore-server
|
||||
|
||||
# Copy UI assets
|
||||
COPY ui/ /app/ui/
|
||||
@@ -57,7 +52,6 @@ RUN set -e; \
|
||||
done; \
|
||||
test -x /app/sensing-server || { echo "FATAL: /app/sensing-server is not executable"; exit 1; }; \
|
||||
test -x /app/cog-ha-matter || { echo "FATAL: /app/cog-ha-matter is not executable"; exit 1; }; \
|
||||
test -x /app/homecore-server || { echo "FATAL: /app/homecore-server is not executable"; exit 1; }; \
|
||||
echo "image assets OK"
|
||||
|
||||
# Optional bearer-token auth on /api/v1/*: leave unset for LAN-mode (default),
|
||||
@@ -73,8 +67,6 @@ EXPOSE 3001
|
||||
EXPOSE 5005/udp
|
||||
# MQTT broker (cog-ha-matter embedded broker — Home Assistant + Matter)
|
||||
EXPOSE 1883
|
||||
# HOMECORE HA-compatible REST + WebSocket (homecore-server)
|
||||
EXPOSE 8123
|
||||
|
||||
ENV RUST_LOG=info
|
||||
|
||||
|
||||
@@ -24,13 +24,10 @@ services:
|
||||
environment:
|
||||
- RUST_LOG=info
|
||||
# CSI_SOURCE controls the data source for the sensing server.
|
||||
# Options: auto (default) — probe for ESP32 UDP then host WiFi; **fail
|
||||
# hard with exit 78 if neither is detected**.
|
||||
# Synthetic data is no longer a silent fallback
|
||||
# (issue #937 fix) — operators must opt in.
|
||||
# Options: auto (default) — probe for ESP32 UDP then fall back to simulation
|
||||
# esp32 — receive real CSI frames from an ESP32 on UDP port 5005
|
||||
# wifi — use host Wi-Fi RSSI/scan data (Windows netsh)
|
||||
# simulated — explicitly generate synthetic CSI for demo mode
|
||||
# simulated — generate synthetic CSI data (no hardware required)
|
||||
- CSI_SOURCE=${CSI_SOURCE:-auto}
|
||||
# MODELS_DIR controls where the server scans for .rvf model files.
|
||||
# Mount a host directory and set this to make models visible:
|
||||
|
||||
@@ -11,65 +11,10 @@
|
||||
# docker run ruvnet/wifi-densepose:latest --model /app/models/my.rvf
|
||||
#
|
||||
# Environment variables:
|
||||
# CSI_SOURCE — data source. Valid values:
|
||||
# auto — try ESP32 then Windows WiFi, **fail-loud if no
|
||||
# real hardware is detected** (issue #937 fix:
|
||||
# the server no longer silently falls back to
|
||||
# synthetic data — that's now opt-in only).
|
||||
# esp32 — listen for UDP CSI on the configured port.
|
||||
# wifi — Windows-native WiFi capture.
|
||||
# simulated — explicit demo mode with synthetic CSI.
|
||||
# Default is `auto`. Set CSI_SOURCE=simulated when you want
|
||||
# fake data tagged as such; never set it implicitly.
|
||||
# CSI_SOURCE — data source: auto (default), esp32, wifi, simulated
|
||||
# MODELS_DIR — directory to scan for .rvf model files (default: data/models)
|
||||
set -e
|
||||
|
||||
# ── Issue #864: fail-closed on default posture ───────────────────────────────
|
||||
# The pre-fix default was: empty RUVIEW_API_TOKEN (auth off) + --bind-addr
|
||||
# 0.0.0.0 + docker-compose publishing :3000/:3001/:5005 → an unauthenticated
|
||||
# attacker on any reachable network segment could read /api/v1/sensing/latest
|
||||
# and the /ws/sensing live stream. That posture is unsafe on guest WiFi,
|
||||
# untrusted LANs, accidentally-port-forwarded hosts, or any reverse-proxied
|
||||
# deployment. Refuse to start with this combination.
|
||||
#
|
||||
# Escape hatches (operator must opt in explicitly):
|
||||
# * Set RUVIEW_API_TOKEN to a strong secret → auth enabled on /api/v1/*.
|
||||
# * Set RUVIEW_ALLOW_UNAUTHENTICATED=1 → preserves the pre-fix behaviour;
|
||||
# only safe on an isolated trust boundary.
|
||||
# * Set RUVIEW_BIND_ADDR to a loopback / private interface → unauth is fine
|
||||
# when the socket isn't reachable. The auto-bind nudges toward 127.0.0.1.
|
||||
#
|
||||
# This check runs only for the default sensing-server path (no args + flag-only
|
||||
# args). The `cog-ha-matter` / `homecore` routes below are excluded because
|
||||
# they own their own auth lifecycle.
|
||||
case "${1:-}" in
|
||||
cog-ha-matter|ha-matter|homecore|homecore-server) ;;
|
||||
*)
|
||||
if [ -z "${RUVIEW_API_TOKEN:-}" ] && [ "${RUVIEW_ALLOW_UNAUTHENTICATED:-}" != "1" ]; then
|
||||
# If the operator hasn't overridden the bind, refuse outright on
|
||||
# the default 0.0.0.0. If they've nailed it to loopback (or a
|
||||
# specific private address they trust), let it run.
|
||||
__bind_default="${RUVIEW_BIND_ADDR:-0.0.0.0}"
|
||||
case "$__bind_default" in
|
||||
127.*|localhost|::1)
|
||||
: ;; # loopback bind is safe even without a token
|
||||
*)
|
||||
echo "[entrypoint] ERROR: refusing to start sensing-server with default" >&2
|
||||
echo "[entrypoint] posture: RUVIEW_API_TOKEN is unset AND bind is" >&2
|
||||
echo "[entrypoint] ${__bind_default}. /ws/sensing streams live sensing" >&2
|
||||
echo "[entrypoint] frames; that data would be readable by anyone who" >&2
|
||||
echo "[entrypoint] can reach this host. Pick one:" >&2
|
||||
echo "[entrypoint] docker run -e RUVIEW_API_TOKEN=\$(openssl rand -hex 32) ..." >&2
|
||||
echo "[entrypoint] docker run -e RUVIEW_BIND_ADDR=127.0.0.1 ..." >&2
|
||||
echo "[entrypoint] docker run -e RUVIEW_ALLOW_UNAUTHENTICATED=1 ... # only on trusted network" >&2
|
||||
echo "[entrypoint] See https://github.com/ruvnet/RuView/issues/864" >&2
|
||||
exit 64
|
||||
;;
|
||||
esac
|
||||
fi
|
||||
;;
|
||||
esac
|
||||
|
||||
# Route to cog-ha-matter (ADR-116) when invoked as:
|
||||
# docker run <image> cog-ha-matter [--flags]
|
||||
# or via the short alias `ha-matter`. Strips the keyword and execs the
|
||||
@@ -83,14 +28,6 @@ case "${1:-}" in
|
||||
--sensing-url "${SENSING_URL:-http://127.0.0.1:3000}" \
|
||||
"$@"
|
||||
;;
|
||||
homecore|homecore-server)
|
||||
# Route to the HOMECORE native Rust port of Home Assistant
|
||||
# (ADRs 126-134, v0.10.0). Default bind matches HA at :8123.
|
||||
shift
|
||||
exec /app/homecore-server \
|
||||
--bind "${HOMECORE_BIND:-0.0.0.0:8123}" \
|
||||
"$@"
|
||||
;;
|
||||
esac
|
||||
|
||||
# If the first argument looks like a flag (starts with -), prepend the
|
||||
@@ -103,7 +40,7 @@ if [ "${1#-}" != "$1" ] || [ -z "$1" ]; then
|
||||
--ui-path /app/ui \
|
||||
--http-port 3000 \
|
||||
--ws-port 3001 \
|
||||
--bind-addr "${RUVIEW_BIND_ADDR:-0.0.0.0}" \
|
||||
--bind-addr 0.0.0.0 \
|
||||
"$@"
|
||||
fi
|
||||
|
||||
|
||||
@@ -1,117 +0,0 @@
|
||||
# RuView Streaming Engine v0.3.0 — Auditable Environmental Intelligence
|
||||
|
||||
## What this is
|
||||
|
||||
Most WiFi-sensing stacks emit a number and hope you trust it. **RuView's streaming
|
||||
engine is built so you don't have to.** Every conclusion it reaches — "someone is
|
||||
in the living room," "fall risk elevated," "the room layout changed" — carries a
|
||||
full evidence trail: which sensors saw it, how much they agreed, which calibration
|
||||
and model produced it, and what privacy policy it was emitted under.
|
||||
|
||||
The throughline is **trust**. If you ask *"why should I believe this when it says a
|
||||
person fell?"*, the engine answers with signal evidence, sensor agreement,
|
||||
calibration provenance, and an auditable privacy posture — not just a confidence
|
||||
score.
|
||||
|
||||
This release lands the ADR-135→146 series: the data contracts, the
|
||||
trust/privacy/audit machinery, and the algorithms — all real, tested, and
|
||||
composed into one end-to-end pipeline cycle.
|
||||
|
||||
## The two layers that make it auditable
|
||||
|
||||
- **WorldGraph (`wifi-densepose-worldgraph`)** — the *where & why* graph. A typed
|
||||
graph of rooms, sensors, RF links, person tracks, object anchors, events, and
|
||||
beliefs, connected by typed edges: `observes`, `located_in`, `derived_from`,
|
||||
`contradicts`, `privacy_limited_by`. The privacy posture is *visible in the
|
||||
persisted graph* — an auditor can read exactly what was suppressed and why.
|
||||
- **Trusted semantic records** — the *what we believe right now* record. Every
|
||||
semantic state carries model version, calibration version, evidence refs,
|
||||
confidence, expiry, and privacy action. High-stakes actions (caregiver
|
||||
escalation) require **multi-signal agreement**, not a single noisy primitive.
|
||||
|
||||
## What's new in v0.3.0
|
||||
|
||||
| Area | Capability |
|
||||
|------|-----------|
|
||||
| Frame contracts (ADR-136) | `ComplexSample` (LE-canonical), provenance fields on every frame, `CanonicalFrame` BLAKE3 witness, `Stage`/`Versioned`/`QualityScored` traits |
|
||||
| Calibration (ADR-135) | `BaselineCalibration::apply()` stamps a deterministic `calibration_id` onto each frame |
|
||||
| Fusion quality (ADR-137) | `QualityScore` with per-node weights, evidence refs, and contradiction flags; calibration-mismatch detection |
|
||||
| Array coordination (ADR-138) | clock-quality + geometry gating; degraded nodes go "watch-only" |
|
||||
| WorldGraph (ADR-139) | the typed digital twin + privacy rollup + deterministic persistence |
|
||||
| Semantic records (ADR-140) | auditable state records + multi-signal agent routing |
|
||||
| Privacy control plane (ADR-141) | named modes + actions + a BLAKE3 hash-chained, tamper-evident attestation |
|
||||
| Evolution + VoxelMap (ADR-142) | cross-link "the room changed" detection + Bayesian occupancy, privacy-gated to a histogram |
|
||||
| RF-SLAM (ADR-143) | persistent reflector discovery → learned static anchors |
|
||||
| UWB fusion (ADR-144) | range-constraint refinement with outlier rejection (forward-looking) |
|
||||
| Ablation harness (ADR-145) | feature-matrix metrics incl. membership-inference privacy leakage |
|
||||
| RF encoder (ADR-146) | multi-task heads with per-head uncertainty + contrastive batcher (forward-looking) |
|
||||
| **Engine (`wifi-densepose-engine`)** | the composition root: one `process_cycle()` runs the whole trust pipeline |
|
||||
|
||||
## Quick start
|
||||
|
||||
```rust
|
||||
use wifi_densepose_engine::StreamingEngine;
|
||||
use wifi_densepose_bfld::PrivacyMode;
|
||||
use wifi_densepose_geo::types::GeoRegistration;
|
||||
use wifi_densepose_signal::ruvsense::fusion_quality::CalibrationId;
|
||||
|
||||
// 1. Build the engine with a privacy posture + model version.
|
||||
let mut engine = StreamingEngine::new(PrivacyMode::PrivateHome, 1, GeoRegistration::default());
|
||||
|
||||
// 2. Describe the space (rooms + sensors are WorldGraph nodes).
|
||||
let room = engine.add_room("living_room", "Living Room");
|
||||
let sensor = engine.add_sensor("esp32-com9", room);
|
||||
engine.register_node_geometry(0, 1.0, 0.0, 0.0); // ADR-138 array geometry (optional)
|
||||
|
||||
// 3. Each 50 ms cycle: feed per-node CSI frames + the calibration epoch.
|
||||
let out = engine.process_cycle(&node_frames, CalibrationId(0xABCD), room, now_ms)?;
|
||||
|
||||
// 4. The result is a *trusted* belief — fully traceable.
|
||||
println!("class={:?} demoted={} evidence={:?}",
|
||||
out.effective_class, out.demoted, out.provenance.evidence);
|
||||
assert_eq!(out.quality.calibration_id, Some(CalibrationId(0xABCD)));
|
||||
|
||||
// 5. Persist the world model; reload reproduces the same query results.
|
||||
let snapshot = engine.snapshot_json()?; // RVF payload — never raw RF frames
|
||||
```
|
||||
|
||||
Per-node calibration (mismatch demotes privacy automatically):
|
||||
|
||||
```rust
|
||||
let out = engine.process_cycle_calibrated(
|
||||
&node_frames,
|
||||
&[Some(CalibrationId(1)), Some(CalibrationId(2))], // disagree → CalibrationIdMismatch
|
||||
room, now_ms)?;
|
||||
assert!(out.demoted); // privacy class demoted to Restricted
|
||||
assert_eq!(out.quality.calibration_id, None); // no single calibration epoch
|
||||
```
|
||||
|
||||
## Validated (acceptance tests that prove the architecture)
|
||||
|
||||
- **ADR-137** `two calibrated frames → calibration mismatch → QualityScore contradiction → Restricted → calibration_id None → witness stable`
|
||||
- **ADR-139** `live_frame → fusion → worldgraph_update → privacy_rollup → persist → reload → same_contents` (no raw RF persisted)
|
||||
- **ADR-140** `raw snapshot → semantic primitive → SemanticStateRecord → agreement rule → expired record rejected`
|
||||
- **ADR-142** `3 links drift 30 frames → ChangePoint → VoxelMap accumulates → low-confidence suppressed → VoxelGate Restricted histogram → ADR-137 contradiction`
|
||||
|
||||
## Performance & safety
|
||||
|
||||
- **~6.35 µs per full cycle** (4 nodes / 56 subcarriers) — ~7,800× under the 50 ms / 20 Hz budget (criterion: `cargo bench -p wifi-densepose-engine`).
|
||||
- New crates are `#![forbid(unsafe_code)]`; no hardcoded secrets; input validated at boundaries; privacy demotion is monotonic; mode changes are hash-chain attested.
|
||||
- `wifi-densepose-core` and `wifi-densepose-bfld` build `#![no_std]` for the ESP32-S3 on-device path.
|
||||
|
||||
## Build & test
|
||||
|
||||
```bash
|
||||
cd v2
|
||||
cargo build --release --workspace --no-default-features # optimized build
|
||||
cargo test --workspace --no-default-features # full suite
|
||||
cargo test -p wifi-densepose-engine # 13 integration tests
|
||||
cargo bench -p wifi-densepose-engine # per-cycle latency
|
||||
```
|
||||
|
||||
## Status (honest)
|
||||
|
||||
Integrated and validated end-to-end: ADR-135/136/137/138/139/141/142/143 via the
|
||||
`wifi-densepose-engine` composition root. Forward-looking / pending: live 20 Hz
|
||||
sensing-server loop wiring, UWB hardware (ADR-144), and RF-encoder model training
|
||||
(ADR-146). Each GitHub issue (#840–#850) lists what is *Built* vs *Integration glue*.
|
||||
@@ -156,25 +156,6 @@ docker inspect ruvnet/wifi-densepose:python --format='{{.Size}}'
|
||||
# Expected: ~569 MB
|
||||
```
|
||||
|
||||
### Step 10b: Verify CIR Deterministic Proof (ADR-134)
|
||||
|
||||
```bash
|
||||
bash scripts/verify-cir-proof.sh
|
||||
```
|
||||
|
||||
**Expected:** `VERDICT: PASS (CIR hash matches)` once the `cir` module is implemented.
|
||||
|
||||
Currently outputs `BLOCKED` because `expected_cir_features.sha256` contains a placeholder.
|
||||
After the CIR implementation lands, regenerate and commit the hash:
|
||||
|
||||
```bash
|
||||
cd v2 && cargo run -p wifi-densepose-signal --bin cir_proof_runner \
|
||||
--release --no-default-features -- --generate-hash \
|
||||
> ../archive/v1/data/proof/expected_cir_features.sha256
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Step 11: Verify ESP32 Flash (requires hardware on COM7)
|
||||
|
||||
```bash
|
||||
@@ -231,8 +212,6 @@ Each row is independently verifiable. Status reflects audit-time findings.
|
||||
| 31 | On-device ESP32 ML inference | No | **NO** | Firmware streams raw I/Q; inference runs on aggregator |
|
||||
| 32 | Real-world CSI dataset bundled | No | **NO** | Only synthetic reference signal (seed=42) |
|
||||
| 33 | 54,000 fps measured throughput | Claimed | **NOT MEASURED** | Criterion benchmarks exist but not run at audit time |
|
||||
| 34 | CIR estimation (ADR-134, ISTA via NeumannSolver) | Yes | **PASS** | `archive/v1/data/proof/expected_cir_features.sha256`, `scripts/verify-cir-proof.sh`; regenerate after intentional changes: `cd v2 && cargo run -p wifi-densepose-signal --bin cir_proof_runner --release --no-default-features -- --generate-hash > ../archive/v1/data/proof/expected_cir_features.sha256` |
|
||||
| 35 | Empty-room baseline calibration (ADR-135, Welford + von Mises) | Yes | **PASS** | `archive/v1/data/proof/expected_calibration_features.sha256`, `scripts/verify-calibration-proof.sh`; regenerate after intentional changes: `cd v2 && cargo run -p wifi-densepose-signal --bin calibration_proof_runner --release --no-default-features -- --generate-hash > ../archive/v1/data/proof/expected_calibration_features.sha256` |
|
||||
|
||||
---
|
||||
|
||||
@@ -242,8 +221,6 @@ Each row is independently verifiable. Status reflects audit-time findings.
|
||||
|--------|-------|
|
||||
| Witness commit SHA | `96b01008f71f4cbe2c138d63acb0e9bc6825286e` |
|
||||
| Python proof hash (numpy 2.4.2, scipy 1.17.1) | `8c0680d7d285739ea9597715e84959d9c356c87ee3ad35b5f1e69a4ca41151c6` |
|
||||
| CIR proof hash (ADR-134) | `120bd7b1f549f57f3773971a389c48c2bdd99b4ab1f205935867a16e95583995` |
|
||||
| Calibration proof hash (ADR-135) | `d6bce07ecb1648e6936561df44bf4a3bfc17bb0ba5f692646b2301d105b52f67` |
|
||||
| ESP32 frame magic | `0xC5110001` |
|
||||
| Workspace crate version | `0.2.0` |
|
||||
|
||||
|
||||
@@ -57,7 +57,7 @@ This witness separates what was **empirically observed on real silicon today** f
|
||||
|
||||
| # | Claim | Why it's not verified |
|
||||
|---|---|---|
|
||||
| **B1** | "Wi-Fi 6 HE-LTF: 242 subcarriers per HE20 frame" | The only AP in range (`ruv.net`) is 11n-only. Every captured frame is 128 bytes = 64 subcarriers (HT-LTF, `ppdu_type=0`). No HE-SU/HE-MU/HE-TB observed. Even if an 11ax AP were available, **whether ESP-IDF v5.4's CSI callback exposes HE-LTF subcarriers via `wifi_csi_info_t.buf` is an open question** — the public API was designed for HT-LTF, and the driver may quietly downconvert. **Validate by capturing CSI against an 11ax AP and comparing `info->len` between HT and HE frames.**<br><br>**RESOLVED WITH MEASUREMENT (2026-06-11, external — issue #1005, production deployment by @stuinfla):** the open question is answered in both directions. **IDF v5.4's driver blob downconverts** (148 B / 64-subcarrier HT frames, PPDU byte 0x00, on a confirmed-HE link); **IDF v5.5.2 delivers true HE-LTF** — 532 B frames = 256 bins (242 active HE20 tones), PPDU byte 0x01 (HE-SU), ~90% of frames, same board/AP/link. Setup: XIAO ESP32-C6 → hostapd on Intel AX210, 2.4 GHz ch 6, `ieee80211ax=1`. No firmware change required (`acquire_csi_su=1` was already set); the gate was purely the IDF driver version. Three C6 nodes ran this mode simultaneously with ADR-110 ESP-NOW sync. Requires the issue-#1005 version-guard fix in `c6_sync_espnow.c` to build on v5.5.x. |<br><br>**REPLICATED IN-HOUSE (2026-06-11):** same source + fix, fresh IDF v5.5.2 toolchain, original COM12 board (`20:6e:f1:17:00:84`), AP `ruv.net` (11ax 2.4 GHz): **84% of 1,525 captured frames at 532 B / PPDU 0x01 (HE-SU)**, HT minority 148 B / 0x00. Evidence grade: MEASURED (two independent rigs). |
|
||||
| **B1** | "Wi-Fi 6 HE-LTF: 242 subcarriers per HE20 frame" | The only AP in range (`ruv.net`) is 11n-only. Every captured frame is 128 bytes = 64 subcarriers (HT-LTF, `ppdu_type=0`). No HE-SU/HE-MU/HE-TB observed. Even if an 11ax AP were available, **whether ESP-IDF v5.4's CSI callback exposes HE-LTF subcarriers via `wifi_csi_info_t.buf` is an open question** — the public API was designed for HT-LTF, and the driver may quietly downconvert. **Validate by capturing CSI against an 11ax AP and comparing `info->len` between HT and HE frames.** |
|
||||
| **B2** | "TWT-bounded deterministic CSI cadence (10 ms wake)" | No 11ax AP in range. The TWT setup *call* was exercised live and the graceful fallback path is now correct (A9), but the agreement itself was never accepted. **Validate by associating with an 11ax AP that has TWT Responder=1, then capturing the timestamped CSI cadence vs the wall clock.** |
|
||||
| **B3** | "±100 µs cross-node alignment over 802.15.4" | 3 boards initialized their radios with correct EUIs (A4/A5), but **none stepped down from candidate-leader to follower** during repeated 35-second multi-board captures. <br><br>**Coex hypothesis REJECTED**: rebuilt + reflashed all 3 boards with `CONFIG_C6_TIMESYNC_CHANNEL=26` (2480 MHz, non-overlapping with WiFi ch 5 at 2432 MHz). Result identical: 3× candidate, 0× "stepping down". So 2.4 GHz radio coex was NOT the cause. <br><br>**Current leading hypothesis**: OpenThread (CONFIG_OPENTHREAD_ENABLED=y) owns the 802.15.4 radio when its stack is initialized — our weak-symbol overrides of `esp_ieee802154_receive_done` / `_transmit_done` may never be called because OpenThread registers strong handlers. Validation in progress: rebuilding with `CONFIG_OPENTHREAD_ENABLED=n` (raw 802.15.4 only, our beacon protocol is private — no need for the Thread stack). If leader election fires under raw-15.4-only, hypothesis confirmed. <br><br>If raw-only also fails, next move is to dump the actual PHY frame bytes via the IEEE 802.15.4 sniffer mode on a 4th board and diagnose at the frame level. |
|
||||
| **B4** | "~5 µA hibernation for battery seed nodes" | No INA / Joulescope current measurement available on this bench. The shipped code uses `esp_deep_sleep_enable_gpio_wakeup` (ext1 path, ESP-IDF default ~10 µA), not a true LP-core polling program. The 5 µA number is the C6 datasheet figure for ULP-level hibernation, not a measured value. **Validate by hooking an INA219/INA226 between the dev board's 3V3 rail and the regulator output, then averaging current over a 60-second cycle with the LP-core armed.** |
|
||||
|
||||
+1
-1
@@ -1,4 +1,4 @@
|
||||
# ADR-166: Quality Engineering Response — Security Hardening & Code Quality
|
||||
# ADR-050: Quality Engineering Response — Security Hardening & Code Quality
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
@@ -1,8 +1,4 @@
|
||||
# ADR-167 Appendix: DDD Bounded Contexts — Tauri Desktop Frontend
|
||||
|
||||
> Appendix to [ADR-052](ADR-052-tauri-desktop-frontend.md). Renumbered from ADR-052
|
||||
> to ADR-167 to resolve the ADR-052 duplicate-number collision (per ADR-164 Gap Register
|
||||
> G1); the parent decision remains ADR-052.
|
||||
# ADR-052 Appendix: DDD Bounded Contexts — Tauri Desktop Frontend
|
||||
|
||||
This document maps out the domain model for the RuView Tauri desktop application
|
||||
described in ADR-052. It defines bounded contexts, their aggregates, entities,
|
||||
@@ -162,7 +158,7 @@ Represents an over-the-air firmware update to a running node.
|
||||
| `target_node` | `MacAddress` | Target node MAC |
|
||||
| `target_ip` | `IpAddr` | Target node IP |
|
||||
| `firmware` | `FirmwareBinary` | The binary being pushed |
|
||||
| `psk` | `Option<SecureString>` | PSK for authentication (ADR-166) |
|
||||
| `psk` | `Option<SecureString>` | PSK for authentication (ADR-050) |
|
||||
| `phase` | `OtaPhase` | Uploading / Rebooting / Verifying / Done / Failed |
|
||||
| `progress` | `Progress` | Upload progress |
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
| Status | Proposed |
|
||||
| Date | 2026-03-06 |
|
||||
| Deciders | ruv |
|
||||
| Depends on | ADR-012 (ESP32 CSI Mesh), ADR-039 (Edge Intelligence), ADR-040 (WASM Programmable Sensing), ADR-044 (Provisioning Enhancements), ADR-166 (Security Hardening, renumbered from ADR-050), ADR-051 (Server Decomposition) |
|
||||
| Depends on | ADR-012 (ESP32 CSI Mesh), ADR-039 (Edge Intelligence), ADR-040 (WASM Programmable Sensing), ADR-044 (Provisioning Enhancements), ADR-050 (Security Hardening), ADR-051 (Server Decomposition) |
|
||||
| Issue | [#177](https://github.com/ruvnet/RuView/issues/177) |
|
||||
|
||||
## Context
|
||||
@@ -211,7 +211,7 @@ pub struct FlashProgress {
|
||||
// commands/ota.rs
|
||||
|
||||
/// Push firmware to a node via HTTP OTA (port 8032).
|
||||
/// Includes PSK authentication per ADR-166.
|
||||
/// Includes PSK authentication per ADR-050.
|
||||
#[tauri::command]
|
||||
async fn ota_update(
|
||||
node_ip: String,
|
||||
@@ -801,7 +801,7 @@ Total estimated effort: ~11 weeks for a single developer.
|
||||
- ADR-039: ESP32 Edge Intelligence
|
||||
- ADR-040: WASM Programmable Sensing
|
||||
- ADR-044: Provisioning Tool Enhancements
|
||||
- ADR-166: Quality Engineering — Security Hardening (renumbered from ADR-050)
|
||||
- ADR-050: Quality Engineering — Security Hardening
|
||||
- ADR-051: Sensing Server Decomposition
|
||||
- `firmware/esp32-csi-node/` — ESP32 firmware source
|
||||
- `firmware/esp32-csi-node/provision.py` — Current provisioning script
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
# ADR-080: QE Analysis Remediation Plan
|
||||
|
||||
- **Status:** Proposed — P0 security findings #1–#3 **RESOLVED** on the shipped Rust sensing-server boundary (2026-06-13; closes ADR-164 G11)
|
||||
- **Status:** Proposed
|
||||
- **Date:** 2026-04-06
|
||||
- **Source:** [QE Analysis Gist (2026-04-05)](https://gist.github.com/proffesor-for-testing/a6b84d7a4e26b7bbef0cf12f932925b7)
|
||||
- **Full Reports:** [proffesor-for-testing/RuView `qe-reports` branch](https://github.com/proffesor-for-testing/RuView/tree/qe-reports/docs/qe-reports)
|
||||
@@ -13,38 +13,25 @@ An 8-agent QE swarm analyzed ~305K lines across Rust, Python, C firmware, and Ty
|
||||
|
||||
Address the 15 prioritized issues from the QE analysis in three waves: P0 (immediate), P1 (this sprint), P2 (this quarter).
|
||||
|
||||
## Security P0 closure note (2026-06-13) — Rust sensing-server boundary
|
||||
|
||||
The three P0 security findings below were logged against the **Python v1** API
|
||||
(`archive/v1/src/…`). ADR-164 G11 re-scoped them to the *shipped* boundary:
|
||||
`wifi-densepose-sensing-server` (Rust). They were verified against the current
|
||||
Rust crate and closed on branch `fix/adr-080-sensing-server-security`. Each fix
|
||||
(or already-fixed finding) is pinned by a test that fails on the old behavior.
|
||||
**The Python v1 paths remain as-is** — v1 is archived and not the shipped
|
||||
surface; this closure governs the live Rust server only.
|
||||
|
||||
## P0 — Fix Immediately
|
||||
|
||||
### 1. Rate Limiter Bypass / XFF spoofing (Security HIGH) — **RESOLVED (verified absent on Rust boundary)**
|
||||
### 1. Rate Limiter Bypass (Security HIGH)
|
||||
|
||||
- **Original location (v1):** `archive/v1/src/middleware/rate_limit.py:200-206`
|
||||
- **Location:** `archive/v1/src/middleware/rate_limit.py:200-206`
|
||||
- **Problem:** Trusts `X-Forwarded-For` without validation. Any client bypasses rate limits via header spoofing.
|
||||
- **Rust verification (2026-06-13):** The Rust sensing-server has **no XFF-trusting control to bypass** — there is no IP-based rate-limiter and no IP-allowlist, and neither security middleware reads a forwarded header. `bearer_auth.rs` authenticates on the token alone (`require_bearer` inspects only the `AUTHORIZATION` header); `host_validation.rs` decides on the `Host` header only. A repo-wide grep for `x-forwarded-for|forwarded|peer_addr|client_ip|real-ip` over `wifi-densepose-sensing-server` returns nothing. The only "rate limiter" is the MQTT *sample-rate* gate (`mqtt/state.rs`), a per-entity publish throttle with no IP/header input.
|
||||
- **Resolution:** No code change needed (no vulnerable surface). Regression tests pin the immunity: `bearer_auth::tests::xff_header_never_affects_auth_decision` (spoofed XFF never flips a 401↔200 decision) and `host_validation::tests::forwarded_headers_never_bypass_host_allowlist` (spoofed `X-Forwarded-Host: localhost` never lets a foreign `Host: evil.com` past the allowlist). Residual: if an IP-based control is ever added, it must derive the peer from the socket (`ConnectInfo<SocketAddr>`) and only honor XFF from an explicit `--trusted-proxy` CIDR — captured as guidance in the test docstrings.
|
||||
- **Fix:** Validate forwarded headers against trusted proxy list, or use connection IP directly.
|
||||
|
||||
### 2. Exception Details Leaked in Responses (Security HIGH, CWE-209) — **RESOLVED**
|
||||
### 2. Exception Details Leaked in Responses (Security HIGH)
|
||||
|
||||
- **Original location (v1):** `archive/v1/src/api/routers/pose.py:140`, `stream.py:297`, +5 endpoints
|
||||
- **Problem:** Internal error/stack-trace detail serialized into client responses.
|
||||
- **Rust finding (2026-06-13):** Six handlers in `wifi-densepose-sensing-server/src/main.rs` serialized the internal error `Display` into the JSON body: `edge_registry_endpoint` returned a panicked `spawn_blocking` `JoinError` (`"task … panicked"`) in a `500` and the raw upstream error in a `503`; `delete_model`/`delete_recording`/`start_recording` returned `std::io::Error` strings (OS detail / path); `calibration_start`/`calibration_stop` returned the `FieldModel` error chain.
|
||||
- **Fix:** New `src/error_response.rs` module — `internal_error` / `internal_error_json` / `upstream_unavailable` log the full detail **server-side only** (tagged with a correlation id) and return a generic body (`{"error":"internal_error","correlation_id":…}`) with no `panicked`, no file paths, no Debug chain. All six call-sites rewired. Pinned by `error_response::tests::internal_error_body_does_not_leak_detail` (leak-substring guard, verified to fail on the reverted old body) + 4 sibling tests.
|
||||
- **Location:** `archive/v1/src/api/routers/pose.py:140`, `stream.py:297`, +5 endpoints
|
||||
- **Problem:** Stack traces visible regardless of environment.
|
||||
- **Fix:** Wrap with generic error responses in production; log details server-side only.
|
||||
|
||||
### 3. WebSocket JWT in URL (Security HIGH, CWE-598) — **RESOLVED (verified absent on Rust boundary)**
|
||||
### 3. WebSocket JWT in URL (Security HIGH, CWE-598)
|
||||
|
||||
- **Original location (v1):** `archive/v1/src/api/routers/stream.py:74`, `archive/v1/src/middleware/auth.py:243`
|
||||
- **Location:** `archive/v1/src/api/routers/stream.py:74`, `archive/v1/src/middleware/auth.py:243`
|
||||
- **Problem:** Tokens in query strings visible in logs/proxies/browser history.
|
||||
- **Rust verification (2026-06-13):** The Rust sensing-server never reads a token from the URL. `require_bearer` (`bearer_auth.rs`) inspects only the `Authorization` header; the WebSocket handlers (`ws_sensing_handler`/`ws_introspection_handler`/`ws_pose_handler`) take a bare `WebSocketUpgrade` with no `Query` extractor; the single `Query` in the crate (`EdgeRegistryParams`) is a non-secret `refresh` flag.
|
||||
- **Resolution:** No code change needed (no query-token path exists). Regression test `bearer_auth::tests::query_string_token_is_never_accepted` proves `?token=`/`?access_token=` in the URL never authenticates (stays `401`) while the same token in the header succeeds (`200`) — verified to fail if a query-token path is re-introduced.
|
||||
- **Fix:** Use WebSocket subprotocol or first-message auth pattern.
|
||||
|
||||
### 4. Rust Tests Not in CI
|
||||
|
||||
|
||||
@@ -259,75 +259,14 @@ Validation runs against:
|
||||
- **ADR-083** (Proposed) — Per-cluster Pi compute hop. Defines the
|
||||
device class that hosts the sketch bank.
|
||||
|
||||
## Pass 2 — randomized rotation + multi-bit (ADR-156 §8, landed 2026-06)
|
||||
|
||||
The "Open question" below ("does `BinaryQuantized` need a randomized
|
||||
rotation pre-pass?") is now **answered with measured numbers** via
|
||||
ADR-156 §10. Summary:
|
||||
|
||||
- **Pass 2 (randomized rotation) is implemented** —
|
||||
`crates/wifi-densepose-ruvector/src/rotation.rs`: a deterministic
|
||||
`R = H·D` (Fast Hadamard Transform + seeded ±1 sign flips), `O(d log d)`
|
||||
/ `O(d)`, norm-preserving, reproducible from a stored `u64` seed. Opt-in
|
||||
via `Sketch::from_embedding_rotated` / `SketchBank::with_rotation`;
|
||||
Pass-1 API and wire format unchanged.
|
||||
- **Measured top-K coverage** (anisotropic planted-cluster fixture,
|
||||
cosine ground truth, dim=128 N=2048 K=8): rotation lifts coverage
|
||||
**36.13% → 46.39%** at the strict `candidate_k = K` bar, and Pass-2
|
||||
reaches the **≥90% acceptance bar at candidate_k = 24 (~3× over-fetch)**.
|
||||
Multi-bit (≤4-bit) reaches 74% at the strict bar. **Honest verdict:
|
||||
neither rotation nor ≤4-bit multi-bit clears the strict-K 90% bar on
|
||||
this distribution; the bar is met via the over-fetch "candidate set"
|
||||
pattern this ADR specifies** (Decision §"the canonical pattern" — sketch
|
||||
picks the candidate set, full precision refines). Full numbers and
|
||||
reproduce commands in ADR-156 §10.
|
||||
- **Pre-existing `SketchBank::topk` bug fixed** — the `n > k` heap path
|
||||
returned the k *farthest* sketches (min-heap mistaken for max-heap);
|
||||
only the `n ≤ k` fast path had test coverage. Fixed + regression-pinned
|
||||
(`topk_heap_path_returns_nearest`,
|
||||
`tight_clusters_give_high_coverage_with_overfetch`). This makes every
|
||||
prior top-K acceptance number in this ADR depend on the fixed path; the
|
||||
≥90% coverage criterion is only meaningful post-fix.
|
||||
|
||||
## Pass 2b — RaBitQ unbiased distance estimator (ADR-156 §11, landed 2026-06)
|
||||
|
||||
The **real** RaBitQ contribution (Gao & Long, SIGMOD 2024) — an
|
||||
**unbiased estimator of the inner product / distance** from the 1-bit
|
||||
code + per-vector side info, not just sign bits — is now implemented and
|
||||
**MEASURED against this ADR's ≥90% strict-K bar**:
|
||||
|
||||
- **Implemented** — `crates/wifi-densepose-ruvector/src/estimator.rs`:
|
||||
`EstimatorSketch` (Pass-2 sign code + 8 B/vec side info:
|
||||
`residual_norm` + `x_dot_o = ⟨x̄, o'⟩`), `DistanceEstimator`
|
||||
(`⟨o',q'⟩ ≈ ⟨x̄,q'⟩ / x_dot_o`, the paper's unbiased rescale), and
|
||||
`EstimatorBank` reranking candidates by the estimate instead of raw
|
||||
Hamming. **Zero-centroid simplification** (`c = 0`) documented;
|
||||
paper-faithful centroid path also built (`with_centroid`). Additive —
|
||||
Pass-1/Pass-2 and the wire format are unchanged.
|
||||
- **MEASURED strict-K coverage** (same fixture as §"Pass 2", cosine
|
||||
ground truth): the estimator lifts the strict `candidate_k = K` bar
|
||||
**46.39% (Pass-2 sign) → 49.71% (estimator, cosine rerank)** — a real
|
||||
**+3.3 pp** lift, but **still ~40 pp short of the ≥90% strict bar.**
|
||||
At over-fetch the estimator does better than sign (95.12% vs 91.60% at
|
||||
candidate_k = 24). **Honest verdict: the unbiased estimator does NOT
|
||||
clear the strict-K 90% bar on this distribution** — the binding
|
||||
constraint is the 1-bit code's information ceiling, not estimator
|
||||
variance. The ≥90% acceptance bar is still met only via the over-fetch
|
||||
"candidate set" pattern this ADR's Decision specifies; the estimator
|
||||
**reduces the over-fetch factor** needed but does not remove it. This
|
||||
is a **published negative**, reported as such. Full numbers + reproduce
|
||||
commands in ADR-156 §11.
|
||||
|
||||
## Open questions
|
||||
|
||||
- **Does `BinaryQuantized` need a randomized rotation pre-pass for
|
||||
RuView's embedding distributions?** **ANSWERED (ADR-156 §10):** rotation
|
||||
is built and measured — it helps (+10pp at strict K) but is not
|
||||
sufficient alone for strict-K 90% on the tested anisotropic
|
||||
distribution; the over-fetch candidate-set pattern meets the bar.
|
||||
Pure sign quantization assumes zero-centered, isotropic embeddings; the
|
||||
rotation decorrelates anisotropic coords as the RaBitQ paper
|
||||
(Gao & Long, SIGMOD 2024) prescribes.
|
||||
RuView's embedding distributions?** Pure sign quantization assumes
|
||||
zero-centered, isotropic embeddings. If AETHER / spectrogram
|
||||
distributions are skewed (likely for spectrogram), add a
|
||||
`randomized_rotation` pre-pass following the original RaBitQ paper
|
||||
(Gao & Long, SIGMOD 2024). Decided after pass-1 benchmark.
|
||||
- **Sketch dimension target.** Default to the embedding's native
|
||||
dimension (128 for AETHER, 256 for spectrogram). Higher-dimensional
|
||||
sketches (Johnson-Lindenstrauss-projected to 512) trade compute for
|
||||
|
||||
@@ -19,7 +19,7 @@ The production CSI node firmware (`firmware/esp32-csi-node`) was built around th
|
||||
|
||||
| C6 capability | What it enables for sensing | Why we can't get it on S3 |
|
||||
|---|---|---|
|
||||
| **802.11ax (Wi-Fi 6) HE-LTF CSI** | 242 subcarriers per HE20 frame (vs 52 for HT-LTF), HE-MU/HE-TB PPDU types, OFDMA-aware channel sounding. **Hardware-confirmed 2026-06-11** (issue #1005, external production deployment): requires **ESP-IDF ≥ 5.5** — the v5.4 driver blob silently downconverts to 64-subcarrier HT even on a confirmed-HE link; v5.5.2 delivers 532 B frames = 256 bins (242 active tones), PPDU 0x01 (HE-SU). See WITNESS-LOG-110 §B1 (resolved). | S3 radio is HT-only (n) |
|
||||
| **802.11ax (Wi-Fi 6) HE-LTF CSI** | 242 subcarriers per HE20 frame (vs 52 for HT-LTF), HE-MU/HE-TB PPDU types, OFDMA-aware channel sounding | S3 radio is HT-only (n) |
|
||||
| **802.15.4 (Thread / Zigbee)** | Cross-node time-sync over a separate radio — frees Wi-Fi airtime for CSI, ±100 µs alignment possible without coordination traffic on the sensing channel | S3 has no 802.15.4 |
|
||||
| **TWT (Target Wake Time)** | Sensor negotiates a deterministic wake slot with the AP; CSI cadence becomes scheduler-bounded instead of opportunistic | Requires 802.11ax — S3 can't speak it |
|
||||
| **LP-core + hibernation (~5 µA)** | Always-on motion gate runs on a separate RISC-V LP core in deep sleep; HP core stays off until a real event | S3 ULP is FSM-only, ~10 µA floor |
|
||||
|
||||
@@ -1,285 +0,0 @@
|
||||
# ADR-125: RuView ↔ Apple Home native HAP bridge — direct HomeKit accessory advertisement from the Seed
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Proposed |
|
||||
| **Date** | 2026-05-25 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codename** | **APPLE-FABRIC** — RuView speaks HomeKit directly so Apple HomePod / Apple TV act as the discovery + automation surface with zero Home-Assistant middle layer |
|
||||
| **Relates to** | [ADR-115](ADR-115-home-assistant-integration.md) (HA-DISCO MQTT publisher), [ADR-116](ADR-116-cog-ha-matter-seed.md) (cog-ha-matter §P7 left HAP/Matter as a feature-flag stub), [ADR-118](ADR-118-bfld-beamforming-feedback-layer-for-detection.md) (BFLD presence + identity-risk events), [ADR-122](ADR-122-bfld-ruview-ha-matter-exposure.md) (BFLD HA/Matter exposure) |
|
||||
| **Tracking issue** | TBD |
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
### 1.1 The misunderstanding worth correcting once
|
||||
|
||||
A naive integration tries to **push** data to a HomePod — open a socket, send a JSON-RPC, call an MQTT topic on `homepod.local`. Apple intentionally does not expose that surface. The HomePod is not an endpoint; it is the **Home Hub + Matter Controller + HomeKit Controller + Siri endpoint** for the Apple Home ecosystem on the LAN. It **discovers** accessories that advertise themselves on the local network via Bonjour/mDNS using the HomeKit Accessory Protocol (HAP) or Matter.
|
||||
|
||||
The correct direction of flow is therefore:
|
||||
|
||||
```text
|
||||
RuView / Seed
|
||||
↓ (advertise HAP / Matter accessory on LAN)
|
||||
HomeKit / Matter accessory
|
||||
↓ (mDNS discovery)
|
||||
HomePod
|
||||
↓ (forwards to Apple Home automation graph)
|
||||
Apple Home ecosystem (iPhone, Watch, Mac, Siri, automations)
|
||||
```
|
||||
|
||||
### 1.2 What we ship today and where it stops
|
||||
|
||||
ADR-115 ships an **MQTT auto-discovery publisher** that talks to Home Assistant. ADR-116's `cog-ha-matter` Cognitum cog wraps that publisher into a Seed-installable artifact with mDNS, an embedded rumqttd broker, RuVector-backed thresholds, and an Ed25519 witness chain. ADR-122 explicitly extends the same publisher with the BFLD presence / identity-risk / Soul-Match topics so a Home Assistant install sees them as auto-discovered entities. The current path to HomePod therefore runs:
|
||||
|
||||
```text
|
||||
RuView sensing-server ──► cog-ha-matter (MQTT HA-DISCO + HA-MIND)
|
||||
↓
|
||||
Home Assistant broker
|
||||
↓
|
||||
Home Assistant HomeKit Bridge add-on
|
||||
↓
|
||||
HomePod
|
||||
```
|
||||
|
||||
This works and the auto-discovery is real, but it introduces a hard dependency: an operator must run Home Assistant, install its HomeKit Bridge integration, and pair the bridge in the Apple Home app. The Seed alone does not appear in Apple Home.
|
||||
|
||||
ADR-116 §P7 anticipated this — the `cog-ha-matter` `Cargo.toml` already carries a `matter = []` feature stub with the comment "matter-rs is added in P7; intentionally absent in P1 to keep the dep surface small until the SDK choice is validated." This ADR closes that box.
|
||||
|
||||
### 1.3 Why now
|
||||
|
||||
Three forces line up in 2026-05:
|
||||
|
||||
1. **The BFLD privacy gate (ADR-118 / 120 / 121) is shipped.** Class-2 and class-3 frames are the only ones eligible to cross the Matter boundary (ADR-122 §2.4). Without that gate we could not safely expose RuView signals to a consumer ecosystem. With it, every Anonymous / Restricted event is safe to advertise as a HomeKit sensor.
|
||||
2. **`@ruvnet/rvagent` (ADR-124) is on npm.** The MCP surface that lets agents query RuView is live. A first-class Apple-Home presence widens RuView's reach from "agents that speak MCP" to "anyone with an iPhone and a HomePod" — the consumer wedge.
|
||||
3. **The Cognitum Seed Docker image now bundles `cog-ha-matter`** (this branch's `Dockerfile.rust` change, see #794) — the runtime where a HAP advertiser would live is finally a single-image deployment.
|
||||
|
||||
### 1.4 Strategic framing
|
||||
|
||||
The combination is asymmetric:
|
||||
|
||||
| Layer | RuView contributes | Apple Home contributes |
|
||||
|-------|---------------------|------------------------|
|
||||
| Sensing | Passive RF presence, breathing, heart rate, fall risk, BFLD identity-risk, through-wall occupancy, longitudinal wellness | (none — Apple has no native RF sensing surface) |
|
||||
| Adoption | (limited — researcher-grade hardware today) | iPhone, Watch, Mac, HomePod, Apple TV installed base; consumer trust; voice; on-device intelligence |
|
||||
| UX | (utility CLI + a Web UI) | Home app, Siri, automation engine, notifications, accessibility |
|
||||
| Trust | Ed25519 witness chain, privacy class gate, local-first | Apple HomeKit local pairing, end-to-end encrypted, no cloud requirement |
|
||||
|
||||
RuView supplies the **invisible cognition layer** Apple cannot provide on its own; Apple supplies the **distribution and UX** that an open sensing stack cannot bootstrap. Direct HAP integration removes the only structural barrier between those two layers — Home Assistant as a mandatory intermediary.
|
||||
|
||||
---
|
||||
|
||||
## 2. Decision
|
||||
|
||||
Ship a **native HomeKit / Matter accessory** in the Seed runtime so a freshly-imaged Cognitum Seed appears in the Apple Home app under `Add Accessory → More Options` with **zero Home-Assistant dependency**.
|
||||
|
||||
Concretely:
|
||||
|
||||
1. Add a `hap-accessory` workspace component that advertises a set of HomeKit characteristics over mDNS using HAP-1.1 (HomeKit Accessory Protocol).
|
||||
2. The component subscribes to `wifi-densepose-sensing-server`'s WebSocket / BFLD `MqttEvent` stream and maps each privacy-class-2/3 event onto a HomeKit characteristic update.
|
||||
3. The same Docker image that ships `sensing-server` and `cog-ha-matter` ships the new advertiser as a third entrypoint:
|
||||
|
||||
```bash
|
||||
docker run --network host ruvnet/wifi-densepose:latest hap-accessory --privacy-mode
|
||||
```
|
||||
|
||||
`--network host` (or a macvlan bridge) is required because HAP pairing depends on the accessory and the controller seeing each other's mDNS broadcasts on the same L2 segment — same constraint Home Assistant's HomeKit Bridge has.
|
||||
|
||||
### 2.1 Two implementation tracks (decided here together; ship 2.1.a first)
|
||||
|
||||
#### 2.1.a — **HAP-python sidecar** (fastest to ship, lands first)
|
||||
|
||||
Add a tiny Python entrypoint `bridges/hap-python/ruview_hap.py` using the well-maintained [`HAP-python`](https://github.com/ikalchev/HAP-python) library. The Dockerfile gets a thin Python runtime stage; the entrypoint script polls `sensing-server` over HTTP and pushes characteristic updates into the HAP loop.
|
||||
|
||||
```python
|
||||
# bridges/hap-python/ruview_hap.py (≈80 LOC)
|
||||
from pyhap.accessory import Accessory
|
||||
from pyhap.accessory_driver import AccessoryDriver
|
||||
from pyhap.const import CATEGORY_SENSOR
|
||||
import urllib.request, json, threading, time
|
||||
|
||||
SENSING_URL = "http://127.0.0.1:3000/api/v1"
|
||||
|
||||
class RuViewSensor(Accessory):
|
||||
category = CATEGORY_SENSOR
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
s_motion = self.add_preload_service('MotionSensor')
|
||||
self.c_motion = s_motion.configure_char('MotionDetected')
|
||||
s_occ = self.add_preload_service('OccupancySensor')
|
||||
self.c_occ = s_occ.configure_char('OccupancyDetected')
|
||||
s_temp = self.add_preload_service('TemperatureSensor')
|
||||
self.c_temp = s_temp.configure_char('CurrentTemperature')
|
||||
threading.Thread(target=self._poll, daemon=True).start()
|
||||
|
||||
def _poll(self):
|
||||
while True:
|
||||
try:
|
||||
v = json.loads(urllib.request.urlopen(f"{SENSING_URL}/vitals").read())
|
||||
self.c_motion.set_value(bool(v.get("motion_present")))
|
||||
self.c_occ.set_value(int(bool(v.get("occupancy"))))
|
||||
if "ambient_temp_c" in v:
|
||||
self.c_temp.set_value(v["ambient_temp_c"])
|
||||
except Exception:
|
||||
pass
|
||||
time.sleep(1.0)
|
||||
|
||||
driver = AccessoryDriver(port=51826)
|
||||
driver.add_accessory(accessory=RuViewSensor(driver, 'RuView Sense'))
|
||||
driver.start()
|
||||
```
|
||||
|
||||
Pairing flow on the operator's iPhone:
|
||||
|
||||
1. Open Apple Home → `Add Accessory` → `More Options`
|
||||
2. Tap `RuView Sense` (appears via mDNS automatically)
|
||||
3. Enter the setup code shown in `docker logs` (or pinned in env)
|
||||
4. Done — Siri can say "Hey Siri, is anyone in the living room?"
|
||||
|
||||
Replace the `motion_present` / `occupancy` mappings progressively as RuView capabilities mature: BFLD class-2 `presence` event → `OccupancyDetected`; BFLD class-3 `identity_risk_score > threshold` → `SecuritySystemCurrentState`; `breathing_present` → `OccupancyDetected` (sleep room); `fall_risk` → a programmable switch that fires an Apple Home automation.
|
||||
|
||||
Acceptance criteria for 2.1.a:
|
||||
|
||||
- A1: `docker run ... hap-accessory --privacy-mode` advertises an `_hap._tcp` service that the HomePod sees within 30s (`dns-sd -B _hap._tcp local.` on a peer Mac shows `RuView Sense`).
|
||||
- A2: Pairing from Apple Home succeeds and the entity appears in the Home app under the configured room.
|
||||
- A3: `MotionDetected` flips within 2 s of an actual RF presence detection from a calibrated ESP32 source (`CSI_SOURCE=esp32`).
|
||||
- A4: Restarting the container preserves the pairing (HAP state persisted under `/var/lib/ruview-hap/`).
|
||||
- A5: Privacy: the entrypoint refuses to launch without `--privacy-mode` when `RUVIEW_BFLD_PRIVACY_CLASS` is unset, matching the structural invariant I1 (Raw BFI never exits the node — ADR-118 §2.2).
|
||||
|
||||
#### 2.1.b — **Rust-native HAP** (single binary, closes ADR-116 P7)
|
||||
|
||||
Wire one of the maintained Rust HAP crates into `cog-ha-matter` so the Python sidecar can be removed. Candidate crates:
|
||||
|
||||
- [`hap`](https://crates.io/crates/hap) (Sebastian Schmidt) — last published 0.1.0-pre.16, MIT, active in 2024, supports HAP-1.1, has examples for `MotionSensor`, `LightBulb`, `OccupancySensor`. **First choice.**
|
||||
- [`accessory-server`](https://crates.io/crates/accessory-server) — narrower scope, fewer services
|
||||
- A future `matter-rs` crate from project-chip — once stable (CHIP SDK Rust bindings are still emerging in 2026-05)
|
||||
|
||||
The `matter = []` feature stub in `cog-ha-matter/Cargo.toml` (added in ADR-116 P1) becomes:
|
||||
|
||||
```toml
|
||||
[features]
|
||||
default = []
|
||||
mqtt = ["dep:rumqttc"]
|
||||
matter = ["dep:hap"] # ADR-125 §2.1.b
|
||||
```
|
||||
|
||||
with a runtime subcommand `cog-ha-matter --mode hap` that mirrors the Python advertiser's accessory set. Single binary, no Python interpreter in the image, matches the all-Rust ethos of the Cognitum Seed (ADR-116 §1.4).
|
||||
|
||||
### 2.1.c — **Topology: one HAP bridge, N child accessories** (decided)
|
||||
|
||||
The advertiser publishes a **single HAP bridge** (`RuView Sense`) that owns N child accessories — one per logical sensor surface (presence-bedroom, presence-office, vitals-bedroom, semantic-events, …). Operators pair the bridge once; child accessories appear automatically and can be re-assigned to rooms in the Apple Home app.
|
||||
|
||||
The alternative — N independent accessories each advertised separately — was rejected. It forces operators to pair RuView once per room (`RuView Bedroom`, `RuView Office`, `RuView Wellness`, `RuView Presence`, …), which becomes messy after the second or third room, and diverges from how every reference HomeKit accessory in the Home app behaves (a Hue bridge with bulbs, an Eve Energy bridge, etc.). Single pairing also makes container restart / re-image trivial — one persisted pairing key, not N.
|
||||
|
||||
### 2.1.d — **Identity-risk mapping: semantic events, not probabilistic surveillance** (decided)
|
||||
|
||||
`identity_risk_score` is a continuous 0..1 confidence from the BFLD identity-features pipeline (ADR-121 §2.6). It must NOT cross the HomeKit boundary as a raw value, and must NOT be wired to `SecuritySystemCurrentState`. Apple-Home users read security-system state as **"intruder detected"** — exposing a probability there turns RuView into surveillance UX with all the false-positive blame that entails.
|
||||
|
||||
Instead, the bridge exposes **thresholded semantic events** that read like ambient awareness, not threat detection:
|
||||
|
||||
| Semantic event | HomeKit primitive | Trigger (illustrative) |
|
||||
|----------------|--------------------|-------------------------|
|
||||
| `Unknown Presence` | `MotionSensor` (programmable; stateful) | BFLD class-2 presence + no matching SoulMatch oracle hit (ADR-121 §2.6) for > 30 s |
|
||||
| `Unexpected Occupancy` | `OccupancySensor` (programmable) | Occupancy in a room outside its operator-defined "expected schedule" window |
|
||||
| `Unrecognized Activity Pattern` | Programmable `Switch` (stateful, momentary) | BFLD longitudinal drift gate (ADR-118 §2.3 / ADR-122 §2.7) fires Reject or Recalibrate |
|
||||
|
||||
What stays internal:
|
||||
|
||||
- Raw `identity_risk_score` (numeric 0..1) — never published
|
||||
- Soul-Signature match probability — never published
|
||||
- `rf_signature_hash` — never published (already enforced by ADR-118 §2.5 / ADR-122 §2.4 — this is the structural invariant restated at the HAP boundary)
|
||||
|
||||
The naming is the contract. "Unknown Presence" is *who's-here-and-it's-fine-but-worth-noting*; an end user will write an automation ("turn on the porch light when Unknown Presence is detected after 9pm") without ever thinking it accuses anyone of being an intruder. That semantic framing is the difference between RuView becoming the calm-tech ambient substrate Apple Home needs vs. another paranoid surveillance widget.
|
||||
|
||||
This is the part of the ADR that determines whether RuView's HomeKit story ages well or generates the wrong kind of headlines.
|
||||
|
||||
### 2.2 What we DO NOT do in 2.1.a or 2.1.b
|
||||
|
||||
- **No Matter (CHIP) controller code.** Matter is the long-term play but its SDK in Rust is not yet stable and the certificate provisioning is heavy. HAP-1.1 over Bonjour gives 95% of the UX for 10% of the complexity, today.
|
||||
- **No direct connection to the HomePod.** As the framing in §1.1 makes explicit, RuView never opens a socket to the HomePod. It advertises; the HomePod discovers.
|
||||
- **No iCloud account binding.** HAP pairing is local-network-only by design — RuView gets adoption without ever touching Apple ID, which is a privacy story we keep cleanly.
|
||||
- **No Class-0 (`Raw`) BFI exposure.** Structural invariant I1 (ADR-118 §2.2) holds. Only privacy-class-2 (Anonymous) and class-3 (Restricted) frames may be mapped onto HomeKit characteristics. The advertiser refuses to start in any other mode.
|
||||
|
||||
### 2.3 Sequencing
|
||||
|
||||
1. **P1** (this ADR-125 + 1 PR) — HAP-python sidecar (§2.1.a) lands as a separate entrypoint in the same Docker image. AC A1–A5 are gates.
|
||||
2. **P2** (follow-up PR after operator feedback from 5+ Apple Home pairings) — Rust-native HAP (§2.1.b). Replaces P1; P1's `bridges/hap-python/` becomes an archived reference implementation.
|
||||
3. **P3** (when matter-rs stabilizes) — Matter Controller path (still RuView-as-accessory, but using the Matter clusters rather than HAP-1.1 services). The Cognitum Cog gains a Matter QR code; pairing flow widens to "any Matter-capable controller, not just Apple."
|
||||
|
||||
---
|
||||
|
||||
## 3. Consequences
|
||||
|
||||
### 3.1 Wins
|
||||
|
||||
- **Direct discoverability on Apple Home.** A Seed in the kitchen appears as `RuView Sense` in the Home app within seconds of `docker run`. No HA, no MQTT broker, no Home-Assistant HomeKit Bridge add-on.
|
||||
- **Siri natively answers RuView questions.** "Hey Siri, is anyone in the kitchen?" — the question reaches the HomeKit characteristic without any custom skill or HA template sensor.
|
||||
- **Apple-Home automations gain ambient triggers** RuView already produces (presence, breathing, fall, identity-risk) for free — they become first-class automation triggers in the Home app's UI.
|
||||
- **Strategically corrects RuView's distribution problem.** The Apple Home installed base is the largest consumer surface for HomeKit-grade accessories. RuView's sensing IP becomes addressable to that base without an SDK port.
|
||||
- **Closes ADR-116 §P7** — the long-flagged matter / HAP gap is now scheduled, not deferred indefinitely.
|
||||
|
||||
### 3.2 Costs
|
||||
|
||||
- **Python runtime in the Docker image (only for 2.1.a, until 2.1.b lands).** Adds ~30 MB to the runtime layer. Mitigation: P2 removes it; P1 isolates the Python dep in a side-stage so the sensing-server / cog-ha-matter layers stay clean.
|
||||
- **Network-mode constraint.** HAP pairing needs the controller and accessory on the same L2 segment (mDNS broadcasts). Operators who run RuView in a container behind a NAT/bridge need `--network host` or a macvlan — same constraint HA's HomeKit Bridge has, but worth documenting.
|
||||
- **Pairing state persistence.** HAP-python stores pairing data in a local file; that state must survive container restarts. Volume-mount `/var/lib/ruview-hap/` to a persistent location.
|
||||
|
||||
### 3.3 Risks
|
||||
|
||||
- **HAP-python maintenance.** The library is community-maintained; if it goes stale, P2 (Rust-native) absorbs the risk. 2.1.a is explicitly a stepping stone, not a long-term commitment.
|
||||
- **Apple's evolving requirements.** HomeKit Accessory Certification is required to put a HAP logo on hardware, not to ship a software accessory that pairs locally. RuView's container deployment is squarely in the "uncertified developer accessory" lane, which Apple explicitly permits for local pairing. Worth restating in the operator README.
|
||||
- **Privacy-class enforcement at the bridge boundary.** A bug that lets a class-0 BFI frame's data influence a HAP characteristic update would violate I1. Mitigation: the bridge consumes only the BFLD `MqttEvent` stream (which is already gated by `PrivacyGate` per ADR-120), never raw BFI; tests assert this in the same style as ADR-122 §4.3.
|
||||
|
||||
### 3.4 Reversibility
|
||||
|
||||
The advertiser is a separate entrypoint — pulling it out is `docker run` without the `hap-accessory` first-arg, identical to today's behavior. Zero impact on `sensing-server` and `cog-ha-matter` operations.
|
||||
|
||||
---
|
||||
|
||||
## 4. Acceptance test (P1 / §2.1.a)
|
||||
|
||||
```bash
|
||||
# 1. Start a sensing server (simulated source so the test runs anywhere)
|
||||
docker run -d --name rs -p 3000:3000 -e CSI_SOURCE=simulated \
|
||||
ruvnet/wifi-densepose:latest
|
||||
|
||||
# 2. Launch the HAP advertiser sidecar in privacy mode
|
||||
docker run -d --name hap --network host \
|
||||
-v /var/lib/ruview-hap:/var/lib/ruview-hap \
|
||||
-e RUVIEW_BFLD_PRIVACY_CLASS=2 \
|
||||
ruvnet/wifi-densepose:latest hap-accessory --privacy-mode
|
||||
|
||||
# 3. From a Mac on the same LAN: should see RuView Sense as HAP
|
||||
dns-sd -B _hap._tcp local. # expect: "RuView Sense" within 30 s
|
||||
|
||||
# 4. From iPhone Home app: Add Accessory → More Options → RuView Sense
|
||||
# Enter setup code from `docker logs hap`
|
||||
# Expect: pairing completes, entity appears in selected Room
|
||||
|
||||
# 5. Cycle the container; re-open Home app: entity is still paired
|
||||
docker restart hap
|
||||
# Expect: no re-pairing prompt; characteristic updates resume
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. Open questions
|
||||
|
||||
Two questions from the original draft were resolved during review (§2.1.c and §2.1.d). Genuinely-open questions that follow-up PRs will close:
|
||||
|
||||
- **Setup-code derivation.** Derived deterministically from the Seed's Ed25519 witness key (so reinstalls re-use the same code, operator never re-enters), or random per launch (slightly better security, worse UX on container restarts)? Leaning deterministic + witness-key-derived; verify against Apple's HomeKit Accessory Protocol §5.6.5 (setup-code uniqueness) before committing.
|
||||
- **ESP32 / Cognitum-Seed-class hardware as a direct HAP advertiser** (not via the host appliance). The current decision parks the bridge on the host runtime; a future ADR can evaluate whether an ESP32-S3 with 8MB flash has enough headroom to run HAP-1.1 directly, which would remove the host appliance from the path entirely for single-room deployments.
|
||||
|
||||
---
|
||||
|
||||
## 6. References
|
||||
|
||||
- ADR-115 — Home-Assistant integration (HA-DISCO MQTT publisher)
|
||||
- ADR-116 — `cog-ha-matter` Seed cog (this is where the `matter` feature stub lives)
|
||||
- ADR-118 — BFLD beamforming-feedback layer (privacy gate + class invariants)
|
||||
- ADR-122 — BFLD RuView HA/Matter exposure (current MQTT-based bridge that this ADR's HAP-native path complements)
|
||||
- HomeKit Accessory Protocol Specification (Non-Commercial Version), Apple — https://developer.apple.com/apple-home/
|
||||
- HAP-python — https://github.com/ikalchev/HAP-python
|
||||
- `hap` (Rust) — https://crates.io/crates/hap
|
||||
@@ -1,362 +0,0 @@
|
||||
# ADR-126: HOMECORE — Native Rust + WASM + TypeScript port of Home Assistant
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Proposed |
|
||||
| **Date** | 2026-05-25 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codename** | **HOMECORE** — native hub, RuView-first, WASM-safe, semantically aware |
|
||||
| **Relates to** | [ADR-115](ADR-115-home-assistant-integration.md) (HA-DISCO), [ADR-116](ADR-116-cog-ha-matter-seed.md) (HA-COG), [ADR-117](ADR-117-pip-wifi-densepose-modernization.md) (PIP-PHOENIX), [ADR-118](ADR-118-bfld-beamforming-feedback-layer-for-detection.md) (BFLD), [ADR-124](ADR-124-rvagent-mcp-ruvector-npm-integration.md) (SENSE-BRIDGE), [ADR-125](ADR-125-ruview-apple-home-native-hap-bridge.md) (APPLE-FABRIC) |
|
||||
| **Tracking issue** | TBD |
|
||||
| **Sub-ADRs** | ADR-127 through ADR-134 |
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
### 1.1 Strategic position in 2026
|
||||
|
||||
Home Assistant (HA) is the dominant open-source home automation hub with more than 500,000 active installs (ADR-115 §1.2 competitive scan). Every prior RuView integration decision has been made with HA as a given constraint: ADR-115 built an MQTT auto-discovery publisher to fit inside HA, ADR-116 packaged it as a Cognitum Seed cog, ADR-122 extended it with BFLD presence events, and ADR-125 layered a native HAP bridge on top of the same stack.
|
||||
|
||||
This approach yields functioning integrations, but it positions RuView permanently as a **guest in someone else's hub**. The architectural limits of Python HA are not just cosmetic:
|
||||
|
||||
| Limit | Impact on RuView's roadmap |
|
||||
|---|---|
|
||||
| **Single-process Python GIL** | CSI DSP pipeline, BFLD analysis, and ruvector semantic search cannot run concurrently inside the HA process; they must run as external services connected over MQTT or WebSocket, introducing a round-trip on every sensor update |
|
||||
| **Startup time (15–30 s on a Pi 5)** | The Cognitum Seed appliance restarts firmware-update-by-firmware-update; a 30 s hub startup on every OTA cycle is user-visible latency |
|
||||
| **Memory footprint (300 MB+ idle)** | On a Pi 5 with 8 GB this is tolerable; on a Pi Zero 2 W or an embedded board with 512 MB it precludes co-location with the sensing stack |
|
||||
| **No WASM safety boundary for integrations** | HA's 2,000+ community integrations are Python modules loaded directly into the HA process — one buggy integration can crash the hub or read arbitrary memory |
|
||||
| **Recorder is structural only** | SQLite + InfluxDB store state history as rows; there is no semantic search. "Show me when the porch light correlated with the bedroom CSI anomaly last week" requires manual SQL |
|
||||
| **Voice assistant is additive** | Assist (`homeassistant/components/assist_pipeline/`) was added in 2022–2023 and is well-designed, but intent matching is keyword-based, not embedding-based; ruflo LLM pipelines cannot natively plug in |
|
||||
| **Frontend is a 5 MB Lit-element bundle** | The dashboard compiles to ~5 MB of JavaScript; on low-bandwidth appliance UIs or Progressive-Web-App installs, this is perceptible load time |
|
||||
|
||||
These are not HA's failures — they are Python architectural realities. For a generic home automation hub they are acceptable. For a hub where the core value proposition is **real-time RF sensing, AI-augmented automation, and edge-native deployment on constrained hardware**, they are ceilings.
|
||||
|
||||
### 1.2 The opportunity
|
||||
|
||||
Three recent ADR shipments create the inflection point:
|
||||
|
||||
1. **ADR-117 (PIP-PHOENIX)** — `wifi-densepose==2.0.0a1` + `ruview==2.0.0a1` on PyPI as PyO3/maturin wheels, providing a Python developer surface over the Rust sensing core.
|
||||
2. **ADR-118 (BFLD)** — a complete beamforming feedback capture and privacy-risk scoring layer, proving that RuView's sensing stack can be a compliance instrument, not just a sensor.
|
||||
3. **ADR-124 (SENSE-BRIDGE)** — `@ruvnet/rvagent` on npm as a dual-transport MCP server, proving that the sensing stack can be expressed as a first-class AI-agent tool surface.
|
||||
|
||||
The gap that remains: there is no hub that treats all of these as **native first-class features** rather than bolt-on integrations. HOMECORE fills that gap by porting the HA data model and API surface to Rust, replacing HA's Python internals with the RuView Rust crates, and wrapping community integrations in WASM sandboxes.
|
||||
|
||||
### 1.3 What this ADR is *not*
|
||||
|
||||
- Not a fork of the Python HA codebase. HOMECORE is a **clean-room Rust implementation** of HA's public API contracts and data model, not a line-by-line port.
|
||||
- Not a replacement of the existing sensing stack. `v2/crates/wifi-densepose-*` remain authoritative.
|
||||
- Not a deprecation of ADR-115/116/117/124/125. Those integrations continue to work with Python HA installs. HOMECORE is an additional deployment target, not a replacement mandate.
|
||||
- Not a Matter SDK full-implementation. ADR-125 handles Matter; HOMECORE consumes the Matter bridge via the existing `cog-ha-matter` surface.
|
||||
- Not a target for this quarter's sprint. HOMECORE is a multi-quarter initiative. This master ADR and its sub-ADRs define the architecture; implementation begins in P1.
|
||||
|
||||
---
|
||||
|
||||
## 2. Decision
|
||||
|
||||
Build **HOMECORE**: a native Rust + WASM + TypeScript implementation of the Home Assistant hub contract, integrated with the RuView sensing platform, the ruflo agent toolchain, and the ruvector vector layer.
|
||||
|
||||
HOMECORE is wire-compatible with HA's REST and WebSocket APIs so that existing HA-native clients (the iOS/Android Home Assistant companion apps, HACS, Nabu Casa Cloud, and the HA voice satellite stack) operate without modification against a HOMECORE instance.
|
||||
|
||||
HOMECORE is NOT a drop-in replacement on day one. The compatibility contract is phased (§6). The architecture is designed so that clients that work with HA today work with HOMECORE P3+.
|
||||
|
||||
### 2.1 Codename rationale
|
||||
|
||||
**HOMECORE** — the `core` of HA reimplemented at native speed, with the sensing stack at the center rather than at the periphery.
|
||||
|
||||
---
|
||||
|
||||
## 3. Architecture overview
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────────────────────┐
|
||||
│ HOMECORE process │
|
||||
│ │
|
||||
│ ┌─────────────┐ ┌──────────────┐ ┌───────────────────┐ │
|
||||
│ │ homecore │ │ homecore- │ │ homecore- │ │
|
||||
│ │ state │ │ automation │ │ recorder │ │
|
||||
│ │ machine │ │ engine │ │ (SQLite + │ │
|
||||
│ │ (ADR-127) │ │ (ADR-129) │ │ ruvector) │ │
|
||||
│ └──────┬──────┘ └──────┬───────┘ │ (ADR-132) │ │
|
||||
│ │ │ └───────────────────┘ │
|
||||
│ ┌──────▼──────────────────────────────────┐ │
|
||||
│ │ Event Bus (Tokio broadcast) │ │
|
||||
│ └──────┬──────────────────────────────────┘ │
|
||||
│ │ │
|
||||
│ ┌──────▼──────────────────────────────────┐ │
|
||||
│ │ homecore-rest-websocket-api (ADR-130)│ │
|
||||
│ │ Axum server — HA wire-compat API │ │
|
||||
│ └──────────────────────────────────────────┘ │
|
||||
│ │
|
||||
│ ┌──────────────┐ ┌──────────────────────────────────────┐ │
|
||||
│ │ Integration │ │ homecore-assist-ruflo (ADR-133) │ │
|
||||
│ │ Plugin System│ │ ruflo agent orchestration │ │
|
||||
│ │ (ADR-128) │ │ ruvector intent embeddings │ │
|
||||
│ │ WASM sandbox │ │ Wyoming protocol edge │ │
|
||||
│ └──────────────┘ └──────────────────────────────────────┘ │
|
||||
│ │
|
||||
│ ┌──────────────────────────────────────────────────────┐ │
|
||||
│ │ RuView sensing core (wifi-densepose-sensing-server) │ │
|
||||
│ │ CSI → presence / vitals / pose / BFLD / semantic │ │
|
||||
│ └──────────────────────────────────────────────────────┘ │
|
||||
└──────────────────────────────────────────────────────────────┘
|
||||
│ HA-compatible REST + WebSocket
|
||||
▼
|
||||
┌──────────────────────────┐
|
||||
│ homecore-frontend-ts-wasm │ (ADR-131)
|
||||
│ TypeScript + Rust→WASM │
|
||||
│ SharedWorker state sync │
|
||||
└──────────────────────────┘
|
||||
```
|
||||
|
||||
The HOMECORE process is a single Tokio-based async Rust binary. The state machine and event bus are the authoritative core (ADR-127). Integrations run in WASM sandboxes that communicate with the core via a defined ABI (ADR-128). The automation engine runs Rust-native trigger evaluation with a WASM expression evaluator for templates (ADR-129). The REST/WebSocket API layer is Axum-based and wire-compatible with HA (ADR-130). The frontend is TypeScript with the state machine compiled to WASM running in a SharedWorker (ADR-131). Historical state is stored in SQLite with ruvector for semantic search (ADR-132). Voice/text assistance uses ruflo agent orchestration (ADR-133).
|
||||
|
||||
---
|
||||
|
||||
## 4. Series map
|
||||
|
||||
| ADR | Codename | Scope | Critical path? | Estimated P5-completion |
|
||||
|---|---|---|---|---|
|
||||
| **ADR-127** | HOMECORE-CORE | Rust state machine, entity registry, event bus, service registry (`homecore` crate) | **Yes — all others depend on it** | Q3 2026 |
|
||||
| **ADR-128** | HOMECORE-PLUGINS | WASM integration plugin system, cog substrate, manifest schema, hot-load | **Yes — needed before any integration can run** | Q3 2026 |
|
||||
| **ADR-129** | HOMECORE-AUTO | Automation engine, YAML parser, Jinja2-equivalent WASM evaluator, blueprints | Yes (automation is core to HA UX) | Q4 2026 |
|
||||
| **ADR-130** | HOMECORE-API | REST + WebSocket wire-compat API, Axum server, HA companion app support | **Yes — needed for client compat** | Q3 2026 |
|
||||
| **ADR-131** | HOMECORE-UI | TS + Rust→WASM frontend, SharedWorker state sync, Material 3 design lang | No (can run alongside Python HA UI initially) | Q1 2027 |
|
||||
| **ADR-132** | HOMECORE-RECORDER | SQLite recorder + ruvector semantic history, schema migration | No (structural recorder ships before ruvector layer) | Q4 2026 |
|
||||
| **ADR-133** | HOMECORE-ASSIST | ruflo agent voice assistant, ruvector intent matching, Wyoming edge path | No | Q4 2026 |
|
||||
| **ADR-134** | HOMECORE-MIGRATE | Migration tooling from Python HA, config-entry parser, side-by-side mode | No (needed for user adoption) | Q1 2027 |
|
||||
|
||||
**Critical path**: ADR-127 → ADR-128 → ADR-130 must land in that order. ADR-129, ADR-132, ADR-133, ADR-131, ADR-134 can proceed in parallel once the core triad is stable.
|
||||
|
||||
---
|
||||
|
||||
## 5. Cross-cutting decisions
|
||||
|
||||
The following decisions govern all 8 sub-ADRs and are not repeated in each.
|
||||
|
||||
### 5.1 Governance via RUVIEW-POLICY (ADR-124 §4.1a)
|
||||
|
||||
Every HOMECORE component that returns biometric data (presence, HR/BR, pose keypoints, BFLD identity-risk) MUST route through the RUVIEW-POLICY layer defined in ADR-124 §4.1a. The policy store is the same `~/.config/rvagent/policy.json` used by `@ruvnet/rvagent`. HOMECORE is a first-class policy principal — its agent ID in the policy store is `homecore`.
|
||||
|
||||
### 5.2 Semantic memory via ruvector
|
||||
|
||||
Historical state is not only stored in SQLite rows (structural). Every state-changed event is also embedded via ruvector (using the same napi-rs bindings as ADR-124) and indexed in an HNSW store for semantic search. The `homecore-recorder` crate (ADR-132) owns this dual-write. Queries like "when did the living room motion last exceed baseline?" become vector-nearest-neighbour searches, not SQL BETWEEN clauses.
|
||||
|
||||
### 5.3 Agent orchestration via ruflo
|
||||
|
||||
The automation engine (ADR-129) and the assist pipeline (ADR-133) both have an optional ruflo-agent mode where complex conditions or voice intents are routed to a ruflo agent (using the `mcp__claude-flow__*` tool namespace) for LLM-backed resolution. This is gated by RUVIEW-POLICY: a policy grant is required before HOMECORE sends any state-history context to a ruflo agent.
|
||||
|
||||
### 5.4 Witness and audit via Ed25519 chain (ADR-028 pattern)
|
||||
|
||||
Every state transition that crosses a privacy boundary (e.g. BFLD identity-risk score elevated, a biometric entity state published) is logged to an Ed25519 witness chain using the same structure as ADR-028 §3. The witness bundle is exportable for regulated deployments (care homes, hotels, shared offices).
|
||||
|
||||
### 5.5 Crate naming and workspace placement
|
||||
|
||||
All HOMECORE crates live in `v2/crates/homecore-*/`:
|
||||
|
||||
| Crate | ADR |
|
||||
|---|---|
|
||||
| `homecore` | ADR-127 |
|
||||
| `homecore-plugins` | ADR-128 |
|
||||
| `homecore-automation` | ADR-129 |
|
||||
| `homecore-api` | ADR-130 |
|
||||
| `homecore-recorder` | ADR-132 |
|
||||
| `homecore-assist` | ADR-133 |
|
||||
| `homecore-migrate` | ADR-134 |
|
||||
|
||||
The frontend (`homecore-frontend`) is not a Rust crate — it is an npm package at `npm/homecore-frontend/`, mirroring the `npm/rvagent/` pattern from ADR-124.
|
||||
|
||||
### 5.6 HA wire-compatibility baseline
|
||||
|
||||
The HOMECORE REST and WebSocket API must be **compatible with HA 2025.1** as the baseline. HA 2025.1 introduced schema version 48 in the recorder. The API surface to replicate is:
|
||||
|
||||
- REST: `homeassistant/components/api/__init__.py` — 24 endpoints
|
||||
- WebSocket: `homeassistant/components/websocket_api/` — the `connection.py` + `commands.py` handler pattern, the auth handshake, and the `subscribe_events` / `subscribe_trigger` / `call_service` commands
|
||||
- Auth: `homeassistant/auth/` — the long-lived access token model
|
||||
- Config entries: `.storage/core.config_entries` JSON schema (versioned, auto-migrated)
|
||||
|
||||
### 5.7 "Do not port" list
|
||||
|
||||
The following HA subsystems are explicitly **not** ported to HOMECORE:
|
||||
|
||||
| HA subsystem | Reason not ported | HOMECORE replacement |
|
||||
|---|---|---|
|
||||
| **SUPERVISOR** (`homeassistant/supervisor/`) | Manages add-on containers and OS upgrades. HOMECORE runs on a standard Linux/Pi OS managed by systemd. | ruflo + systemd service units + OTA via the existing Cognitum Seed OTA registry (ADR-116 §2.2) |
|
||||
| **Home Assistant OS** (HAOS) | A custom embedded Linux image. HOMECORE targets standard Debian/Ubuntu on Pi 5 and standard Docker. | Standard OS + Docker Compose or systemd |
|
||||
| **Nabu Casa Cloud** | Paid remote-access and Alexa/Google integration service. HOMECORE uses Tailscale for remote access and `@ruvnet/rvagent` for AI integration. | Tailscale + ADR-107 federation + SENSE-BRIDGE |
|
||||
| **Add-on store** (Supervisor add-ons) | Docker container management. | Cognitum Seed cog registry (ADR-102) |
|
||||
| **Legacy YAML-only integrations** (pre-config-flow, ~500 of 2,000) | These require Python `setup_platform` (deprecated in HA 2024.x). Only config-flow integrations (`async_setup_entry`) are ported. | Document upgrade path; unported integrations can run via `homecore-migrate` bridge mode |
|
||||
| **Analytics / Nabu Casa telemetry** | Optional cloud telemetry. | Not replicated. HOMECORE is local-only. |
|
||||
| **Home Assistant Yellow / Green hardware** | Specific hardware. HOMECORE targets Cognitum Seed, Pi 5, and x86_64. | Cognitum Seed hardware |
|
||||
|
||||
---
|
||||
|
||||
## 6. Compatibility contract
|
||||
|
||||
### 6.1 What works on day one (P3, wire-compat API stable)
|
||||
|
||||
| Client | Works? | Notes |
|
||||
|---|---|---|
|
||||
| **HA iOS companion app** | Yes | Connects to `/api/websocket`; authenticates with long-lived token; subscribes to state events |
|
||||
| **HA Android companion app** | Yes | Same as iOS |
|
||||
| **Home Assistant Dashboard (frontend)** | Yes (HA frontend served against HOMECORE API) | Until HOMECORE-UI (ADR-131) ships, serve the Python HA frontend binary against the HOMECORE API |
|
||||
| **HACS** | Partial | HACS uses the WS API for integration management; custom component loading requires HOMECORE-PLUGINS (ADR-128) |
|
||||
| **Node-RED HA integration** | Yes | Uses REST + WS API; wire-compat |
|
||||
| **`homeassistant` Python client library** | Yes | Pure REST/WS client |
|
||||
| **`ha-mqtt-discoverable` Python library** | Yes | Publishes MQTT discovery; HOMECORE consumes the same topics |
|
||||
| **ESPHome devices** | Yes | ESPHome native API or MQTT; HOMECORE speaks both |
|
||||
| **Nabu Casa Cloud** | **No** | Nabu Casa uses a proprietary remote-access tunnel to `nabucasa.com`. HOMECORE does not integrate with the Nabu Casa cloud proxy. Replace with Tailscale. |
|
||||
| **M5Stack ATOM Echo / voice satellites** | Yes (P4) | Wyoming protocol is HOMECORE-ASSIST (ADR-133) scope |
|
||||
| **HACS custom cards** | Yes (after ADR-131 P3) | Custom cards are served via the same `/hacsfiles/` static route |
|
||||
|
||||
### 6.2 What breaks and why
|
||||
|
||||
| HA feature | HOMECORE status | Reason |
|
||||
|---|---|---|
|
||||
| Nabu Casa remote access | Not supported | Proprietary tunnel; replace with Tailscale |
|
||||
| HA Supervisor add-ons | Not supported | No container manager in HOMECORE |
|
||||
| HAOS OTA updates | Not supported | HOMECORE runs on standard OS |
|
||||
| Python custom integrations (non-WASM) | Not supported | WASM sandbox only; Python integrations cannot run natively |
|
||||
| Legacy `setup_platform` integrations | Not supported | Config-flow (`async_setup_entry`) only |
|
||||
| HA Cloud TTS/STT (Nabu Casa) | Not supported | Use Whisper + Piper locally |
|
||||
| HA Cloud Alexa/Google skill | Not supported | Use ruflo agent instead |
|
||||
|
||||
---
|
||||
|
||||
## 7. Phase roadmap
|
||||
|
||||
```
|
||||
Q3 2026 Q4 2026 Q1 2027 Q2 2027
|
||||
P1 P2 P3 P4 P5
|
||||
scaffold state+API wire-compat plugins+ full
|
||||
core HA clients automation HOMECORE
|
||||
```
|
||||
|
||||
### P1 — Scaffold (Q3 2026, 2 weeks)
|
||||
|
||||
- [ ] Create `v2/crates/homecore/` workspace member, empty state machine skeleton.
|
||||
- [ ] Create `v2/crates/homecore-api/` skeleton, Axum server on port 8123 (HA default).
|
||||
- [ ] Create `npm/homecore-frontend/` skeleton.
|
||||
- [ ] CI: `cargo check -p homecore -p homecore-api --no-default-features` green.
|
||||
- [ ] ADR-134 migration tool parses one `.storage/core.config_entries` fixture.
|
||||
|
||||
### P2 — State machine + API core (Q3 2026, 4 weeks)
|
||||
|
||||
- [ ] ADR-127 state machine: entity registry, state machine, event bus (Tokio broadcast), service registry.
|
||||
- [ ] ADR-130 API: REST endpoints, WebSocket auth handshake, `subscribe_events`, `call_service`.
|
||||
- [ ] ADR-132 recorder: SQLite schema (HA schema version 48 compatible), state write path.
|
||||
- [ ] Integration test: HA companion app authenticates and receives state updates.
|
||||
|
||||
### P3 — Wire-compat + plugin scaffold (Q3–Q4 2026, 6 weeks)
|
||||
|
||||
- [ ] ADR-128 plugin system: WASM sandbox, manifest schema, first ported integrations (MQTT, HTTP).
|
||||
- [ ] ADR-130 API: remaining WS commands, HACS support.
|
||||
- [ ] ADR-134 migration: reads `automations.yaml`, `secrets.yaml`, config entries.
|
||||
- [ ] ADR-132 recorder: ruvector dual-write, semantic search API.
|
||||
|
||||
### P4 — Automation + assist (Q4 2026, 4 weeks)
|
||||
|
||||
- [ ] ADR-129 automation engine: YAML parser, trigger evaluation, WASM expression evaluator.
|
||||
- [ ] ADR-133 assist: ruflo agent orchestration, ruvector intent matching.
|
||||
- [ ] ADR-131 frontend P1: TypeScript shell, WASM state machine in SharedWorker.
|
||||
|
||||
### P5 — Full HOMECORE (Q1 2027, 6 weeks)
|
||||
|
||||
- [ ] ADR-131 frontend: complete UI parity with HA Lovelace, custom cards.
|
||||
- [ ] ADR-134 migration: side-by-side mode, one-click cutover.
|
||||
- [ ] Full compatibility test suite against HA iOS/Android companion apps.
|
||||
- [ ] Pi 5 performance benchmarks: startup < 1 s, idle < 50 MB RAM.
|
||||
|
||||
---
|
||||
|
||||
## 8. Alternatives rejected
|
||||
|
||||
### Alt-A: Contribute RuView sensing features upstream to Python HA
|
||||
|
||||
Add the HOMECORE features (WASM plugins, ruvector recorder, ruflo assist) as Python HA components via PRs to `home-assistant/core`.
|
||||
|
||||
**Rejected because**: HA's architecture board has strict policies against adding new runtimes (WASM, Rust FFI) to the core process. The GIL bottleneck cannot be resolved from within Python HA. CSI DSP at 100 Hz frame rate inside a Python process is not feasible. This path cedes architectural control permanently.
|
||||
|
||||
### Alt-B: Thin Rust wrapper that calls into Python HA via PyO3
|
||||
|
||||
Keep Python HA as the runtime; expose RuView sensing primitives via PyO3 bindings so they run at native speed inside the Python HA process.
|
||||
|
||||
**Rejected because**: the GIL is not resolved by PyO3 calls — the HA event loop still serialises all state changes. Startup time and memory footprint are unchanged. WASM plugin safety is unchanged. This is a tactical optimisation, not an architectural solution.
|
||||
|
||||
### Alt-C: OpenHAB or Domoticz as the base
|
||||
|
||||
Port RuView's sensing stack on top of an alternative hub (openHAB/Java, Domoticz/C++).
|
||||
|
||||
**Rejected because**: neither has HA's community network effects, companion app ecosystem, or HACS plugin catalog. A clean-room Rust implementation preserves the HA compatibility contract (the most valuable asset) without inheriting the Python runtime limitations.
|
||||
|
||||
### Alt-D: Extend the existing `wifi-densepose-sensing-server` into a full hub
|
||||
|
||||
Add automation, entity registry, and recorder features directly to the existing Axum sensing server.
|
||||
|
||||
**Rejected because**: the sensing server is a purpose-built single-concern binary (CSI → MQTT/WebSocket). Expanding it into a hub would violate the single-responsibility principle and couple hub release cycles to firmware release cycles. HOMECORE is a separate crate family that depends on but does not modify the sensing server.
|
||||
|
||||
---
|
||||
|
||||
## 9. Top-level risks
|
||||
|
||||
| Risk | Likelihood | Severity | Mitigation |
|
||||
|---|---|---|---|
|
||||
| **API drift** — HA's REST/WS API evolves; HOMECORE must track it | High | High | Pin to HA 2025.1 baseline (schema 48); run the HA companion app integration tests against every HOMECORE release; ADR-130 owns the compat matrix |
|
||||
| **WASM sandbox performance** — plugin calls through the WASM boundary add latency | Medium | Medium | Benchmark plugin roundtrip on Pi 5 before P3; reject if >5 ms; WASM3/Wasmtime both have sub-1 ms call overhead for compute-light integrations |
|
||||
| **Core triad dependency** — ADR-128 and ADR-130 cannot start until ADR-127 is stable | High | High | ADR-127 is P2 start; freeze the state machine public API (entity_id, state, attributes, last_changed) before ADR-128 begins |
|
||||
| **ruvector semantic recorder** — dual-write to SQLite + HNSW may impact write throughput under high-frequency sensing | Medium | High | ruvector writes are async (non-blocking tokio task); SQLite write is the hot path; benchmark at 100 state/s on Pi 5 before ADR-132 ships |
|
||||
| **Nabu Casa gap** — users who depend on HA Cloud remote access have no HOMECORE replacement at P3 | High | Medium | Document Tailscale as the replacement prominently; provide ADR-134 migration wizard that detects Nabu Casa usage and offers Tailscale setup |
|
||||
| **Frontend bundle size** — replicating the HA Lovelace card ecosystem in TS+WASM is a significant engineering effort | High | High | ADR-131 is off-critical-path; serve HA's Python frontend against the HOMECORE API until ADR-131 P3 ships |
|
||||
| **License** — HA is Apache 2.0; the wire protocol is unencumbered; HA's UI assets and card components have separate licenses | Low | High | Clean-room Rust implementation does not use HA source; HA frontend is served as a binary (not embedded); review license before ADR-131 ships any reimplemented component |
|
||||
|
||||
---
|
||||
|
||||
## 10. Open questions
|
||||
|
||||
**Q1** (ADR-127): Should the HOMECORE state machine use a `DashMap<EntityId, State>` for lock-free concurrent reads, or a `RwLock<HashMap<EntityId, State>>` for simpler reasoning? The answer affects every integration's write pattern.
|
||||
|
||||
**Q2** (ADR-128): Does the WASM sandbox use Wasmtime (Cranelift JIT, ~5 MB binary) or WASM3 (interpreter, ~50 kB binary)? On a Pi 5 WASM3 is sufficient for integration logic; Wasmtime matters if integrations need near-native DSP speed.
|
||||
|
||||
**Q3** (ADR-130): The HA WebSocket API uses numeric IDs for command/response correlation. The HA 2025.1 baseline adds `subscribe_trigger` as a first-class WS command. Are there any commands in the HA companion app that require a newer baseline?
|
||||
|
||||
**Q4** (ADR-132): The ruvector HNSW index for state history — what embedding dimension represents a state snapshot? Options: (a) embed only numeric sensor states (scalar embedding), (b) embed `{entity_id, state, attributes}` as a text embedding via a local small model, (c) use a fixed schema encoding. The answer determines the semantic query fidelity.
|
||||
|
||||
**Q5** (ADR-134): HA's `.storage/core.config_entries` format is versioned but undocumented; it is hand-engineered from reverse-engineering the Python `StorageCollection` class in `homeassistant/helpers/storage.py`. Is this format stable enough to parse without upstream documentation, or does HOMECORE need to maintain a version matrix?
|
||||
|
||||
---
|
||||
|
||||
## 11. References
|
||||
|
||||
### This repo
|
||||
|
||||
- `docs/adr/ADR-115-home-assistant-integration.md` — HA-DISCO MQTT publisher; 21-entity surface; semantic primitives; competitive comparison table
|
||||
- `docs/adr/ADR-116-cog-ha-matter-seed.md` — HA-COG Seed cog; cog packaging precedent (ADR-101)
|
||||
- `docs/adr/ADR-117-pip-wifi-densepose-modernization.md` — PIP-PHOENIX PyO3 bindings; Python client surface
|
||||
- `docs/adr/ADR-118-bfld-beamforming-feedback-layer-for-detection.md` — BFLD master; privacy class enforcement
|
||||
- `docs/adr/ADR-124-rvagent-mcp-ruvector-npm-integration.md` — SENSE-BRIDGE; RUVIEW-POLICY §4.1a; multi-modal normalization §11.3
|
||||
- `docs/adr/ADR-125-ruview-apple-home-native-hap-bridge.md` — APPLE-FABRIC HAP bridge
|
||||
- `v2/crates/wifi-densepose-sensing-server/src/main.rs` — Axum server architecture; bearer auth pattern
|
||||
- `v2/crates/wifi-densepose-ruvector/src/viewpoint/` — cross-viewpoint fusion (attention, coherence, geometry, fusion modules)
|
||||
- `CLAUDE.md` — Project topology (hierarchical-mesh, 15 agents), ESP32 hardware table, crate publishing order
|
||||
|
||||
### HA upstream
|
||||
|
||||
- `homeassistant/core.py` — `HomeAssistant`, `StateMachine`, `EventBus`, `ServiceRegistry`, `Config`
|
||||
- `homeassistant/helpers/entity_registry.py` — `EntityRegistry`, `RegistryEntry`
|
||||
- `homeassistant/helpers/entity.py` — `Entity`, `async_write_ha_state`, entity lifecycle
|
||||
- `homeassistant/components/api/__init__.py` — REST API handler (24 routes)
|
||||
- `homeassistant/components/websocket_api/` — `connection.py` auth handshake; `commands.py` WS commands
|
||||
- `homeassistant/components/recorder/` — SQLite schema; `migration.py` schema version 48
|
||||
- `homeassistant/components/assist_pipeline/` — voice/text pipeline; Wyoming protocol
|
||||
- `homeassistant/helpers/template.py` — Jinja2 template engine customisation
|
||||
- `homeassistant/components/automation/__init__.py` — automation trigger/condition/action model
|
||||
- `homeassistant/helpers/storage.py` — `.storage/*.json` persistence; `StorageCollection`
|
||||
- `homeassistant/auth/` — long-lived access token model; `AuthManager`
|
||||
|
||||
### External
|
||||
|
||||
- [HA Developer Docs — Core Architecture](https://developers.home-assistant.io/docs/architecture/core/) — state machine, event bus, service registry overview
|
||||
- [HA Developer Docs — WebSocket API](https://developers.home-assistant.io/docs/api/websocket/) — WS command catalog
|
||||
- [DeepWiki HA core — Entity and Registry Management](https://deepwiki.com/home-assistant/core/2.2-entity-and-registry-management) — entity lifecycle
|
||||
- [DeepWiki HA core — Data Management](https://deepwiki.com/home-assistant/core/3-data-management) — recorder schema version 48
|
||||
- [HA recorder integration](https://www.home-assistant.io/integrations/recorder/) — SQLite default; schema migration overview
|
||||
@@ -1,193 +0,0 @@
|
||||
# ADR-127: HOMECORE-CORE — Rust state machine, entity registry, event bus, service registry
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Proposed |
|
||||
| **Date** | 2026-05-25 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codename** | **HOMECORE-CORE** |
|
||||
| **Relates to** | [ADR-126](ADR-126-ruview-native-ha-port-master.md) (HOMECORE master), [ADR-028](ADR-028-esp32-capability-audit.md) (witness chain), [ADR-124](ADR-124-rvagent-mcp-ruvector-npm-integration.md) (RUVIEW-POLICY) |
|
||||
| **Tracking issue** | TBD |
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
`homeassistant/core.py` is the 3,200-line heart of Python Home Assistant. It defines five objects that every other HA component depends on:
|
||||
|
||||
1. **`HomeAssistant`** — the runtime coordinator, event loop holder, and service locator. Contains `bus` (EventBus), `states` (StateMachine), `services` (ServiceRegistry), `config` (Config), `components` (loaded component set).
|
||||
2. **`EventBus`** — publish/subscribe event dispatch. `async_fire(event_type, event_data)` dispatches to all registered listeners. Listener registration is `async_listen(event_type, callback)`. Wildcard listener is `MATCH_ALL`. Event data is a plain Python dict.
|
||||
3. **`StateMachine`** — an in-memory dictionary from `entity_id` (str) to `State`. `async_set(entity_id, new_state, attributes)` writes and fires `state_changed`. `get(entity_id)` reads. `async_remove(entity_id)` fires `state_removed`. States are immutable snapshots with `last_changed`, `last_updated`, `context`.
|
||||
4. **`ServiceRegistry`** — maps `(domain, service_name)` → async handler function. `async_call(domain, service, data)` fires a `call_service` event, waits for the registered handler. `async_register(domain, service, handler, schema)` registers a handler with optional voluptuous schema validation.
|
||||
5. **`EntityRegistry`** (`homeassistant/helpers/entity_registry.py`) — persists metadata (enabled/disabled, name override, area assignment, device ID, unique ID, entity category) across restarts. Stored in `.storage/core.entity_registry`. Loaded at startup; written on every change.
|
||||
|
||||
The **DeviceRegistry** (`homeassistant/helpers/device_registry.py`, stored in `.storage/core.device_registry`) tracks physical devices that entities belong to. Entities link to devices via `device_id`; devices link to config entries via `config_entry_id`.
|
||||
|
||||
### 1.1 Why these specific files matter
|
||||
|
||||
Python HA's `core.py` is a single-process Python 3.12 module that:
|
||||
- Holds the asyncio event loop directly
|
||||
- Serialises all state-changed writes through `asyncio.Lock`
|
||||
- Fires event listeners in the same event loop iteration that fired the event (listeners cannot block)
|
||||
- Is single-threaded by design — concurrent writes to the state machine are impossible without explicit async primitives
|
||||
|
||||
For HOMECORE the same semantic requirements apply, but the implementation must support:
|
||||
- **Concurrent reads** from dozens of integration WASM sandboxes polling current state
|
||||
- **High-frequency writes** from the RuView sensing stack (CSI at 100 Hz; state updates at up to 20 Hz per entity)
|
||||
- **Ordered delivery** of state_changed events to automation triggers (ADR-129) and recorder (ADR-132) subscribers
|
||||
- **Zero-copy reads** where possible for the REST API (ADR-130) path
|
||||
|
||||
---
|
||||
|
||||
## 2. Decision
|
||||
|
||||
Implement the `homecore` Rust crate at `v2/crates/homecore/` with the following design.
|
||||
|
||||
### 2.1 State machine: `DashMap` + Tokio broadcast
|
||||
|
||||
The primary state store is a `DashMap<EntityId, Arc<State>>` where:
|
||||
- `EntityId` is a validated newtype around `String` (validated format: `domain.name`)
|
||||
- `State` is a frozen struct: `entity_id`, `state` (String), `attributes` (serde_json::Value), `last_changed` (DateTime<Utc>), `last_updated` (DateTime<Utc>), `context` (Context)
|
||||
- `Arc<State>` allows zero-copy cloning for readers while the writer atomically replaces the map entry
|
||||
|
||||
State changes are published to a `tokio::sync::broadcast::Sender<StateChangedEvent>` channel (capacity: 4,096 events). Any number of receivers subscribe — the recorder, automation engine, WebSocket subscriber handler, and ruvector dual-write task all hold independent receivers. Slow receivers that fall behind by 4,096 events receive a `RecvError::Lagged` and must re-sync from the current state map.
|
||||
|
||||
### 2.2 Event bus: typed + untyped channels
|
||||
|
||||
HOMECORE distinguishes two event categories:
|
||||
|
||||
1. **System events** (typed): `StateChanged`, `ServiceCall`, `ComponentLoaded`, `PlatformDiscovered`, `HomeAssistantStart`, `HomeAssistantStop`. These use Tokio typed broadcast channels with zero allocation on the read path.
|
||||
2. **Integration events** (untyped): integrations fire arbitrary event types (`event_type: String`, `event_data: serde_json::Value`). These use a single `broadcast::Sender<DomainEvent>` where `DomainEvent` carries the type string and data blob. This mirrors HA's `EventBus.async_fire()`.
|
||||
|
||||
### 2.3 Service registry: `HashMap` + mpsc dispatch
|
||||
|
||||
Services are registered as `(Domain, ServiceName) → ServiceHandler` where `ServiceHandler` is a `Box<dyn Fn(ServiceCall) -> BoxFuture<ServiceResponse> + Send + Sync>`. The registry lives in a `tokio::sync::RwLock<HashMap<(Domain, ServiceName), ServiceHandler>>`. Service calls go through the event bus (fire `call_service`) and are dispatched to the handler by an internal router task. This matches HA's indirection: `hass.services.async_call(domain, service, data)` does not call the handler directly; it fires an event.
|
||||
|
||||
### 2.4 Entity registry: persisted metadata sidecar
|
||||
|
||||
The entity registry is a `RwLock<HashMap<EntityId, EntityEntry>>` backed by an async JSON writer that flushes to `.homecore/storage/core.entity_registry` on every write. The schema matches HA's `core.entity_registry` schema (version 13 as of HA 2025.1) so ADR-134 migration can read both formats interchangeably.
|
||||
|
||||
`EntityEntry` fields mirrored from HA:
|
||||
- `entity_id: EntityId`
|
||||
- `unique_id: Option<String>`
|
||||
- `platform: String`
|
||||
- `name: Option<String>` (user override)
|
||||
- `disabled_by: Option<DisabledBy>` (user, integration, config_entry)
|
||||
- `area_id: Option<AreaId>`
|
||||
- `device_id: Option<DeviceId>`
|
||||
- `entity_category: Option<EntityCategory>` (config, diagnostic)
|
||||
- `config_entry_id: Option<ConfigEntryId>`
|
||||
|
||||
### 2.5 Device registry: parallel sidecar
|
||||
|
||||
`DeviceRegistry` mirrors HA's `core.device_registry` schema (version 13). Devices are identified by a set of `(id_type, id_value)` tuples (the `identifiers` field), which matches HA's pattern of accepting multiple identifier types per device (MAC address, serial number, integration-specific ID).
|
||||
|
||||
---
|
||||
|
||||
## 3. HA-side reference table
|
||||
|
||||
| HA module / file | What it does | HOMECORE preserves | Changes | Drops |
|
||||
|---|---|---|---|---|
|
||||
| `homeassistant/core.py` `StateMachine` | In-memory state store, fire `state_changed` | Same semantics: immutable snapshots, `last_changed`, `last_updated`, `context` | `DashMap` instead of asyncio-locked `dict`; `broadcast::Sender` instead of asyncio callbacks | Python asyncio coupling |
|
||||
| `homeassistant/core.py` `EventBus` | Pub/sub event dispatch | `MATCH_ALL` listener; per-type listener; event data dict | Typed system events + untyped domain events; no Python dict — use `serde_json::Value` | `@callback` decorator, HassJob abstraction |
|
||||
| `homeassistant/core.py` `ServiceRegistry` | Register/call services | Same `(domain, service)` key structure; schema validation | Schema validation via `serde` `Deserialize` trait instead of voluptuous | voluptuous, Python type coercions |
|
||||
| `homeassistant/core.py` `HomeAssistant` | Runtime coordinator / service locator | State machine + event bus + services accessible on one struct | Struct with `Arc<HomeCoreInner>` for cheap cloning across tasks | asyncio event loop holder, Python executor |
|
||||
| `homeassistant/helpers/entity_registry.py` | Persist entity metadata | All fields listed in §2.4; file format compatible | Async tokio I/O; no Python pickle | Python-specific persistence helpers |
|
||||
| `homeassistant/helpers/device_registry.py` | Persist device metadata | `identifiers`, `connections`, `manufacturer`, `model`, `name`, `via_device_id` | Async tokio I/O | — |
|
||||
| `homeassistant/helpers/entity.py` | Entity base class | `entity_id`, `state`, `attributes`, `unique_id`, `device_info`, async_write_ha_state semantics | Trait `HomeCoreEntity` instead of class | Python MRO, `@property` decorators |
|
||||
| `homeassistant/helpers/event.py` | Convenience event helpers | `async_track_state_change`, `async_track_time_interval` (as Rust timer tasks) | Rust closures / async tasks | Python asyncio task wrappers |
|
||||
|
||||
---
|
||||
|
||||
## 4. Public API parity table
|
||||
|
||||
| HA Python surface | HOMECORE Rust equivalent |
|
||||
|---|---|
|
||||
| `hass.states.get(entity_id)` | `hass.states.get(&entity_id) -> Option<Arc<State>>` |
|
||||
| `hass.states.async_set(entity_id, state, attributes)` | `hass.states.set(entity_id, state, attributes).await` |
|
||||
| `hass.states.async_remove(entity_id)` | `hass.states.remove(&entity_id).await` |
|
||||
| `hass.states.async_all(domain_filter)` | `hass.states.all(domain_filter) -> Vec<Arc<State>>` |
|
||||
| `hass.bus.async_fire(event_type, data)` | `hass.bus.fire(event_type, data).await` |
|
||||
| `hass.bus.async_listen(event_type, callback)` | `hass.bus.subscribe(event_type) -> broadcast::Receiver<DomainEvent>` |
|
||||
| `hass.services.async_call(domain, service, data)` | `hass.services.call(domain, service, data).await -> ServiceResponse` |
|
||||
| `hass.services.async_register(domain, service, handler, schema)` | `hass.services.register(domain, service, handler)` |
|
||||
| `hass.services.has_service(domain, service)` | `hass.services.has(domain, service) -> bool` |
|
||||
| `entity_registry.async_get(entity_id)` | `entity_registry.get(&entity_id) -> Option<&EntityEntry>` |
|
||||
| `entity_registry.async_update_entity(entity_id, **kwargs)` | `entity_registry.update(entity_id, patch).await` |
|
||||
| `device_registry.async_get_device(identifiers)` | `device_registry.get_by_identifiers(identifiers) -> Option<&DeviceEntry>` |
|
||||
| `Context(user_id, parent_id)` | `Context { id: Uuid, parent_id: Option<Uuid>, user_id: Option<UserId> }` |
|
||||
|
||||
---
|
||||
|
||||
## 5. Phased implementation plan
|
||||
|
||||
### P1 — Skeleton (2 weeks)
|
||||
|
||||
- [ ] Create `v2/crates/homecore/` workspace member with `Cargo.toml`.
|
||||
- [ ] Define `State`, `EntityId`, `Domain`, `ServiceName`, `Context`, `DomainEvent` types.
|
||||
- [ ] `StateMachine`: `DashMap` + broadcast channel; `set()`, `get()`, `remove()`, `all()`.
|
||||
- [ ] `EventBus`: typed broadcast for system events + untyped broadcast for domain events.
|
||||
- [ ] Unit tests: 50 state writes/reads with concurrent readers; verify broadcast delivery.
|
||||
|
||||
### P2 — Service registry + entity registry (2 weeks)
|
||||
|
||||
- [ ] `ServiceRegistry`: `RwLock<HashMap>` + mpsc dispatch task.
|
||||
- [ ] `EntityRegistry`: in-memory + JSON async writer to `.homecore/storage/core.entity_registry`.
|
||||
- [ ] `DeviceRegistry`: in-memory + JSON async writer to `.homecore/storage/core.device_registry`.
|
||||
- [ ] Serialization: `serde` with `#[serde(rename_all = "snake_case")]`; schema version 13 header written to match HA format.
|
||||
- [ ] Unit tests: register service, call service, verify handler invoked; persist and reload entity registry.
|
||||
|
||||
### P3 — Trait surface for integrations (1 week)
|
||||
|
||||
- [ ] `HomeCoreEntity` trait: `entity_id()`, `unique_id()`, `name()`, `device_info()`, `state()`, `attributes()`, `async_write_ha_state(&hass)`.
|
||||
- [ ] `Platform` trait: `async_setup_entry(hass, config_entry) -> Result<()>`.
|
||||
- [ ] `ConfigEntry` struct mirroring HA's `ConfigEntry` fields.
|
||||
- [ ] Integration test: a minimal test integration registers an entity, writes a state, reads it back from the state machine.
|
||||
|
||||
### P4 — Performance validation (1 week)
|
||||
|
||||
- [ ] Benchmark: 1,000 state writes/s on Pi 5; measure latency at p50/p95/p99.
|
||||
- [ ] Benchmark: 100 concurrent WS subscribers each receiving all state_changed events; measure delivery lag.
|
||||
- [ ] Benchmark: broadcast channel saturation test at 4,096 capacity; verify `RecvError::Lagged` handling.
|
||||
- [ ] Acceptance criterion: p99 state write latency < 1 ms on Pi 5 (8 GB, 4 cores).
|
||||
|
||||
---
|
||||
|
||||
## 6. Risks
|
||||
|
||||
| Risk | Likelihood | Severity | Mitigation | Cross-ADR impact |
|
||||
|---|---|---|---|---|
|
||||
| **Broadcast channel lag** — a slow subscriber (e.g. ruvector recorder write) lags behind and drops events | Medium | High | Give recorder its own channel separate from WS subscribers; recorder is the hot path, give it highest priority | ADR-132: recorder write path must be designed to keep up with 100 Hz state writes |
|
||||
| **DashMap contention** — shard count default (16) may be too low for 100 Hz writes on a single entity | Low | Medium | Increase DashMap shard count to 64; benchmark before ADR-130 integration | ADR-130: REST API reads state directly from DashMap — must be lock-free |
|
||||
| **Entity registry format drift** — HA updates `.storage/core.entity_registry` schema; HOMECORE falls behind | Medium | Medium | Pin to schema version 13; version-check on load; fail loudly on unknown version | ADR-134: migration tool reads HA entity registry — must support the same schema version |
|
||||
| **Context propagation** — HA's `Context` is used for audit trails (which automation triggered which service call). HOMECORE must propagate it correctly or automation audits break | High | Low | Derive `Context` from source event at every service call; thread through `ServiceCall.context` field | ADR-129: automation engine must supply context when calling services |
|
||||
|
||||
---
|
||||
|
||||
## 7. Open questions
|
||||
|
||||
**Q1**: Should `EntityId` validation be strict (reject anything that doesn't match `[a-z0-9_]+\.[a-z0-9_]+`) or lenient (accept any UTF-8 string)? HA itself accepts unicode entity IDs since 2024.3. Strict validation simplifies routing; lenient matches HA's actual behaviour.
|
||||
|
||||
**Q2**: The `broadcast::Sender` capacity of 4,096 is chosen based on a worst-case of 100 state writes/s × 40 s of acceptable lag before a slow receiver is declared dead. Is 40 s the right threshold, or should it be configurable per receiver?
|
||||
|
||||
**Q3**: Should the `HomeCoreEntity` trait be object-safe (enabling `Vec<Box<dyn HomeCoreEntity>>`) or use associated types (enabling monomorphisation)? Object safety is required for the WASM plugin boundary (ADR-128); monomorphisation is faster for built-in integrations.
|
||||
|
||||
**Q4**: HA's `State.context` carries a `user_id` that traces which user or automation initiated a state change. HOMECORE uses `UserId` from the auth layer (ADR-130). Is the auth layer a dependency of the core state machine, or should `user_id` be an optional opaque string to avoid circular deps?
|
||||
|
||||
---
|
||||
|
||||
## 8. References
|
||||
|
||||
### HA upstream
|
||||
|
||||
- `homeassistant/core.py` — `HomeAssistant`, `StateMachine` (lines 1–800), `EventBus` (lines 800–1100), `ServiceRegistry` (lines 1100–1500), `Config` (lines 1500–2000)
|
||||
- `homeassistant/helpers/entity_registry.py` — `EntityRegistry`, `RegistryEntry` (all ~1,900 lines); schema version constant `STORAGE_VERSION`
|
||||
- `homeassistant/helpers/device_registry.py` — `DeviceRegistry`, `DeviceEntry`; schema version
|
||||
- `homeassistant/helpers/entity.py` — `Entity` base class; `async_write_ha_state`; entity lifecycle hooks
|
||||
- `homeassistant/helpers/event.py` — `async_track_state_change`, `async_track_time_interval`
|
||||
|
||||
### This repo
|
||||
|
||||
- `v2/crates/wifi-densepose-sensing-server/src/main.rs` — Axum + Tokio architecture pattern used throughout the existing server stack
|
||||
- `docs/adr/ADR-126-ruview-native-ha-port-master.md` — HOMECORE master; §5.5 crate naming; §6 compatibility contract; §5.1 RUVIEW-POLICY
|
||||
- `docs/adr/ADR-028-esp32-capability-audit.md` — witness chain pattern (Ed25519 per state transition)
|
||||
@@ -1,270 +0,0 @@
|
||||
# ADR-128: HOMECORE-PLUGINS — WASM integration plugin system
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Proposed |
|
||||
| **Date** | 2026-05-25 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codename** | **HOMECORE-PLUGINS** |
|
||||
| **Relates to** | [ADR-126](ADR-126-ruview-native-ha-port-master.md) (HOMECORE master), [ADR-127](ADR-127-homecore-state-machine-rust.md) (HOMECORE-CORE), [ADR-102](ADR-102-edge-module-registry.md) (cog registry), [ADR-100](ADR-100-cog-packaging-specification.md) (cog packaging spec) |
|
||||
| **Tracking issue** | TBD |
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
Home Assistant ships approximately 2,000 integrations, each a Python module in `homeassistant/components/<domain>/`. Each integration:
|
||||
|
||||
1. Declares a **manifest** (`manifest.json`) with `domain`, `name`, `version`, `requirements` (pip packages), `dependencies` (other HA integrations), `codeowners`, `iot_class`, `config_flow` (bool), and `quality_scale`.
|
||||
2. Provides **`async_setup`** (global domain setup, called once at HA startup) and/or **`async_setup_entry`** (per-config-entry setup, called when a user adds an integration via the UI).
|
||||
3. Imports Python packages from `requirements` at load time — these are installed into HA's Python environment by the loader at first run.
|
||||
4. Communicates with the HA core exclusively through the `hass` object (the `HomeAssistant` instance) — setting states, calling services, registering services, subscribing to events.
|
||||
|
||||
In Python HA, integrations run **in-process** with the hub. A buggy integration can crash the event loop, read arbitrary HA memory, or import packages that conflict with other integrations. HA mitigates this via code review and quality scale requirements, but there is no runtime isolation boundary.
|
||||
|
||||
### 1.1 The Cognitum Seed cog system
|
||||
|
||||
The project already has a cog system (ADR-102, ADR-100) for the Cognitum Seed appliance. A **cog** is a signed, sandboxed module that installs from the Seed app registry. ADR-101 (`cog-pose-estimation`) shipped signed aarch64/x86_64 binaries with a model weight blob. ADR-116 (`cog-ha-matter`) shipped HA+Matter integration as a cog.
|
||||
|
||||
The cog system uses a different packaging model from HA integrations (binary artifacts vs Python packages), but the same conceptual pattern: a manifest, a lifecycle hook, and communication through a defined interface.
|
||||
|
||||
HOMECORE-PLUGINS unifies these two patterns: every HOMECORE integration is a **WASM module** that speaks the cog ABI, can be hot-loaded without restarting the hub, and is sandboxed by the WASM runtime.
|
||||
|
||||
---
|
||||
|
||||
## 2. Decision
|
||||
|
||||
HOMECORE integrations are **WASM modules** loaded by a Rust host runtime (`homecore-plugins` crate). Each plugin:
|
||||
|
||||
1. Compiles to a `.wasm` binary (from Rust, AssemblyScript, Go, or any WASM-targeting language).
|
||||
2. Declares a `manifest.json` (superset of HA's manifest schema — see §3).
|
||||
3. Exports exactly three WASM functions: `setup_entry(config_entry_ptr, config_entry_len) → i32`, `call_service(call_ptr, call_len) → i32`, and `receive_event(event_ptr, event_len) → i32`.
|
||||
4. Imports a set of **host functions** from the HOMECORE host runtime: `hc_state_get`, `hc_state_set`, `hc_event_fire`, `hc_service_call`, `hc_log`, `hc_entity_register`.
|
||||
5. Communicates with the host exclusively through those imports — no direct memory access outside its own linear memory.
|
||||
|
||||
The WASM runtime is **Wasmtime** (Cranelift JIT on Pi 5 and x86_64; interpretation mode available for low-memory targets via `--features wasm3`).
|
||||
|
||||
### 2.1 Why WASM over Python-in-process
|
||||
|
||||
| Criterion | Python in-process (HA today) | WASM sandbox (HOMECORE) |
|
||||
|---|---|---|
|
||||
| Memory isolation | None — any integration can read any HA object | WASM linear memory; host allocates shared buffer only for ABI calls |
|
||||
| Crash isolation | Integration panic = HA event loop crash | WASM trap = plugin terminated, hub continues |
|
||||
| Language support | Python only | Any WASM-targeting language: Rust, Go, AssemblyScript, C, Zig |
|
||||
| Hot-load without restart | No — requires `asyncio.run_coroutine_threadsafe` patching | Yes — Wasmtime `Engine` + `Module::deserialize` from compiled `.cwasm` cache |
|
||||
| Dependency conflicts | pip requirements collide across integrations | Each WASM module carries its own static dependencies (no runtime pip) |
|
||||
| Startup cost per integration | Python import + pip install | Wasmtime JIT compile (~5 ms for a typical 200 kB WASM module); cached to `.cwasm` |
|
||||
|
||||
### 2.2 Cog system as the plugin substrate
|
||||
|
||||
The existing cog system (ADR-102) is the distribution and lifecycle layer. HOMECORE-PLUGINS extends it:
|
||||
|
||||
- **Distribution**: cogs are fetched from the Seed app registry (`app-registry.json`) or from a HOMECORE plugin registry (superset of the cog registry, same JSON schema + a `wasm_module` field).
|
||||
- **Lifecycle**: `cognitum-agent` (ADR-116) already handles OTA update, signature verification, and sandboxed execution. HOMECORE-PLUGINS reuses this lifecycle by treating each HOMECORE integration as a cog with a WASM payload.
|
||||
- **Ed25519 signatures**: every plugin `.wasm` is signed with the publisher's Ed25519 key. The HOMECORE host verifies the signature before compiling the module (same pattern as ADR-028 witness chain).
|
||||
|
||||
---
|
||||
|
||||
## 3. Manifest schema
|
||||
|
||||
HOMECORE's manifest is a superset of HA's `manifest.json`. Fields not present in HA are marked **[HOMECORE]**.
|
||||
|
||||
```json
|
||||
{
|
||||
"domain": "mqtt",
|
||||
"name": "MQTT",
|
||||
"version": "2025.1.0",
|
||||
"documentation": "https://www.home-assistant.io/integrations/mqtt/",
|
||||
"iot_class": "local_push",
|
||||
"config_flow": true,
|
||||
"dependencies": [],
|
||||
"quality_scale": "platinum",
|
||||
"wasm_module": "mqtt.wasm",
|
||||
"wasm_module_hash": "sha256:abcdef...",
|
||||
"wasm_module_sig": "ed25519:<base64>",
|
||||
"publisher_key": "<base64 Ed25519 public key>",
|
||||
"min_homecore_version": "0.1.0",
|
||||
"host_imports_required": ["hc_state_get", "hc_state_set", "hc_event_fire", "hc_service_call"],
|
||||
"homecore_permissions": ["state:write:sensor.*", "state:read:*", "service:call:homeassistant.*"],
|
||||
"cog_id": "homecore-mqtt-2025.1.0"
|
||||
}
|
||||
```
|
||||
|
||||
**[HOMECORE]** fields:
|
||||
- `wasm_module` — relative path to the `.wasm` binary
|
||||
- `wasm_module_hash` — SHA-256 of the wasm binary; verified before execution
|
||||
- `wasm_module_sig` — Ed25519 signature of the wasm binary hash
|
||||
- `publisher_key` — Ed25519 public key of the publisher
|
||||
- `min_homecore_version` — minimum HOMECORE version required
|
||||
- `host_imports_required` — subset of host functions the module needs (security auditable)
|
||||
- `homecore_permissions` — coarse-grained permission claims (glob patterns); future: enforcement via RUVIEW-POLICY layer (ADR-124 §4.1a)
|
||||
- `cog_id` — Seed app registry ID for the cog distribution
|
||||
|
||||
---
|
||||
|
||||
## 4. HA-side reference table
|
||||
|
||||
| HA module / file | What it does | HOMECORE preserves | Changes | Drops |
|
||||
|---|---|---|---|---|
|
||||
| `homeassistant/components/<domain>/manifest.json` | Integration metadata | `domain`, `name`, `version`, `iot_class`, `config_flow`, `dependencies`, `quality_scale`, `documentation` | Add WASM fields; remove `requirements` (no pip) | `requirements` (pip packages) |
|
||||
| `homeassistant/loader.py` | Loads Python modules; installs pip requirements | Manifest parsing; dependency resolution between cogs | WASM module loading via Wasmtime; no pip | Python `importlib`, pip subprocess |
|
||||
| `homeassistant/components/<domain>/__init__.py` | `async_setup` + `async_setup_entry` | `setup_entry` hook (per config entry) | WASM export function instead of Python async function | Python module structure |
|
||||
| `homeassistant/config_entries.py` | Config entry lifecycle management | `ConfigEntry` struct: `entry_id`, `domain`, `title`, `data`, `options`, `state`, `version` | Rust struct; async state machine | Python class hierarchy; `FlowManager` |
|
||||
| `homeassistant/components/<domain>/config_flow.py` | UI configuration flow | Config flow metadata (steps, schemas) | JSON-schema-based flow descriptor shipped in manifest | `voluptuous`, Python UI flow runtime |
|
||||
|
||||
---
|
||||
|
||||
## 5. WASM ABI specification
|
||||
|
||||
### 5.1 Host functions imported by plugins
|
||||
|
||||
```
|
||||
hc_state_get(key_ptr: i32, key_len: i32, out_ptr: i32, out_cap: i32) → i32
|
||||
// Returns JSON-encoded State into out_ptr buffer; returns bytes written or -1 if not found.
|
||||
|
||||
hc_state_set(entity_ptr: i32, entity_len: i32, state_ptr: i32, state_len: i32,
|
||||
attrs_ptr: i32, attrs_len: i32) → i32
|
||||
// Sets state for entity_id; returns 0 on success, negative on error.
|
||||
|
||||
hc_event_fire(event_type_ptr: i32, event_type_len: i32,
|
||||
event_data_ptr: i32, event_data_len: i32) → i32
|
||||
// Fires a domain event.
|
||||
|
||||
hc_service_call(domain_ptr: i32, domain_len: i32,
|
||||
service_ptr: i32, service_len: i32,
|
||||
data_ptr: i32, data_len: i32) → i32
|
||||
// Calls a service synchronously from the plugin's perspective (async on the host).
|
||||
|
||||
hc_entity_register(entry_ptr: i32, entry_len: i32) → i32
|
||||
// Registers an entity with the entity registry; entry is JSON-encoded EntityEntry.
|
||||
|
||||
hc_log(level: i32, msg_ptr: i32, msg_len: i32) → void
|
||||
// Structured log output; level: 0=debug, 1=info, 2=warn, 3=error.
|
||||
```
|
||||
|
||||
### 5.2 WASM exports required by host
|
||||
|
||||
```
|
||||
setup_entry(config_entry_ptr: i32, config_entry_len: i32) → i32
|
||||
// Called when a config entry is set up. config_entry is JSON-encoded ConfigEntry.
|
||||
// Returns 0 on success, negative error code on failure.
|
||||
|
||||
call_service_handler(domain_ptr: i32, domain_len: i32,
|
||||
service_ptr: i32, service_len: i32,
|
||||
data_ptr: i32, data_len: i32) → i32
|
||||
// Called when a service registered by this plugin is invoked.
|
||||
|
||||
receive_event(event_type_ptr: i32, event_type_len: i32,
|
||||
event_data_ptr: i32, event_data_len: i32) → i32
|
||||
// Called when an event type the plugin subscribed to fires.
|
||||
// Subscription is declared in manifest `subscribed_events` array.
|
||||
|
||||
alloc(size: i32) → i32
|
||||
// Host calls this to allocate a buffer inside the WASM linear memory
|
||||
// before writing data for a callback. Required for ABI memory passing.
|
||||
|
||||
dealloc(ptr: i32, size: i32) → void
|
||||
// Host calls this to free a previously allocated buffer.
|
||||
```
|
||||
|
||||
### 5.3 Execution model
|
||||
|
||||
Each WASM module instance runs in its own Wasmtime `Store`. The host calls WASM exports from a dedicated Tokio task per plugin. Incoming events are queued in an `mpsc::Sender<PluginEvent>` per plugin; the plugin task drains the queue and calls `receive_event`. This isolates plugin execution from the hot state-machine path.
|
||||
|
||||
---
|
||||
|
||||
## 6. Public API parity table
|
||||
|
||||
| HA integration pattern | HOMECORE WASM equivalent |
|
||||
|---|---|
|
||||
| `async_setup_entry(hass, entry)` Python async function | `setup_entry(config_entry_json)` WASM export |
|
||||
| `hass.states.async_set(entity_id, state, attrs)` | `hc_state_set(...)` host import |
|
||||
| `hass.states.get(entity_id)` | `hc_state_get(...)` host import |
|
||||
| `hass.bus.async_fire(event_type, data)` | `hc_event_fire(...)` host import |
|
||||
| `hass.services.async_call(domain, service, data)` | `hc_service_call(...)` host import |
|
||||
| `hass.services.async_register(domain, service, handler)` | Declared in manifest `registered_services`; `call_service_handler` WASM export handles all |
|
||||
| `async_track_state_change(hass, entity_ids, callback)` | Declared in manifest `subscribed_state_entities`; `receive_event` called with `state_changed` events |
|
||||
| Config flow `FlowManager.async_init()` | Config flow metadata in manifest; UI calls HOMECORE-API `/config/config_entries/flow` |
|
||||
| `ConfigEntry.entry_id`, `.domain`, `.data`, `.options` | Same fields in `ConfigEntry` JSON passed to `setup_entry` |
|
||||
|
||||
---
|
||||
|
||||
## 7. Phased implementation plan
|
||||
|
||||
### P1 — WASM host skeleton (2 weeks)
|
||||
|
||||
- [ ] Create `v2/crates/homecore-plugins/` workspace member.
|
||||
- [ ] Wasmtime dependency; compile a trivial WASM module that calls `hc_log` and verify it runs.
|
||||
- [ ] Define the host function ABI in a `host_api.rs` module; write the Wasmtime `Linker` registration for all 6 host functions.
|
||||
- [ ] Manifest schema: `serde`-deserialised `Manifest` struct; validate required fields.
|
||||
- [ ] Hash + Ed25519 signature verification of `.wasm` bytes before compilation.
|
||||
|
||||
### P2 — State machine bridge (2 weeks)
|
||||
|
||||
- [ ] Wire `hc_state_get` and `hc_state_set` to the `homecore` state machine (ADR-127).
|
||||
- [ ] Wire `hc_event_fire` to the event bus.
|
||||
- [ ] Wire `hc_service_call` to the service registry.
|
||||
- [ ] Wire `hc_entity_register` to the entity registry.
|
||||
- [ ] Write a test plugin in Rust compiled to WASM: registers one entity, writes its state via host imports, verifies the state machine sees the update.
|
||||
|
||||
### P3 — Config entry lifecycle + hot-load (2 weeks)
|
||||
|
||||
- [ ] `ConfigEntryManager` — tracks loaded plugins, calls `setup_entry` on new config entries, handles teardown.
|
||||
- [ ] Hot-load: watch a directory for new `.wasm` + `manifest.json` pairs; load without hub restart.
|
||||
- [ ] Wasmtime compiled module cache: serialize to `.cwasm` after first JIT compile; deserialize on subsequent loads (sub-1 ms plugin restart).
|
||||
- [ ] Integration test: MQTT plugin loaded at runtime, registers `sensor.test` entity, state readable via HOMECORE-API.
|
||||
|
||||
### P4 — Cog registry integration (1 week)
|
||||
|
||||
- [ ] Fetch plugin from Seed app registry `app-registry.json`; verify Ed25519 signature against publisher key.
|
||||
- [ ] Expose `/api/homecore/plugins` REST endpoint (HOMECORE-API ADR-130 extension): list loaded plugins, load new plugin by URL, unload plugin.
|
||||
- [ ] First-party plugin: ship an MQTT plugin WASM module that provides the same function as HA's `homeassistant/components/mqtt/`.
|
||||
|
||||
### P5 — Permission enforcement (1 week)
|
||||
|
||||
- [ ] Enforce `homecore_permissions` claims: reject `hc_state_set` calls that write to entities outside the plugin's declared `state:write:*` pattern.
|
||||
- [ ] Log all permission denials to the Ed25519 witness chain.
|
||||
- [ ] Expose permission audit via `/api/homecore/plugins/<domain>/audit`.
|
||||
|
||||
---
|
||||
|
||||
## 8. Risks
|
||||
|
||||
| Risk | Likelihood | Severity | Mitigation | Cross-ADR impact |
|
||||
|---|---|---|---|---|
|
||||
| **ADR-127 state machine not stable** — plugin ABI calls into the state machine; if the API changes, all plugins break | High (early phase) | High | Freeze the `hc_state_get`/`hc_state_set` ABI in P1; never change pointer/length convention; version the host ABI in the manifest `min_homecore_version` | ADR-127 must freeze public API before ADR-128 P2 begins |
|
||||
| **Wasmtime binary size** — adding Wasmtime to HOMECORE adds ~15 MB to the binary on Pi 5 | Medium | Medium | Use Cranelift JIT only; skip LLVM optimizer. Alternative: `wasm3` feature flag (~50 kB) for constrained hardware | ADR-126: binary size target < 50 MB idle RAM; Wasmtime itself uses ~5 MB RAM at runtime |
|
||||
| **ABI memory overhead** — every state read/write from a plugin must JSON-encode/decode through shared memory | Medium | Medium | Cap state value size at 64 kB; use a pool allocator for ABI buffers; profile on Pi 5 at 10 state writes/s per plugin | ADR-130: REST API reads state from DashMap directly, bypassing plugin ABI — no overhead there |
|
||||
| **Community plugin trust** — WASM sandbox prevents crashes but cannot prevent malicious plugins from calling `hc_service_call` to turn off all lights | Medium | High | `homecore_permissions` permission claims (P5); future: RUVIEW-POLICY enforcement (ADR-124 §4.1a) for biometric data access | ADR-124 RUVIEW-POLICY must be made aware of HOMECORE as a policy principal |
|
||||
|
||||
---
|
||||
|
||||
## 9. Open questions
|
||||
|
||||
**Q1**: Should the WASM module ABI use JSON-over-shared-memory (current proposal) or a more compact binary encoding (MessagePack, FlatBuffers)? JSON is simpler to debug and matches HA's existing JSON-everywhere convention; MessagePack cuts ABI overhead by ~4×. Decide before P2 implementation.
|
||||
|
||||
**Q2**: HA's `config_flow.py` is a multi-step UI wizard with voluptuous schema validation. HOMECORE's config flow is described in the manifest JSON. Is a JSON-schema-based config flow sufficient for the 100 most popular integrations, or do some require imperative step logic that can't be expressed declaratively?
|
||||
|
||||
**Q3**: Should existing Python HA community integrations be automatically compilable to WASM via a transpilation layer (e.g. CPython compiled to WASM via Pyodide), or should HOMECORE accept only natively compiled WASM modules? Pyodide+WASM would make migration easier but adds ~25 MB per plugin and loses the performance argument.
|
||||
|
||||
**Q4**: The `host_imports_required` manifest field lists which host functions the plugin needs. Should this be verified at load time (reject plugin that imports undeclared functions) or only advisory? Strict enforcement prevents surprises; advisory aids migration.
|
||||
|
||||
---
|
||||
|
||||
## 10. References
|
||||
|
||||
### HA upstream
|
||||
|
||||
- `homeassistant/loader.py` — integration loader; pip requirement installation; `async_setup_entry` invocation
|
||||
- `homeassistant/config_entries.py` — `ConfigEntry`, `ConfigEntryState`, `ConfigEntriesError`, `FlowManager`
|
||||
- `homeassistant/components/mqtt/manifest.json` — canonical example of HA manifest structure
|
||||
- `homeassistant/components/mqtt/__init__.py` — `async_setup_entry` pattern for a complex integration with services
|
||||
- `homeassistant/components/mqtt/config_flow.py` — multi-step config flow example
|
||||
|
||||
### This repo
|
||||
|
||||
- `docs/adr/ADR-102-edge-module-registry.md` — cog registry architecture; `app-registry.json` schema
|
||||
- `docs/adr/ADR-100-cog-packaging-specification.md` — cog packaging spec; Ed25519 signing
|
||||
- `docs/adr/ADR-101-pose-estimation-cog.md` — cog lifecycle precedent
|
||||
- `docs/adr/ADR-127-homecore-state-machine-rust.md` — state machine ABI that plugins call
|
||||
- `docs/adr/ADR-126-ruview-native-ha-port-master.md` — §5.7 "do not port" list (legacy Python integrations)
|
||||
@@ -1,212 +0,0 @@
|
||||
# ADR-129: HOMECORE-AUTO — Automation engine, script runner, and template evaluator
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Proposed |
|
||||
| **Date** | 2026-05-25 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codename** | **HOMECORE-AUTO** |
|
||||
| **Relates to** | [ADR-126](ADR-126-ruview-native-ha-port-master.md) (HOMECORE master), [ADR-127](ADR-127-homecore-state-machine-rust.md) (HOMECORE-CORE), [ADR-129 implicit](ADR-129-homecore-automation-engine.md), [ADR-133](ADR-133-homecore-assist-ruflo.md) (HOMECORE-ASSIST) |
|
||||
| **Tracking issue** | TBD |
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
Home Assistant's automation system is defined across three components:
|
||||
|
||||
1. **`homeassistant/components/automation/__init__.py`** — the automation manager: loads automation YAML, evaluates trigger platforms, calls the script executor when conditions pass. The core class is `AutomationEntity` which extends `ToggleEntity`. Automations are themselves HA entities with `state = on/off`.
|
||||
|
||||
2. **`homeassistant/components/script/__init__.py`** — the script executor: a sequence of actions (service calls, conditions, delays, events, template variables, `choose`, `parallel`, `repeat`, `wait_for_trigger`). Scripts are entities too (`ScriptEntity` extends `ToggleEntity`). The execution engine supports five run modes: `single`, `restart`, `queued`, `parallel`, `ignore_first`.
|
||||
|
||||
3. **`homeassistant/helpers/template.py`** — HA's Jinja2 customisation layer: wraps the upstream `jinja2` Python library with HA-specific globals (`states()`, `is_state()`, `state_attr()`, `now()`, `utcnow()`, `as_timestamp()`, `distance()`, `closest()`, etc.), custom filters (`regex_match`, `round`, `timestamp_local`), and a sandboxed `Environment` that prevents file I/O and dangerous evaluations.
|
||||
|
||||
### 1.1 Scale and surface
|
||||
|
||||
HA's automation YAML supports:
|
||||
- **17 trigger platforms** (state, time, numeric_state, template, event, homeassistant, zone, geo_location, device, calendar, conversation, mqtt, webhook, tag, sun, time_pattern, persistent_notification)
|
||||
- **7 condition types** (state, numeric_state, time, template, zone, sun, device)
|
||||
- **22+ action types** (call_service, delay, wait_template, fire_event, device_action, choose, if, parallel, repeat, sequence, stop, set_conversation_response, ...)
|
||||
|
||||
The YAML schema is validated by `voluptuous` schemas defined in `homeassistant/helpers/config_validation.py` (~5,000 lines).
|
||||
|
||||
### 1.2 Jinja2 is the critical surface
|
||||
|
||||
HA templates are used not only in automations but in dashboard cards, notification messages, and script variables. The HA frontend sends template strings to the API's `POST /api/template` endpoint for server-side evaluation. Any HOMECORE instance that claims API compatibility must execute Jinja2-compatible templates or existing automations will break.
|
||||
|
||||
Full Jinja2 support in Rust without Python is non-trivial. The approach chosen here uses a **WASM-compiled MiniJinja** (the `minijinja` Rust crate compiled with HA-specific extension functions) rather than a full Python Jinja2 re-implementation.
|
||||
|
||||
---
|
||||
|
||||
## 2. Decision
|
||||
|
||||
Build the `homecore-automation` crate with three components:
|
||||
|
||||
1. **YAML parser**: `serde_yaml` + custom validator that parses HA's automation and script YAML into typed Rust structs. Validates trigger, condition, and action schemas at load time.
|
||||
2. **Trigger evaluator**: a Tokio task per loaded automation that subscribes to the HOMECORE event bus (ADR-127) and evaluates trigger conditions in Rust. When a trigger fires and conditions pass, it enqueues the automation action sequence.
|
||||
3. **Action executor**: a script runner that processes action sequences. Service calls go to the HOMECORE service registry. Delays use `tokio::time::sleep`. Template evaluation uses MiniJinja. Complex conditions (optional) can route to a ruflo agent (ADR-133).
|
||||
|
||||
### 2.1 Template evaluator: MiniJinja + HA-compatible extension functions
|
||||
|
||||
`minijinja` (crates.io version 2.x) is a production-quality Jinja2 implementation in pure Rust. It is missing 5–10% of Jinja2's surface area (notably: `{% block %}` / `{% extends %}` template inheritance, and some Jinja2 Python-specific filters), but covers 100% of HA's automation template usage.
|
||||
|
||||
HA-specific globals added on top of MiniJinja:
|
||||
|
||||
```rust
|
||||
env.add_global("states", minijinja::Value::from_function(ha_states_global));
|
||||
env.add_global("is_state", minijinja::Value::from_function(ha_is_state_global));
|
||||
env.add_global("state_attr", minijinja::Value::from_function(ha_state_attr_global));
|
||||
env.add_global("now", minijinja::Value::from_function(ha_now_global));
|
||||
env.add_global("utcnow", minijinja::Value::from_function(ha_utcnow_global));
|
||||
env.add_global("as_timestamp", minijinja::Value::from_function(ha_as_timestamp_global));
|
||||
env.add_global("distance", minijinja::Value::from_function(ha_distance_global));
|
||||
env.add_global("iif", minijinja::Value::from_function(ha_iif_global));
|
||||
```
|
||||
|
||||
Each global function reads from the HOMECORE state machine (ADR-127) via an `Arc<StateMachine>` captured at environment construction time. Template evaluation is synchronous (MiniJinja is sync) but runs in a `tokio::task::spawn_blocking` wrapper to avoid blocking the async executor.
|
||||
|
||||
### 2.2 WASM evaluator for untrusted template strings
|
||||
|
||||
Dashboard card templates submitted via `POST /api/template` come from user-authored YAML, not first-party code. HA evaluates these in the same Python process, relying on Jinja2's `SandboxedEnvironment` for safety. HOMECORE uses a **WASM-sandboxed MiniJinja** evaluator:
|
||||
|
||||
- A single WASM module (`homecore-template-eval.wasm`) is compiled from the MiniJinja crate with the HA extension globals stubbed to call host functions.
|
||||
- Template strings are passed into the WASM module via the HOMECORE plugin ABI (ADR-128 §5.1).
|
||||
- The WASM sandbox prevents file I/O, network access, and infinite loops (via Wasmtime fuel metering — 100,000 instructions per template evaluation).
|
||||
- Result is returned as a string to the HOMECORE API.
|
||||
|
||||
This is the same Wasmtime host already used for integration plugins (ADR-128) — no additional WASM runtime dependency.
|
||||
|
||||
---
|
||||
|
||||
## 3. HA-side reference table
|
||||
|
||||
| HA module / file | What it does | HOMECORE preserves | Changes | Drops |
|
||||
|---|---|---|---|---|
|
||||
| `automation/__init__.py` `AutomationEntity` | Automation as a toggle entity (on/off) with triggers/conditions/actions | Automation is a HOMECORE entity with same on/off state semantics | Rust struct `AutomationEntity` implementing `HomeCoreEntity` trait | Python class hierarchy, voluptuous schema |
|
||||
| `automation/__init__.py` `TriggerActionConfig` | Trigger → condition → action pipeline | Full trigger/condition/action pipeline | Typed Rust enums per trigger platform | Python dict-based config |
|
||||
| `automation/trigger.py` | Delegates to per-platform trigger modules (`homeassistant/components/<platform>/trigger.py`) | Same per-platform dispatch | Rust match arm per trigger type | Python dynamic module import |
|
||||
| `script/__init__.py` `Script` | Script entity + action sequence executor | Same 22 action types | Rust enum `Action` with all variants | Python asyncio coroutines |
|
||||
| `script/__init__.py` run modes | `single`, `restart`, `queued`, `parallel`, `ignore_first` | All 5 run modes | Tokio-based concurrency control (semaphore for `queued`, `parallel`) | Python asyncio task management |
|
||||
| `helpers/template.py` `Template` | Jinja2 evaluation + HA globals | Same HA global function names and signatures | MiniJinja instead of Python Jinja2; WASM sandbox for user templates | Python `jinja2` library; `voluptuous` coercions in templates |
|
||||
| `helpers/config_validation.py` | `cv.template`, `cv.entity_id`, time period validators | Same validation semantics | Rust custom deserializers implementing `serde::Deserialize` | voluptuous; Python regex |
|
||||
| `components/automation/blueprint.py` | Blueprint system (reusable automation templates with input variables) | Blueprint YAML schema + variable substitution | Pure Rust YAML substitution | Python Blueprint class hierarchy |
|
||||
|
||||
---
|
||||
|
||||
## 4. Public API parity table
|
||||
|
||||
| HA automation surface | HOMECORE equivalent |
|
||||
|---|---|
|
||||
| `automation.trigger` (state, time, numeric_state, template, event, ...) | `Trigger` enum with variants for all 17 HA trigger platforms |
|
||||
| `automation.condition` (state, numeric_state, time, template, zone, sun, device) | `Condition` enum with variants for all 7 condition types |
|
||||
| `automation.action` — call_service, delay, fire_event, choose, if, parallel, repeat, wait_template, stop | `Action` enum with variants for all 22 action types |
|
||||
| `script.run_mode` — single, restart, queued, parallel | `RunMode` enum with 5 variants |
|
||||
| `POST /api/template` (REST eval of a template string) | Same endpoint in HOMECORE-API (ADR-130); backed by WASM-sandboxed MiniJinja |
|
||||
| Automation entity: `state = on|off`, `attributes.last_triggered`, `attributes.id` | `AutomationEntity` struct with same attribute names |
|
||||
| `automation.trigger` service (manually trigger an automation) | `homecore.automation.trigger` service; same service call data schema |
|
||||
| `automation.reload` service (reload automations.yaml) | `homecore.automation.reload` service |
|
||||
| `automation.toggle` service | Standard `HomeCoreEntity` toggle service |
|
||||
| Blueprint YAML with `blueprint:` key and `input:` variables | Blueprint parsed by HOMECORE YAML parser; same substitution semantics |
|
||||
|
||||
---
|
||||
|
||||
## 5. Trigger platform mapping
|
||||
|
||||
| HA trigger platform | HOMECORE implementation |
|
||||
|---|---|
|
||||
| `state` | Subscribe to `state_changed` broadcast; match `entity_id`, `from`, `to`, `for` |
|
||||
| `numeric_state` | Subscribe to `state_changed`; parse state as f64; compare against `above`/`below` |
|
||||
| `time` | `tokio::time::sleep_until` to next occurrence; re-arm after fire |
|
||||
| `time_pattern` | Cron-style evaluation using `cron` crate; tokio timer task |
|
||||
| `template` | Re-evaluate template on every `state_changed`; fire when template transitions from false to true |
|
||||
| `event` | Subscribe to named domain event on event bus |
|
||||
| `homeassistant` (start/stop) | Subscribe to `HomeAssistantStart` / `HomeAssistantStop` typed events |
|
||||
| `zone` | Subscribe to `zone.entered` / `zone.left` events from the device tracker integration |
|
||||
| `mqtt` | Subscribe to MQTT topic via the MQTT plugin (ADR-128); fire event when message arrives |
|
||||
| `webhook` | HOMECORE-API registers a webhook path; fires event on POST |
|
||||
| `calendar` | Subscribe to calendar event from calendar integration |
|
||||
| `conversation` | Subscribe to `conversation.user_input` event; match intent/sentence |
|
||||
| `geo_location` | Subscribe to `geo_location.entered` / `geo_location.left` |
|
||||
| `sun` | Compute sunrise/sunset from latitude/longitude in `homecore.config`; tokio timer |
|
||||
| `device` | Delegate to integration-specific device trigger via WASM plugin |
|
||||
| `persistent_notification` | Subscribe to `persistent_notification.create` event |
|
||||
| `tag` | Subscribe to `tag.scanned` event from NFC/QR integration |
|
||||
|
||||
---
|
||||
|
||||
## 6. Phased implementation plan
|
||||
|
||||
### P1 — YAML parser (2 weeks)
|
||||
|
||||
- [ ] Define Rust enums for `Trigger`, `Condition`, `Action`, `RunMode` with `serde` deserialization.
|
||||
- [ ] Parse an existing `automations.yaml` from a real HA install with zero errors (test fixture).
|
||||
- [ ] Validator: reject unknown trigger platforms with a clear error message.
|
||||
- [ ] Unit tests: parse 50 automation fixtures covering all 17 trigger types and 22 action types.
|
||||
|
||||
### P2 — State and event triggers (2 weeks)
|
||||
|
||||
- [ ] Implement `state`, `numeric_state`, `event`, `homeassistant`, `time`, `time_pattern` trigger evaluators.
|
||||
- [ ] `ConditionEvaluator` for `state`, `numeric_state`, `time` conditions.
|
||||
- [ ] `ActionExecutor` for `call_service`, `delay`, `fire_event`, `stop` action types.
|
||||
- [ ] Integration test: load one automation (state trigger → call_service action); verify fires correctly when state changes.
|
||||
|
||||
### P3 — Full action set + MiniJinja (3 weeks)
|
||||
|
||||
- [ ] MiniJinja + HA extension globals; `POST /api/template` endpoint wired to WASM evaluator.
|
||||
- [ ] `template` trigger + `template` condition evaluators.
|
||||
- [ ] `choose`, `if`, `parallel`, `repeat`, `wait_template`, `sequence` action types.
|
||||
- [ ] All 5 `RunMode` variants (concurrency control via Tokio semaphore/mutex).
|
||||
- [ ] Integration test: `automations.yaml` from ADR-134 migration fixture loads and runs correctly.
|
||||
|
||||
### P4 — Blueprint system + ruflo agent condition (1 week)
|
||||
|
||||
- [ ] Blueprint YAML parser + input variable substitution.
|
||||
- [ ] Optional ruflo agent condition: `condition: ruflo_agent` with `query: "..."` routes to ruflo LLM (ADR-133 §3.3); gated by RUVIEW-POLICY.
|
||||
- [ ] `automation.reload` service.
|
||||
- [ ] Performance benchmark: 100 automations loaded; 100 state changes/s; verify trigger evaluation stays < 5 ms per state change.
|
||||
|
||||
---
|
||||
|
||||
## 7. Risks
|
||||
|
||||
| Risk | Likelihood | Severity | Mitigation | Cross-ADR impact |
|
||||
|---|---|---|---|---|
|
||||
| **MiniJinja gaps** — some HA templates use Jinja2 features MiniJinja doesn't support (template inheritance, Python-specific filters) | Medium | Medium | Document the MiniJinja-vs-Jinja2 delta before P3 ships; provide a migration guide for affected templates; defer the 5% of templates that fail to a Python-compat shim (ADR-134) | ADR-134: migration tool must warn on templates that use unsupported Jinja2 features |
|
||||
| **Template performance** — synchronous MiniJinja in `spawn_blocking` adds overhead under high automation fan-out | Low | Low | Benchmark at 50 automations each evaluating a template trigger on every state_changed (worst case); if > 2 ms add a template-evaluation cache keyed by (template_hash, relevant_entity_states) | ADR-127: state machine must expose a "relevant states snapshot" API for caching |
|
||||
| **ADR-127 state machine API not frozen** — trigger evaluators call `hass.states.all()` and subscribe to broadcasts; if those APIs change, trigger code must update | High (early) | High | ADR-127 must freeze its public API before ADR-129 P2 begins; use a `HomeCoreRef` trait (version 1.0 stable) | ADR-127 owns this dependency |
|
||||
| **Complex action YAML** — real-world automations use deeply nested `choose`/`if`/`parallel` blocks; parsing is non-trivial | Medium | Medium | Use a corpus of 500 public HA automations from the HA community (MIT-licensed) as parse-test fixtures in CI | None |
|
||||
|
||||
---
|
||||
|
||||
## 8. Open questions
|
||||
|
||||
**Q1**: MiniJinja does not support all Python-specific Jinja2 filters (e.g. `map`, `select`, `reject` with Python lambda arguments). HA's `homeassistant/helpers/template.py` adds custom equivalents of several of these. How many real-world HA automations use these filters? A corpus analysis of public HA configs on GitHub would answer this before P3 implementation.
|
||||
|
||||
**Q2**: HA's `template` trigger supports a `value_template` that can reference `trigger.to_state`, `trigger.from_state`, and `trigger.for`. This requires passing trigger context into the template evaluation scope. Is this context threading straightforward in MiniJinja, or does it require a custom context type?
|
||||
|
||||
**Q3**: The `conversation` trigger in HA uses the Assist pipeline's intent matching to fire automations based on voice commands. HOMECORE-ASSIST (ADR-133) owns the pipeline. Should the `conversation` trigger be implemented in ADR-129 (automation engine dependency on ADR-133) or in ADR-133 (assist pipeline fires automation events that ADR-129 listens to)?
|
||||
|
||||
**Q4**: HA blueprints have a community sharing mechanism (blueprint.exchange). Should HOMECORE support importing blueprints from HA's blueprint exchange directly, or only local blueprints?
|
||||
|
||||
---
|
||||
|
||||
## 9. References
|
||||
|
||||
### HA upstream
|
||||
|
||||
- `homeassistant/components/automation/__init__.py` — `AutomationEntity`, `AutomationConfig`, trigger/condition/action pipeline
|
||||
- `homeassistant/components/script/__init__.py` — `Script`, `ScriptEntity`, run modes, action sequence execution
|
||||
- `homeassistant/helpers/template.py` — `Template` class, `TemplateEnvironment`, all HA-specific Jinja2 globals and filters
|
||||
- `homeassistant/helpers/config_validation.py` — voluptuous schema definitions for all automation/script YAML elements
|
||||
- `homeassistant/components/automation/blueprint.py` — Blueprint input substitution
|
||||
|
||||
### This repo
|
||||
|
||||
- `docs/adr/ADR-127-homecore-state-machine-rust.md` — state machine and event bus that triggers subscribe to
|
||||
- `docs/adr/ADR-133-homecore-assist-ruflo.md` — ruflo agent condition + conversation trigger dependency
|
||||
- `docs/adr/ADR-134-homecore-migration-from-python-ha.md` — migration tool reads `automations.yaml`
|
||||
|
||||
### External
|
||||
|
||||
- [minijinja crates.io](https://crates.io/crates/minijinja) — Jinja2-compatible template engine in Rust
|
||||
- [HA Automation Templating docs](https://www.home-assistant.io/docs/automation/templating/) — HA-specific template globals reference
|
||||
@@ -1,218 +0,0 @@
|
||||
# ADR-130: HOMECORE-API — Wire-compatible REST and WebSocket API
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Proposed |
|
||||
| **Date** | 2026-05-25 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codename** | **HOMECORE-API** |
|
||||
| **Relates to** | [ADR-126](ADR-126-ruview-native-ha-port-master.md) (HOMECORE master), [ADR-127](ADR-127-homecore-state-machine-rust.md) (HOMECORE-CORE), [ADR-055](ADR-055-integrated-sensing-server.md) (sensing-server Axum pattern), [ADR-124](ADR-124-rvagent-mcp-ruvector-npm-integration.md) (SENSE-BRIDGE — bearer auth pattern) |
|
||||
| **Tracking issue** | TBD |
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
Home Assistant's HTTP and WebSocket APIs are the primary interface for every non-frontend client: the iOS companion app, the Android companion app, HACS, Node-RED, the `homeassistant` Python client library, ESPHome native API clients, external automation scripts, and the hundreds of third-party HA dashboard projects.
|
||||
|
||||
The API surface is defined in two Python modules:
|
||||
|
||||
1. **`homeassistant/components/api/__init__.py`** — 24 REST API routes mounted at `/api/`. Key routes: `GET /api/`, `GET /api/states`, `GET /api/states/<entity_id>`, `POST /api/states/<entity_id>`, `GET /api/events`, `POST /api/events/<event_type>`, `GET /api/services`, `POST /api/services/<domain>/<service>`, `GET /api/error_log`, `GET /api/config`, `POST /api/template`, `POST /api/check_config`, `GET /api/history/period/<datetime>` (deprecated — recorder), `POST /api/logbook/` (deprecated — recorder).
|
||||
|
||||
2. **`homeassistant/components/websocket_api/`** — the WebSocket API handler (`connection.py` handles auth handshake; `commands.py` handles 30+ command types). Key commands: `auth`, `subscribe_events`, `unsubscribe_events`, `call_service`, `get_states`, `get_services`, `get_config`, `subscribe_trigger`, `render_template`, `validate_config`, `subscribe_entities` (entity registry updates), `config/entity_registry/list`, and many more.
|
||||
|
||||
### 1.1 Auth model
|
||||
|
||||
HA uses **long-lived access tokens (LLAT)** as the primary auth mechanism for non-UI clients. Tokens are created in the HA user profile UI and stored in `.storage/auth`. The REST API accepts `Authorization: Bearer <token>` or the `api_password` legacy header (deprecated since HA 2022.x). The WebSocket API requires an `auth` message with `access_token` as the first message after connection.
|
||||
|
||||
### 1.2 Why wire-compat matters
|
||||
|
||||
The iOS and Android HA companion apps (>100,000 installs combined) hardcode the HA API paths and WebSocket command schemas. Any implementation that deviates from the exact JSON schemas causes the apps to fail silently — not with a meaningful error, but by returning empty entity lists or missing state updates. Wire-compat is therefore a hard requirement, not a nice-to-have.
|
||||
|
||||
The baseline for compatibility is **HA 2025.1** (the version that introduced SQLite recorder schema version 48). Any HOMECORE instance claiming compliance with this ADR must pass the companion app integration test suite.
|
||||
|
||||
---
|
||||
|
||||
## 2. Decision
|
||||
|
||||
Implement the `homecore-api` crate as an Axum-based server that replicates the HA REST and WebSocket API on port 8123. The implementation is informed by — but does not copy — `homeassistant/components/api/__init__.py` and `homeassistant/components/websocket_api/`.
|
||||
|
||||
The server reuses the Axum + Tokio architecture established in `v2/crates/wifi-densepose-sensing-server/src/main.rs` and its bearer auth pattern (`v2/crates/wifi-densepose-sensing-server/src/bearer_auth.rs`).
|
||||
|
||||
### 2.1 REST API route table
|
||||
|
||||
| Route | Method | HA source line (approx.) | HOMECORE status |
|
||||
|---|---|---|---|
|
||||
| `/api/` | GET | `api/__init__.py:74` | P2 — returns `{ "message": "API running." }` |
|
||||
| `/api/config` | GET | `api/__init__.py:97` | P2 — returns `homecore.config` as JSON |
|
||||
| `/api/states` | GET | `api/__init__.py:116` | P2 — returns `hass.states.all()` as JSON array |
|
||||
| `/api/states/<entity_id>` | GET | `api/__init__.py:130` | P2 |
|
||||
| `/api/states/<entity_id>` | POST | `api/__init__.py:145` | P2 — writes state; fires `state_changed` |
|
||||
| `/api/events` | GET | `api/__init__.py:168` | P3 |
|
||||
| `/api/events/<event_type>` | POST | `api/__init__.py:180` | P3 — fires domain event |
|
||||
| `/api/services` | GET | `api/__init__.py:192` | P2 |
|
||||
| `/api/services/<domain>/<service>` | POST | `api/__init__.py:206` | P2 |
|
||||
| `/api/template` | POST | `api/__init__.py:222` | P3 — WASM MiniJinja evaluator (ADR-129) |
|
||||
| `/api/check_config` | POST | `api/__init__.py:240` | P4 |
|
||||
| `/api/error_log` | GET | `api/__init__.py:252` | P3 |
|
||||
| `/api/history/period/<datetime>` | GET | `api/__init__.py:270` | P4 — recorder query (ADR-132) |
|
||||
| `/api/logbook/` | POST | `api/__init__.py:310` | P4 — recorder query |
|
||||
| `/api/camera_proxy/<entity_id>` | GET | `api/__init__.py:330` | P4 — proxy to camera integration |
|
||||
| `/api/calendar/<entity_id>` | GET | `api/__init__.py:348` | P4 |
|
||||
| `/api/webhook/<webhook_id>` | POST/GET | `api/__init__.py:368` | P3 — fires `webhook.<id>` event |
|
||||
| `/api/intent/handle` | POST | `api/__init__.py:400` | P4 — HOMECORE-ASSIST (ADR-133) |
|
||||
| `/auth/token` | POST | `auth/providers/__init__.py` | P2 — issue LLAT from username/password |
|
||||
| `/auth/authorize` | GET/POST | `auth/providers/__init__.py` | P3 — OAuth2 flow |
|
||||
| `/frontend/` static assets | GET | `frontend/__init__.py` | P1 — serve HA Python frontend static files until ADR-131 ships |
|
||||
|
||||
### 2.2 WebSocket API command table
|
||||
|
||||
| WS command type | HA source | HOMECORE status |
|
||||
|---|---|---|
|
||||
| `auth` (handshake) | `websocket_api/connection.py:55` | P2 |
|
||||
| `subscribe_events` | `websocket_api/commands.py:120` | P2 |
|
||||
| `unsubscribe_events` | `websocket_api/commands.py:145` | P2 |
|
||||
| `call_service` | `websocket_api/commands.py:160` | P2 |
|
||||
| `get_states` | `websocket_api/commands.py:200` | P2 |
|
||||
| `get_services` | `websocket_api/commands.py:218` | P2 |
|
||||
| `get_config` | `websocket_api/commands.py:230` | P2 |
|
||||
| `subscribe_trigger` | `websocket_api/commands.py:250` | P3 |
|
||||
| `render_template` | `websocket_api/commands.py:280` | P3 |
|
||||
| `validate_config` | `websocket_api/commands.py:300` | P3 |
|
||||
| `subscribe_entities` | `websocket_api/commands.py:320` | P3 — entity registry update stream |
|
||||
| `config/entity_registry/list` | `websocket_api/commands.py:370` | P3 |
|
||||
| `config/entity_registry/update` | `websocket_api/commands.py:400` | P3 |
|
||||
| `config/area_registry/list` | `websocket_api/commands.py:450` | P3 |
|
||||
| `config/device_registry/list` | `websocket_api/commands.py:480` | P3 |
|
||||
| `config/config_entries/list` | `websocket_api/commands.py:510` | P3 |
|
||||
| `lovelace/config` (dashboard) | `lovelace/dashboard.py` | P4 — reads from HOMECORE storage |
|
||||
| `media_player/*` | `websocket_api/commands.py:600` | P4 |
|
||||
|
||||
### 2.3 Auth implementation
|
||||
|
||||
HOMECORE-API implements long-lived access tokens as JWTs signed with an Ed25519 key (generated at first startup, stored in `.homecore/auth_key.pem`). Token format:
|
||||
|
||||
```json
|
||||
{
|
||||
"sub": "<user_id>",
|
||||
"iss": "homecore",
|
||||
"iat": <unix_timestamp>,
|
||||
"exp": <unix_timestamp or null for LLAT>,
|
||||
"type": "long_lived_access_token"
|
||||
}
|
||||
```
|
||||
|
||||
The HA companion app sends `Authorization: Bearer <token>` on every REST request. The WebSocket auth handshake sends `{ "type": "auth", "access_token": "<token>" }`. Both paths validate the JWT against the stored Ed25519 key.
|
||||
|
||||
Legacy `api_password` is deliberately not supported (removed in HA 2022.x and never properly secure).
|
||||
|
||||
---
|
||||
|
||||
## 3. HA-side reference table
|
||||
|
||||
| HA module / file | What it does | HOMECORE preserves | Changes | Drops |
|
||||
|---|---|---|---|---|
|
||||
| `components/api/__init__.py` | 24 REST routes + JSON response schemas | All response schemas byte-compatible with HA 2025.1 | Axum router instead of HA's custom HTTP component; `serde_json` instead of Python `json` | Python HTTP request context; HA's built-in CORS middleware (replicated in Axum) |
|
||||
| `components/websocket_api/connection.py` | WS auth handshake; per-connection state; message dispatch | Auth handshake flow: `auth_required` → `auth` message → `auth_ok` or `auth_invalid` | Axum `WebSocketUpgrade` extractor; per-connection `tokio::task` | Python asyncio message handling |
|
||||
| `components/websocket_api/commands.py` | 30+ WS command handlers | All command type strings; response envelope `{ id, type, result }` or error `{ id, type, error: { code, message } }` | Rust match dispatch; Tokio broadcast receiver per subscription | Python class-based command handler registration |
|
||||
| `auth/providers/__init__.py` | Auth providers; LLAT issuance; OAuth2 flow | LLAT issuance; token validation | Ed25519 JWT instead of HA's custom token serializer; same token `type` field values | Nabu Casa cloud auth; multi-provider auth chain |
|
||||
| `components/http/__init__.py` | Aiohttp-based HTTP server setup; CORS; trusted proxies | CORS headers; `X-Forwarded-For` trusted proxy handling | Axum Tower middleware | Aiohttp; Python SSL context |
|
||||
|
||||
---
|
||||
|
||||
## 4. Public API parity table
|
||||
|
||||
| HA API surface | HOMECORE exact equivalent |
|
||||
|---|---|
|
||||
| `GET /api/states` → `[{entity_id, state, attributes, last_changed, last_updated, context}]` | Identical JSON schema; `last_changed` / `last_updated` in ISO 8601 |
|
||||
| `GET /api/services` → `{domain: {service: {description, fields}}}` | Identical schema; service descriptions read from plugin manifests |
|
||||
| WS `subscribe_events` → `{type: "event", event: {event_type, data, origin, time_fired, context}}` | Identical envelope; `time_fired` in ISO 8601 |
|
||||
| WS `call_service` → `{type: "result", success: true, result: {context}}` | Identical; `context.id` is a UUID |
|
||||
| WS `get_states` → `{type: "result", result: [{entity_id, state, attributes, ...}]}` | Identical schema |
|
||||
| REST `POST /api/services/<domain>/<service>` → 200 with called service list | Identical; same `target` field support |
|
||||
| REST `POST /api/template` → 200 with evaluated string | Identical; same error response `{message: "..."}` on template error |
|
||||
| Auth WS flow: `auth_required` → `auth` → `auth_ok` | Identical message type strings; same `ha_version` field in `auth_required` |
|
||||
| REST `Authorization: Bearer <token>` | Identical header name; JWT instead of HA's opaque token format (transparent to clients) |
|
||||
|
||||
---
|
||||
|
||||
## 5. Phased implementation plan
|
||||
|
||||
### P1 — Axum skeleton + static frontend (1 week)
|
||||
|
||||
- [ ] Create `v2/crates/homecore-api/` workspace member.
|
||||
- [ ] Axum router on port 8123; Tower CORS middleware (allow `http://homeassistant.local:8123`).
|
||||
- [ ] Static file handler: serve HA's Python frontend build from a configurable path (default `./frontend/build/`). This allows using the Python HA frontend as-is until ADR-131 ships.
|
||||
- [ ] `GET /api/` returns `{ "message": "API running." }`.
|
||||
- [ ] CI: `cargo check -p homecore-api`; HTTP smoke test.
|
||||
|
||||
### P2 — Core REST + WebSocket auth + states (3 weeks)
|
||||
|
||||
- [ ] Axum WebSocket upgrade at `/api/websocket`.
|
||||
- [ ] Auth: Ed25519 JWT issuance at `/auth/token`; validation middleware.
|
||||
- [ ] WS auth handshake: `auth_required` → `auth` → `auth_ok` / `auth_invalid`.
|
||||
- [ ] WS commands: `get_states`, `subscribe_events`, `unsubscribe_events`, `call_service`, `get_services`, `get_config`.
|
||||
- [ ] REST: `/api/states`, `/api/states/<entity_id>` (GET + POST), `/api/services`, `/api/services/<domain>/<service>`, `/api/config`.
|
||||
- [ ] Integration test: HA iOS companion app authenticates and displays entity list against HOMECORE.
|
||||
|
||||
### P3 — Remaining WS commands + entity registry API (3 weeks)
|
||||
|
||||
- [ ] WS: `subscribe_trigger`, `render_template`, `validate_config`, `subscribe_entities`, entity/area/device registry commands.
|
||||
- [ ] REST: `/api/template`, `/api/webhook/<id>`, `/api/error_log`, `/api/events`, `/api/events/<type>`.
|
||||
- [ ] `/auth/authorize` OAuth2 flow for UI login.
|
||||
- [ ] HACS smoke test: HACS connects, lists integrations.
|
||||
|
||||
### P4 — Recorder + history API (2 weeks)
|
||||
|
||||
- [ ] `/api/history/period/<datetime>` backed by ADR-132 recorder SQLite.
|
||||
- [ ] `/api/logbook/` backed by ADR-132 recorder.
|
||||
- [ ] `/api/camera_proxy/`, `/api/calendar/`, `/api/intent/handle`.
|
||||
- [ ] Companion app full feature test: automations, notifications, history charts.
|
||||
|
||||
---
|
||||
|
||||
## 6. Risks
|
||||
|
||||
| Risk | Likelihood | Severity | Mitigation | Cross-ADR impact |
|
||||
|---|---|---|---|---|
|
||||
| **JSON schema drift** — HA updates a response field name between 2025.1 and HOMECORE release | Medium | High | Maintain a JSON-schema test fixture set generated from HA 2025.1; run against HOMECORE in CI | ADR-134: migration tool depends on the same JSON schemas; must stay in sync |
|
||||
| **WS subscription fan-out** — 50 concurrent HA companion app sessions each subscribed to `subscribe_events` ALL; every state change creates 50 serialization tasks | Medium | Medium | Broadcast serialized JSON once; clone the `Bytes` arc to each subscriber sender; do not re-serialize per subscriber | ADR-127: broadcast channel capacity must handle subscriber fan-out without lagging |
|
||||
| **Auth token format** — HA companion apps may validate the token format (JWT vs opaque). HOMECORE uses JWT; HA uses a custom opaque token. Tokens are never decoded client-side in standard clients, but non-standard clients may inspect them | Low | Low | JWTs are base64url-encoded JSON; any client checking `token.startsWith("ey")` will see a JWT. HA's own tokens are also base64url but not JWTs. Document the difference; test with the iOS app specifically | None |
|
||||
| **Port 8123 conflict** — HOMECORE runs on the same port as HA; side-by-side mode (ADR-134) requires HOMECORE on a different port until cutover | High | Medium | ADR-134 side-by-side mode runs HOMECORE on port 8124; companion app can be pointed at port 8124 for testing | ADR-134 owns the cutover mechanism |
|
||||
|
||||
---
|
||||
|
||||
## 7. Open questions
|
||||
|
||||
**Q1**: The HA WebSocket API uses incremental integer IDs (`id: 1, 2, 3, ...`) for command/response correlation within a session. HOMECORE uses the same scheme. What is the maximum `id` value the companion app supports before wrapping? If the app doesn't wrap and HOMECORE processes > 2^31 commands per session, this becomes an overflow issue in extremely long-lived sessions.
|
||||
|
||||
**Q2**: The `subscribe_entities` WS command (added in HA 2021.x) sends entity registry change events in addition to state change events. The iOS companion app uses this to maintain a local entity list without polling. Is the full `subscribe_entities` delta schema (including `action: "create" | "update" | "remove"`) fully documented, or must it be reverse-engineered from the companion app source?
|
||||
|
||||
**Q3**: HA's `/auth/token` endpoint accepts `grant_type=password` (username/password) and `grant_type=refresh_token`. HOMECORE's initial implementation supports password grant only. Is refresh token support required for the companion app (it caches tokens between sessions) or does the companion app re-authenticate on each launch?
|
||||
|
||||
**Q4**: CORS policy: HA's default CORS allows `http://localhost:*` and `http://homeassistant.local:*`. The HOMECORE-UI frontend (ADR-131) will be served from a different origin in development. What CORS policy should HOMECORE-API use in production vs development mode?
|
||||
|
||||
---
|
||||
|
||||
## 8. References
|
||||
|
||||
### HA upstream
|
||||
|
||||
- `homeassistant/components/api/__init__.py` — 24 REST routes with exact URL paths, methods, and JSON response schemas
|
||||
- `homeassistant/components/websocket_api/connection.py` — auth handshake protocol; per-connection state management
|
||||
- `homeassistant/components/websocket_api/commands.py` — 30+ command type handlers with exact type strings and result schemas
|
||||
- `homeassistant/components/http/__init__.py` — CORS setup; trusted proxy handling; aiohttp-based server
|
||||
- `homeassistant/auth/providers/__init__.py` — token issuance; `AuthManager`; LLAT format
|
||||
- `homeassistant/auth/__init__.py` — `AuthManager.async_create_long_lived_access_token`
|
||||
|
||||
### This repo
|
||||
|
||||
- `v2/crates/wifi-densepose-sensing-server/src/main.rs` — Axum server architecture (REST + WebSocket); pattern for this ADR
|
||||
- `v2/crates/wifi-densepose-sensing-server/src/bearer_auth.rs` — Bearer auth middleware pattern
|
||||
- `docs/adr/ADR-127-homecore-state-machine-rust.md` — state machine that REST/WS routes read from
|
||||
- `docs/adr/ADR-126-ruview-native-ha-port-master.md` — §6 compatibility contract with companion apps
|
||||
|
||||
### External
|
||||
|
||||
- [HA WebSocket API Developer Docs](https://developers.home-assistant.io/docs/api/websocket/) — authoritative command type catalog
|
||||
- [HA REST API](https://developers.home-assistant.io/docs/api/rest/) — REST endpoint schemas
|
||||
@@ -1,130 +0,0 @@
|
||||
# ADR-132: HOMECORE-RECORDER — State History + Semantic Search
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Accepted |
|
||||
| **Date** | 2026-05-25 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codename** | **HOMECORE-RECORDER** |
|
||||
| **Crate** | `v2/crates/homecore-recorder` |
|
||||
| **Relates to** | [ADR-126](ADR-126-ruview-native-ha-port-master.md) (HOMECORE master — series map row ADR-132), [ADR-127](ADR-127-homecore-state-machine-rust.md) (HOMECORE-CORE state machine), [ADR-124](ADR-124-rvagent-mcp-ruvector-npm-integration.md) (ruvector/SENSE-BRIDGE), [ADR-130](ADR-130-homecore-rest-websocket-api.md) (HOMECORE-API query surface, downstream) |
|
||||
| **Tracking issue** | [#800](https://github.com/ruvnet/RuView/pull/800) (HOMECORE intake) |
|
||||
|
||||
> **Documented retroactively (2026-06-12).** The `homecore-recorder` crate shipped under
|
||||
> the ADR-126 series map (which planned an "ADR-132 HOMECORE-RECORDER") but the standalone
|
||||
> ADR file was never written; the crate's `Cargo.toml`, `README.md`, `lib.rs`, `schema.rs`,
|
||||
> and `semantic.rs` all cite "ADR-132". This ADR reverse-documents the decision that the
|
||||
> shipped, tested code already embodies (ADR-164 Gap G3 / Coverage-Gaps Lens §A). It does
|
||||
> **not** introduce new design; it records what is built. Date reflects the crate's intake
|
||||
> era (first commit `e96ebaea8`, 2026-05-25); real-impl pass landed in `7c8071145`
|
||||
> (2026-06-11).
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
ADR-126 (the HOMECORE master) decided to reimplement Home Assistant (HA) natively in Rust.
|
||||
HA persists every state change to a SQLite *recorder* database; downstream features
|
||||
(history graphs, the logbook, long-term statistics, automation conditions that reference
|
||||
past state) all read that store. HOMECORE therefore needs a durable state-history backbone.
|
||||
|
||||
Two forces shape the decision:
|
||||
|
||||
1. **Migration / coexistence.** Users adopting HOMECORE will have an existing HA
|
||||
`recorder` database. Reusing HA's on-disk schema (rather than inventing a new one) lets
|
||||
HOMECORE read an existing HA `home-assistant_v2.db` directly and lets HA-aware tooling
|
||||
read HOMECORE's store. This is the same trust boundary that `homecore-migrate`
|
||||
(ADR-165) handles for `.storage/*.json`.
|
||||
2. **Semantic queries.** HA history is queried with SQL `BETWEEN`/`WHERE` clauses. The
|
||||
HOMECORE platform already carries ruvector (ADR-124) for vector search, so the recorder
|
||||
can additionally embed state changes and answer natural-language queries
|
||||
("which kitchen devices were warm at 3 PM?") via k-NN — a capability HA does not have.
|
||||
|
||||
The recorder is the **durable-state surface**: if it is wrong, history, logbook, and
|
||||
historical-condition automations are all wrong. ADR-164 flagged it as a CRITICAL coverage
|
||||
gap precisely because such a load-bearing crate had no governing ADR.
|
||||
|
||||
## 2. Decision
|
||||
|
||||
Ship `homecore-recorder` as a SQLite state-history recorder with an HA-compatible schema
|
||||
and an optional ruvector-backed semantic index, in three phases. P1 and P2 are built and
|
||||
tested; P3 is planned.
|
||||
|
||||
### 2.1 Storage — SQLite with the HA recorder schema (P1, shipped)
|
||||
|
||||
- Persist via `sqlx` with the SQLite backend only (no Postgres, no TLS feature set).
|
||||
- Mirror HA recorder **schema v48** so the store is bidirectionally readable
|
||||
(`src/schema.rs`):
|
||||
- `state_attributes` — shared attribute JSON blobs, deduped by an FNV-1a 64-bit hash
|
||||
stored as a signed `i64` (matches HA's dedup key);
|
||||
- `states` — one row per state write (`entity_id`, `state`, `attributes_id` FK,
|
||||
`last_changed_ts`/`last_updated_ts` as REAL Unix seconds, `context_id` UUID);
|
||||
- `events` — domain events (`event_type`, `event_data` JSON, `time_fired_ts`);
|
||||
- `recorder_runs` — boot/shutdown bookends for history-gap detection.
|
||||
- All DDL uses `CREATE TABLE IF NOT EXISTS`, so schema application is idempotent and safe
|
||||
on every startup.
|
||||
- Default persistence path `.homecore/home.db` (configurable).
|
||||
|
||||
### 2.2 Capture — listener on the HOMECORE event bus (P1, shipped)
|
||||
|
||||
- `RecorderListener` subscribes to the HOMECORE event bus (ADR-127) and captures
|
||||
`StateChanged` events, writing snapshots through `Recorder` (`src/listener.rs`,
|
||||
`src/db.rs`).
|
||||
- A `DedupEngine` (`src/dedup.rs`) skips redundant writes when the state hash is unchanged,
|
||||
matching HA's stateful-listener behaviour.
|
||||
|
||||
### 2.3 Semantic search — ruvector HNSW (P2, shipped, feature-gated)
|
||||
|
||||
- Behind the `ruvector` Cargo feature, the `Recorder` additionally calls a `SemanticIndex`
|
||||
implementation (`src/semantic.rs`) that embeds state attributes and stores vectors in a
|
||||
`ruvector-core` HNSW index for k-NN search.
|
||||
- P2 embeddings are **hash-based** (sha2) — a deliberate, honest placeholder. They give a
|
||||
working HNSW surface without claiming sentence-level semantic quality.
|
||||
- When the feature is off, `NullSemanticIndex` satisfies the `SemanticIndex` trait bound
|
||||
with no allocation, so the structural recorder ships independently of ruvector.
|
||||
|
||||
### 2.4 Real sentence embeddings (P3, planned — not yet built)
|
||||
|
||||
- Replace the hash embeddings with ruvector-attention sentence embeddings (dim → 384). Not
|
||||
implemented; tracked as a follow-up. The README and `Cargo.toml` label this P3 explicitly.
|
||||
|
||||
### 2.5 Test evidence (as shipped)
|
||||
|
||||
- P1: 14 tests (`cargo test -p homecore-recorder --no-default-features`).
|
||||
- P2: 20 tests (`cargo test -p homecore-recorder --features ruvector`).
|
||||
|
||||
## 3. Consequences
|
||||
|
||||
**Positive.**
|
||||
|
||||
- HA-schema compatibility makes migration (ADR-165) and coexistence cheap: HOMECORE can
|
||||
read an existing HA `recorder.db`, and any SQLite tool can read HOMECORE's history.
|
||||
- The semantic index is **additive** and feature-gated: the durable structural recorder has
|
||||
no hard dependency on ruvector, so the storage backbone ships first.
|
||||
- Standard SQLite means no proprietary export format; history is directly queryable.
|
||||
|
||||
**Negative / honest limits.**
|
||||
|
||||
- P2 semantic search uses **hash embeddings**, not real sentence embeddings — query quality
|
||||
is limited until P3. This is disclosed in the crate docs and here; it must not be cited as
|
||||
semantic-quality-validated.
|
||||
- No per-crate benchmarks exist yet; the latency figures in the README
|
||||
(state-write p50 < 2 ms, semantic search < 10 ms on 1 M records) are design targets /
|
||||
estimates, **needs verification** with a criterion baseline.
|
||||
- Pinning to HA schema v48 couples HOMECORE to a specific HA recorder schema generation;
|
||||
future HA schema bumps require an explicit migration step.
|
||||
|
||||
**Neutral.**
|
||||
|
||||
- This ADR governs the recorder crate only. The query/REST surface over recorder data is
|
||||
HOMECORE-API (ADR-130, P3); automation conditions on historical state are
|
||||
HOMECORE-automation (ADR-129, P3).
|
||||
|
||||
## 4. Links
|
||||
|
||||
- Crate: `v2/crates/homecore-recorder/` — `Cargo.toml`, `README.md`, `src/lib.rs`,
|
||||
`src/db.rs`, `src/schema.rs`, `src/dedup.rs`, `src/listener.rs`, `src/semantic.rs`.
|
||||
- [ADR-126](ADR-126-ruview-native-ha-port-master.md) — HOMECORE master (series map: ADR-132 = HOMECORE-RECORDER).
|
||||
- [ADR-165](ADR-165-homecore-migrate-from-home-assistant.md) — HOMECORE-MIGRATE (reads HA `.storage`; P2 exports a side-by-side recorder DB).
|
||||
- [ADR-164](ADR-164-adr-corpus-gap-analysis.md) — gap analysis that surfaced this missing ADR (Gap G3).
|
||||
- [Home Assistant Recorder integration](https://www.home-assistant.io/integrations/recorder/).
|
||||
@@ -1,176 +0,0 @@
|
||||
# ADR-133: HOMECORE-ASSIST — Voice/Intent Pipeline + Ruflo Agent Bridge
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Proposed |
|
||||
| **Date** | 2026-05-25 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codename** | **HOMECORE-ASSIST** |
|
||||
| **Relates to** | [ADR-126](ADR-126-ruview-native-ha-port-master.md) (HOMECORE master), [ADR-127](ADR-127-homecore-state-machine-rust.md) (HOMECORE-CORE), [ADR-130](ADR-130-homecore-rest-websocket-api.md) (HOMECORE-API), [ADR-124](ADR-124-rvagent-mcp-ruvector-npm-integration.md) (SENSE-BRIDGE) |
|
||||
| **Tracking issue** | TBD |
|
||||
| **Crate** | `v2/crates/homecore-assist` |
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
Home Assistant's Assist pipeline (`homeassistant/components/assist_pipeline/`) provides
|
||||
voice-to-intent-to-response processing. It chains:
|
||||
|
||||
1. **STT** (speech-to-text) — Whisper, cloud, or satellite
|
||||
2. **NLU** (natural language understanding) — intent recognition via regex/slots
|
||||
3. **Intent handler** — maps intent to a HA service call
|
||||
4. **TTS** (text-to-speech) — synthesises the response for the caller
|
||||
|
||||
HA's intent model (`homeassistant/helpers/intent.py`) is keyword/regex based. Every
|
||||
intent is a named template with slot definitions and a handler that dispatches to HA
|
||||
services. The built-in intents (`homeassistant/components/conversation/default_agent.py`)
|
||||
cover `HassTurnOn`, `HassTurnOff`, `HassLightSet`, `HassNevermind`, `HassCancelAll`,
|
||||
`HassGetState`, `HassGetWeather`, and many others.
|
||||
|
||||
HOMECORE needs a wire-compatible Assist pipeline so that:
|
||||
- The HA iOS/Android companion app's "Assist" button works against HOMECORE.
|
||||
- The HOMECORE-API WebSocket `assist` command (ADR-130 §2.2) has a handler.
|
||||
- The ruflo agent toolchain (ADR-124) can provide LLM-grade intent disambiguation as a
|
||||
drop-in upgrade path for the P1 regex recognizer.
|
||||
|
||||
### 1.1 Ruflo integration approach
|
||||
|
||||
Ruflo's agent runner exposes an MCP-over-stdio interface (`node ruflo-agent.js`).
|
||||
HOMECORE-ASSIST manages a long-lived subprocess (Q3 Windows concern below), sends
|
||||
utterance JSON, and receives intent JSON back. In P1 we ship only the trait surface
|
||||
and a `NoopRunner` stub; the real subprocess management is P2.
|
||||
|
||||
### 1.2 Ruvector semantic intent matching (P2)
|
||||
|
||||
`ruvector-core` provides embedding + cosine-similarity primitives. P2 will add a
|
||||
`SemanticIntentRecognizer` that embeds the utterance and compares it to a HNSW index
|
||||
of intent exemplars, falling back to the P1 regex recognizer when similarity < 0.75.
|
||||
This is the mechanism that allows "dim the lights" to match `HassLightSet` without an
|
||||
explicit regex entry.
|
||||
|
||||
---
|
||||
|
||||
## 2. Design
|
||||
|
||||
### 2.1 Module layout (`v2/crates/homecore-assist/`)
|
||||
|
||||
| Module | Contents |
|
||||
|--------|----------|
|
||||
| `intent` | `IntentName` newtype, `Intent` (name + slots), `IntentResponse` (speech + optional card + optional data) |
|
||||
| `recognizer` | `IntentRecognizer` trait; `RegexIntentRecognizer` (P1); `SemanticIntentRecognizer` stub (P2) |
|
||||
| `handler` | `IntentHandler` trait; built-in handlers: `HassTurnOn`, `HassTurnOff`, `HassLightSet`, `HassNevermind`, `HassCancelAll` |
|
||||
| `runner` | `RufloRunner` trait + `RufloRunnerOpts`; `NoopRunner` (P1 stub); real subprocess runner (P2) |
|
||||
| `pipeline` | `AssistPipeline`: wires recognizer → handler → response; exposes `async fn process(utterance, language) -> IntentResponse` |
|
||||
|
||||
### 2.2 Built-in intent handlers (P1)
|
||||
|
||||
| Handler | HA service call | Slot |
|
||||
|---------|-----------------|------|
|
||||
| `HassTurnOn` | `homeassistant.turn_on` / `light.turn_on` / `switch.turn_on` | `entity_id` |
|
||||
| `HassTurnOff` | `homeassistant.turn_off` / `light.turn_off` / `switch.turn_off` | `entity_id` |
|
||||
| `HassLightSet` | `light.turn_on` | `entity_id`, `brightness` (0–255), `color_name` |
|
||||
| `HassNevermind` | — (no-op, returns acknowledgement) | — |
|
||||
| `HassCancelAll` | — (fires `homeassistant_stop_all_scripts` domain event) | — |
|
||||
|
||||
### 2.3 IntentResponse
|
||||
|
||||
```rust
|
||||
pub struct IntentResponse {
|
||||
pub speech: String,
|
||||
pub card: Option<Card>,
|
||||
pub data: Option<serde_json::Value>,
|
||||
}
|
||||
|
||||
pub struct Card {
|
||||
pub title: String,
|
||||
pub content: String,
|
||||
}
|
||||
```
|
||||
|
||||
### 2.4 RufloRunner trait
|
||||
|
||||
```rust
|
||||
#[async_trait]
|
||||
pub trait RufloRunner: Send + Sync + 'static {
|
||||
async fn spawn(&mut self, opts: RufloRunnerOpts) -> Result<(), AssistError>;
|
||||
async fn send_request(&self, payload: serde_json::Value) -> Result<RufloResponse, AssistError>;
|
||||
async fn shutdown(&mut self) -> Result<(), AssistError>;
|
||||
}
|
||||
```
|
||||
|
||||
`RufloResponse` is `{ intent: Option<Intent>, speech: Option<String> }`.
|
||||
|
||||
### 2.5 Pipeline
|
||||
|
||||
```rust
|
||||
pub struct AssistPipeline<R, H> {
|
||||
recognizer: R,
|
||||
handler: H,
|
||||
runner: Option<Box<dyn RufloRunner>>,
|
||||
}
|
||||
|
||||
impl<R: IntentRecognizer, H: IntentHandler> AssistPipeline<R, H> {
|
||||
pub async fn process(&self, utterance: &str, language: &str, hc: &HomeCore)
|
||||
-> Result<IntentResponse, AssistError>;
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. Questions & Answers
|
||||
|
||||
### Q1 — Why not reuse HA's existing `homeassistant.helpers.intent` via PyO3?
|
||||
|
||||
PyO3 bridges add a GIL lock on every cross-language call; the Assist pipeline processes
|
||||
hundreds of short utterances per day from voice satellites. A native Rust recognizer is
|
||||
simpler and faster. Python HA can still connect as an external integration via MQTT or
|
||||
the HOMECORE WebSocket API.
|
||||
|
||||
### Q2 — How does `RegexIntentRecognizer` handle ambiguity?
|
||||
|
||||
Patterns are tried in registration order; the first match wins. Slot extraction uses
|
||||
named capture groups. A future P2 upgrade can run all patterns, score them by slot
|
||||
completeness, and return the highest-scoring match.
|
||||
|
||||
### Q3 — Windows subprocess teardown (ruflo runner subprocess on Windows)
|
||||
|
||||
`tokio::process::Child` on Windows does not automatically kill the child process when
|
||||
the `Child` struct is dropped — `SIGTERM` is not a Windows concept, and `TerminateProcess`
|
||||
is not called automatically. Options for P2:
|
||||
|
||||
1. Call `child.start_kill()` in a `Drop` impl (requires a `Runtime` handle — tricky in sync Drop).
|
||||
2. Wrap `Child` in an `Arc<Mutex<Option<Child>>>` and call `kill()` in an `async fn shutdown()`.
|
||||
3. Use a Windows job object to bind the subprocess lifetime to the parent process.
|
||||
|
||||
**P2 decision**: implement option 2 (explicit `async shutdown()`) + register a `tokio::signal`
|
||||
handler for `Ctrl+C` / `SIGINT` that calls `shutdown()` before exit. Document the Windows caveat
|
||||
in the crate README and in `runner.rs`. Job object approach (option 3) is deferred to P3 only
|
||||
if option 2 proves insufficient in fleet testing.
|
||||
|
||||
### Q4 — Why is `SemanticIntentRecognizer` a P2 stub?
|
||||
|
||||
The ruvector HNSW index requires the vector store to be populated at startup with intent
|
||||
exemplars. That startup path requires deciding on a serialization format (HNSW index files
|
||||
vs. an in-memory array at compile time), which intersects with ADR-084 (RabitQ) and ADR-067
|
||||
(ruvector v2.0.5). P2 will define the exemplar format and populate the index.
|
||||
|
||||
---
|
||||
|
||||
## 4. Consequences
|
||||
|
||||
- **Positive**: HOMECORE-API `assist` WebSocket command gets a functional backend.
|
||||
- **Positive**: Ruflo LLM pipelines can upgrade intent matching by swapping the `RufloRunner` impl.
|
||||
- **Positive**: P1 ships with zero new heavy dependencies (no subprocess spawning, no ML runtime).
|
||||
- **Negative**: Regex matching has limited coverage; long-tail utterances will return "I'm not sure".
|
||||
- **Deferral**: ruvector semantic recognizer and real subprocess runner both land in P2.
|
||||
|
||||
---
|
||||
|
||||
## 5. Implementation phases
|
||||
|
||||
| Phase | Scope |
|
||||
|-------|-------|
|
||||
| **P1** (this ADR) | `intent`, `recognizer` (regex), `handler` (5 built-ins), `runner` (trait + noop), `pipeline` (end-to-end wiring), 10–15 tests |
|
||||
| **P2** | Real `tokio::process::Child` runner with Windows-safe teardown; `SemanticIntentRecognizer` with ruvector HNSW |
|
||||
| **P3** | STT/TTS bridge, satellite protocol, cloud fallback |
|
||||
@@ -1,545 +0,0 @@
|
||||
# ADR-134: First-Class Channel Impulse Response (CIR) Support
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Proposed |
|
||||
| **Date** | 2026-05-28 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codebase target** | `wifi-densepose-signal` (new module `ruvsense/cir.rs`) |
|
||||
| **Relates to** | ADR-014 (SOTA Signal Processing), ADR-017 (RuVector Signal+MAT), ADR-029 (RuvSense Multistatic), ADR-030 (Persistent Field Model), ADR-042 (Coherent Human Channel Imaging), ADR-110 (ESP32-C6 Firmware Extension) |
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
### 1.1 The Gap
|
||||
|
||||
Searching for `CIR`, `channel_impulse`, and `ifft` across the entire Rust workspace (`v2/crates/**`) and Python source (`archive/v1/src/**`) finds zero production code that computes a per-link Channel Impulse Response from CSI. The only `IFFT` call in production is in `wifi-densepose-mat/src/ml/vital_signs_classifier.rs:386`, which applies a bandpass `fft → freq_mask → ifft` to a 1-D vital-sign time series — unrelated to channel sounding.
|
||||
|
||||
This is a concrete absence in a codebase that already documents CIR extensively. Four research documents propose CIR as the next major signal-processing tier:
|
||||
|
||||
- `docs/research/sota-surveys/ruview-multistatic-fidelity-sota-2026.md` — bandwidth → multipath separability table; explicit `Δτ = 1/BW` formula; states "at 20 MHz the entire room collapses into a single CIR cluster."
|
||||
- `docs/research/architecture/ruvsense-multistatic-fidelity-architecture.md` — proposes `ruvector-solver::NeumannSolver` for sparse CIR recovery (Section 2.1); uses `link_gates[i].is_coherent(cir)` in pseudocode (line 583); shows CIR as Stage 2 in the pipeline diagram (Section 4.1).
|
||||
- `docs/research/rf-topological-sensing/02-csi-edge-weight-computation.md` — gives `h_ij(τ,t) = IFFT{H_ij(f_k,t)}`, lists RMS delay spread, tap count, and dominant-tap ratio as edge-weight features, and describes ESPRIT for multipath decomposition.
|
||||
- ADR-042 — calls for complex-valued CIR in the coherent diffraction tomography path.
|
||||
|
||||
Three relevant ADRs are Proposed but unimplemented: ADR-029 (RuvSense multistatic, where `reconstruct_cir()` is referenced in pseudocode but never written), ADR-030 (persistent field model, where CIR baseline subtraction is central), ADR-042 (CHCI, where coherent phase is the primary input).
|
||||
|
||||
### 1.2 Hardware Tiers in Scope
|
||||
|
||||
| Tier | Device | Bandwidth | Usable subcarriers | Native CIR resolution | Min path separation | Ranging |
|
||||
|------|--------|-----------|--------------------|-----------------------|---------------------|---------|
|
||||
| A-HE | ESP32-C6, HE-LTF (802.11ax HE-SU/MU/TB) | 20 MHz | ~242 | 50 ns | 15 m | No |
|
||||
| A | ESP32-S3, HT20 | 20 MHz | 56 | 50 ns | 15 m | No |
|
||||
| B | ESP32-S3, HT40 | 40 MHz | 114 | 25 ns | 7.5 m | Yes |
|
||||
| C | Nexmon BCM43455c0 (Pi 5/4/3B+) via rvCSI | 80 MHz | ≥256 | 12.5 ns | 3.75 m | Yes |
|
||||
|
||||
Sub-Nyquist sparse recovery (see Section 2) can push native resolution by approximately 3× for sufficiently sparse channels. The ADR-029 research document explicitly targets HT40 (Tier B) as the primary deployment mode for RuvSense.
|
||||
|
||||
**Preferred deployment ordering:** Tier A-HE (ESP32-C6 as STA against an 11ax AP) is the preferred Tier A target — 4.7× more active subcarriers than S3 HT20 at identical bandwidth yields a statistically stronger ISTA solve and higher `dominant_tap_ratio` stability under noise, without any additional hardware cost. Tier A (S3 HT20) is the fallback when no 11ax AP is present. Tier B (S3 HT40) is selected when sub-room ranging is required. Tier C (Nexmon Pi install) is used when maximum resolution is needed and a dedicated Pi sensing node is deployed.
|
||||
|
||||
Tier A-HE and Tier A share identical native CIR resolution (50 ns / 15 m path separation) and are both non-ranging. Tier A-HE's advantage is **statistical, not numerical**: because Φ is a normalised DFT submatrix with G = 3K, the condition number κ(Φ) ≈ 1 identically across all tiers (σ² ≈ 3 uniformly — see §2.3 for the derivation). The real gain is measurement SNR: 4.7× more independent frequency observations average down noise by √(242/52) ≈ **2.16×**, producing fewer ghost taps and tighter dominant-tap peaks under realistic ESP32 noise levels.
|
||||
|
||||
### 1.3 Why CIR Now
|
||||
|
||||
The multistatic coherence gate in `ruvsense/multistatic.rs` currently operates on frequency-domain amplitude and phase vectors. The pseudocode in the architecture document calls `link_gates[i].is_coherent(cir)` — passing a CIR, not a raw CSI frame. Without CIR, the coherence gate cannot distinguish a direct-path tap fade from a reflected-path arrival. Without CIR, `ruvsense/tomography.rs` cannot isolate the direct-path component for ranging, and `wifi-densepose-mat/src/localization/triangulation.rs` cannot perform time-of-arrival triangulation. This ADR closes that gap with a single, well-bounded implementation decision.
|
||||
|
||||
---
|
||||
|
||||
## 2. Decision
|
||||
|
||||
### 2.1 Chosen Algorithm: ISTA with a DFT Dictionary (L1-Regularized Sparse CIR Recovery)
|
||||
|
||||
The primary CIR estimator is **ISTA** (Iterative Shrinkage-Thresholding Algorithm) with an L1 penalty and a delay-domain DFT dictionary, implemented by wrapping the existing `ruvector-solver::NeumannSolver`. This is not zero-padded IFFT. It is compressed sensing recovery that super-resolves the delay domain beyond the Nyquist limit.
|
||||
|
||||
The problem: given the measured frequency-domain CSI vector `H ∈ ℂ^K` (K = 56 or 114 or 256 subcarriers), find the sparse delay-domain representation `x ∈ ℂ^G` (G > K, a finer delay grid) such that:
|
||||
|
||||
```
|
||||
minimise ‖H - Φx‖₂² + λ‖x‖₁
|
||||
```
|
||||
|
||||
where `Φ ∈ ℂ^{K×G}` is a sub-DFT dictionary matrix with columns `φ_g = [1, e^{-j2πΔf·τ_g}, …, e^{-j2π(K-1)Δf·τ_g}]^T`, and `τ_g` are the delay-grid points spaced at `1/(G·Δf)`. For ESP32-S3 HT20 with K=56, Δf=312.5 kHz, and G=168 (3× oversampling), the effective delay resolution improves from 50 ns to 17 ns (path separation ~5 m), without any additional hardware.
|
||||
|
||||
ISTA is already the algorithmic pattern used in `ruvsense/tomography.rs` for voxel-space reconstruction. The `ruvector_solver::NeumannSolver` is already wired into the workspace and used in `fresnel.rs:280` and `train/subcarrier.rs:225`. There is no new dependency.
|
||||
|
||||
### 2.2 Why Not the Alternatives
|
||||
|
||||
The table below is the decision record, not a menu of supported options.
|
||||
|
||||
| Algorithm | Verdict | Key reason rejected |
|
||||
|-----------|---------|---------------------|
|
||||
| **Zero-padded IFFT** | Rejected | Sidelobe leakage of -13 dB contaminates adjacent taps; no super-resolution; unacceptable for ranging in rooms where taps are 5-15 m apart. CIRSense (arXiv:2510.11374) independently confirms this by showing standard IFFT requires ≥160 MHz for reliable tap separation in indoor rooms — our ESP32 hardware cannot provide that bandwidth. |
|
||||
| **ISTA / L1 (this ADR)** | **Chosen** | Directly reuses `NeumannSolver`; matches pattern in `tomography.rs`; well-understood convergence in 20-50 iterations at K=56; λ is the single tunable hyperparameter; super-resolves by 3× over Nyquist; no eigendecomposition cost. |
|
||||
| **OMP / CoSaMP** | Rejected | Greedy order matters when taps are correlated (specular + body reflection within one Nyquist bin). OMP commits to a tap permanently on each iteration; early wrong choices degrade the remaining solution irreversibly. ISTA's continuous shrinkage avoids this. ISTA and OMP yield similar results at high SNR; at low SNR (NLOS links, distant nodes) ISTA is measurably better per Chronos (NSDI 2016) and the pulse-shape paper (arXiv:2306.15320). |
|
||||
| **MUSIC / Root-MUSIC / ESPRIT** | Rejected | Requires building a spatial-smoothed covariance matrix `R = (1/(K-L+1)) Σ h_i h_i^H` and then full eigendecomposition. On the aggregator this is O(L³) per link per frame. With 12 links at 20 Hz, this is 240 eigendecompositions/s of 20×20 Hermitian matrices — feasible, but not worth the complexity when ISTA achieves comparable resolution at far lower cost. MUSIC also requires knowing the number of paths P in advance; ISTA does not. MUSIC is superior for angle-of-arrival estimation (its original purpose in SpotFi) but not for the delay-domain CIR that this ADR targets. |
|
||||
| **SAGE / CLEAN** | Rejected | Iterative deconvolution methods that require a point-spread function model. CLEAN (radio astronomy origin) works well when the PSF is known and shift-invariant — neither holds for 56-subcarrier WiFi with hardware-specific IQ imbalance. SAGE is theoretically optimal but the E-step requires per-path complex amplitude updates, making implementation significantly more complex than ISTA for comparable output quality at our SNR regimes. |
|
||||
| **Neural/deep CIR** | Rejected | No trained model, no paired CIR ground truth in this codebase, and the neural approach requires offline training data that matches each deployment's multipath structure. The 2024-2025 literature on neural CIR (arXiv:2601.06467 "Neuro-Wideband" paper) requires extrapolation across ≥200 MHz — not applicable to 20 MHz ESP32 inputs. Add after a training dataset is collected; not as the initial implementation. |
|
||||
| **Treat ESP32-C6 HE-LTF as identical to ESP32-S3 HT20 for CIR purposes** | Rejected | Ignores the 4.7× subcarrier count difference (242 vs 52 K_active). Note that κ(Φ) ≈ 1 identically across tiers (Φ is a normalised DFT submatrix; σ² = G/K = 3 uniformly), so the gain is not numerical conditioning — it is statistical: 4.7× more independent frequency observations suppress noise by 2.16×, producing fewer ghost taps and higher `dominant_tap_ratio` stability. This is a free accuracy improvement that requires only correct pilot masking (a separate `HE20_PILOT_INDICES` constant) and a per-tier `CirConfig`. Treating the C6 as a slow S3 silently discards the largest available accuracy improvement without any hardware change. |
|
||||
|
||||
### 2.3 Per-Bandwidth Strategy
|
||||
|
||||
There is one algorithm for all tiers, parameterised by bandwidth. The question of whether CIR is worth computing at all is answered by the SOTA survey: "at 20 MHz the entire room collapses into a single CIR cluster." This is not a reason to skip CIR at 20 MHz — it is a reason to be precise about what CIR at 20 MHz provides.
|
||||
|
||||
| Tier | K_active subcarriers | G delay bins (3×) | Effective delay res. | Path sep. | Recommended λ | Iterations |
|
||||
|------|---------------------|--------------------|---------------------|-----------|----------------|------------|
|
||||
| A-HE (HE20, ESP32-C6) | 242 | 726 | ~17 ns | ~5 m | 0.03 | 32 |
|
||||
| A (HT20, ESP32-S3) | 52 | 168 | ~17 ns | ~5 m | 0.05 | 30 |
|
||||
| B (HT40, ESP32-S3) | 108 | 342 | ~9 ns | ~2.7 m | 0.03 | 35 |
|
||||
| C (HT80, Nexmon) | 242 | 768 | ~4 ns | ~1.2 m | 0.02 | 40 |
|
||||
|
||||
Tier A-HE uses 802.11ax HE-LTF subcarrier spacing (78.125 kHz in HE-SU 20 MHz) and 802.11ax pilot pattern (8 pilot subcarriers per 802.11ax spec, distinct from the HT20 pilot pattern at ±7, ±21). The resulting K_active matches Tier C in count (242 vs ≥242) but spans only 20 MHz — same native resolution, substantially better statistical SNR from measurement averaging. Tier A-HE is the preferred substrate for ADR-029 RuvSense nodes whenever a compatible AP is present. ADR-110 (Accepted, v0.7.0-esp32) is the firmware substrate that delivers HE-LTF PPDU classification (`csi_collector.c`, frame bytes 18–19), TWT wake slots (`c6_twt.c`), and 802.15.4 epoch timestamps (`c6_timesync_get_epoch_us()`).
|
||||
|
||||
**Sensing matrix condition number — κ(Φ) ≈ 1 by construction:** Φ is a normalised DFT submatrix with columns `φ_g = e^{-j2πΔf·τ_g}·(1/√K)` and G = 3K. When active subcarrier indices are uniformly distributed (as they are for all standard 802.11 tier configurations), Φ Φ^H ≈ (G/K)·I = 3·I. Empirical power iteration (100 iterations, both extremes) confirms σ²_max ≈ σ²_min ≈ 3.000 and κ(Φ) = σ_max/σ_min ≈ **1.00 across all tiers** (HT20, HT40, HE20, HE40). The condition number does not improve with K. The Tier A-HE benefit is therefore purely statistical: 4.7× more independent frequency observations suppress noise by √(K_HE/K_HT) = √(242/52) ≈ **2.16×**, not via a better-conditioned linear system.
|
||||
|
||||
Minimum viable bandwidth for useful CIR: **both Tier A-HE and Tier A (20 MHz) are useful** for presence-based features (tap count, RMS delay spread, dominant-tap ratio) and for coherence gating. Neither is useful for sub-room ranging (>5 m path separation floor). Tier B (40 MHz) opens direct-path triangulation at room scale. The SOTA survey states this explicitly in the bandwidth-separability table.
|
||||
|
||||
The ADR does not gate CIR on bandwidth — it gates downstream consumers. The coherence gate in `multistatic.rs` works at any tier. The ToF triangulation path in `triangulation.rs` is gated behind a minimum bandwidth check (`if cir.bandwidth_hz < 40e6 { return None }`).
|
||||
|
||||
#### 2.3a Soft-AP HE Caveat
|
||||
|
||||
IDF v5.4 soft-AP does **not** advertise HE capabilities. When the ESP32-C6 is configured as a soft-AP, connecting stations negotiate at 802.11bgn rates and the C6 receives HT-LTF frames, not HE-LTF. The 242-subcarrier HE-LTF sensing matrix is only available when the **C6 operates as a STA associated to an external 802.11ax (Wi-Fi 6) AP**.
|
||||
|
||||
This constraint is explicitly noted in `firmware/esp32-csi-node/main/c6_softap_he.c:163`:
|
||||
|
||||
```c
|
||||
// IDF v5.4 soft-AP does not advertise HE; STAs associate at 11bgn.
|
||||
// HE-LTF CSI (242 subcarriers) requires STA mode against an 11ax AP.
|
||||
// See: https://github.com/espressif/esp-idf/issues/XXXXX
|
||||
```
|
||||
|
||||
The same constraint applies to iTWT validation (WITNESS-LOG-110 §A0.6): TWT setup also requires STA mode. Operators deploying ESP32-C6 nodes expecting Tier A-HE SNR benefit must ensure an 11ax AP is in range. If no 11ax AP is available, the firmware falls back to HT20 association (Tier A); the `CirEstimator` detects this from frame byte 18–19 PPDU type (provided by ADR-110's `csi_collector.c`) and selects the appropriate `CirConfig` automatically.
|
||||
|
||||
#### 2.3b Measured Performance (2026-05-28, release build, 1× shared `CirEstimator`)
|
||||
|
||||
All figures are Criterion median latency on an x86 aggregator (single-threaded). The `CirEstimator` instance is shared across all links in the multi-link scenario (one `Send + Sync` shared reference).
|
||||
|
||||
**Latency per `estimate()` call:**
|
||||
|
||||
| Config | K_active | G | Single estimate | 12-link sequential | Amortised per-link | Constructor |
|
||||
|--------|----------|---|-----------------|--------------------|--------------------|-------------|
|
||||
| HT20 (Tier A) | 52 | 156 | 2.72 ms | 17.69 ms | ~1.47 ms | 422 µs |
|
||||
| HT40 (Tier B) | 114 | 342 | 13.43 ms | 74.35 ms | ~6.20 ms | 2.03 ms |
|
||||
| HE20 (Tier A-HE) | 242 | 726 | 3.20 ms | — | est. ~3 ms | — |
|
||||
| HE40 (future) | 484 | 1452 | 9.71 ms | — | est. ~6 ms | — |
|
||||
|
||||
Notable: **HE20 (3.20 ms) is faster than HT40 (13.43 ms)** despite 2.1× higher K. This is because ISTA convergence is iteration-count-dominated, and HE20's 4.7× more measurements per iteration tighten the residual faster — HE20 converges in ~32 iters vs HT40's 35+. The naive "more subcarriers = more compute" intuition does not hold when iterations to convergence also decrease.
|
||||
|
||||
**Cycle-budget verdict at 20 Hz RuvSense target (50 ms cycle):**
|
||||
|
||||
| Scenario | Time used / 50 ms budget | Verdict |
|
||||
|----------|--------------------------|---------|
|
||||
| HT20, 1 link | 5% | comfortable |
|
||||
| HE20, 1 link | 6% | comfortable |
|
||||
| HT40, 1 link | 27% | tight |
|
||||
| HT20, 12-link multistatic | 35% | OK |
|
||||
| **HT40, 12-link multistatic** | **149%** | **exceeds budget** |
|
||||
|
||||
HT40 at 12-link multistatic (74 ms / 50 ms cycle) **does not fit the 20 Hz budget** on a single aggregator thread. Mitigation: either (a) parallel-per-link execution across aggregator cores (divides to ~6.2 ms wall-clock at 12 cores), or (b) reduce super-resolution from G = 3K to G = 2K (cuts matrix size by 33%, reducing latency to approximately 9–10 ms sequential). Tier A-HE on C6 fits comfortably even at 12 links sequential (~38 ms, 77% budget) and trivially when parallelised.
|
||||
|
||||
**Memory — `Vec<Complex32>` allocation per `CirEstimator::new()`:**
|
||||
|
||||
| Config | Φ matrix size |
|
||||
|--------|--------------|
|
||||
| HT20 (Tier A) | 65 KB |
|
||||
| HT40 (Tier B) | 312 KB |
|
||||
| HE20 (Tier A-HE) | 1.4 MB |
|
||||
| HE40 (future) | 5.6 MB |
|
||||
|
||||
Sharing one `CirEstimator` instance across all same-tier links is **mandatory at HE20 and above**. Per-link instantiation at 12 HE20 links would consume 12 × 1.4 MB = 16.8 MB for sensing matrices alone, which is unacceptable on an embedded aggregator. The `Arc<CirEstimator>` pattern (one instance per tier, cloned `Arc` per link thread) is the intended deployment.
|
||||
|
||||
### 2.4 Pilot and Null Carrier Handling
|
||||
|
||||
ESP32-S3 CSI delivers 64 OFDM tones, of which:
|
||||
- 6 are null (DC subcarrier + edge guards, indices ±28 to ±32 in HT20): **set to complex zero** before forming `H`.
|
||||
- 4 are pilot subcarriers (indices ±7, ±21 in HT20): **excluded from the L1 optimisation** by masking the corresponding rows in `Φ`. The pilot tones carry known symbols with hardware-added phase noise; including them injects systematic error into the delay estimate. Their indices are available from `CsiFrame.metadata.antenna_config` indirectly, but for ESP32-S3 the pilot indices are standardised per 802.11n HT20 and are hard-coded as constants in the `CirEstimator`.
|
||||
|
||||
The resulting effective `K` passed to the solver is 56 − 4 = **52 active data subcarriers** for HT20 (Tier A). For HT40, 114 − 6 = **108 active** (Tier B). For Nexmon HT80, pilots are masked per 802.11n spec (≈14 pilots), leaving ≈242 active (Tier C).
|
||||
|
||||
**Tier A-HE (ESP32-C6, HE-LTF):** 802.11ax HE-SU 20 MHz uses a 256-tone FFT with 242 data+pilot subcarriers (±121 around DC), of which **8 are pilot subcarriers** per IEEE 802.11ax-2021 Table 27-47 (HE-SU 20 MHz pilot locations differ from HT20; the 8 pilots are at ±7, ±21, ±43, ±57 in the 0-based 0..255 indexing). After masking 8 pilots, K_active = **242** (not 248; the remaining 6 tones outside ±121 are also null/guard). These pilot indices are distinct from the HT20 constants and are hard-coded as a separate `HE20_PILOT_INDICES` constant in `cir.rs`. The PPDU type field from ADR-110's `csi_collector.c` (frame bytes 18–19) identifies the frame as HE-SU/HE-MU/HE-TB and selects the correct pilot mask at runtime.
|
||||
|
||||
This pilot-exclusion step happens inside `CirEstimator::estimate()` before the solver runs. The `Cir` output struct always reports the full `G` delay bins; the caller does not need to know about the masking.
|
||||
|
||||
### 2.5 Phase Sanitization Order
|
||||
|
||||
**CIR estimation runs after `phase_sanitizer.rs` and after `ruvsense/phase_align.rs`.**
|
||||
|
||||
Justification: the ISTA solver minimises `‖H - Φx‖₂²` in the complex domain. If `H` contains hardware-induced phase offsets (SFO, CFO, LO noise), the solver will attempt to fit those offsets as phantom multipath taps at small delays, creating ghost peaks near τ=0. The `PhaseSanitizer` removes 2π discontinuities and z-score outliers. The `phase_align.rs` LO offset estimator removes the inter-packet carrier phase random walk (circular mean of the static-subcarrier phasor). Only after both stages is `H` a clean estimate of the environmental channel transfer function.
|
||||
|
||||
The ordering is: raw CSI frame → `phase_sanitizer.rs` → `phase_align.rs` (if multi-antenna or multi-packet) → `CirEstimator::estimate()` → `Cir`.
|
||||
|
||||
For single-packet, single-antenna Tier A inputs where `phase_align.rs` is unavailable, the `CirEstimator` applies conjugate multiplication (`H[k] * conj(H_ref[k])`) using the static-environment reference frame stored in `CirEstimator::reference_csi`. This is the same cancellation approach used in `csi_ratio.rs` (ADR-014).
|
||||
|
||||
### 2.6 Proposed Rust API
|
||||
|
||||
The new module is `v2/crates/wifi-densepose-signal/src/ruvsense/cir.rs`. It is exported from `ruvsense/mod.rs` as `pub mod cir`.
|
||||
|
||||
```rust
|
||||
use num_complex::Complex32;
|
||||
use wifi_densepose_core::types::CsiFrame;
|
||||
|
||||
// ---- Configuration ----------------------------------------------------------
|
||||
|
||||
/// Per-bandwidth configuration for CIR estimation.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CirConfig {
|
||||
/// Number of delay-domain bins (dictionary columns). Should be 3× K.
|
||||
/// Default: 168 for HT20, 342 for HT40, 768 for HT80.
|
||||
pub delay_bins: usize,
|
||||
/// L1 regularisation strength. Sparser channels → lower λ.
|
||||
/// Default: 0.05 (HT20), 0.03 (HT40), 0.02 (HT80).
|
||||
pub lambda: f32,
|
||||
/// Maximum ISTA iterations. Default: 30 (HT20) / 35 (HT40) / 40 (HT80).
|
||||
pub max_iter: usize,
|
||||
/// ISTA convergence tolerance (‖x_new − x_old‖₂). Default: 1e-4.
|
||||
pub tol: f32,
|
||||
/// Pilot subcarrier indices (0-based within the measured K subcarriers)
|
||||
/// to exclude from the sensing matrix Φ. Hard-coded per 802.11n spec.
|
||||
/// HT20: [7, 21, 35, 49] (±7, ±21 mapped to 0..55). HT40: [11, 25, 89, 103].
|
||||
pub pilot_indices: Vec<usize>,
|
||||
/// Minimum usable bandwidth in Hz before ranging is disabled downstream.
|
||||
/// Default: 40e6 (40 MHz) — Tier A CIR is presence-only.
|
||||
pub ranging_min_bandwidth_hz: f64,
|
||||
}
|
||||
|
||||
impl CirConfig {
|
||||
/// Construct default config for a given bandwidth in MHz.
|
||||
pub fn for_bandwidth_mhz(bw_mhz: u16) -> Self { /* … */ }
|
||||
}
|
||||
|
||||
impl Default for CirConfig {
|
||||
fn default() -> Self { Self::for_bandwidth_mhz(20) }
|
||||
}
|
||||
|
||||
// ---- Output type ------------------------------------------------------------
|
||||
|
||||
/// Channel Impulse Response in the delay domain.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Cir {
|
||||
/// Complex tap amplitudes, length = `config.delay_bins`.
|
||||
/// Index 0 = zero-delay (direct path candidate).
|
||||
pub taps: Vec<Complex32>,
|
||||
/// Delay of each tap in seconds. `tap_delay[i] = i / (delay_bins * subcarrier_spacing_hz)`.
|
||||
pub tap_delays_s: Vec<f64>,
|
||||
/// Channel bandwidth that produced this CIR (Hz).
|
||||
pub bandwidth_hz: f64,
|
||||
/// Sub-carrier spacing (Hz). 312_500.0 for 802.11n HT20/HT40.
|
||||
pub subcarrier_spacing_hz: f64,
|
||||
/// RMS delay spread (seconds), weighted by tap power.
|
||||
pub rms_delay_spread_s: f64,
|
||||
/// Index of the dominant tap (highest |tap|²).
|
||||
pub dominant_tap_idx: usize,
|
||||
/// Ratio: dominant-tap power / total power. High (>0.7) = strong LOS.
|
||||
pub dominant_tap_ratio: f32,
|
||||
/// Number of taps above the noise threshold (|tap|² > noise_floor_power).
|
||||
pub active_tap_count: usize,
|
||||
/// Whether ranging is meaningful given the bandwidth.
|
||||
pub ranging_valid: bool,
|
||||
}
|
||||
|
||||
impl Cir {
|
||||
/// ToF of the dominant tap in seconds (proxy for direct-path travel time).
|
||||
/// Returns `None` if `ranging_valid` is false (Tier A, 20 MHz only).
|
||||
pub fn dominant_tap_tof_s(&self) -> Option<f64> {
|
||||
if self.ranging_valid {
|
||||
Some(self.tap_delays_s[self.dominant_tap_idx])
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---- Estimator --------------------------------------------------------------
|
||||
|
||||
/// Errors from CIR estimation.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum CirError {
|
||||
#[error("CsiFrame has no complex data (amplitude-only)")]
|
||||
NoComplexData,
|
||||
#[error("Subcarrier count mismatch: got {got}, expected {expected}")]
|
||||
SubcarrierMismatch { got: usize, expected: usize },
|
||||
#[error("Phase sanitization required before CIR estimation")]
|
||||
UnsanitizedPhase,
|
||||
#[error("ISTA solver failed: {0}")]
|
||||
SolverFailed(String),
|
||||
}
|
||||
|
||||
/// Stateful CIR estimator. Holds a pre-computed sensing matrix Φ and a
|
||||
/// reusable FFT plan for efficient repeated calls.
|
||||
///
|
||||
/// `CirEstimator` is `Send + Sync`: the sensing matrix is immutable after
|
||||
/// construction, and the solver state is stack-local to each `estimate()` call.
|
||||
pub struct CirEstimator {
|
||||
config: CirConfig,
|
||||
/// Sensing matrix Φ ∈ ℂ^{K_active × G}, row-major, pre-computed at construction.
|
||||
sensing_matrix: Vec<Complex32>,
|
||||
/// Number of active (non-pilot) subcarriers.
|
||||
k_active: usize,
|
||||
/// Static-environment reference frame for conjugate-multiplication fallback.
|
||||
/// Set via `set_reference_csi()` after the first quiescent frames.
|
||||
reference_csi: Option<Vec<Complex32>>,
|
||||
}
|
||||
|
||||
impl CirEstimator {
|
||||
/// Construct an estimator for the given config.
|
||||
/// Builds the sensing matrix at construction time; O(K×G) work, done once.
|
||||
pub fn new(config: CirConfig) -> Self { /* … */ }
|
||||
|
||||
/// Update the reference CSI used for single-antenna conjugate-mult fallback.
|
||||
/// Call this with averaged quiescent frames (no motion, no people).
|
||||
pub fn set_reference_csi(&mut self, reference: Vec<Complex32>) { /* … */ }
|
||||
|
||||
/// Estimate the CIR from a single CSI frame.
|
||||
///
|
||||
/// # Phase precondition
|
||||
///
|
||||
/// The caller is responsible for passing a frame whose phase has already
|
||||
/// been processed by `PhaseSanitizer` and, if multi-antenna, by `phase_align.rs`.
|
||||
/// Passing raw hardware phase will produce ghost taps.
|
||||
///
|
||||
/// # Per-antenna strategy
|
||||
///
|
||||
/// For multi-antenna frames (n_spatial_streams > 1), `estimate()` runs the
|
||||
/// solver independently on each row of `frame.data` and returns the
|
||||
/// incoherent-average CIR (tap magnitudes averaged across antennas, phases
|
||||
/// from the highest-amplitude antenna). This matches the approach used in
|
||||
/// the tomography module.
|
||||
pub fn estimate(&self, frame: &CsiFrame) -> Result<Cir, CirError> { /* … */ }
|
||||
}
|
||||
|
||||
// Marker impls — sensing matrix is immutable after construction.
|
||||
unsafe impl Send for CirEstimator {}
|
||||
unsafe impl Sync for CirEstimator {}
|
||||
```
|
||||
|
||||
**Design decisions within the API:**
|
||||
|
||||
- `Vec<Complex32>` not `ndarray`: The sensing matrix and tap vector are kept as flat `Vec<Complex32>` to avoid pulling `ndarray` into the hot path. The existing `NeumannSolver` in `ruvector_solver` operates on `CsrMatrix<f32>`, which the ISTA wrapper will construct from the real/imag split of `Φ`.
|
||||
- **No owned FFT plan**: The 802.11 subcarrier grid is small enough (K ≤ 256) that a reused plan via `rustfft::FftPlanner` provides no measurable benefit over construction per call at 20 Hz update rate.
|
||||
- **`Send + Sync`**: The estimator is stateless per `estimate()` call except for `reference_csi`, which is updated only from the control path (single writer). Use a `RwLock<Option<Vec<Complex32>>>` in the actual implementation for multi-threaded aggregators.
|
||||
- **Multi-antenna**: Incoherent-average across antennas (magnitudes averaged, not complex). Coherent averaging requires phase-calibrated antennas (ADR-042 CHCI path); this ADR targets the incoherent case available from current ESP32 hardware.
|
||||
|
||||
### 2.7 Downstream Consumers
|
||||
|
||||
**`ruvsense/multistatic.rs` — coherence gate moves to tap-delay domain**
|
||||
|
||||
The existing `CoherenceGate` in `ruvsense/coherence_gate.rs` operates on raw frequency-domain amplitude/phase vectors from `FusedSensingFrame`. Add an overload:
|
||||
|
||||
```rust
|
||||
impl CoherenceGate {
|
||||
/// Gate using CIR tap magnitudes instead of raw subcarrier amplitudes.
|
||||
/// More robust: tap magnitude changes are isolated to specific delay bins
|
||||
/// rather than spread across all subcarriers.
|
||||
pub fn update_cir(&mut self, cir: &Cir, pose: &Pose) -> GateDecision { /* … */ }
|
||||
}
|
||||
```
|
||||
|
||||
The coherence metric becomes: compare the tap magnitude vector `|taps|` against the running Welford mean/variance of tap magnitudes. A tap that gains or loses power (body entering a delay bin) produces a coherence drop on that specific delay, rather than modulating all 56 subcarriers simultaneously. This reduces false gates from broadband interference.
|
||||
|
||||
The `reconstruct_cir()` call site in the `process_cycle()` pseudocode (architecture doc, line 578) is the implementation target:
|
||||
|
||||
```rust
|
||||
// In multistatic.rs RuvSenseAggregator::process_cycle():
|
||||
let cirs: Vec<Cir> = self.link_buffers.iter()
|
||||
.map(|buf| self.cir_estimator.estimate(buf.latest_sanitized_frame()))
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
|
||||
let coherent_links: Vec<(usize, &Cir)> = cirs.iter().enumerate()
|
||||
.filter(|(i, cir)| self.link_gates[*i].is_cir_coherent(cir))
|
||||
.collect();
|
||||
```
|
||||
|
||||
**Tier A-HE additional inputs in `multistatic.rs`** (P1 follow-ups, not blocking this ADR):
|
||||
|
||||
- **802.15.4 epoch timestamp**: When the link source is a Tier A-HE ESP32-C6 node (identified by PPDU type from ADR-110), the frame carries a sub-100 µs epoch from `c6_timesync_get_epoch_us()`. In `process_cycle()`, attach this epoch to the `CsiFrame` metadata so that multi-link CIR estimates can be temporally aligned to a shared 802.15.4 reference rather than the aggregator's local clock. This is required for coherent multi-link CIR phase comparison (CHCI path, ADR-042) but is not required for the incoherent coherence gate or `dominant_tap_ratio` features. Mark as `// TODO(ADR-134 P1): attach c6 802.15.4 epoch` in the implementation stub.
|
||||
|
||||
- **TWT wake-slot ID for frame independence**: ADR-110's TWT schedule assigns each C6 node a dedicated wake slot (slot ID from `c6_twt.c`). When frames arrive from different TWT slots, the inter-frame CSI phase is independently sampled — the ISTA per-frame independence assumption holds exactly. When a node misses a TWT slot and re-transmits in a later slot, the independence assumption breaks and the `dominant_tap_ratio` estimate for that frame should be down-weighted. Wire `twt_slot_id` from the frame metadata into `CoherenceGate::update_cir()` to detect and down-weight retransmitted frames. Mark as `// TODO(ADR-134 P1): consume twt_slot_id` in the stub.
|
||||
|
||||
**Cycle-budget constraint on HT40 multi-link (see §2.3b for measurements)**
|
||||
|
||||
Measured latency shows HT40 at 12-link multistatic takes ~74 ms, exceeding the 50 ms cycle budget at 20 Hz. The `RuvSenseAggregator::process_cycle()` implementation must not invoke `CirEstimator::estimate()` for all Tier B links sequentially on the main cycle thread. Required: dispatch CIR estimation across Rayon threadpool workers (`par_iter()` over link buffers) when tier == HT40. Tier A-HE at 12 links sequential (~38 ms) fits within budget and does not require parallelisation, though it benefits from it. Tier A at 12 links sequential (18 ms) has comfortable headroom. Add a `CYCLE_BUDGET_WARNING` log at DEBUG level if a sequential estimate run exceeds 45 ms.
|
||||
|
||||
**`wifi-densepose-ruvector/src/viewpoint/coherence.rs` — no change to phase-phasor logic**
|
||||
|
||||
The existing `CrossViewpointAttention` in `viewpoint/coherence.rs` computes a differential phasor coherence score in the frequency domain. CIR does not replace this — it augments it. The phase-phasor metric remains the primary edge weight for viewpoint fusion because it is more sensitive to small motions (body within a Fresnel zone). CIR-derived features (tap count, RMS delay spread) become secondary features passed to the attention mechanism as geometric priors, not replacements for phasor coherence.
|
||||
|
||||
**`wifi-densepose-mat/src/localization/triangulation.rs` — conditional direct-path ToF**
|
||||
|
||||
When `cir.ranging_valid` is true (Tier B or C), the dominant tap's ToF `cir.dominant_tap_tof_s()` is a candidate direct-path range measurement. The triangulation module already imports `ruvector_solver::NeumannSolver` for TDoA solving. Wire in the CIR ToF as an additional observation:
|
||||
|
||||
```rust
|
||||
// In triangulation.rs, within the TDoA system builder:
|
||||
if let Some(tof) = cir.dominant_tap_tof_s() {
|
||||
let range_m = tof * SPEED_OF_LIGHT;
|
||||
// Add as an additional row in the TDoA linear system.
|
||||
// Weight by dominant_tap_ratio (high ratio = reliable LOS measurement).
|
||||
tdoa_builder.add_range(link_id, range_m, cir.dominant_tap_ratio);
|
||||
}
|
||||
```
|
||||
|
||||
This is a conditional enhancement. Tier A (20 MHz) links contribute no ranging; Tier B/C links contribute one ranging measurement each. The existing TDoA solver handles mixed inputs because it is already weighted least-squares via NeumannSolver.
|
||||
|
||||
**`wifi-densepose-vitals` — CIR provides marginal improvement only for heartbeat**
|
||||
|
||||
For breathing detection (`bvp.rs`, `ruvsense/breathing.rs`): breathing produces a periodic modulation of the direct-path tap magnitude at 0.15–0.5 Hz. Filtering `|cir.taps[dominant_tap_idx]|` through the existing bandpass pipeline is equivalent to doing the same on the peak-subcarrier amplitude — no architectural change needed. The existing Fresnel model (`fresnel.rs`) already models this at the subcarrier level.
|
||||
|
||||
For heartbeat detection at 0.8–2.0 Hz: CIR provides a minor SNR benefit by isolating the direct-path tap from multipath interference. This is a marginal improvement in Tier A/B. At Tier C (Nexmon, 80 MHz), isolated direct-path taps become more stable and the heartbeat band SNR improvement is measurable (~2 dB). CIR integration with vitals is therefore: **pass `cir.taps[cir.dominant_tap_idx]` magnitude time series to the existing vital-sign pipeline as an additional input stream**. No new module in `wifi-densepose-vitals` is needed for this ADR; it is a one-line addition to the aggregator's vitals path.
|
||||
|
||||
### 2.8 Feature Gating
|
||||
|
||||
New Cargo feature: `cir` in `wifi-densepose-signal/Cargo.toml`.
|
||||
|
||||
```toml
|
||||
[features]
|
||||
default = ["cir"]
|
||||
|
||||
cir = ["ruvector-solver"]
|
||||
```
|
||||
|
||||
`ruvector-solver` is already in the workspace (used by `fresnel.rs` and `train/subcarrier.rs`). The feature gate does not add a new dependency — it conditionally compiles `ruvsense/cir.rs`. The feature is **default-on** because:
|
||||
|
||||
1. It adds no new crate dependencies.
|
||||
2. The `CirEstimator` is zero-cost if never instantiated — the sensing matrix is only allocated on `CirEstimator::new()`.
|
||||
3. Downstream consumers (`multistatic.rs`, `triangulation.rs`) will conditionally compile their CIR branches with `#[cfg(feature = "cir")]`.
|
||||
|
||||
### 2.9 Test Plan
|
||||
|
||||
**Tier 1 — Deterministic synthetic channel (unit test, no hardware)**
|
||||
|
||||
Inject a known two-tap channel: direct path at τ₁ = 30 ns with complex amplitude α₁ = 0.8e^{jπ/4}, reflected path at τ₂ = 80 ns with α₂ = 0.3e^{j3π/4}. Compute the expected CSI vector `H[k] = α₁·e^{-j2πk·Δf·τ₁} + α₂·e^{-j2πk·Δf·τ₂}` for K=56, Δf=312.5 kHz. Pass to `CirEstimator::estimate()`. Assert:
|
||||
- `cir.active_tap_count` is 2 (with noise_floor = -25 dB relative to α₁ power).
|
||||
- `cir.tap_delays_s[cir.dominant_tap_idx]` is within one delay bin of τ₁ = 30 ns.
|
||||
- `cir.dominant_tap_ratio` > 0.7 (direct path dominates).
|
||||
- The second peak delay is within one delay bin of τ₂ = 80 ns.
|
||||
|
||||
This test must be deterministic (no random seed) and must pass under `cargo test --workspace --no-default-features --features cir`. It follows the pattern established by `verify.py` for the Python pipeline.
|
||||
|
||||
**Tier 2 — Phase corruption robustness**
|
||||
|
||||
Same two-tap channel but add a random per-subcarrier phase ramp (SFO) and a constant phase offset (CFO). Without sanitization: assert the test fails (ghost tap at τ=0 from CFO). With `phase_sanitizer.rs` applied before `estimate()`: assert the same pass conditions as Tier 1. This validates the ordering decision in Section 2.5.
|
||||
|
||||
**Tier 3 — Per-bandwidth regression (unit test)**
|
||||
|
||||
For K ∈ {56, 114, 256} with the two-tap channel, assert that the dominant-tap delay estimate error is < 1 delay bin, confirming the 3× super-resolution holds across all tiers.
|
||||
|
||||
**Tier 4 — Real hardware capture (integration test, COM9)**
|
||||
|
||||
Using the existing ESP32-S3 on COM9 (ruvzen), capture 200 CSI frames in a static room (no motion). Assert:
|
||||
- `cir.active_tap_count` is consistent across frames (variance < 1 tap count over 200 frames).
|
||||
- `cir.dominant_tap_ratio` > 0.5 (LOS dominant path present).
|
||||
- `cir.rms_delay_spread_s` is in the range [10 ns, 200 ns] (reasonable for a room).
|
||||
|
||||
This test documents expected tap statistics for the ADR-028 witness bundle (see Section 2.10). The test is gated behind `#[cfg(feature = "hardware-test")]` and is not run in CI.
|
||||
|
||||
**Tier 5 — Tier A-HE hardware bench (integration test, COM12)**
|
||||
|
||||
Using the ESP32-C6 on COM12 (ruvzen, `MR60BHA2` sensor slot — see CLAUDE.local.md hardware table) associated to an 11ax AP, capture 600 CSI frames (30 seconds at 20 Hz) in the same static room used for Tier 4. Assert:
|
||||
- `cir.active_tap_count` is consistent across frames (variance < 1 tap count over 600 frames).
|
||||
- `cir.dominant_tap_ratio` > 0.5 (same threshold as Tier 4).
|
||||
- `cir.dominant_tap_ratio` averaged over 600 frames is ≥ 20% higher than the Tier 4 S3 baseline from the same room and session — confirming the statistical SNR gain (√(242/52) ≈ 2.16×) from K_active=242 vs K_active=52 (not a conditioning improvement; κ(Φ) ≈ 1 at both tiers).
|
||||
- Frame metadata shows PPDU type = HE-SU (not HT20), confirming the C6 is receiving HE-LTF frames (not falling back to Tier A).
|
||||
|
||||
This test is gated behind `#[cfg(feature = "hardware-test")]` and is not run in CI. It validates the Tier A-HE preference claim and provides the baseline for any future ADR targeting C6-specific optimisations.
|
||||
|
||||
### 2.10 Witness and Proof
|
||||
|
||||
Per ADR-028, any new signal stage receives a witness entry. The witness additions for CIR:
|
||||
|
||||
**WITNESS-LOG-028.md** — add two rows:
|
||||
|
||||
| Row | Capability | Evidence | Hash |
|
||||
|-----|-----------|----------|------|
|
||||
| W-34 | CIR sparse recovery (synthetic 2-tap, HT20) | `cargo test cir::tests::two_tap_recovery -- --nocapture` output + tap delay error < 1 bin | SHA-256 of stdout |
|
||||
| W-35 | CIR phase-ordering correctness | `cargo test cir::tests::phase_corruption_rejected` passes with sanitizer, fails without | SHA-256 of test binary |
|
||||
|
||||
**`verify.py` extension**: Add a `cir_recovery_check()` function that feeds the same synthetic two-tap channel through `CirEstimator` via a Python ctypes/cffi shim, computes the dominant-tap delay, and asserts < 1 bin error. Hash the function output and compare to `expected_features.sha256`. This integrates CIR into the deterministic proof chain.
|
||||
|
||||
The `source-hashes.txt` in the witness bundle adds the SHA-256 of `ruvsense/cir.rs` alongside the existing firmware binaries.
|
||||
|
||||
---
|
||||
|
||||
## 3. Consequences
|
||||
|
||||
### 3.1 Positive
|
||||
|
||||
- **Coherence gate precision**: The `multistatic.rs` coherence gate can now isolate motion to specific delay bins. A body walking across one end of a room no longer corrupts the coherence score of the direct-path tap, eliminating false gate triggers on multi-node links.
|
||||
- **Direct-path ranging (Tier B/C)**: At 40 MHz and above, the dominant-tap ToF provides a real range measurement for TDoA triangulation, closing a gap in `triangulation.rs` that currently estimates position from angle-of-arrival only.
|
||||
- **Reuses `NeumannSolver`**: Zero new crate dependencies. The ISTA loop wraps the existing solver interface exactly as `fresnel.rs` and `subcarrier.rs` do.
|
||||
- **Foundation for ADR-030 and ADR-042**: The persistent field model (ADR-030) requires a per-link CIR baseline for perturbation extraction. The coherent diffraction tomography (ADR-042) requires complex CIR as input. Both are unblocked by this ADR.
|
||||
- **Test-harness compatible**: The synthetic test channel plugs directly into the `verify.py` proof infrastructure without new tooling.
|
||||
|
||||
### 3.2 Negative
|
||||
|
||||
- **Memory cost**: Measured `Vec<Complex32>` allocation per `CirEstimator::new()`: HT20 = 65 KB, HT40 = 312 KB, HE20 = 1.4 MB (see §2.3b). Sharing one `Arc<CirEstimator>` per tier across all same-tier links is mandatory at HE20+; per-link instantiation at 12 HE20 links costs 16.8 MB for sensing matrices alone.
|
||||
- **Latency — HT40 12-link budget breach**: Measured median `estimate()` latency: HT20 = 2.72 ms, HT40 = 13.43 ms, HE20 = 3.20 ms (see §2.3b for full table). HT40 at 12-link multistatic sequential = 74.35 ms, which exceeds the 50 ms cycle budget at 20 Hz. HT20 (17.69 ms) and HE20 (est. ~38 ms) both fit. CIR runs on the aggregator, not the ESP32. HT40 multistatic requires Rayon parallelisation (see §2.7). An ESP32-S3 or ESP32-C6 at 240 MHz cannot run any multi-link CIR recovery in the 50 ms budget.
|
||||
- **New test fixture**: The two-tap synthetic test requires a `Complex32` construction helper and a tolerance-aware tap-peak detector — ~50 lines of test utility code.
|
||||
- **Phase ordering is a hard precondition**: If a caller invokes `CirEstimator::estimate()` on an unsanitized frame, the result is silently wrong (ghost taps, not an error). The `CirError::UnsanitizedPhase` variant provides a partial guard via a heuristic check (phase variance > 10 rad² across subcarriers suggests unsanitized SFO/CFO), but this is not a proof of correctness.
|
||||
|
||||
### 3.3 Risks
|
||||
|
||||
| Risk | Probability | Impact | Mitigation |
|
||||
|------|-------------|--------|------------|
|
||||
| `NeumannSolver` convergence at low K with high noise | Medium | Ghost taps in HT20 when channel has few paths and low SNR | κ(Φ) ≈ 1 by construction (normalised DFT submatrix, G = 3K), so numerical ill-conditioning is not the risk. The risk is low SNR at K=52 (2.16× weaker than K=242 at same noise floor). Mitigate with Tikhonov diagonal regularisation (`A + λI`) inside the sensing matrix build step, same as `fresnel.rs:269`, which absorbs residual noise not addressed by measurement averaging. |
|
||||
| Dominant-tap ambiguity when LOS is blocked (NLOS-only links) | High at long NLOS ranges | `dominant_tap_idx` points to a reflected path, not direct path | `dominant_tap_ratio` < 0.3 flags this; `ranging_valid` logic gates on ratio > 0.5 |
|
||||
| ISTA step-size instability at high λ | Low | Oscillating tap magnitudes across frames | Bound λ to `[1e-4, 0.2]` in `CirConfig` validation; add a step-size line search in the first iteration |
|
||||
| ESP32 hardware delivers amplitude-only CSI (no complex) for some firmware versions | Low | `CirError::NoComplexData` at runtime | Firmware audit: `wifi_csi_info_t.buf` in ESP-IDF 5.4 delivers I/Q; document minimum firmware version in `hardware/esp32/README.md` |
|
||||
|
||||
---
|
||||
|
||||
## 4. Rationale and Comparison to Alternative Designs
|
||||
|
||||
### 4.1 Why Not Compute CIR in Python (`archive/v1/`)
|
||||
|
||||
The Python pipeline in `archive/v1/src/` is frozen. ADR-011 established that new signal stages go into the Rust workspace, not into the Python archive. The Python proof (`verify.py`) validates the pipeline hash, not the algorithm; its `cir_recovery_check()` extension calls the compiled Rust binary, not Python CIR code.
|
||||
|
||||
### 4.2 Why Not Rely on rvCSI Exclusively
|
||||
|
||||
`vendor/rvcsi` (ADR-095/096) provides a `CsiFrame`/`CsiWindow`/`CsiEvent` schema and Nexmon adapter, but the published `rvcsi-dsp` crate does not currently implement CIR estimation (as of May 2026 — confirmed by crate source). Even when rvCSI adds CIR, the WiFi-DensePose workspace needs CIR as a first-class type integrated with `CsiFrame` (the `wifi-densepose-core` type), not as a foreign struct requiring FFI translation on every frame at 20 Hz. rvCSI's CIR, when published, can be accepted as an alternative input source by converting to `Cir` at the adapter boundary; the downstream consumers in `multistatic.rs` and `triangulation.rs` will not need to change.
|
||||
|
||||
### 4.3 Why Not Frequency-Domain Only Forever
|
||||
|
||||
The three research documents (SOTA survey, architecture, edge-weight computation) all converge on the same conclusion: frequency-domain CSI features are sufficient for presence and coarse gesture, but insufficient for:
|
||||
|
||||
1. **Tap-isolated coherence gating** (the multistatic coherence gate confounds body motion with environmental drift when both appear as broadband subcarrier modulations).
|
||||
2. **Direct-path ranging** (subcarrier phase slope gives bearing, not range, unless combined with a CIR ToF).
|
||||
3. **Field normal modes** (ADR-030 requires a per-link CIR baseline to extract structural perturbations from environmental drift).
|
||||
|
||||
Deferring CIR indefinitely means these three capabilities remain permanently gated behind the current frequency-domain accuracy ceiling. CIRSense (arXiv:2510.11374, October 2025) independently validates that CIR-domain features yield 3× higher accuracy with 4.5× better computational efficiency compared to raw CSI features for respiration monitoring — the canonical WiFi sensing task in this codebase.
|
||||
|
||||
---
|
||||
|
||||
## 5. Related ADRs
|
||||
|
||||
| ADR | Relationship |
|
||||
|-----|-------------|
|
||||
| ADR-014 (SOTA Signal Processing) | **Extended**: CIR adds a 7th signal module alongside the 6 in ADR-014 |
|
||||
| ADR-017 (RuVector Signal+MAT) | **Enables**: ADR-017's coherence gate pseudocode references CIR; now implementable |
|
||||
| ADR-029 (RuvSense Multistatic) | **Unblocks**: `reconstruct_cir()` stub in `process_cycle()` now has a concrete implementation |
|
||||
| ADR-030 (Persistent Field Model) | **Prerequisite fulfilled**: baseline CIR per link is required for perturbation extraction |
|
||||
| ADR-042 (Coherent Human Channel Imaging) | **Foundation layer**: CHCI's coherent diffraction tomography consumes `Cir` as primary input |
|
||||
| ADR-095/096 (rvCSI) | **Complementary**: rvCSI provides the Nexmon adapter for Tier C; CIR estimation runs on top |
|
||||
| ADR-028 (ESP32 Capability Audit) | **Witness extended**: two new rows W-34, W-35 added to `WITNESS-LOG-028.md` |
|
||||
| ADR-110 (ESP32-C6 Firmware Extension) | **Substrate**: HE-LTF PPDU classification (frame bytes 18–19), TWT wake slots (`c6_twt.c`), and 802.15.4 epoch timestamps (`c6_timesync_get_epoch_us()`) — all shipped in v0.7.0-esp32. Tier A-HE `CirConfig` depends on PPDU type from ADR-110 for automatic tier detection. |
|
||||
|
||||
---
|
||||
|
||||
## 6. References
|
||||
|
||||
### Production Code
|
||||
- `v2/crates/wifi-densepose-signal/src/ruvsense/multistatic.rs` — current amplitude/phase coherence gate; `reconstruct_cir()` call site
|
||||
- `v2/crates/wifi-densepose-signal/src/phase_sanitizer.rs` — must run before `CirEstimator::estimate()`
|
||||
- `v2/crates/wifi-densepose-signal/src/fresnel.rs:280` — `NeumannSolver` usage pattern this ADR mirrors
|
||||
- `v2/crates/wifi-densepose-train/src/subcarrier.rs:225` — second `NeumannSolver` usage in workspace
|
||||
- `v2/crates/wifi-densepose-mat/src/ml/vital_signs_classifier.rs:386` — the only IFFT in production (unrelated to CIR)
|
||||
|
||||
### Research Documents
|
||||
- `docs/research/sota-surveys/ruview-multistatic-fidelity-sota-2026.md` — bandwidth table, 20 MHz separability analysis
|
||||
- `docs/research/architecture/ruvsense-multistatic-fidelity-architecture.md` — `NeumannSolver` CIR proposal (§2.1), pipeline diagram (§4.1), `is_coherent(cir)` pseudocode (line 583)
|
||||
- `docs/research/rf-topological-sensing/02-csi-edge-weight-computation.md` — IFFT formula, CIR features, ESPRIT for multipath decomposition
|
||||
|
||||
### External Papers
|
||||
- Kotaru et al., "SpotFi: Decimeter Level Localization Using WiFi," ACM SIGCOMM 2015 — MUSIC for AoA; spatial smoothing from K subcarriers
|
||||
- Vasisht et al., "Decimeter-Level Localization with a Single WiFi Access Point," NSDI 2016 (Chronos) — BPDN for sparse CIR across stitched channels
|
||||
- CIRSense, arXiv:2510.11374 (October 2025) — CIR delay-domain sensing; ISTA sparse recovery; 3× accuracy vs CSI, 4.5× compute efficiency; validated at 160 MHz (informative for Tier C)
|
||||
- "Pulse Shape-Aided Multipath Delay Estimation for Fine-Grained WiFi Sensing," arXiv:2306.15320 — OMP vs ISTA comparison at low SNR
|
||||
- "Neuro-Wideband WiFi Sensing via Self-Conditioned CSI Extrapolation," arXiv:2601.06467 (January 2026) — neural CIR extrapolation requiring ≥200 MHz; explains why neural approach is rejected for this ADR
|
||||
- Zheng et al., "Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi," MobiSys 2019 (Widar 3.0) — BVP as domain-independent alternative to CIR; relevant to vitals-path decision
|
||||
@@ -1,664 +0,0 @@
|
||||
# ADR-135: Empty-Room Baseline Calibration
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Proposed |
|
||||
| **Date** | 2026-05-28 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codebase target** | `wifi-densepose-signal` (new module `ruvsense/calibration.rs`); `wifi-densepose-cli` (new `calibrate` subcommand) |
|
||||
| **Relates to** | ADR-014 (SOTA Signal Processing), ADR-028 (ESP32 Capability Audit), ADR-029 (RuvSense Multistatic), ADR-030 (Persistent Field Model), ADR-110 (ESP32-C6 Firmware Extension), ADR-134 (First-Class CIR Support) |
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
### 1.1 The Gap
|
||||
|
||||
Searching across the Rust workspace (`v2/crates/**`) for `BaselineCalibration`, `empty_room`, `static_baseline`, and `calibrate` finds no production module that captures an empty-room CSI reference and stores it for real-time subtraction. The closest existing code is `ruvsense/field_model.rs`, which runs an SVD decomposition of calibration frames to extract electromagnetic eigenmodes for ADR-030's drift detection tier. That is a layer above what this ADR addresses: before eigenmodes can be reliably computed, each link needs a per-subcarrier statistical baseline that removes hardware-induced gain bias and environment-fixed multipath from the sensing signal.
|
||||
|
||||
The absence is consequential. Three production issues trace directly to missing baseline calibration:
|
||||
|
||||
- **False motion triggers** from environmental loading: thermal expansion of walls, HVAC vibration, and furniture reflections cause slow CSI amplitude drift that sits below the motion threshold but corrupts long-window variance estimates. The `ruvsense/coherence_gate.rs` coherence check cannot distinguish this drift from a slowly approaching person.
|
||||
- **Phase-coherent algorithms degrade silently**: `CirEstimator` (ADR-134) assumes that the phase-cleaned CSI `H` represents the environmental channel. Without baseline subtraction, `H` also contains the fixed-geometry direct path and primary reflections from walls and furniture. The ISTA solver correctly fits these as low-delay taps, but they consume regularisation budget that should be reserved for body-perturbed taps. `dominant_tap_ratio` is systematically inflated, making NLOS-body detection harder.
|
||||
- **Multi-node coherence scores are not comparable**: Without a per-link baseline, the amplitude scale of one ESP32-S3 link at 2.4 GHz differs from another at 5 GHz even in the same room, because RSSI, antenna gain, and cable loss vary per node. Multistatic fusion in `ruvsense/multistatic.rs` applies attention weighting that implicitly assumes comparable amplitude scales across links. Hardware normalization (`hardware_norm.rs`) resamples to a canonical subcarrier grid and applies z-score normalization using population statistics — but those statistics are computed from the full signal including environmental-loading drift, not from a known-empty reference.
|
||||
|
||||
ADR-030 (Persistent Field Model, Proposed) describes the SVD-decomposition tier and assumes calibration data exists. ADR-134 (CIR, Proposed) documents at §2.5 that `CirEstimator::set_reference_csi()` should be called "with averaged quiescent frames" — but does not specify how those frames are collected, persisted, or invalidated. This ADR closes that gap.
|
||||
|
||||
### 1.2 What "Baseline" Means Here
|
||||
|
||||
An empty-room baseline is a per-subcarrier statistical summary of the channel transfer function `H(f_k)` when the room contains no people. It captures:
|
||||
|
||||
- The static environment geometry: direct path, wall and furniture reflections, resonances.
|
||||
- Hardware-specific gain offsets per subcarrier, which are stable across reboots on the same ESP32 unit.
|
||||
- Long-term ambient drift not corrected by `phase_sanitizer.rs` (which operates per-frame, not across frames).
|
||||
|
||||
What a baseline is **not**: it is not a calibration for inter-packet phase noise (CFO/SFO), which `phase_sanitizer.rs` and `phase_align.rs` already handle. Those two stages must run before baseline comparison.
|
||||
|
||||
### 1.3 Hardware Context
|
||||
|
||||
| Tier | Device | Port | Active subcarriers | Bandwidth | Baseline memory (host) |
|
||||
|------|--------|------|--------------------|-----------|------------------------|
|
||||
| A | ESP32-S3 | COM9 | 52 (HT20) | 20 MHz | ~7 KB per link |
|
||||
| A-HE | ESP32-C6 | COM12 | 242 (HE20, STA mode against 11ax AP) | 20 MHz | ~31 KB per link |
|
||||
| B | ESP32-S3 | COM9 | 108 (HT40) | 40 MHz | ~14 KB per link |
|
||||
|
||||
All hardware runs ADR-110 v0.7.0-esp32 firmware. ESP32-C6 on COM12 provides `c6_timesync_get_epoch_us()` (±100 µs 802.15.4 epoch) for multi-node capture synchronization. The C6 falls back to HT20 when no 802.11ax AP is present; the calibration module detects this from `CsiMetadata.bandwidth_mhz` and selects the appropriate subcarrier mask.
|
||||
|
||||
NVS flash budget: ESP32-S3 has 8 MB flash / 4 MB data partition (ADR-028 confirmed). A full Tier A-HE HE20 baseline (242 subcarriers × 4 stats × f32 = ~3.9 KB) fits comfortably in NVS. The NVS key namespace is `ruvcal` with key `b_<link_id>`. Device-side NVS storage is **optional** — the host holds the authoritative baseline in a TOML file and pushes it to device NVS only when fleet-wide simultaneous capture is configured. See Section 2.4.
|
||||
|
||||
### 1.4 Pipeline Position
|
||||
|
||||
```
|
||||
Raw CSI frame
|
||||
→ phase_sanitizer.rs (SFO/CFO removal, per-frame)
|
||||
→ phase_align.rs (LO phase offset, multi-antenna)
|
||||
→ CalibrationRecorder::record() ← NEW (calibration mode only)
|
||||
→ BaselineCalibration::subtract() ← NEW (runtime mode)
|
||||
→ CirEstimator::estimate() (ADR-134)
|
||||
→ multistatic.rs / motion.rs / vitals
|
||||
```
|
||||
|
||||
During calibration mode, the `CalibrationRecorder` accumulates frames. At runtime, `BaselineCalibration::subtract()` removes the static environment before the signal enters any downstream consumer. CIR estimation and coherence gating both receive baseline-subtracted CSI.
|
||||
|
||||
---
|
||||
|
||||
## 2. Decision
|
||||
|
||||
### 2.1 Captured Statistics: Minimum Sufficient Set
|
||||
|
||||
The baseline captures per-subcarrier **amplitude mean and variance** plus per-subcarrier **circular phase mean and circular variance** (concentration parameter `κ` from the von Mises model). No per-link spatial covariance matrix is captured.
|
||||
|
||||
**Amplitude statistics (per subcarrier k, per spatial stream s):**
|
||||
- `amp_mean[s][k]`: Welford running mean of `|H[s][k]|`.
|
||||
- `amp_m2[s][k]`: Welford M2 accumulator for variance. Variance is `m2 / (n - 1)`.
|
||||
|
||||
**Phase statistics (per subcarrier k, per spatial stream s, after sanitization and LO removal):**
|
||||
- `phase_sin_mean[s][k]`, `phase_cos_mean[s][k]`: running means of `sin(φ)` and `cos(φ)`. The circular mean is `atan2(phase_sin_mean, phase_cos_mean)`.
|
||||
- `phase_circular_variance[s][k]`: `1 - sqrt(phase_sin_mean² + phase_cos_mean²)`, the standard estimator of circular dispersion (Mardia & Jupp, 2000). Range is [0, 1]; 0 = perfectly concentrated, 1 = maximally dispersed.
|
||||
|
||||
**What is rejected and why:**
|
||||
|
||||
| Statistic | Verdict | Reason |
|
||||
|-----------|---------|--------|
|
||||
| Per-link spatial covariance (K×K Hermitian) | Rejected | For K=242 (HE20), the full covariance matrix is 242×242×8 bytes = 469 KB per link. Not warranted for a calibration baseline: ADR-030's field model already computes spatial covariance from calibration frames for the eigenmode decomposition. This ADR's baseline is the input to ADR-030, not a substitute for it. |
|
||||
| Higher-order moments (skewness, kurtosis) | Rejected | Non-Gaussian amplitude distributions on WiFi subcarriers arise primarily from Rician fading; skewness does not improve motion/person detection at any currently deployed tier. |
|
||||
| Cross-subcarrier covariance | Rejected | Same argument as spatial covariance. Off-diagonal entries of the subcarrier covariance encode correlated fading but require 52²/2 = 1,352 entries per stream for HT20 alone, and their incremental value over per-subcarrier variance is not supported by the literature for presence detection. |
|
||||
| Time-domain correlation function | Rejected | Belongs to CIR estimation (ADR-134), not to baseline calibration. |
|
||||
|
||||
The chosen set — amplitude mean/variance and circular phase mean/variance — is the minimum that enables three downstream operations:
|
||||
1. Static-environment subtraction for motion detectors (amplitude mean).
|
||||
2. Drift scoring against a known reference (amplitude z-score relative to baseline variance).
|
||||
3. Phase-coherent baseline for `CirEstimator::set_reference_csi()` (circular mean gives the expected phase vector for the static environment).
|
||||
|
||||
### 2.2 Algorithm: Welford Online, Not Batched
|
||||
|
||||
The calibration recorder uses **Welford's online algorithm** (Welford, 1962) for both amplitude and phase statistics. This is the same `WelfordStats` struct already implemented in `ruvsense/field_model.rs` — the calibration module imports it directly.
|
||||
|
||||
The alternative — batched mean-of-N (accumulate all frames in memory, compute offline) — is rejected on two grounds:
|
||||
|
||||
1. **Memory**: 60 seconds of HE20 frames at 20 Hz = 1,200 frames × 242 subcarriers × 2 streams × 16 bytes = ~9.3 MB of raw complex data. On an embedded aggregator or the Raspberry Pi 5 (cognitum-v0, 8 GB) this is acceptable, but it requires allocating the full buffer before calibration begins, blocking streaming. Welford's algorithm requires O(K × S) state regardless of frame count.
|
||||
2. **Streaming interoperability**: Welford allows the recorder to emit a live `deviation_from_partial_baseline()` score that the operator can monitor in real time during calibration, giving feedback that the room is truly empty. Batched computation cannot do this.
|
||||
|
||||
For circular phase statistics, Welford's algorithm cannot be applied directly to phase angles (wrap-around violates the linear update assumption). Instead the recorder maintains running sums of `sin(φ)` and `cos(φ)` — a standard technique equivalent to Welford on the unit-circle projection (Fisher, 1993). This is numerically equivalent to the maximum-likelihood estimator for the von Mises concentration parameter under the assumption of a unimodal phase distribution, which holds for a static empty room (no multipath ambiguity).
|
||||
|
||||
### 2.3 Capture Duration: 30 Seconds Default, Configurable
|
||||
|
||||
The default capture duration is **30 seconds** at the standard 20 Hz sensing rate, yielding 600 frames per spatial stream per subcarrier.
|
||||
|
||||
**Justification against alternatives:**
|
||||
|
||||
- **60 seconds** (common in the SOTA literature, including Domino arXiv:2509.13807): provides better statistical stability for the circular phase estimate at the cost of doubling operator wait time. With 600 frames, the standard error of the mean amplitude per subcarrier is `σ / √600 < 0.002 × σ` — negligible for sensing purposes at any tier.
|
||||
- **10 seconds / 200 frames**: the minimum for a Welford estimate to reach asymptotic variance at typical ESP32 CSI SNR. At 200 frames the circular variance estimate `1 - R̄` has a standard deviation of ~0.04 (Fisher, 1993, Eq. 3.24), corresponding to roughly ±0.04 rad² uncertainty in phase concentration. This is acceptable for amplitude-only downstream stages but degrades the phase-coherent CIR reference. Not the default.
|
||||
- **Per-link tradeoff**: a 12-link multistatic room requires 30 s of guaranteed emptiness. Longer captures reduce the practical window in which recalibration is feasible (e.g., during a 30-minute care visit). The 30-second default is the shortest duration that produces a phase-concentration estimate with standard deviation < 0.02 rad².
|
||||
|
||||
The `--duration` CLI flag accepts any value from 10 to 600 seconds. Values below 10 seconds are rejected with an error; values above 300 seconds emit a warning.
|
||||
|
||||
### 2.4 Persistence Format
|
||||
|
||||
**Host-side: TOML**
|
||||
|
||||
The authoritative baseline on the host (aggregator, cognitum-v0, or ruvzen Windows box) is stored as a TOML file at the path specified by `--output`. The format is human-readable so operators can inspect and manually flag a stale baseline. Fields are:
|
||||
|
||||
```toml
|
||||
[meta]
|
||||
schema_version = 1
|
||||
captured_at_utc = "2026-05-28T14:32:00Z"
|
||||
device_id = "esp32s3-com9"
|
||||
bandwidth_mhz = 20
|
||||
tier = "A" # A | A-HE | B
|
||||
n_streams = 1
|
||||
n_subcarriers = 52
|
||||
frame_count = 600
|
||||
|
||||
[[stream]]
|
||||
stream_idx = 0
|
||||
|
||||
[stream.amp_mean] # length = n_subcarriers
|
||||
values = [0.421, 0.418, ...]
|
||||
|
||||
[stream.amp_variance]
|
||||
values = [0.0012, 0.0009, ...]
|
||||
|
||||
[stream.phase_cos_mean]
|
||||
values = [0.871, 0.864, ...]
|
||||
|
||||
[stream.phase_sin_mean]
|
||||
values = [0.122, 0.134, ...]
|
||||
|
||||
[stream.phase_circular_variance]
|
||||
values = [0.031, 0.028, ...]
|
||||
```
|
||||
|
||||
TOML is chosen over JSON (no comments, awkward for large arrays), bincode (not human-inspectable, format stability risks across serde versions), and rkyv (zero-copy but requires unsafe and pinned schema). The TOML files are small (Tier A: ~8 KB, Tier A-HE: ~40 KB) and load in < 1 ms at runtime. The `toml` crate is already in the workspace (`wifi-densepose-sensing-server/Cargo.toml`).
|
||||
|
||||
**Device NVS: little-endian binary**
|
||||
|
||||
When `--push-nvs` is passed, the CLI additionally serialises the baseline into a compact binary format and writes it to the device's NVS partition under namespace `ruvcal`, key `b_0` (stream 0). The binary format:
|
||||
|
||||
```
|
||||
Offset Size Field
|
||||
0 4 Magic: 0xCA1_1_BA5E (LE u32)
|
||||
4 2 Schema version: 1 (LE u16)
|
||||
6 2 n_subcarriers (LE u16)
|
||||
8 1 n_streams
|
||||
9 1 tier (0=A, 1=A-HE, 2=B)
|
||||
10 4 frame_count (LE u32)
|
||||
14 4×K×S amp_mean (f32 LE, K×S packed, stream-major)
|
||||
14+4KS 4×K×S amp_variance (f32 LE)
|
||||
14+8KS 4×K×S phase_cos_mean (f32 LE)
|
||||
14+12KS 4×K×S phase_sin_mean (f32 LE)
|
||||
14+16KS 4×K×S phase_circular_variance (f32 LE)
|
||||
```
|
||||
|
||||
For Tier A (K=52, S=1): total = 14 + 5×52×4 = 1,054 bytes. Well within NVS single-key limits (4,000 bytes default). For Tier A-HE (K=242, S=1): 14 + 5×242×4 = 4,854 bytes — slightly above the default NVS 4,000 byte limit per key. **Resolution**: use two NVS keys (`b_0_amp` for amplitude stats, `b_0_phase` for phase stats), each 2,434 bytes. The CLI serialises to two keys when K×S×4 > 1,980 bytes.
|
||||
|
||||
Host and device use different formats because TOML is not parsed on the ESP32 and the binary format would be awkward to inspect on the host. The CLI handles both directions; no device code changes are required.
|
||||
|
||||
### 2.5 Stale-Baseline Detection
|
||||
|
||||
A baseline becomes stale when the static channel has changed significantly enough that baseline-subtracted frames no longer represent motion-only signals. The two causes are:
|
||||
- **Environmental loading**: furniture moved, new appliances added, HVAC pattern change.
|
||||
- **Hardware state change**: device rebooted and auto-gain-control settled at a different level; antenna cable degraded.
|
||||
|
||||
Detection uses the **Welford z-score of recent frames against the baseline amplitude mean**. At runtime, the `CalibrationDeviationScore` computed by `BaselineCalibration::deviation()` returns a per-subcarrier z-score `z[k] = (|H_live[k]| - amp_mean[k]) / sqrt(amp_variance[k])`. The staleness check aggregates this over time:
|
||||
|
||||
```
|
||||
drift_score(t) = mean_over_k( median_over_window_W( |z[k,t']|² ) for t' in [t-W, t] )
|
||||
```
|
||||
|
||||
where the inner `median` operates over a rolling window of W frames. `median` is used instead of `mean` because a single person present during an otherwise empty period should not be flagged as staleness — median suppresses transient occupancy outliers.
|
||||
|
||||
**Parameters:**
|
||||
- `W = 300 frames` (15 seconds at 20 Hz): long enough to average out occupancy transients, short enough to detect a furniture-rearrangement event within half a minute.
|
||||
- Staleness threshold: `drift_score > 4.0`. This corresponds to a mean squared z-score of 4 across all subcarriers, i.e., the amplitude is on average 2σ above the calibration baseline across most subcarriers. This threshold was validated by the field_model.rs team: the `BaselineExpired` error in `field_model.rs` fires at a similar magnitude of environmental shift.
|
||||
|
||||
When `drift_score > 4.0` is sustained for `3 × W = 900 frames` (45 seconds), the system emits a `BaselineDrift` event (see §2.6). A single window above threshold triggers a `BaselineWarn` log only.
|
||||
|
||||
The 3-window confirmation guard prevents false staleness calls during extended occupied periods (e.g., a person sitting still for 10 minutes will raise z-scores, but is not an indicator of environmental change).
|
||||
|
||||
### 2.6 Recalibration Trigger
|
||||
|
||||
**Default behaviour: operator-initiated.**
|
||||
|
||||
The system does not recalibrate automatically. The operator issues `wifi-densepose calibrate --port COM9 --duration 30 --output baseline.toml` from a terminal, or calls `POST /api/calibrate` on the cognitum-v0 appliance dashboard (`http://cognitum-v0:9000`). Automatic recalibration is a configurable option, not the default, for the following reason: automatic recalibration requires confidence that the room is empty at the time of recalibration. There is no reliable mechanism in the current codebase to verify room emptiness from CSI alone (it is the very thing being calibrated), so automatic recalibration risks capturing an occupied baseline and silently degrading sensing accuracy.
|
||||
|
||||
**Configurable modes (all off by default):**
|
||||
|
||||
| Mode | Config key | Condition |
|
||||
|------|-----------|-----------|
|
||||
| Drift-triggered | `recalibrate_on_drift = true` | `drift_score > 4.0` sustained 45 s AND `drift_score < drift_score + 2σ` (i.e., the drift has stabilised, suggesting the room reached a new static state, not that someone is walking around) |
|
||||
| Periodic | `recalibrate_period_hours = N` | Every N hours; captures a reference frame silently; requires `--background` mode |
|
||||
| API-triggered | always available | `POST /api/calibrate` with optional `duration_secs` body parameter |
|
||||
|
||||
When drift-triggered recalibration is enabled, it waits for `drift_score` to plateau (derivative < 0.1 per 30-frame window) before starting capture, using this as a heuristic that the room has stabilised in a new static configuration (furniture moved to a final position, not a person in transit).
|
||||
|
||||
The `CalibrationDeviationScore::drift_score` field is published on the sensing WebSocket at `ws://localhost:8765` as a standard sensing field so the cognitum-v0 dashboard and Home Assistant integration (ADR-115) can expose baseline health.
|
||||
|
||||
### 2.7 Multi-Tier PHY Handling
|
||||
|
||||
An ESP32-C6 may associate as HT20 (Tier A) when no 802.11ax AP is in range, or as HE20 (Tier A-HE) when one is available. The two modes produce different subcarrier counts (52 vs 242 K_active) and different pilot patterns. They are **not interchangeable baselines**.
|
||||
|
||||
**Decision: one baseline file per PHY tier per link. Tier change invalidates the existing baseline.**
|
||||
|
||||
When the aggregator receives a frame from a C6 link and `CsiMetadata.bandwidth_mhz` and the PPDU type (from ADR-110's `csi_collector.c` frame byte 18–19) indicate a tier different from the currently loaded baseline, `BaselineCalibration::subtract()` returns `CalibrationError::TierMismatch { expected, actual }`. The aggregator logs this at WARN level and falls back to no-baseline-subtraction mode for that link until the operator recalibrates.
|
||||
|
||||
The rationale for invalidation rather than interpolation: interpolating a 52-subcarrier baseline to 242 subcarriers (or vice versa) requires assumptions about per-subcarrier correlation that are not validated in this codebase. The hardware-norm resample path (`hardware_norm.rs`) uses Catmull-Rom for subcarrier grid normalisation, but that normalises across hardware types at the same tier — not across tier transitions on the same device.
|
||||
|
||||
In practice, tier transitions are rare: they occur when the AP is rebooted (dropping 802.11ax), when the C6 moves out of 11ax AP range, or when the operator changes the AP. The operator is expected to recalibrate after a tier change.
|
||||
|
||||
### 2.8 Fleet-Wide Simultaneous Capture
|
||||
|
||||
The operator can calibrate the full multistatic array with a single command:
|
||||
|
||||
```
|
||||
wifi-densepose calibrate --all-nodes --duration 30 --output baselines/
|
||||
```
|
||||
|
||||
This issues a simultaneous capture barrier across all configured nodes using the 802.15.4 epoch from ADR-110 (`c6_timesync_get_epoch_us()` on C6 nodes; local clock interpolated to 802.15.4 domain for S3 nodes).
|
||||
|
||||
**Protocol skeleton:**
|
||||
|
||||
1. The CLI sends a `CalibrateStart { start_epoch_us, duration_ms }` UDP control packet to each node's UDP control port (default 5006). Nodes begin accumulating frames from `start_epoch_us` for `duration_ms` milliseconds, tagging each with the 802.15.4 epoch. S3 nodes use their local hardware timer; C6 nodes use `c6_timesync_get_epoch_us()`.
|
||||
2. The aggregator simultaneously opens a UDP receive socket per node and applies `CalibrationRecorder::record()` to each incoming frame. Frame ordering within the window is irrelevant because Welford statistics are commutative.
|
||||
3. At `start_epoch_us + duration_ms + 500 ms` (500 ms guard for last-frame arrival), the CLI finalises each `CalibrationRecorder`, serialises each `BaselineCalibration` to `baselines/<device_id>.toml`, and optionally pushes NVS binary to each device.
|
||||
4. A summary JSON `baselines/summary.json` lists each node, tier, frame count, and the mean `drift_score` relative to any previous baseline, allowing the operator to spot nodes that were occupied during calibration.
|
||||
|
||||
Fleet capture requires that all C6 nodes are associated (not in AP setup mode). Seed nodes that have not yet been provisioned (`seed-2` through `seed-5` from CLAUDE.local.md fleet table) are skipped with a warning. `cognitum-seed-1` is the only fully provisioned seed as of this writing.
|
||||
|
||||
The 802.15.4 timesync barrier is optional for calibration accuracy (Welford statistics are order-independent) but is required when the calibration baseline will also be used to compute the inter-node phase alignment for ADR-042's CHCI path.
|
||||
|
||||
### 2.9 Proposed Rust API
|
||||
|
||||
The new module is `v2/crates/wifi-densepose-signal/src/ruvsense/calibration.rs`, exported from `ruvsense/mod.rs` as `pub mod calibration`.
|
||||
|
||||
```rust
|
||||
use num_complex::Complex32;
|
||||
use wifi_densepose_core::types::CsiFrame;
|
||||
|
||||
// ---- Error type -------------------------------------------------------------
|
||||
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum CalibrationError {
|
||||
#[error("Tier mismatch: baseline is {expected}, frame is {actual}")]
|
||||
TierMismatch { expected: String, actual: String },
|
||||
|
||||
#[error("Subcarrier count mismatch: baseline has {expected}, frame has {got}")]
|
||||
SubcarrierMismatch { expected: usize, got: usize },
|
||||
|
||||
#[error("Stream count mismatch: baseline has {expected}, frame has {got}")]
|
||||
StreamMismatch { expected: usize, got: usize },
|
||||
|
||||
#[error("Insufficient frames: need at least {needed}, recorded {got}")]
|
||||
InsufficientFrames { needed: usize, got: usize },
|
||||
|
||||
#[error("Baseline not yet finalised (still recording)")]
|
||||
NotFinalised,
|
||||
|
||||
#[error("Baseline data corrupted: {0}")]
|
||||
Corrupt(String),
|
||||
|
||||
#[error("Phase precondition violated: frame phase has not been sanitized")]
|
||||
UnsanitizedPhase,
|
||||
|
||||
#[error("TOML serialisation error: {0}")]
|
||||
TomlSerialise(String),
|
||||
|
||||
#[error("TOML deserialisation error: {0}")]
|
||||
TomlDeserialise(String),
|
||||
}
|
||||
|
||||
// ---- Configuration ----------------------------------------------------------
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CalibrationConfig {
|
||||
/// Number of frames to accumulate before finalising. Default: 600 (30 s × 20 Hz).
|
||||
pub target_frames: usize,
|
||||
/// Minimum frames accepted by `finalize()`. Default: 200.
|
||||
pub min_frames: usize,
|
||||
/// Staleness window in frames. Default: 300.
|
||||
pub drift_window_frames: usize,
|
||||
/// Drift score threshold for BaselineDrift event. Default: 4.0.
|
||||
pub drift_threshold: f32,
|
||||
/// Duration (frames) above drift_threshold before emitting BaselineDrift. Default: 900.
|
||||
pub drift_confirm_frames: usize,
|
||||
}
|
||||
|
||||
impl Default for CalibrationConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
target_frames: 600,
|
||||
min_frames: 200,
|
||||
drift_window_frames: 300,
|
||||
drift_threshold: 4.0,
|
||||
drift_confirm_frames: 900,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---- Recorder ---------------------------------------------------------------
|
||||
|
||||
/// Accumulates CSI frames from an empty room to build a baseline.
|
||||
///
|
||||
/// # Phase precondition
|
||||
///
|
||||
/// The caller is responsible for passing frames whose phase has been
|
||||
/// processed by `PhaseSanitizer` and `phase_align.rs` before calling
|
||||
/// `record()`. Unsanitized phase will be detected by a heuristic
|
||||
/// (per-subcarrier phase variance > 10 rad²) and rejected with
|
||||
/// `CalibrationError::UnsanitizedPhase`.
|
||||
///
|
||||
/// # Concurrency
|
||||
///
|
||||
/// `CalibrationRecorder` requires `&mut self` for `record()`. It is not
|
||||
/// `Sync`. Wrap in a `Mutex` if shared across threads.
|
||||
pub struct CalibrationRecorder {
|
||||
config: CalibrationConfig,
|
||||
frame_count: usize,
|
||||
n_streams: usize,
|
||||
n_subcarriers: usize,
|
||||
// Amplitude Welford accumulators: [stream][subcarrier]
|
||||
amp_mean: Vec<Vec<f64>>,
|
||||
amp_m2: Vec<Vec<f64>>,
|
||||
// Circular phase accumulators: [stream][subcarrier]
|
||||
phase_sin_sum: Vec<Vec<f64>>,
|
||||
phase_cos_sum: Vec<Vec<f64>>,
|
||||
}
|
||||
|
||||
impl CalibrationRecorder {
|
||||
/// Create a new recorder. The first `record()` call sets the
|
||||
/// expected subcarrier and stream counts.
|
||||
pub fn new(config: CalibrationConfig) -> Self;
|
||||
|
||||
/// Accept one sanitized CSI frame into the running statistics.
|
||||
///
|
||||
/// Returns the current frame count after this update.
|
||||
pub fn record(&mut self, frame: &CsiFrame) -> Result<usize, CalibrationError>;
|
||||
|
||||
/// Returns `true` if `target_frames` have been accumulated.
|
||||
pub fn is_complete(&self) -> bool;
|
||||
|
||||
/// Returns the current frame count.
|
||||
pub fn frame_count(&self) -> usize;
|
||||
|
||||
/// Finalise the baseline from accumulated statistics.
|
||||
///
|
||||
/// Consumes `self`. Returns an error if fewer than `min_frames` were
|
||||
/// recorded.
|
||||
pub fn finalize(self) -> Result<BaselineCalibration, CalibrationError>;
|
||||
}
|
||||
|
||||
// ---- Baseline ---------------------------------------------------------------
|
||||
|
||||
/// A fully finalised empty-room baseline.
|
||||
///
|
||||
/// Stores per-subcarrier amplitude mean/variance and circular phase
|
||||
/// mean/variance for each spatial stream. Immutable after construction.
|
||||
/// `Clone` is cheap (Vec of f32).
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct BaselineCalibration {
|
||||
/// Device ID from which this baseline was captured.
|
||||
pub device_id: String,
|
||||
/// UTC timestamp of calibration (Unix seconds).
|
||||
pub captured_at_unix_s: i64,
|
||||
/// PHY tier string: "A", "A-HE", or "B".
|
||||
pub tier: String,
|
||||
/// Bandwidth in MHz.
|
||||
pub bandwidth_mhz: u16,
|
||||
/// Number of spatial streams.
|
||||
pub n_streams: usize,
|
||||
/// Number of active (non-pilot, non-null) subcarriers.
|
||||
pub n_subcarriers: usize,
|
||||
/// Total frames used to build this baseline.
|
||||
pub frame_count: usize,
|
||||
// Per-stream, per-subcarrier statistics (stream-major layout).
|
||||
pub amp_mean: Vec<Vec<f32>>,
|
||||
pub amp_variance: Vec<Vec<f32>>,
|
||||
pub phase_cos_mean: Vec<Vec<f32>>,
|
||||
pub phase_sin_mean: Vec<Vec<f32>>,
|
||||
/// Circular variance ∈ [0, 1]: 0 = concentrated, 1 = dispersed.
|
||||
pub phase_circular_variance: Vec<Vec<f32>>,
|
||||
}
|
||||
|
||||
impl BaselineCalibration {
|
||||
/// Compute a deviation score for one live frame against this baseline.
|
||||
///
|
||||
/// Returns `CalibrationError::TierMismatch` if the frame's bandwidth
|
||||
/// or subcarrier count do not match the baseline.
|
||||
pub fn deviation(&self, frame: &CsiFrame) -> Result<CalibrationDeviationScore, CalibrationError>;
|
||||
|
||||
/// Subtract the baseline amplitude mean from `frame.data` (in-place,
|
||||
/// stream-by-stream, subcarrier-by-subcarrier).
|
||||
///
|
||||
/// After subtraction, `frame.data[s][k]` represents the perturbation
|
||||
/// from the static environment, suitable for motion detection and CIR
|
||||
/// estimation.
|
||||
///
|
||||
/// Phase is not modified by subtraction; downstream callers that need
|
||||
/// phase-coherent baseline removal should use
|
||||
/// `reference_csi_vector()` to set `CirEstimator::set_reference_csi()`.
|
||||
pub fn subtract(&self, frame: &mut CsiFrame) -> Result<(), CalibrationError>;
|
||||
|
||||
/// Returns the expected complex CSI vector for the static environment
|
||||
/// (amplitude mean × exp(j × circular_mean_phase)), suitable for passing
|
||||
/// to `CirEstimator::set_reference_csi()`.
|
||||
///
|
||||
/// Returns one vector per spatial stream: `Vec<Vec<Complex32>>`.
|
||||
pub fn reference_csi_vector(&self) -> Vec<Vec<Complex32>>;
|
||||
|
||||
/// Serialise to TOML bytes.
|
||||
pub fn to_toml(&self) -> Result<Vec<u8>, CalibrationError>;
|
||||
|
||||
/// Deserialise from TOML bytes.
|
||||
pub fn from_toml(buf: &[u8]) -> Result<Self, CalibrationError>;
|
||||
|
||||
/// Serialise to compact NVS binary (see §2.4 for format).
|
||||
pub fn to_nvs_bytes(&self) -> Vec<u8>;
|
||||
|
||||
/// Deserialise from NVS binary.
|
||||
pub fn from_nvs_bytes(buf: &[u8]) -> Result<Self, CalibrationError>;
|
||||
}
|
||||
|
||||
// ---- Deviation score --------------------------------------------------------
|
||||
|
||||
/// Per-frame deviation from the static baseline.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CalibrationDeviationScore {
|
||||
/// Per-subcarrier amplitude z-score: (|H[k]| − mean[k]) / std[k].
|
||||
/// Positive = higher than baseline, negative = lower.
|
||||
pub amplitude_z: Vec<Vec<f32>>,
|
||||
/// RMS amplitude z-score across all subcarriers and streams.
|
||||
/// Motion threshold: > 3.0 = likely occupied frame.
|
||||
pub rms_amplitude_z: f32,
|
||||
/// Per-subcarrier circular phase deviation in radians: |φ_live[k] − φ_baseline[k]|.
|
||||
pub phase_deviation_rad: Vec<Vec<f32>>,
|
||||
/// Mean circular phase deviation across all subcarriers.
|
||||
pub mean_phase_deviation_rad: f32,
|
||||
/// Instantaneous drift score (see §2.5 for definition).
|
||||
pub drift_score: f32,
|
||||
/// Whether the drift_score sustained above threshold (staleness flag).
|
||||
pub baseline_stale: bool,
|
||||
}
|
||||
```
|
||||
|
||||
**Design decisions within the API:**
|
||||
|
||||
- `record()` takes `&mut self`, not `&self` with interior mutability. The recording path is inherently single-threaded (one receiver loop per link). Interior mutability would add `Mutex` overhead for no benefit.
|
||||
- `subtract()` takes `&mut CsiFrame` and modifies `frame.data` in place. It does not modify `frame.amplitude` or `frame.phase` — callers that read `frame.amplitude` downstream are expected to call `CsiFrame::recompute_amplitude_phase()` (a new method to be added to `wifi_densepose_core::types::CsiFrame`) or to use `frame.data` directly.
|
||||
- `to_nvs_bytes()` / `from_nvs_bytes()` are fallible via `panic!` for magic mismatch but return `Result` for truncation. This matches the pattern in `csi.rs::parse_esp32_vitals()`.
|
||||
- `BaselineCalibration` is `Clone` because the CLI needs to hold one copy while pushing NVS and another while writing TOML.
|
||||
|
||||
### 2.10 CLI Surface
|
||||
|
||||
The `wifi-densepose calibrate` subcommand is added to `wifi-densepose-cli/src/lib.rs` as a new `Commands::Calibrate(CalibrateCommand)` variant.
|
||||
|
||||
```
|
||||
wifi-densepose calibrate [OPTIONS]
|
||||
|
||||
OPTIONS:
|
||||
--port <PORT> Serial port or UDP address of the ESP32 node
|
||||
(e.g., COM9 on Windows, /dev/ttyS8 on WSL).
|
||||
For fleet mode, omit and use --all-nodes.
|
||||
--duration <SECS> Capture duration in seconds [default: 30]
|
||||
--output <PATH> Path to write the TOML baseline file
|
||||
[default: baseline_<device_id>.toml]
|
||||
--tier <TIER> Expected PHY tier: A | A-HE | B
|
||||
[default: detected from first frame]
|
||||
--push-nvs After capturing, serialise to NVS binary and
|
||||
write to device flash via the provisioning tool.
|
||||
--all-nodes Fleet mode: capture from all configured nodes
|
||||
simultaneously using 802.15.4 epoch sync.
|
||||
--server <ADDR> Aggregator address for --all-nodes mode
|
||||
[default: 127.0.0.1:5006]
|
||||
--min-frames <N> Minimum frames before finalise() is accepted
|
||||
[default: 200]
|
||||
--drift-check After capturing, compare against an existing
|
||||
baseline at --output and print the drift score.
|
||||
```
|
||||
|
||||
**Defaults justified:**
|
||||
|
||||
- `--duration 30`: justified in §2.3.
|
||||
- `--output baseline_<device_id>.toml`: the device ID is embedded in the first received `CsiMetadata.device_id`. The operator does not need to specify it for single-node mode.
|
||||
- `--tier detected`: the first frame's `bandwidth_mhz` and PPDU type (for C6) determine the tier. The flag exists for cases where the operator wants to force Tier A even if the device is capable of Tier A-HE (e.g., to pre-generate a fallback baseline).
|
||||
|
||||
### 2.11 Downstream Consumers
|
||||
|
||||
| Consumer | What it receives | Change required |
|
||||
|----------|-----------------|-----------------|
|
||||
| `ruvsense/multistatic.rs` | Baseline-subtracted `CsiFrame.data` via `BaselineCalibration::subtract()` | `MultistaticConfig` gains a `baseline: Option<Arc<BaselineCalibration>>` field; `process_cycle()` calls `subtract()` on each node's latest frame before passing to the attention gate |
|
||||
| `ruvsense/cir.rs` (ADR-134) | Static-environment reference via `BaselineCalibration::reference_csi_vector()` passed to `CirEstimator::set_reference_csi()` | No API change to `CirEstimator`; the aggregator setup path calls `set_reference_csi()` at startup if a baseline file is present |
|
||||
| `motion.rs` | `CalibrationDeviationScore.rms_amplitude_z` as a primary motion signal | Replaces the existing amplitude variance threshold with a baseline-relative z-score; threshold changes from an absolute amplitude variance to `rms_amplitude_z > 3.0` |
|
||||
| `features.rs` | `CalibrationDeviationScore` fields available as additional features | `SignalFeatures` gains `baseline_rms_z: Option<f32>` and `baseline_drift_score: Option<f32>` fields; `None` when no baseline is loaded |
|
||||
| `wifi-densepose-vitals` | No change | Breathing and heart-rate detection filters operate in the 0.15–2.0 Hz band; slow baseline drift is below 0.001 Hz and is already filtered. The vital-sign pipeline benefits marginally from baseline subtraction at the amplitude level but this is not required for the current implementation. |
|
||||
| `ruvsense/field_model.rs` | Calibration frames passed through `CalibrationRecorder` before SVD decomposition | The field model now takes baseline-subtracted frames as input. The Welford mean accumulator in `field_model.rs::FieldModelBuilder` is superseded for the per-subcarrier-mean step — the calibration module handles it. `FieldModelBuilder` ingests `BaselineCalibration` directly to skip its internal mean step. |
|
||||
|
||||
**CIR interaction detail**: ADR-134's §2.5 specifies that the `CirEstimator` applies conjugate multiplication using `reference_csi` for single-antenna fallback. `BaselineCalibration::reference_csi_vector()` produces the correct complex reference vector: `amp_mean[s][k] × exp(j × atan2(phase_sin_mean, phase_cos_mean))`. This is more accurate than the previously described approach of averaging quiescent frames on the fly, because the baseline uses 600 frames (30 s) rather than a small number of recent frames, reducing the noise on the reference vector by a factor of ~√600/√10 ≈ 7.7× compared to a 0.5 s on-the-fly average.
|
||||
|
||||
### 2.12 Test Plan
|
||||
|
||||
**Tier 1 — Deterministic synthetic stationary channel (unit test)**
|
||||
|
||||
Generate a synthetic CSI frame representing a static 2-tap channel (direct path + one wall reflection, identical parameters to the ADR-134 Tier 1 test): `H[k] = α₁·e^{-j2πkΔf·τ₁} + α₂·e^{-j2πkΔf·τ₂}`. Add zero-mean Gaussian amplitude noise (σ = 0.02 × |α₁|) and constant phase offset δ = π/8 per subcarrier (simulating LO drift already corrected by `phase_align.rs`). Feed 600 copies of this frame to `CalibrationRecorder`. Call `finalize()`. Assert:
|
||||
|
||||
- `baseline.amp_mean[0][k]` is within 2σ/√600 of `|α₁·e^{-j2πkΔf·τ₁} + α₂·e^{-j2πkΔf·τ₂}|` for all k.
|
||||
- `baseline.phase_circular_variance[0][k]` < 0.005 (highly concentrated — noise σ = 0.02 does not produce meaningful phase variance).
|
||||
- `CalibrationDeviationScore.rms_amplitude_z` for the same static frame is < 1.0 (not flagged as motion).
|
||||
|
||||
**Tier 2 — Perturbation detection (unit test)**
|
||||
|
||||
Same baseline. Inject one frame with amplitude perturbed at 10 random subcarriers by +3σ (simulating a person present). Assert `rms_amplitude_z > 3.0` and that the perturbed subcarrier indices are among the top-10 `|amplitude_z|` entries in `CalibrationDeviationScore`.
|
||||
|
||||
**Tier 3 — TOML round-trip (unit test)**
|
||||
|
||||
Serialise the Tier 1 baseline to `to_toml()`, deserialise with `from_toml()`, assert field-level equality to within f32 precision.
|
||||
|
||||
**Tier 4 — NVS binary round-trip (unit test)**
|
||||
|
||||
Same as Tier 3 using `to_nvs_bytes()` / `from_nvs_bytes()`. Assert magic word `0xCA11BA5E` at offset 0 and schema version = 1.
|
||||
|
||||
**Tier 5 — Stale-baseline detection (unit test)**
|
||||
|
||||
Start with the Tier 1 baseline. Feed 900 frames with amplitude uniformly increased by `5σ` at all subcarriers (simulating furniture moved). Assert that `CalibrationDeviationScore.baseline_stale` becomes `true` at or before frame 900.
|
||||
|
||||
**Tier 6 — Real hardware capture (integration test, COM9)**
|
||||
|
||||
Using the ESP32-S3 on COM9 (ruvzen), capture a 30-second baseline in a static empty room. Then capture 200 live frames in the same room (still empty). Assert:
|
||||
- `CalibrationDeviationScore.rms_amplitude_z` < 2.0 for all 200 frames.
|
||||
- `CalibrationDeviationScore.drift_score` < 1.0.
|
||||
- Walking through the room during the live phase: at least 10 consecutive frames show `rms_amplitude_z > 3.0`.
|
||||
|
||||
This test is gated behind `#[cfg(feature = "hardware-test")]` and is not run in CI.
|
||||
|
||||
**Tier 7 — Determinism proof (CI-compatible)**
|
||||
|
||||
To extend the ADR-028 witness proof chain: using the same synthetic 600-frame stream from Tier 1, compute the SHA-256 of `to_nvs_bytes()` output. Record this hash in `archive/v1/data/proof/expected_features.sha256` under the key `calibration_nvs_baseline_v1`. The `verify.py` extension function `calibration_baseline_check()` regenerates the same 600-frame synthetic stream, runs `CalibrationRecorder`, serialises, and asserts the hash matches. This makes the calibration algorithm deterministic end-to-end, consistent with the ADR-028 proof methodology.
|
||||
|
||||
### 2.13 Witness / Proof
|
||||
|
||||
Per ADR-028, the following rows are added to `docs/WITNESS-LOG-028.md`:
|
||||
|
||||
| Row | Capability | Evidence | Hash |
|
||||
|-----|-----------|----------|------|
|
||||
| W-36 | CalibrationRecorder Welford correctness (synthetic 600-frame stationary) | `cargo test calibration::tests::stationary_baseline -- --nocapture` | SHA-256 of amp_mean output |
|
||||
| W-37 | BaselineCalibration NVS binary round-trip | `cargo test calibration::tests::nvs_round_trip` passes | SHA-256 of serialised bytes |
|
||||
| W-38 | Drift detection fires within 900 frames (synthetic 5σ perturbation) | `cargo test calibration::tests::stale_detection` | SHA-256 of test binary |
|
||||
|
||||
`source-hashes.txt` in the witness bundle gains `SHA-256(ruvsense/calibration.rs)`.
|
||||
|
||||
---
|
||||
|
||||
## 3. Consequences
|
||||
|
||||
### 3.1 Positive
|
||||
|
||||
- **Motion detector reliability**: replacing absolute amplitude variance thresholds with baseline-relative z-scores reduces false positives from HVAC and thermal drift. The `rms_amplitude_z > 3.0` threshold is scale-invariant across hardware tiers.
|
||||
- **CIR quality improvement**: `CirEstimator` receives a 600-frame static reference rather than a 10-frame rolling average. Ghost taps near τ=0 from the dominant static path are suppressed earlier in the ISTA solve, freeing regularisation budget for body-perturbed taps. Effective `dominant_tap_ratio` dynamic range increases by the ratio `√600/√10 ≈ 7.7×` in reference SNR — the ISTA warm-start quality directly improves.
|
||||
- **Multi-node amplitude comparability**: after baseline subtraction, each link's `CsiFrame.data` is zero-centred on the static environment. Multistatic attention weighting can use amplitude magnitude directly without per-link gain normalisation.
|
||||
- **ADR-030 field model simplification**: `FieldModelBuilder` no longer needs its own per-subcarrier Welford mean pass; it consumes the finished `BaselineCalibration` and proceeds directly to SVD. Duplicate code is removed.
|
||||
- **Fleet-wide recalibration is one command**: the `--all-nodes` flag with 802.15.4 epoch sync enables house-wide calibration in a single 30-second window, closing the operational gap for multi-room deployments.
|
||||
|
||||
### 3.2 Negative
|
||||
|
||||
- **Calibration ceremony required at install**: operators must capture a 30-second empty-room baseline before the system produces reliable motion scores. Systems shipped without a baseline fall back to uncalibrated mode (no `subtract()` call, absolute variance thresholds). This is not a regression — the current code has no baseline — but it is a new operational step.
|
||||
- **Baseline invalidated by furniture changes**: any significant room change (moved sofa, new TV) requires recalibration. The `drift_score > 4.0` alarm notifies the operator, but does not self-heal.
|
||||
- **Two NVS keys for Tier A-HE**: the 4,854-byte HE20 baseline does not fit in a single default NVS key. The two-key scheme (`b_0_amp` / `b_0_phase`) adds complexity to the device-side NVS reader if that is ever implemented. For the current scope (host-side reader only), this is not a practical problem.
|
||||
- **New `recompute_amplitude_phase()` method needed on `CsiFrame`**: `subtract()` modifies `frame.data` but `frame.amplitude` and `frame.phase` become stale. The method is simple (`amplitude = data.mapv(|c| c.norm()); phase = data.mapv(|c| c.arg())`) but it adds one public API surface to `wifi-densepose-core`.
|
||||
|
||||
### 3.3 Risks
|
||||
|
||||
| Risk | Probability | Impact | Mitigation |
|
||||
|------|-------------|--------|------------|
|
||||
| Operator captures baseline with person present | Medium (single-person household) | Silently corrupted baseline; baseline-subtracted frames look like a "hole" where the person was | The CLI prints real-time `rms_amplitude_z` during capture; high z-scores (>2.0) during capture trigger a WARNING banner. Post-capture, `--drift-check` compares against a previous baseline to flag anomalies |
|
||||
| Tier change (HT20 → HE20) invalidates baseline mid-session | Medium (C6 nodes near AP boundary) | `TierMismatch` error at runtime; system falls to uncalibrated mode | `TierMismatch` logged at WARN; operator notified via WebSocket event; auto-recalibration configurable |
|
||||
| Phase circular variance underestimated for subcarriers with multimodal phase distribution (two equally strong reflected paths at ±π/2) | Low (requires geometric coincidence) | `phase_circular_variance` near 1.0; phase reference from `reference_csi_vector()` is noisy for those subcarriers | `phase_circular_variance > 0.5` per-subcarrier is flagged in the TOML with a comment; CIR estimator down-weights the corresponding rows in Φ by masking them (same mechanism as pilot exclusion in §2.4 of ADR-134) |
|
||||
| ESP32-S3 auto-gain-control shifts between baseline capture and runtime | Low (AGC settles within 5 frames) | Amplitude mean baseline offset; all `amp_z` scores biased | AGC-locked mode (`esp_wifi_set_csi_config` with `rx_chain` pin) is available in firmware v0.7.0; recommend enabling for dedicated sensing nodes via `provision.py --pin-agc` flag |
|
||||
|
||||
---
|
||||
|
||||
## 4. Rationale and Comparison to Alternative Designs
|
||||
|
||||
### 4.1 Why Not "Skip Calibration, Rely on Differential Signals Only"
|
||||
|
||||
The dominant approach in academic WiFi sensing papers (2018–2022) is to use differential or conjugate-product CSI — dividing each frame by a running average of recent frames — rather than an explicit empty-room baseline. This avoids the calibration ceremony at the cost of three concrete problems in this codebase:
|
||||
|
||||
- **Differential signals accumulate bias under environmental loading**. A piece of furniture that moves over 10 minutes produces a slow CSI drift that appears as a 10-minute "motion" event in a conjugate-product system with a 1-second window, or becomes invisible in a system with a 1-hour window. There is no window size that eliminates environmental loading without also suppressing slow human motion (a resting person's micromotion is < 0.01 Hz). The IEEE Transactions 2024 paper "Experimental Evaluation of Long-Term Concept Drift and Its Mitigation in WiFi CSI Sensing" (IEEE Xplore document 10975920) demonstrates that concept drift from environmental factors causes systematic accuracy degradation over hours to days, which no differential window eliminates.
|
||||
- **Differential signals cannot be compared across nodes**. Multi-node coherence scoring requires a shared zero-mean reference. If each node has its own differential reference (its own recent history), drift rates differ across nodes and coherence scores are not interpretable.
|
||||
- **`CirEstimator` requires an absolute complex reference**. ADR-134 §2.5 describes conjugate multiplication: `H[k] * conj(H_ref[k])`. The `H_ref` in that context must be a stable, long-term static reference to avoid ghost taps — not a 0.5-second recent average, which still contains transient motion in active households.
|
||||
|
||||
### 4.2 Why Not "Calibrate at Factory, Ship Coefficients"
|
||||
|
||||
Per-device factory calibration would require: (a) a known-geometry, electromagnetically clean test chamber per device, and (b) the firmware to store calibration at production time. ESP32 hardware calibration (PHY RF calibration, `esp_phy_store_cal_data_to_nvs`) is a different concept — it corrects transmit chain IQ imbalance, not the per-room environmental channel. Room geometry is not known at factory. Per-room baseline is the only physically meaningful calibration for ambient sensing applications.
|
||||
|
||||
### 4.3 Why Not "Use a Neural Network-Learned Baseline"
|
||||
|
||||
Neural baseline subtraction (training a denoising autoencoder on empty-room CSI) has been proposed in several transfer learning papers. The objection from ADR-134 §2.2 for neural CIR applies equally here: there is no paired empty-room dataset for this codebase, and the feature distribution of "empty room" is inherently location-specific. A neural baseline trained in one room may produce negative subtraction values in a different room's frequency-selective geometry. The per-subcarrier Welford mean is a degenerate (optimal) estimator under Gaussian noise: it requires no training data, has a closed-form convergence guarantee, and generalises perfectly to any room because it operates on that room's own captures.
|
||||
|
||||
### 4.4 Why Welford Over Exponential Moving Average (EMA)
|
||||
|
||||
EMA (`mean_new = α × x + (1 − α) × mean_old`) is simpler to implement and provides continuous adaptation but has two drawbacks for a calibration baseline:
|
||||
|
||||
- **α is a free parameter** with no principled setting. Too small an α causes slow adaptation (baseline lags environmental loading); too large adapts immediately to occupancy (person present → person absorbed into baseline → false negative forever).
|
||||
- **EMA variance** requires a separate squared-error accumulator and is less numerically stable than Welford at finite precision.
|
||||
|
||||
Welford provides the exact sample variance in a single pass with no free parameters and no numerical issues. The existing `WelfordStats` in `field_model.rs` is reused directly. The only EMA advantage (continuous adaptation without a discrete recalibrate event) is a liability here: the baseline must be stable while the room is occupied and only updated on explicit operator command.
|
||||
|
||||
---
|
||||
|
||||
## 5. Related ADRs
|
||||
|
||||
| ADR | Relationship |
|
||||
|-----|-------------|
|
||||
| ADR-014 (SOTA Signal Processing) | **Extended**: calibration baseline subtraction becomes the zeroth stage of the signal pipeline, before any feature extraction |
|
||||
| ADR-028 (ESP32 Capability Audit) | **Witness extended**: three new rows W-36 through W-38 added to `WITNESS-LOG-028.md`; calibration NVS binary hash added to `source-hashes.txt` |
|
||||
| ADR-029 (RuvSense Multistatic) | **Enables**: `MultistaticConfig.baseline` field unblocks amplitude-comparable multi-node coherence scoring |
|
||||
| ADR-030 (Persistent Field Model) | **Simplified**: `FieldModelBuilder` no longer computes its own per-subcarrier Welford mean; it ingests `BaselineCalibration` as input |
|
||||
| ADR-110 (ESP32-C6 Firmware Extension) | **Substrate**: 802.15.4 epoch from `c6_timesync_get_epoch_us()` enables fleet-wide simultaneous capture barrier (§2.8); PPDU type (frame bytes 18–19) enables automatic tier detection for C6 nodes |
|
||||
| ADR-115 (Home Assistant Integration) | **Consumer**: `CalibrationDeviationScore.drift_score` and `baseline_stale` are published on the WebSocket stream and picked up by the HA MQTT publisher as `sensor.wifi_baseline_drift` and `binary_sensor.wifi_baseline_stale` |
|
||||
| ADR-134 (First-Class CIR Support) | **Prerequisite improved**: `BaselineCalibration::reference_csi_vector()` replaces the on-the-fly quiescent-frame average described in ADR-134 §2.5; CIR ghost taps from the static environment are suppressed more reliably |
|
||||
|
||||
---
|
||||
|
||||
## 6. References
|
||||
|
||||
### Production Code
|
||||
|
||||
- `v2/crates/wifi-densepose-signal/src/ruvsense/field_model.rs` — `WelfordStats` struct reused; `FieldModelBuilder` to be simplified
|
||||
- `v2/crates/wifi-densepose-signal/src/ruvsense/cir.rs` — `CirEstimator::set_reference_csi()` call site
|
||||
- `v2/crates/wifi-densepose-signal/src/phase_sanitizer.rs` — runs before calibration recording
|
||||
- `v2/crates/wifi-densepose-signal/src/ruvsense/phase_align.rs` — runs before calibration recording
|
||||
- `v2/crates/wifi-densepose-signal/src/hardware_norm.rs` — cross-hardware amplitude normalisation; operates before baseline for `canonical_grid` resampling, after baseline for `z-score` normalisation
|
||||
- `v2/crates/wifi-densepose-signal/src/ruvsense/multistatic.rs` — primary consumer of `BaselineCalibration::subtract()`
|
||||
- `v2/crates/wifi-densepose-signal/src/motion.rs` — secondary consumer of `CalibrationDeviationScore.rms_amplitude_z`
|
||||
- `v2/crates/wifi-densepose-cli/src/lib.rs` — `Commands::Calibrate` variant to be added
|
||||
- `v2/crates/wifi-densepose-sensing-server/src/cli.rs` — `Args` struct for sensing-server CLI context
|
||||
- `firmware/esp32-csi-node/provision.py` — provisioning tool; `--push-nvs` integration point
|
||||
- `archive/v1/data/proof/verify.py` — deterministic proof chain; `calibration_baseline_check()` extension
|
||||
- `archive/v1/data/proof/expected_features.sha256` — hash entry `calibration_nvs_baseline_v1` to be added
|
||||
|
||||
### External Papers
|
||||
|
||||
- Welford, B.P. (1962). "Note on a Method for Calculating Corrected Sums of Squares and Products." *Technometrics*, 4(3), 419–420. — Online mean/variance algorithm used for both amplitude and (via sin/cos projection) phase statistics.
|
||||
- Mardia, K.V. & Jupp, P.E. (2000). *Directional Statistics*. Wiley. Ch. 2–3. — Circular variance estimator `1 − R̄` and its standard error; von Mises maximum-likelihood estimator for the concentration parameter.
|
||||
- Ma, Y. et al. (2023). "Optimal Preprocessing of WiFi CSI for Sensing Applications." *IEEE Transactions on Wireless Communications* (published 2024, arXiv:2307.12126). — Derives the theoretically optimal gain and phase error correction for commodity WiFi CSI; confirms that a per-subcarrier amplitude model reduces sensing noise by 40% over no-correction baseline. Validates the amplitude-mean-subtraction approach chosen here.
|
||||
- Kong, R. & Chen, H. (2025). "Domino: Dominant Path-based Compensation for Hardware Impairments in Modern WiFi Sensing." arXiv:2509.13807. IEEE ICASSP 2026. — Shows that operating on the dominant static CIR path as a reference achieves >2× accuracy over existing compensation methods for respiration monitoring. Validates the principle that a stable static reference (this ADR's baseline) materially improves sensing over no-reference methods.
|
||||
- IEEE Xplore document 10975920 (2025). "Experimental Evaluation of Long-Term Concept Drift and Its Mitigation in WiFi CSI Sensing." — Demonstrates that environmental loading causes accuracy degradation over hours/days in CSI sensing systems that rely on differential signals only; motivates the explicit operator-initiated recalibration model chosen in §2.6.
|
||||
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Reference in New Issue
Block a user