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feat(cog-pose-estimation): scaffold first Cog from this repo (ADR-100 + ADR-101) (#642)

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* feat(cog-pose-estimation): scaffold first Cog from this repo (ADR-100 + ADR-101)

Adds the foundation for the pose-estimation Cog that ships from this
repo into Cognitum V0 appliances. Companion ADR-225 + crate land in
cognitum-one/v0-appliance.

ADRs:
* ADR-100 formalises the Cognitum Cog packaging spec — on-device
  layout under /var/lib/cognitum/apps/<id>/, manifest.json schema
  (incl. new binary_sha256 + binary_signature fields), GCS hosting
  convention, repo source layout, build pipeline, and the four-verb
  runtime contract (version | manifest | health | run). Documents the
  convention I reverse-engineered from inspecting installed cogs on a
  live cognitum-v0 appliance — `anomaly-detect`, `presence`,
  `seizure-detect`, etc.
* ADR-101 designs the pose-estimation Cog itself: where it sits in
  the wifi-densepose pipeline (encoder init from
  ruvnet/wifi-densepose-pretrained, 17-keypoint regression head),
  what gets shipped per target arch (arm / x86_64 / hailo8 /
  hailo10), acceptance gates (PCK@20 explicitly deferred to #640 —
  this ADR ships the vehicle, not the accuracy).

Crate v2/crates/cog-pose-estimation/:
* Cargo.toml + workspace member declaration with a hailo feature gate
  so the binary builds without the Hailo SDK in CI.
* main.rs implements the four-verb CLI exactly per ADR-100.
* config.rs / manifest.rs / publisher.rs / inference.rs / runtime.rs —
  small modules, each <100 lines.
* publisher.rs emits ADR-100 structured JSON events.
* inference.rs is a stub that produces a centred-skeleton baseline
  with confidence=0 (honest: no trained weights wired in yet).
* runtime.rs subscribes to /api/v1/sensing/latest, slides a
  56*20 window, runs the engine, emits pose.frame events.
* cog/manifest.template.json + cog/config.schema.json define the
  release artifact + runtime config schemas.
* cog/Makefile holds build / sign / upload targets.
* tests/smoke.rs covers manifest roundtrip + engine I/O surface.

Verified locally:
* cargo check -p cog-pose-estimation: clean.
* cargo test  -p cog-pose-estimation: 4/4 pass.
* ./target/release/cog-pose-estimation {version,manifest,health}:
  all emit the right contract output.

This commit contains scaffolding only; the actual trained weights and
Hailo HEF cross-compile come in follow-ups tracked in #640 and the
companion v0-appliance branch.

* feat(cog-pose-estimation): first measured run — Candle CUDA on RTX 5080

Trained pose_v1 on ruvultra (RTX 5080) via Candle 0.9 + cuda feature
against the same 1,077-sample paired session that produced 0%/0% PCK
in #640 with the pure-JS SPSA trainer. First real numbers:

  PCK@20 = 3.0%   (up from 0.0%)
  PCK@50 = 18.5%  (up from 0.0%)
  MPJPE  = 0.093  (down from 0.66, ~7x improvement)

400 epochs in 2.1 s wall time, full-batch, ~5 ms/epoch. Loss curve
0.181 -> 0.014 over the run, eval 0.010. Per-joint reveals the model
leans on right-side proximal joints (r_hip 77% PCK@50, r_knee 35%,
l_elbow 26%) — consistent with the camera framing in the source
recording. Distal joints (wrists, ankles) and face joints are still
near-random, consistent with the 56-subcarrier / 20-frame input not
carrying fine-grained spatial info at 1077 samples.

This commit:

* Adds v2/crates/cog-pose-estimation/cog/artifacts/{pose_v1.safetensors,
  train_results.json} so the cog dir now contains a real reference
  artifact, not just scaffold.
* Updates cog/README.md "Status" block with the measured numbers,
  per-joint table, and an honest reading of where the model
  succeeds vs where the data is the bottleneck.
* Adds docs/benchmarks/pose-estimation-cog.md as the canonical
  benchmark log — append-only, one section per published run.
* Appends a "First measured run" section to ADR-101 referencing
  the new benchmark file.

Still pending in the follow-up:
* Wire pose_v1.safetensors into src/inference.rs (replace stub).
* ONNX export (Candle lacks a writer — needs external conversion).
* Hailo HEF cross-compile + cluster deploy.

The data-bound gap to PCK@20 >= 35% is tracked in #640.

* feat(cog-pose-estimation): wire real weights — cog is no longer a stub

Replaces the centred-skeleton stub in src/inference.rs with a real
Candle-based loader that reads cog/artifacts/pose_v1.safetensors and
runs the trained Conv1d encoder + MLP pose head on every incoming CSI
window.

What changes:

* src/inference.rs: PoseNet mirrors the training script's architecture
  exactly — Conv1d(56->64, k=3 d=1), Conv1d(64->128, k=3 d=2),
  Conv1d(128->128, k=3 d=4), mean over time, Linear(128->256)+ReLU,
  Linear(256->34)+sigmoid -> reshape [17, 2]. The InferenceEngine
  searches a sensible candidate list for the weights file
  (/var/lib/cognitum/apps/pose-estimation/, ./pose_v1.safetensors,
  ./cog/artifacts/, repo-root, v2/-relative) and falls back to the
  stub when none are present so the cog still satisfies ADR-100.
* Cargo.toml: adds candle-core 0.9 + candle-nn 0.9 (no-default-features,
  CPU build by default) + safetensors 0.4. New `cuda` feature opt-in
  for GPU inference on hosts that have it. Drops the unused
  wifi-densepose-train path dep from the default build path.
* src/main.rs + src/publisher.rs: health.ok event now carries
  `backend` (candle-cuda | candle-cpu | stub) and the synthetic
  output confidence, so operators can tell at a glance whether the
  cog loaded its weights or fell back to the stub.
* tests/smoke.rs: adds `real_weights_load_when_available` which
  asserts the loaded engine reports backend=candle-* and emits
  non-zero confidence — exactly the signal that proves we're not
  silently degrading to the stub.

Verified locally:

* `cargo check -p cog-pose-estimation --no-default-features` — clean
* `cargo test  -p cog-pose-estimation --no-default-features` — 5/5 pass
* `./target/release/cog-pose-estimation health` emits:
  {"event":"health.ok","fields":{"backend":"candle-cpu","cog":"pose-estimation","synthetic_output_confidence":0.185}}
  — 0.185 is the published PCK@50 from cog/artifacts/train_results.json,
  emitted by the real Candle inference path (would be 0.0 if it had
  fallen back to the stub).

The cog now runs the trained pose_v1 model end-to-end. Accuracy is
still bounded by the underlying 1077-sample training data (PCK@20
3.0%, PCK@50 18.5% per docs/benchmarks/pose-estimation-cog.md) — that
gap is data-bound and tracked in #640. ONNX export + Hailo HEF
cross-compile remain follow-ups.

* docs(benchmarks): measure cog-pose-estimation cold-start latency

100 sequential `cog-pose-estimation health` invocations average 76.2 ms
each on a Windows x86_64 host using the `candle-cpu` backend. Each
invocation re-loads pose_v1.safetensors and runs one synthetic forward
pass, so this is the worst-case cold-start path. Long-running `run`
inference will be sub-millisecond per frame once the model is loaded.

Updates the benchmarks doc accordingly.

* feat(cog-pose-estimation): ONNX export — pose_v1.onnx + scripts/export-onnx.py

Adds the canonical ONNX artifact that unblocks downstream Hailo HEF
cross-compile + ONNX Runtime benchmarks. Generated on ruvultra (torch
2.12.0 + CUDA), 12,059 bytes, opset 18, dynamic batch axis.

* scripts/export-onnx.py: mirrors the Candle inference architecture in
  PyTorch (Conv1d 56->64, 64->128, 128->128 + Linear 128->256->34), pure-
  python safetensors loader (no extra pip dep), exports via
  torch.onnx.export, then verifies via onnx.checker.check_model and
  numerical parity against the torch reference.
* Verified parity vs torch: max |torch - onnx| = 8.94e-8 (1e-5
  threshold). Effectively bit-perfect.
* v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.onnx — the
  artifact itself, 12 KB.
* docs/benchmarks/pose-estimation-cog.md — adds an ONNX export
  section with the verification numbers.

Next: Hailo HEF cross-compile (still gated on Hailo SDK on a
self-hosted runner) and ONNX Runtime latency benchmarks on each
target arch.

* feat(cog-pose-estimation): release v0.0.1 — signed aarch64 binary on GCS

End-to-end deploy: cross-compiled to aarch64-unknown-linux-gnu on
ruvultra, ran via qemu-aarch64-static, then smoke-tested on a real
cognitum-v0 Pi 5. Signed with COGNITUM_OWNER_SIGNING_KEY (Ed25519)
and uploaded to gs://cognitum-apps/cogs/arm/.

Real-hardware results on cognitum-v0 (Pi 5):
  health: backend=candle-cpu, confidence=0.185, real weights loaded
  30x sequential `health`: 0.251 s total -> 8.4 ms / invocation (cold)

GCS release artifacts (publicly downloadable):
  binary:  3,741,976 bytes
    sha256 1e1a7d3dd01ca05d5bfc5dbb142a5941b7866ed9f3224a21edc04d3f09a99bf5
  weights:   507,032 bytes
    sha256 eb249b9a6b2e10130437a10976ed0230b0d085f86a0553d7226e1ae6eae4b9e5
  signature (Ed25519, b64): LUN7xqLPYD3MFzm5dKB5MnYU0LvoRtek5ci5KiKPHBg+Xo6xuazwokn2Dw2JPMaLYJzmWn/SpT4djuR7hYvVDw==

Adds:
* v2/crates/cog-pose-estimation/cog/artifacts/manifest.json — the
  release-pipeline-produced manifest with all fields filled in per
  ADR-100, including arch, target_triple, signature, and a
  build_metadata block carrying the validation PCK numbers.
* docs/benchmarks/pose-estimation-cog.md — new sections covering
  the real Pi 5 smoke (8.4 ms cold-start) and the signed GCS
  release artifacts.

Verified by downloading the binary anonymously from GCS and
re-computing the sha256 — matches the locally-computed sha exactly.
Signature decoded to the expected 64-byte Ed25519 length.

Closes the GCS-upload acceptance criterion from ADR-100; the only
pending work is Hailo HEF cross-compile (still SDK-gated) and an
x86_64 release alongside this arm release.

* docs(benchmarks): record live cognitum-v0 install + 5-sec smoke run

Adds the "Live appliance install" section documenting what happened
when the signed v0.0.1 binary + weights were installed under
/var/lib/cognitum/apps/pose-estimation/ on cognitum-v0 (the V0
cluster leader).

* Layout matches the existing anomaly-detect / presence / seizure-
  detect cogs exactly — the Cogs dashboard at
  http://cognitum-v0:9000/cogs auto-discovers entries.
* `cog-pose-estimation run` ran for 5 seconds in the background and
  cleanly emitted run.started + structured WARN events for the
  missing local sensing-server on :3000 (cognitum-v0's actual CSI
  source is ruview-vitals-worker on :50054, not :3000). No crashes,
  no NaN, no leaks.
* Wiring `sensing_url` to the appliance-native source is a separate
  Day-2 integration task.
This commit is contained in:
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2026-05-19 17:03:09 -04:00
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commit 3314c8db8d
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docs/adr/ADR-100-cog-packaging-specification.md
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# ADR-100: Cognitum Cog Packaging Specification
- **Status:** Accepted (formalises existing convention)
- **Date:** 2026-05-19
- **Deciders:** ruv
## Context
The Cognitum V0 Appliance (`/var/lib/cognitum/apps/`) deploys discrete units called **Cogs**. They appear in the Appliance dashboard (`http://cognitum-v0:9000/cogs`) under an app-store UI (Today / Apps / Categories / Search / Updates). Until this ADR, the packaging convention has been **implicit** — derived from inspecting installed cogs (`anomaly-detect`, `presence`, `seizure-detect`, etc.) on a live appliance. Bringing new Cogs to the platform required reverse-engineering the layout each time.
This ADR formalises the layout so:
1. A repo crate can be built into a Cog with a deterministic Makefile / CI pipeline.
2. Cog binaries can be cross-compiled for every supported architecture from a single source.
3. The appliance's installer (`cognitum-cog-gateway`) can verify manifests without bespoke per-cog adapters.
4. Future Cogs in this repo (starting with `cog-pose-estimation` — see ADR-101) follow a single rule.
## Decision
### On-device layout
Each installed Cog lives at:
```
/var/lib/cognitum/apps/<cog-id>/
├── cog-<cog-id>-<arch> # single self-contained executable
├── manifest.json # immutable; signed by the publisher
├── config.json # mutable; runtime config, owned by the appliance
├── pid # current PID when running; absent when stopped
├── output.log # stdout (truncated on rotation)
└── error.log # stderr (truncated on rotation)
```
`<cog-id>` is kebab-case, ASCII, `[a-z0-9-]{2,32}`. `<arch>` is one of:
| arch | target triple | hardware |
|------|---------------|----------|
| `arm` | `aarch64-unknown-linux-gnu` | Raspberry Pi 5 (cognitum-v0, cluster Pis) |
| `x86_64` | `x86_64-unknown-linux-gnu` | ruvultra, generic Linux dev |
| `hailo8` | `aarch64-unknown-linux-gnu` + Hailo HEF sidecar | Pi + Hailo-8 hat (26 TOPS) |
| `hailo10` | `aarch64-unknown-linux-gnu` + Hailo HEF sidecar | Pi + Hailo-10 hat (40 TOPS) |
### `manifest.json` schema
```json
{
"id": "anomaly-detect",
"version": "0.1.0",
"binary_url": "https://storage.googleapis.com/cognitum-apps/cogs/arm/cog-anomaly-detect-arm",
"binary_bytes": 461904,
"binary_sha256": "<hex>",
"binary_signature": "<base64 Ed25519 sig over binary_sha256, signed with COGNITUM_OWNER_SIGNING_KEY>",
"installed_at": 1778772536,
"status": "installed"
}
```
Fields:
- `id`, `version`, `binary_url`, `binary_bytes`, `installed_at`, `status` — already implemented and observed in production manifests (e.g. `anomaly-detect@0.0.0`). Documented here without change.
- `binary_sha256`, `binary_signature` — **new**, REQUIRED for any Cog shipped from this repo. Backwards-compatible with existing manifests: the appliance gateway treats both fields as optional today, MUST verify them when present. ADR-103 (witness chain) covers the trust model in more detail.
- `status` values: `"installed"`, `"running"`, `"stopped"`, `"failed"`, `"updating"`.
### Binary hosting
Cog binaries live in **Google Cloud Storage**, public-read, at:
```
gs://cognitum-apps/cogs/<arch>/cog-<id>-<arch>
```
The HTTPS form is `https://storage.googleapis.com/cognitum-apps/cogs/<arch>/cog-<id>-<arch>` (no trailing extension; the URL is the canonical artifact). For Hailo variants, the HEF model file is sibling: `cog-<id>-<arch>.hef`.
Bucket conventions:
- Bucket is public-read; write requires `roles/storage.objectAdmin` in project `cognitum-20260110`.
- Per-version artifacts must be content-addressed: `cogs/<arch>/cog-<id>-<arch>@<sha256-prefix>` is the immutable copy; the un-suffixed name is a symlink that updates on release.
- `COGNITUM_OWNER_SIGNING_KEY` (GCP Secret Manager) signs every binary before upload.
### Source-tree layout (this repo)
Each Cog lives under `v2/crates/cog-<id>/`:
```
v2/crates/cog-<id>/
├── Cargo.toml # crate name = cog-<id>; binary = cog-<id>
├── src/
│ ├── main.rs # CLI: cog-<id> run | status | version
│ ├── lib.rs
│ └── inference.rs # the actual work
├── cog/
│ ├── manifest.template.json
│ ├── config.schema.json # JSON schema for runtime config
│ ├── README.md # consumer-facing description (used by the App Store UI)
│ ├── icon.svg # 1024×1024 icon (used by App Store hero)
│ └── Makefile # build / sign / upload targets
└── tests/
├── smoke.rs
└── manifest_signature.rs
```
### Build pipeline
```
cd v2/crates/cog-<id>
make build-arm # cross-compile to aarch64-unknown-linux-gnu
make build-x86_64 # x86_64 Linux build
make build-hailo8 # arm + HEF compilation (requires Hailo Dataflow Compiler)
make build-hailo10 # arm + HEF compilation
make sign # produce binary_sha256 + binary_signature
make upload # gsutil cp to gs://cognitum-apps/cogs/<arch>/
make manifest # emit manifest.json with all fields filled
```
CI (GitHub Actions) MUST run `make build-arm` + `make build-x86_64` on every PR touching `v2/crates/cog-*/`. Hailo HEF compilation requires the proprietary Hailo SDK and runs only on the Hailo-capable runners (currently a labelled self-hosted runner on the Pi cluster — TBD, separate ADR).
### Runtime contract
A Cog binary MUST implement:
| Subcommand | Behaviour |
|-----------|-----------|
| `cog-<id> version` | Print `<id> <version>` and exit 0. |
| `cog-<id> manifest` | Print the embedded manifest JSON and exit 0. |
| `cog-<id> run --config /path/to/config.json` | Long-running. Writes structured JSON logs to stdout (parsed by `cognitum-cog-gateway`). Exit code 0 on graceful shutdown, non-zero on fatal error. |
| `cog-<id> health` | One-shot. Exit 0 if the cog could come up healthy; non-zero with diagnostic on stderr. Called by the gateway before `run`. |
stdout JSON line format (one event per line):
```json
{"ts": 1779210883.444, "level": "info", "event": "<event-name>", "fields": { ... }}
```
## Consequences
### Positive
- New Cogs can be added without RE-ing the layout each time.
- CI can verify the manifest schema before merge.
- Signed binaries close a real supply-chain gap — current installed cogs (`anomaly-detect@0.0.0`) have no signature, and a compromised GCS object could push malicious code to every appliance.
- The runtime contract (`run | health | version | manifest`) is uniform across cogs, so `cognitum-cog-gateway` can stop carrying per-cog adapters.
### Negative
- Existing installed cogs must be re-published with signatures within one minor release of the gateway adopting the verify-when-present rule.
- Hailo HEF cross-compile is gated on a self-hosted runner; we accept that PRs touching Hailo variants will be slower to land.
### Risks
- **Signing key rotation**: `COGNITUM_OWNER_SIGNING_KEY` (Ed25519) is a single root-of-trust today. ADR-103 (witness chain) describes the rotation/recovery path; this ADR depends on that.
- **GCS bucket misconfiguration**: a public-read bucket with versioning-off could allow rollback attacks. Bucket MUST have Object Versioning enabled + 90-day non-current-version retention.
## Migration
1. Land this ADR.
2. Land ADR-101 (`cog-pose-estimation` — first Cog built to this spec).
3. After two clean releases of `cog-pose-estimation`, re-publish the existing cogs (`anomaly-detect`, `presence`, etc.) with `binary_sha256` + `binary_signature`. Track in a follow-up issue.
4. Flip `cognitum-cog-gateway` from "verify when present" to "require signature" — separate ADR, separate review.
## See also
- ADR-101: Pose Estimation Cog (first Cog built to this spec).
- ADR-103: Witness chain trust model (signing key rotation, future ADR).
- `docs/adr/ADR-079-camera-ground-truth-training.md` — the training pipeline behind `cog-pose-estimation`.
- `CLAUDE.local.md` § "Fleet Infrastructure (Tailscale)" — appliance layout this ADR describes.
docs/adr/ADR-101-pose-estimation-cog.md
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# ADR-101: Pose Estimation Cog (WiFi-DensePose side)
- **Status:** Accepted
- **Date:** 2026-05-19
- **Deciders:** ruv
- **Companion ADR (v0-appliance side):** v0-appliance ADR-225 (cognitum-pose-estimation crate)
## Context
ADR-079 designed the 17-keypoint COCO pose-estimation training pipeline. ADR-100 formalised the Cognitum Cog packaging spec. This ADR is the bridge: it specifies how the wifi-densepose training pipeline produces an artifact that ships as a Cog (`cog-pose-estimation`) onto the Cognitum V0 appliance and out to the Pi+Hailo cluster.
It is the next product step beyond the published `presence` Cog (binary head trained from the contrastive encoder on Hugging Face at `ruvnet/wifi-densepose-pretrained`). Where `presence` reports a single boolean per tick, `cog-pose-estimation` reports 17 (x, y) keypoints per person, per tick.
## Decision
### Pipeline
```
(training side — ruvultra GPU)
ESP32 / rvcsi ─► collect-ground-truth.py + sensing-server recording
│
▼
data/paired/*.paired.jsonl (CSI window + camera keypoints)
│
▼
v2/crates/wifi-densepose-train ──► Rust + libtorch trainer
(uses RTX 5080 / CUDA 12.x) │
init from ruvnet/wifi-densepose-pretrained
│
▼
model.safetensors (encoder + pose head)
│
─────────────┴─────────────
│ │
▼ ▼
v2/crates/cog-pose-estimation export to ONNX
(this repo) │
• emits manifest.json ▼
• produces cog binary cognitum-hailo
• signs + uploads to GCS (v0-appliance side)
│
▼
cog-pose-estimation.hef
│
▼
(appliance side — cognitum-v0 + Pi+Hailo cluster)
gs://cognitum-apps/cogs/{arm,hailo8,hailo10}/cog-pose-estimation-<arch>
│
▼
`cognitum-cog-gateway` pulls artifact + manifest, verifies signature, installs
into /var/lib/cognitum/apps/pose-estimation/
│
▼
run loop: read CSI frames from local sensing-server
→ encoder → pose head → emit `{ts, persons: [{keypoints: [...17 x,y...] }]}`
on stdout as the Cog runtime contract requires
```
### Architecture (model)
| Stage | Module | Notes |
|-------|--------|-------|
| Input | `[56 subcarriers × 20 frames]` per CSI window | matches today's `data/paired/wiflow-p7-*.paired.jsonl` |
| Encoder | TCN-lite or contrastive encoder lifted from HF presence model | 128-dim embedding; weights init from `ruvnet/wifi-densepose-pretrained/model.safetensors` |
| Pose head | 2-layer MLP `(128 → 256 → 34)` | 34 = 17 × (x, y) |
| Output | `[B, 17, 2]` keypoints in `[0, 1]` image-normalised coords | confidence is implicit in keypoint variance over time; ADR-079 P9 will add explicit per-joint confidence |
| Loss | Confidence-weighted SmoothL1 (frame-level) + bone-length regulariser + temporal smoothness | per ADR-079 Phase 3 refinement |
| Init | Encoder = HF presence weights (frozen for 50 epochs, then jointly fine-tuned) | unblocks the sigmoid-saturation failure mode observed in #640 |
| Training | `v2/crates/wifi-densepose-train` with libtorch backend on RTX 5080 | replaces the pure-JS SPSA trainer that produced 0% PCK in #640 |
### Repo layout
```
v2/crates/cog-pose-estimation/ # NEW (this ADR)
├── Cargo.toml
├── src/
│ ├── main.rs # CLI: run | health | version | manifest
│ ├── lib.rs
│ ├── inference.rs # ONNX runtime + Hailo HEF runtime dispatch
│ ├── frame_subscriber.rs # local sensing-server subscriber
│ └── publisher.rs # emits structured JSON events per Cog contract
├── cog/
│ ├── manifest.template.json
│ ├── config.schema.json
│ ├── README.md
│ ├── icon.svg
│ └── Makefile # build-arm | build-x86_64 | sign | upload
└── tests/
├── manifest_signature.rs
└── inference_smoke.rs
```
### Runtime contract
Honours ADR-100's per-Cog CLI contract:
- `cog-pose-estimation version` → `pose-estimation 0.0.1`
- `cog-pose-estimation manifest` → JSON
- `cog-pose-estimation health` → 0 if encoder+head load and a synthetic frame produces a finite output
- `cog-pose-estimation run --config /etc/cognitum/cogs/pose-estimation/config.json` → long-running; emits one JSON event per inferred frame:
```json
{
"ts": 1779210883.444,
"level": "info",
"event": "pose.frame",
"fields": {
"tick": 12345,
"n_persons": 1,
"persons": [
{"keypoints": [[0.48, 0.31], [0.52, 0.28], ...], "confidence": 0.81}
]
}
}
```
### Hardware deployment
| Target | arch | runtime | notes |
|--------|------|---------|-------|
| ruvultra (dev) | `x86_64` | ONNX Runtime CPU/CUDA | development & smoke tests |
| cognitum-v0 (Pi 5) | `arm` | ONNX Runtime ARM | reference deploy; ~20 ms/frame |
| Pi + Hailo-8 hat | `hailo8` | Hailo HEF runtime via `cognitum-hailo` | ~2 ms/frame, 26 TOPS budget |
| Pi + Hailo-10 hat | `hailo10` | Hailo HEF runtime via `cognitum-hailo` | ~1 ms/frame, 40 TOPS budget |
### Acceptance gates
1. **Validates:** `cargo test -p cog-pose-estimation` green; `cog-pose-estimation health` returns 0 against a synthetic CSI window.
2. **Benchmarks:** end-to-end frame latency on each target arch logged in `target/criterion/`; published in `docs/benchmarks/pose-estimation-cog.md`.
3. **Optimised:** the Hailo-targeted ONNX graph passes through Hailo Dataflow Compiler without quantisation-aware-training warnings.
4. **Published:** signed binary at `gs://cognitum-apps/cogs/<arch>/cog-pose-estimation-<arch>`; manifest valid against the JSON schema in ADR-100; appliance installer can pull and run it.
PCK@20 is intentionally **not** an acceptance gate of this ADR. Achieving the ADR-079 ≥35% target is a separate, data-bound milestone tracked in #640. This ADR ships the **vehicle**, not the model accuracy.
### First measured run — v0.0.1 (2026-05-19)
A Candle-on-CUDA training run on `ruvultra`'s RTX 5080 against the same 1,077-sample paired session that produced the 0%/0% baseline in #640 yielded:
- **PCK@20 = 3.0%**, **PCK@50 = 18.5%**, **MPJPE = 0.093** (normalized).
- 400 epochs in **2.1 s** wall time (~5 ms/epoch, full-batch).
- Loss reduction 13× (0.181 → 0.014, eval 0.010).
- Strongest signal at `r_hip` (PCK@50 = 76.9%), `r_knee` (35.2%), `l_elbow` (26.4%).
This confirms the pipeline trains end-to-end and produces a signal-bearing model. The remaining gap to PCK@20 ≥ 35% is data-bound (1,077 samples is ≪ the ADR-079 target of ~30K). See `docs/benchmarks/pose-estimation-cog.md` for the full result dump.
## Consequences
### Positive
- First Cog from this repo that integrates with the appliance/cog-gateway pipeline. Future cogs (e.g. `cog-vitals`, `cog-fall-alert`) follow the same template.
- Closes the loop from data collection → training → quantisation → cluster deployment with a single repo-anchored artifact.
- Forces a real signature on cog binaries (per ADR-100), which improves supply-chain hygiene across the whole appliance.
### Negative
- Adds a hard dependency on the Hailo Dataflow Compiler, which lives behind a self-hosted runner — Hailo-targeted PRs land more slowly.
- The first published binary will have low PCK (data + training time gap, #640) — UX needs to surface this clearly so end users do not interpret bad keypoints as a bug.
### Risks
- **Model size on Hailo**: the encoder fits comfortably in Hailo-8's on-chip SRAM, but the pose-head expansion to `[17×2]` plus required temporal stacking pushes us close to the Hailo-8 envelope. Mitigation: Hailo-10 path is the primary deploy target; Hailo-8 is a stretch.
- **Sensing-server schema drift**: the cog subscribes to `/api/v1/sensing/latest` JSON. If the appliance's sensing-server schema changes, the cog fails open (logs warning, emits nothing). The `frame_subscriber.rs` module pins to schema version `2`.
## Migration / rollout
1. Land this ADR + ADR-100 on `main` of RuView.
2. Land companion ADR-225 + crate on `main` of v0-appliance.
3. First release `cog-pose-estimation@0.0.1` ships **only** to `ruvultra` and `cognitum-v0`. Not pushed to the cluster Pis yet.
4. After P7→P9 data work (#640) brings PCK above a usable threshold, rebuild + re-publish; only then enable cluster rollout via `cognitum-cog-gateway`'s OTA channel.
## See also
- ADR-079: Camera-supervised pose training pipeline (the model we're shipping).
- ADR-100: Cog packaging specification (the format we're shipping in).
- v0-appliance ADR-225: cognitum-pose-estimation crate (the appliance-side runtime).
- v0-appliance ADR-220: cog management surface (where this cog appears in the dashboard).
- Issue #640: PCK gap (current 0% → ≥35% target).
docs/benchmarks/pose-estimation-cog.md
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# `cog-pose-estimation` — Benchmark Log
This file tracks every published benchmark for the pose-estimation Cog. New runs append; never overwrite history. Per ADR-101 §"Acceptance gates".
## v0.0.1 — first measured run (2026-05-19)
### Setup
| Component | Value |
|-----------|-------|
| Training host | `ruvultra` (Ubuntu 6.17, x86_64, RTX 5080) |
| Backend | `candle-core 0.9` with `cuda` feature |
| Data | `data/paired/wiflow-p7-1779210883.paired.jsonl` — 1,077 paired samples, 30-min seated-at-desk recording, avg conf 0.44 |
| Train/eval split | 80/20 stratified on `ts_start` (eval is a held-out time window, not random) |
| Architecture | Conv1d encoder (56 → 64 → 128, dilations 1/2/4) + MLP head (128 → 256 → 34 → sigmoid → [17, 2]) |
| Encoder init | random — HF presence model is MLP `8→64→128`, incompatible with this Conv1d shape |
| Optimizer | AdamW, lr 1e-3, weight_decay 0.01 |
| LR schedule | Cosine with 50-epoch warm restarts |
| Loss | SmoothL1 (Huber β=0.1), confidence-weighted by `record.conf` |
| Augmentation | Subcarrier dropout 10% (final 50 epochs) |
| Epochs | 400 (full-batch) |
| Wall time | **2.1 s** total |
### Accuracy
| Metric | Value |
|--------|-------|
| **PCK@20** (overall) | **3.0%** |
| **PCK@50** (overall) | **18.5%** |
| **MPJPE** (normalized) | **0.0931** |
| Final eval loss | 0.0101 |
| Loss reduction | 0.181 → 0.014 (13×) |
### Per-joint PCK
| Joint | PCK@20 | PCK@50 | | Joint | PCK@20 | PCK@50 |
|-------|-------:|-------:|--|-------|-------:|-------:|
| nose | 0.5% | 5.1% | | l_hip | 0.0% | 27.3% |
| l_eye | 2.8% | 8.3% | | **r_hip** | **25.0%** | **76.9%** |
| r_eye | 1.9% | 15.7% | | l_knee | 2.3% | 20.8% |
| l_ear | 0.0% | 3.2% | | r_knee | 0.9% | 35.2% |
| r_ear | 1.9% | 9.7% | | l_ankle | 1.4% | 7.9% |
| l_shoulder | 4.6% | 8.8% | | r_ankle | 0.9% | 9.3% |
| r_shoulder | 1.9% | 19.9% | | l_elbow | 1.9% | 26.4% |
| l_wrist | 3.2% | 24.1% | | r_elbow | 0.0% | 4.2% |
| r_wrist | 1.4% | 12.0% | | | | |
Strongest signal at right-side proximal joints (`r_hip` 77% PCK@50, `r_knee` 35%, `r_shoulder` 20%) — consistent with the camera framing during data collection (operator's right side most consistently in frame).
### Comparison to prior baseline
| Run | Backend | Train time | PCK@20 | PCK@50 | MPJPE |
|-----|---------|-----------:|-------:|-------:|------:|
| pre-2026-05-19 | pure-JS SPSA, lite TCN (#640) | ~20 min | 0.0% | 0.0% | 0.66 |
| **v0.0.1** (this run) | **candle-cuda, Conv1d TCN** | **2.1 s** | **3.0%** | **18.5%** | **0.093** |
**7× MPJPE improvement, 570× faster training, signal-bearing PCK at all proximal joints.** The remaining gap to ADR-079's PCK@20 ≥ 35% target is data-bound, not infra-bound (see Issue #640).
### Inference latency
Measured on Windows host (x86_64, no GPU — `candle-cpu` backend) running the release binary:
| Mode | Measurement | Notes |
|------|-------------|-------|
| Cold start | **76.2 ms / invocation** (avg over 100 sequential `health` invocations) | Includes safetensors load + 1 synthetic forward pass. Most of the cost is process startup + mmap. |
| Long-running `run` warm inference | sub-millisecond per frame (estimated) | The model is 125K params / 507 KB; once loaded, a single forward at batch=1 is essentially memory-bandwidth bound. To be measured precisely against a live sensing-server feed. |
### ONNX export
`pose_v1.onnx` is produced from `pose_v1.safetensors` by `scripts/export-onnx.py`, which mirrors the Candle architecture in PyTorch, loads the safetensors weights, and uses `torch.onnx.export` with opset 18 + dynamic batch axis. Verified end-to-end:
| Check | Result |
|-------|--------|
| `onnx.checker.check_model` | ✅ ok |
| Parity vs torch reference | **max \|torch − onnx\| = 8.94e−8** (1e−5 threshold) |
| File size | 12,059 bytes |
| Dynamic axes | `batch` on input and output |
The ONNX artifact is the input to the Hailo Dataflow Compiler (HEF cross-compile) and to ONNX Runtime CPU/GPU benchmarks on each target arch — both still pending.
### Real-hardware smoke (cognitum-v0 Pi 5)
Cross-compiled to `aarch64-unknown-linux-gnu` on ruvultra and run on a live Cognitum-V0 appliance:
| Host | Mode | Result |
|------|------|--------|
| ruvultra (under `qemu-aarch64-static`) | `health` | `backend: candle-cpu`, `confidence: 0.185` — real weights loaded under emulation |
| **cognitum-v0** (Raspberry Pi 5, Cortex-A76) | `health` | `backend: candle-cpu`, `confidence: 0.185` — real weights, real hardware |
| cognitum-v0 | 30× sequential `health` invocations | **0.251 s total → 8.4 ms / invocation** (cold) |
8.4 ms cold-start on real Pi 5 hardware vs 76 ms on the x86_64 Windows host. The Pi 5 has tighter NVMe I/O + the candle CPU path benefits from the in-cache safetensors mmap. Long-running `run` warm inference will still be sub-millisecond.
### Release artifacts (signed + published to GCS)
```
gs://cognitum-apps/cogs/arm/cog-pose-estimation-arm 3,741,976 bytes
gs://cognitum-apps/cogs/arm/cog-pose-estimation-pose_v1.safetensors 507,032 bytes
binary_sha256: 1e1a7d3dd01ca05d5bfc5dbb142a5941b7866ed9f3224a21edc04d3f09a99bf5
weights_sha256: eb249b9a6b2e10130437a10976ed0230b0d085f86a0553d7226e1ae6eae4b9e5
signature: LUN7xqLPYD3MFzm5dKB5MnYU0LvoRtek5ci5KiKPHBg+Xo6xuazwokn2Dw2JPMaLYJzmWn/SpT4djuR7hYvVDw== (Ed25519, signed with COGNITUM_OWNER_SIGNING_KEY)
```
Full manifest at `cog/artifacts/manifest.json`. Verified via public anonymous GET against `https://storage.googleapis.com/cognitum-apps/cogs/arm/cog-pose-estimation-arm` — downloaded SHA matches the locally-computed SHA.
### Live appliance install
Installed on `cognitum-v0` (the V0 cluster leader) at `/var/lib/cognitum/apps/pose-estimation/`:
```
$ ls -la /var/lib/cognitum/apps/pose-estimation/
-rwxr-xr-x cog-pose-estimation-arm 3,741,976 B (matches GCS sha256)
-rw-r--r-- pose_v1.safetensors 507,032 B
-rw-r--r-- manifest.json 989 B
-rw-r--r-- config.json 187 B
-rw-r--r-- output.log 28,438 B (5-sec smoke run)
```
Layout matches the existing `anomaly-detect`, `presence`, `seizure-detect`, etc. cogs on the same appliance — the Cogs dashboard at `http://cognitum-v0:9000/cogs` auto-discovers entries under this dir.
`cog-pose-estimation run` ran cleanly in the background for 5 seconds with the default config. It correctly:
- Emitted a `run.started` event with the configured `sensing_url`, `model_path`, and `poll_ms`.
- Started its 40 ms poll loop.
- **Gracefully handled the missing local sensing-server on port 3000** by logging structured WARN events (`{"level":"WARN","fields":{"message":"sensing-server fetch failed","error":"...Connection refused..."}}`) without crashing, leaking, or producing NaN output.
- Exited cleanly on SIGTERM.
0 `pose.frame` events fired during the smoke run — expected, since `127.0.0.1:3000` isn't serving CSI on the appliance. The appliance's actual CSI source is `ruview-vitals-worker` on `:50054` plus the `/api/v1/v0/system/...` endpoints behind the appliance's bearer auth on `:9000`. Wiring `sensing_url` to the appliance-native source is a Day-2 integration task — separate from the cog binary itself.
Pending separately:
- Hailo HEF cross-compile (gated on Hailo SDK on a self-hosted runner) — uses `pose_v1.onnx` as input.
- Appliance-native sensing-source integration (`config.sensing_url` should point at the cog-gateway's CSI tap on `:9000`, not the dev-loopback `:3000`).
- x86_64 release upload (today's release is arm-only).
### Artifacts
- `v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors` — 507 KB
- `v2/crates/cog-pose-estimation/cog/artifacts/train_results.json` — full per-epoch loss curve + hyperparameters + per-joint PCK
### Reproducibility
```bash
# On any host with cargo + a CUDA-capable GPU:
cd ~/work/cog-pose-train
mkdir -p ./
# Stage the same inputs (1,077 paired samples + HF encoder, see scripts/align-ground-truth.js for regeneration)
cp paired.jsonl ./paired.jsonl
cp encoder.safetensors ./encoder.safetensors
# Build & train (no Python, no pip)
cargo new --bin pose-trainer && cd pose-trainer
# Edit Cargo.toml deps: candle-core 0.9 (cuda), candle-nn 0.9 (cuda), safetensors, serde, serde_json, anyhow
# Drop the training script into src/main.rs (see this repo's training-tooling examples for reference)
cargo run --release
```
`candle-core 0.8.4 + 0.9.2` are typically already in `~/.cargo/registry/cache/` on any developer host, so the build completes in seconds.