feat(cog-pose-estimation): scaffold first Cog from this repo (ADR-100 + ADR-101)#642
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… + 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.
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.
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.
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.
…t-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.
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.
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 was referenced May 19, 2026
ruvnet
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May 19, 2026
Updates both ADRs to reflect that the first cog (`cog-pose-estimation@0.0.1`) landed today via PRs #642 + #643. ADR-100 (Cog Packaging Specification): * Status line: "first conforming cog shipped 2026-05-19". * Migration step 2 marked complete with PR references and the GCS paths the binaries live at. ADR-101 (Pose Estimation Cog): * Status line: "v0.0.1 shipped 2026-05-19". * New "v0.0.1 shipping status" section that walks through every ADR-100 acceptance gate with concrete pass/fail evidence (binary sizes, sha256 round-trip, signature, manifest path, live install on cognitum-v0, runtime contract, real-weights load assertion, ONNX parity). * Measured-metrics table: training time (2.1 s/400 epochs on RTX 5080), PCK@20/PCK@50/MPJPE, cold-start latency for Windows/ruvultra/Pi 5. * Carries forward the two open follow-ups: Hailo HEF (SDK-gated) and PCK@20 >= 35% (data-bound, #640). * "See also" link to docs/benchmarks/pose-estimation-cog.md. Docs-only; no code changes.
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Summary
Lays the foundation for shipping pose estimation as a Cognitum Cog — the deployable unit that installs into
cognitum-v0's/var/lib/cognitum/apps/<id>/. First Cog from this repo. Companion ADR-225 + appliance-side crate land incognitum-one/v0-appliance.ADRs
manifest.jsonschema (incl. newbinary_sha256+binary_signaturefields for supply-chain hygiene), GCS hosting atgs://cognitum-apps/cogs/<arch>/, source-tree layout, build pipeline, and the four-verb runtime contract (version | manifest | health | run).ruvnet/wifi-densepose-pretrained, 17-keypoint regression head, per-arch deploys (arm/x86_64/hailo8/hailo10). PCK@20 deferred to #640 — this ADR ships the vehicle, not the accuracy.What's in the crate
v2/crates/cog-pose-estimation/:Verified
cargo check -p cog-pose-estimation— cleancargo test -p cog-pose-estimation— 4/4 passcog-pose-estimation version→pose-estimation 0.3.0cog-pose-estimation manifest→ JSON speccog-pose-estimation health→ emits{"event":"health.ok",...}and exits 0What's still pending (NOT in this PR)
cognitum-pose-estimationincognitum-one/v0-appliance(runs the HEF on the appliance) — lands on its own branch.inference.rsis a stub returning a centred-skeleton baseline withconfidence=0(honest: no real model wired in). Replaces with libtorch + HF init in a follow-up tracked under #640.COGNITUM_OWNER_SIGNING_KEYprovisioning is a separate PR.Test plan
cargo check -p cog-pose-estimationcleancargo test -p cog-pose-estimation4/4 passmanifest.jsonschema against a live appliance manifest (e.g./var/lib/cognitum/apps/anomaly-detect/manifest.jsonon cognitum-v0)wifi-densepose-traininto release builds when--no-default-featuresis used🤖 Generated with claude-flow