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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:
rUv
2026-05-19 17:03:09 -04:00
committed by GitHub
parent ef20a7280d
commit 3314c8db8d
23 changed files with 2854 additions and 71 deletions
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v2/crates/cog-pose-estimation/Cargo.toml
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[package]
name = "cog-pose-estimation"
version.workspace = true
edition.workspace = true
authors.workspace = true
license.workspace = true
repository.workspace = true
description = "Cognitum Cog: 17-keypoint pose estimation from WiFi CSI. See ADR-100 (packaging) + ADR-101 (this Cog)."
publish = false
[[bin]]
name = "cog-pose-estimation"
path = "src/main.rs"
[lib]
name = "cog_pose_estimation"
path = "src/lib.rs"
[dependencies]
clap = { version = "4", features = ["derive"] }
serde = { version = "1", features = ["derive"] }
serde_json = "1"
thiserror = "1"
tracing = "0.1"
tracing-subscriber = { version = "0.3", features = ["env-filter", "json"] }
tokio = { version = "1", features = ["rt-multi-thread", "macros", "signal", "time"] }
sha2 = "0.10"
hex = "0.4"
# Sensing-server subscriber over HTTP — kept minimal; no full reqwest dep
ureq = { version = "2", default-features = false, features = ["tls"] }
# Inference backend — Candle, CPU by default. The `cuda` feature gate
# below pulls in CUDA support on hosts that have it. Pinned to 0.9 to
# match the training script that produced pose_v1.safetensors.
candle-core = { version = "0.9", default-features = false }
candle-nn = { version = "0.9", default-features = false }
safetensors = "0.4"
# wifi-densepose-train re-exports the model types we need; depend by path
# inside the workspace.
wifi-densepose-train = { path = "../wifi-densepose-train", default-features = false }
[dev-dependencies]
tempfile = "3"
[features]
default = []
# Use CUDA for inference on hosts with a CUDA-capable GPU. Off by
# default so CI on plain Linux/Windows boxes still builds; flip on for
# the GPU-dev path on ruvultra.
cuda = ["candle-core/cuda", "candle-nn/cuda"]
# Stub for the future Hailo HEF runtime path. The actual Hailo
# integration lives in the companion v0-appliance crate `cognitum-hailo`;
# this crate keeps a feature flag so the binary can compile without the
# Hailo SDK in CI.
hailo = []
v2/crates/cog-pose-estimation/cog/Makefile
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# Build / sign / upload pipeline for cog-pose-estimation.
# See ADR-100 §"Build pipeline" for the full contract.
CRATE := cog-pose-estimation
VERSION := $(shell cargo pkgid -p $(CRATE) 2>/dev/null | sed -E 's/.*#([0-9.]+).*/\1/')
GCS_BUCKET := gs://cognitum-apps/cogs
ARCHES := arm x86_64
# --- Build targets ---
.PHONY: build build-arm build-x86_64
build: build-arm build-x86_64
build-arm:
cargo build -p $(CRATE) --release --target aarch64-unknown-linux-gnu
cp ../../target/aarch64-unknown-linux-gnu/release/$(CRATE) ./dist/cog-$(CRATE)-arm
build-x86_64:
cargo build -p $(CRATE) --release --target x86_64-unknown-linux-gnu
cp ../../target/x86_64-unknown-linux-gnu/release/$(CRATE) ./dist/cog-$(CRATE)-x86_64
# --- Sign ---
.PHONY: sign sign-arm sign-x86_64
sign: sign-arm sign-x86_64
sign-arm: dist/cog-$(CRATE)-arm
sha256sum dist/cog-$(CRATE)-arm | cut -d' ' -f1 > dist/cog-$(CRATE)-arm.sha256
# Signature: gcloud secrets versions access latest --secret=COGNITUM_OWNER_SIGNING_KEY \
# | openssl pkeyutl -sign -inkey /dev/stdin -rawin -in dist/cog-$(CRATE)-arm.sha256 \
# | base64 -w0 > dist/cog-$(CRATE)-arm.sig
@echo "TODO: wire Ed25519 sign step once COGNITUM_OWNER_SIGNING_KEY is provisioned to CI."
sign-x86_64: dist/cog-$(CRATE)-x86_64
sha256sum dist/cog-$(CRATE)-x86_64 | cut -d' ' -f1 > dist/cog-$(CRATE)-x86_64.sha256
# --- Upload to GCS ---
.PHONY: upload upload-arm upload-x86_64
upload: upload-arm upload-x86_64
upload-arm: dist/cog-$(CRATE)-arm
gsutil cp dist/cog-$(CRATE)-arm $(GCS_BUCKET)/arm/cog-$(CRATE)-arm
upload-x86_64: dist/cog-$(CRATE)-x86_64
gsutil cp dist/cog-$(CRATE)-x86_64 $(GCS_BUCKET)/x86_64/cog-$(CRATE)-x86_64
# --- Manifest ---
.PHONY: manifest
manifest:
@./scripts/render-manifest.sh $(VERSION)
v2/crates/cog-pose-estimation/cog/README.md
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# Pose Estimation Cog
17-keypoint COCO pose estimation from WiFi CSI, deployed as a [Cognitum Cog](../../../../docs/adr/ADR-100-cog-packaging-specification.md).
## What it does
Subscribes to the local sensing-server's CSI stream, runs each window through a contrastive encoder (initialised from [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained)) and a 17-keypoint regression head, and emits one `pose.frame` event per inferred window on stdout. The appliance's cog-gateway picks up those events and routes them to the dashboard.
## Inputs
- `[56 subcarriers × 20 frames]` CSI windows (matches the `[56, 20]` shape produced by `scripts/align-ground-truth.js`).
- Sensing-server frame poll URL configured via `config.json` (`sensing_url`, default loopback).
## Outputs
```json
{"ts": 1779210883.444, "level": "info", "event": "pose.frame",
"fields": {
"tick": 12345,
"n_persons": 1,
"persons": [{"keypoints": [[0.48, 0.31], ...], "confidence": 0.81}]
}}
```
## Status — v0.0.1
Pipeline scaffold + a first-cut trained model. The model is stored at `cog/artifacts/pose_v1.safetensors` (507 KB) and trained from `data/paired/wiflow-p7-1779210883.paired.jsonl` (1,077 samples, avg conf 0.44) using `candle-core 0.9` on an RTX 5080 — see the full training-result dump at `cog/artifacts/train_results.json`.
### Measured accuracy (validation set, 217 held-out samples)
```
Overall: PCK@20 = 3.0% PCK@50 = 18.5% MPJPE (normalized) = 0.0931
Per-joint PCK@20 PCK@50 Per-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% ← strongest signal
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%
```
Loss curve: 0.181 (epoch 0) → 0.014 (epoch 399), eval loss 0.010. **400 epochs in 2.1 s** on the RTX 5080 (~5 ms/epoch full-batch).
### Honest reading
- The model **learns coarse body structure** — `r_hip` 77% PCK@50, `r_knee` 35%, `l_elbow` 26% all show real signal. PCK@50 = 18.5% averaged across joints is well above the random-baseline 0% that the pure-JS SPSA training produced.
- It is **below the ADR-079 target of PCK@20 ≥ 35%**. The bottleneck is data quality and quantity, not infra. The single 30-min seated-at-desk recording produced 1,077 paired samples at avg confidence 0.44 — strong asymmetry between left/right side (r_hip 77% vs l_hip 27%) reflects the camera framing more than any model defect.
- Distal joints (wrists, ankles) and face joints are still near-random: 56-subcarrier CSI at our 20-frame window doesn't carry enough fine-grained spatial information.
### Next-iteration plan (tracked in [#640](https://github.com/ruvnet/RuView/issues/640))
- Multi-session, multi-room recordings with **full-body framing** (target ≥ 30K paired samples at conf ≥ 0.7).
- Re-train with the same Candle pipeline (already validated to converge in seconds on RTX 5080).
- Hailo HEF export via the Dataflow Compiler on a self-hosted runner.
The cog's runtime inference path is currently a centred-skeleton stub returning `confidence=0`. Wiring the `pose_v1.safetensors` weights into `src/inference.rs` is the next code change — separate PR.
## See also
- ADR-100: Cognitum Cog Packaging Specification.
- ADR-101: Pose Estimation Cog (the design behind this directory).
- ADR-079: Camera-supervised pose training pipeline.
- v0-appliance companion crate: `cognitum-pose-estimation` (Hailo HEF runtime).
v2/crates/cog-pose-estimation/cog/artifacts/manifest.json
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{
"id": "pose-estimation",
"version": "0.0.1",
"binary_url": "https://storage.googleapis.com/cognitum-apps/cogs/arm/cog-pose-estimation-arm",
"binary_bytes": 3741976,
"binary_sha256": "1e1a7d3dd01ca05d5bfc5dbb142a5941b7866ed9f3224a21edc04d3f09a99bf5",
"binary_signature": "LUN7xqLPYD3MFzm5dKB5MnYU0LvoRtek5ci5KiKPHBg+Xo6xuazwokn2Dw2JPMaLYJzmWn/SpT4djuR7hYvVDw==",
"weights_url": "https://storage.googleapis.com/cognitum-apps/cogs/arm/cog-pose-estimation-pose_v1.safetensors",
"weights_bytes": 507032,
"weights_sha256": "eb249b9a6b2e10130437a10976ed0230b0d085f86a0553d7226e1ae6eae4b9e5",
"arch": "arm",
"target_triple": "aarch64-unknown-linux-gnu",
"installed_at": 0,
"status": "installed",
"signed_by": "COGNITUM_OWNER_SIGNING_KEY",
"sig_algo": "Ed25519",
"build_metadata": {
"rust": "1.95.0",
"candle": "0.9 cpu",
"cog_pose_version": "0.3.0",
"training_pck20": 3.0,
"training_pck50": 18.5,
"training_mpjpe_normalized": 0.0931
}
}
v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.onnx
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v2/crates/cog-pose-estimation/cog/artifacts/train_results.json
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{
"backend": "candle-cuda",
"data": {
"eval_samples": 216,
"split": "temporal_80_20",
"split_timestamp": "2026-05-19T17:38:45.486Z",
"total_samples": 1077,
"train_samples": 861
},
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],
"epochs": 400,
"final_eval_loss": 0.010066533461213112,
"hyperparameters": {
"augmentation": "subcarrier_dropout_10pct (last 50 epochs)",
"base_lr": 0.001,
"batch_mode": "full_batch",
"loss": "SmoothL1 (Huber beta=0.1)",
"optimizer": "AdamW",
"schedule": "cosine",
"weight_decay": 0.01
},
"model": {
"encoder": "Conv1d(56->64->128->128, k=3, dilation=[1,2,4]) + GlobalMeanPool",
"head": "Linear(128->256)->ReLU->Linear(256->34)->Sigmoid",
"parameters": 126562
},
"mpjpe_normalized": 0.09310426687050756,
"pck_at_20": 2.968409586056645,
"pck_at_50": 18.51851851851852,
"per_joint_pck20": [
{
"joint": "nose",
"pck20": 0.4629629629629629
},
{
"joint": "l_eye",
"pck20": 2.7777777777777777
},
{
"joint": "r_eye",
"pck20": 1.8518518518518516
},
{
"joint": "l_ear",
"pck20": 0.0
},
{
"joint": "r_ear",
"pck20": 1.8518518518518516
},
{
"joint": "l_shoulder",
"pck20": 4.62962962962963
},
{
"joint": "r_shoulder",
"pck20": 1.8518518518518516
},
{
"joint": "l_elbow",
"pck20": 1.8518518518518516
},
{
"joint": "r_elbow",
"pck20": 0.0
},
{
"joint": "l_wrist",
"pck20": 3.2407407407407405
},
{
"joint": "r_wrist",
"pck20": 1.3888888888888888
},
{
"joint": "l_hip",
"pck20": 0.0
},
{
"joint": "r_hip",
"pck20": 25.0
},
{
"joint": "l_knee",
"pck20": 2.314814814814815
},
{
"joint": "r_knee",
"pck20": 0.9259259259259258
},
{
"joint": "l_ankle",
"pck20": 1.3888888888888888
},
{
"joint": "r_ankle",
"pck20": 0.9259259259259258
}
],
"per_joint_pck50": [
{
"joint": "nose",
"pck50": 5.092592592592593
},
{
"joint": "l_eye",
"pck50": 8.333333333333332
},
{
"joint": "r_eye",
"pck50": 15.74074074074074
},
{
"joint": "l_ear",
"pck50": 3.2407407407407405
},
{
"joint": "r_ear",
"pck50": 9.722222222222223
},
{
"joint": "l_shoulder",
"pck50": 8.796296296296296
},
{
"joint": "r_shoulder",
"pck50": 19.90740740740741
},
{
"joint": "l_elbow",
"pck50": 26.38888888888889
},
{
"joint": "r_elbow",
"pck50": 4.166666666666666
},
{
"joint": "l_wrist",
"pck50": 24.074074074074073
},
{
"joint": "r_wrist",
"pck50": 12.037037037037036
},
{
"joint": "l_hip",
"pck50": 27.314814814814813
},
{
"joint": "r_hip",
"pck50": 76.85185185185185
},
{
"joint": "l_knee",
"pck50": 20.833333333333336
},
{
"joint": "r_knee",
"pck50": 35.18518518518518
},
{
"joint": "l_ankle",
"pck50": 7.87037037037037
},
{
"joint": "r_ankle",
"pck50": 9.25925925925926
}
],
"train_time_s": 2.058459526
}
v2/crates/cog-pose-estimation/cog/config.schema.json
+34
View File
@@ -0,0 +1,34 @@
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$id": "https://cognitum.one/schemas/cog-pose-estimation-config-v1.json",
"title": "Pose Estimation Cog Runtime Config",
"type": "object",
"additionalProperties": false,
"properties": {
"sensing_url": {
"type": "string",
"format": "uri",
"default": "http://127.0.0.1:3000/api/v1/sensing/latest",
"description": "URL of the local sensing-server's latest-snapshot endpoint."
},
"model_path": {
"type": "string",
"description": "Filesystem path to the model weights (safetensors or Hailo HEF). Resolved relative to /var/lib/cognitum/apps/pose-estimation/ when not absolute."
},
"poll_ms": {
"type": "integer",
"minimum": 10,
"maximum": 1000,
"default": 40,
"description": "How often to poll the sensing-server in milliseconds."
},
"min_confidence": {
"type": "number",
"minimum": 0,
"maximum": 1,
"default": 0.3,
"description": "Drop frames where the inferred pose confidence is below this threshold."
}
},
"required": ["model_path"]
}
v2/crates/cog-pose-estimation/cog/manifest.template.json
+10
View File
@@ -0,0 +1,10 @@
{
"id": "pose-estimation",
"version": "{{VERSION}}",
"binary_url": "https://storage.googleapis.com/cognitum-apps/cogs/{{ARCH}}/cog-pose-estimation-{{ARCH}}",
"binary_bytes": 0,
"binary_sha256": "",
"binary_signature": "",
"installed_at": 0,
"status": "installed"
}
v2/crates/cog-pose-estimation/src/config.rs
+58
View File
@@ -0,0 +1,58 @@
//! Runtime configuration for the pose-estimation Cog.
//!
//! Schema lives at `cog/config.schema.json` so the appliance can validate
//! before launching the cog.
use serde::{Deserialize, Serialize};
use std::path::{Path, PathBuf};
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(deny_unknown_fields)]
pub struct CogConfig {
/// URL of the local sensing-server's frame feed.
/// Defaults to the appliance's loopback sensing-server.
#[serde(default = "default_sensing_url")]
pub sensing_url: String,
/// Path to the model weights bundle (safetensors or HEF).
/// Resolved relative to the cog's install dir if not absolute.
pub model_path: PathBuf,
/// Frame poll interval in milliseconds.
#[serde(default = "default_poll_ms")]
pub poll_ms: u64,
/// Confidence threshold below which a frame's keypoints are not emitted.
#[serde(default = "default_min_confidence")]
pub min_confidence: f32,
}
fn default_sensing_url() -> String {
"http://127.0.0.1:3000/api/v1/sensing/latest".to_string()
}
fn default_poll_ms() -> u64 {
40 // ~25 Hz to match ESP32 CSI rate
}
fn default_min_confidence() -> f32 {
0.3
}
impl CogConfig {
pub fn load(path: &Path) -> Result<Self, ConfigError> {
let raw = std::fs::read_to_string(path)
.map_err(|e| ConfigError::Read(path.to_path_buf(), e))?;
let cfg: CogConfig =
serde_json::from_str(&raw).map_err(|e| ConfigError::Parse(path.to_path_buf(), e))?;
Ok(cfg)
}
}
#[derive(Debug, thiserror::Error)]
pub enum ConfigError {
#[error("failed to read config at {0}: {1}")]
Read(PathBuf, std::io::Error),
#[error("failed to parse config at {0}: {1}")]
Parse(PathBuf, serde_json::Error),
}
v2/crates/cog-pose-estimation/src/inference.rs
+233
View File
@@ -0,0 +1,233 @@
//! Inference engine — loads `pose_v1.safetensors` (produced by the
//! Candle training run on `ruvultra`'s RTX 5080, see
//! `cog/artifacts/pose_v1.safetensors` + `docs/benchmarks/pose-estimation-cog.md`)
//! and runs the encoder + pose head on each CSI window.
//!
//! Architecture mirrors the training script exactly:
//! Conv1d(56 -> 64, k=3, dilation=1, padding=1)
//! Conv1d(64 -> 128, k=3, dilation=2, padding=2)
//! Conv1d(128 -> 128, k=3, dilation=4, padding=4)
//! mean over time -> [128]
//! Linear(128 -> 256) -> ReLU
//! Linear(256 -> 34) -> sigmoid -> reshape [17, 2]
//!
//! When the safetensors file is missing the engine falls back to a
//! centred-skeleton baseline with `confidence=0` so the cog still
//! satisfies the ADR-100 runtime contract and the dashboard surfaces
//! "no model yet" instead of dropping frames silently.
use candle_core::{DType, Device, Tensor};
use candle_nn::{Conv1d, Conv1dConfig, Linear, Module, VarBuilder};
use std::path::Path;
use std::sync::Arc;
/// 56 subcarriers × 20 frames per CSI window — matches the format
/// produced by `scripts/align-ground-truth.js` after #641.
pub const INPUT_SUBCARRIERS: usize = 56;
pub const INPUT_TIMESTEPS: usize = 20;
pub const OUTPUT_KEYPOINTS: usize = 17;
#[derive(Debug, Clone)]
pub struct CsiWindow {
pub data: Vec<f32>, // length INPUT_SUBCARRIERS * INPUT_TIMESTEPS
}
#[derive(Debug, Clone)]
pub struct PoseOutput {
/// Flat `[OUTPUT_KEYPOINTS * 2]` keypoints in `[0, 1]` normalised
/// image coords, ordered (x0, y0, x1, y1, …).
pub keypoints: Vec<f32>,
pub confidence: f32,
}
impl PoseOutput {
pub fn is_finite(&self) -> bool {
self.keypoints.iter().all(|v| v.is_finite()) && self.confidence.is_finite()
}
}
/// Internal model — mirrors the training script's `PoseModel` exactly.
struct PoseNet {
c1: Conv1d,
c2: Conv1d,
c3: Conv1d,
fc1: Linear,
fc2: Linear,
}
impl PoseNet {
fn new(vb: VarBuilder<'_>) -> candle_core::Result<Self> {
let enc = vb.pp("enc");
let head = vb.pp("head");
let c1 = candle_nn::conv1d(
56,
64,
3,
Conv1dConfig { padding: 1, stride: 1, dilation: 1, groups: 1, ..Default::default() },
enc.pp("c1"),
)?;
let c2 = candle_nn::conv1d(
64,
128,
3,
Conv1dConfig { padding: 2, stride: 1, dilation: 2, groups: 1, ..Default::default() },
enc.pp("c2"),
)?;
let c3 = candle_nn::conv1d(
128,
128,
3,
Conv1dConfig { padding: 4, stride: 1, dilation: 4, groups: 1, ..Default::default() },
enc.pp("c3"),
)?;
let fc1 = candle_nn::linear(128, 256, head.pp("fc1"))?;
let fc2 = candle_nn::linear(256, 34, head.pp("fc2"))?;
Ok(Self { c1, c2, c3, fc1, fc2 })
}
/// Forward pass: `[B, 56, 20]` -> `[B, 34]` in `[0, 1]`.
fn forward(&self, x: &Tensor) -> candle_core::Result<Tensor> {
let h = self.c1.forward(x)?.relu()?;
let h = self.c2.forward(&h)?.relu()?;
let h = self.c3.forward(&h)?.relu()?;
// Global average pool over time dim (last dim) -> [B, 128]
let h = h.mean(2)?;
let h = self.fc1.forward(&h)?.relu()?;
let h = self.fc2.forward(&h)?;
// sigmoid -> keep in [0, 1]
candle_nn::ops::sigmoid(&h)
}
}
pub struct InferenceEngine {
inner: Option<Arc<LoadedModel>>,
device: Device,
}
struct LoadedModel {
net: PoseNet,
}
impl InferenceEngine {
/// Create an engine. Tries to load weights from `cog/artifacts/pose_v1.safetensors`
/// (relative to current dir or the cog install dir under
/// `/var/lib/cognitum/apps/pose-estimation/`). Returns a usable
/// engine either way — without weights, `infer` produces the
/// stub output.
pub fn new() -> Result<Self, Box<dyn std::error::Error>> {
Self::with_weights(default_weights_path().as_deref())
}
/// Create an engine with a specific weights path (used by `--config`
/// in `cog-pose-estimation run`). If `weights_path` is `None`, the
/// stub fallback is used.
pub fn with_weights(weights_path: Option<&Path>) -> Result<Self, Box<dyn std::error::Error>> {
let device = pick_device();
let inner = match weights_path {
Some(p) if p.exists() => {
// SAFETY: `from_mmaped_safetensors` mmaps the file for the
// VarBuilder's lifetime. We don't modify the file while the
// VarBuilder is alive, and the file is read-only on disk on
// appliance installs.
let vb = unsafe {
VarBuilder::from_mmaped_safetensors(&[p.to_path_buf()], DType::F32, &device)?
};
let net = PoseNet::new(vb)?;
Some(Arc::new(LoadedModel { net }))
}
_ => None,
};
Ok(Self { inner, device })
}
/// Where the weights actually came from. Useful for the run.started event.
pub fn backend(&self) -> &'static str {
match (&self.inner, &self.device) {
(Some(_), Device::Cuda(_)) => "candle-cuda",
(Some(_), _) => "candle-cpu",
(None, _) => "stub",
}
}
pub fn infer(&self, window: &CsiWindow) -> Result<PoseOutput, Box<dyn std::error::Error>> {
if window.data.len() != INPUT_SUBCARRIERS * INPUT_TIMESTEPS {
return Err(format!(
"expected {} input values, got {}",
INPUT_SUBCARRIERS * INPUT_TIMESTEPS,
window.data.len()
)
.into());
}
let Some(model) = &self.inner else {
// Stub fallback — model not loaded.
return Ok(PoseOutput {
keypoints: vec![0.5f32; OUTPUT_KEYPOINTS * 2],
confidence: 0.0,
});
};
// Build [1, 56, 20] tensor from the flat row-major buffer.
let t = Tensor::from_slice(
&window.data,
(1, INPUT_SUBCARRIERS, INPUT_TIMESTEPS),
&self.device,
)?;
let out = model.net.forward(&t)?; // [1, 34]
let flat: Vec<f32> = out.flatten_all()?.to_vec1()?;
// Confidence from pose_v1 is a published constant rather than per-frame —
// the trained model didn't emit a confidence head. Use the validation-set
// PCK@50 (18.5%) as the published self-reported confidence so downstream
// consumers can gate display decisions on it.
Ok(PoseOutput {
keypoints: flat,
confidence: 0.185,
})
}
}
/// Synthetic CSI window for the `health` subcommand. Zeros — exercises
/// the I/O surface; the model never touches values that produce NaN.
pub struct SyntheticInput;
impl Default for SyntheticInput {
fn default() -> Self {
Self
}
}
impl SyntheticInput {
pub fn as_window(&self) -> CsiWindow {
CsiWindow {
data: vec![0.0; INPUT_SUBCARRIERS * INPUT_TIMESTEPS],
}
}
}
// ---------------------------------------------------------------------------
// Helpers
// ---------------------------------------------------------------------------
fn pick_device() -> Device {
#[cfg(feature = "cuda")]
if let Ok(d) = Device::cuda_if_available(0) {
return d;
}
Device::Cpu
}
fn default_weights_path() -> Option<std::path::PathBuf> {
// Search in the order an installed Cog would see it.
let candidates = [
std::path::PathBuf::from("/var/lib/cognitum/apps/pose-estimation/pose_v1.safetensors"),
std::path::PathBuf::from("./pose_v1.safetensors"),
std::path::PathBuf::from("./cog/artifacts/pose_v1.safetensors"),
// From the repo root.
std::path::PathBuf::from("v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors"),
// From inside v2/.
std::path::PathBuf::from("crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors"),
];
candidates.into_iter().find(|p| p.exists())
}
v2/crates/cog-pose-estimation/src/lib.rs
+19
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//! `cog-pose-estimation` library surface.
//!
//! See `ADR-101` for the design and `ADR-100` for the surrounding Cog
//! packaging spec. This crate is intentionally a thin shell around
//! `wifi-densepose-train`'s exported model types — the heavy lifting
//! (encoder, pose head) lives there.
pub mod config;
pub mod inference;
pub mod manifest;
pub mod publisher;
pub mod runtime;
/// Cog identifier — matches the on-disk path
/// `/var/lib/cognitum/apps/pose-estimation/`.
pub const COG_ID: &str = "pose-estimation";
/// Cog version (sourced from Cargo.toml at build time).
pub const COG_VERSION: &str = env!("CARGO_PKG_VERSION");
v2/crates/cog-pose-estimation/src/main.rs
+116
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@@ -0,0 +1,116 @@
//! `cog-pose-estimation` — Cognitum Cog binary entrypoint.
//!
//! Implements the ADR-100 runtime contract:
//! cog-pose-estimation version
//! cog-pose-estimation manifest
//! cog-pose-estimation health
//! cog-pose-estimation run --config <path>
//!
//! Each subcommand writes structured JSON to stdout. `run` is long-running
//! and emits one `pose.frame` event per inferred CSI window.
use clap::{Parser, Subcommand};
use cog_pose_estimation::{
config::CogConfig,
inference::{InferenceEngine, SyntheticInput},
manifest::ManifestSpec,
publisher::{emit_event, Event},
};
use std::path::PathBuf;
const COG_ID: &str = "pose-estimation";
const COG_VERSION: &str = env!("CARGO_PKG_VERSION");
#[derive(Parser)]
#[command(name = COG_ID, version = COG_VERSION)]
#[command(about = "Cognitum Cog: 17-keypoint pose estimation from WiFi CSI", long_about = None)]
struct Cli {
#[command(subcommand)]
command: Cmd,
}
#[derive(Subcommand)]
enum Cmd {
/// Print `<id> <version>` and exit.
Version,
/// Print the embedded manifest as JSON.
Manifest,
/// One-shot health check. Exit 0 if the cog can come up healthy.
Health,
/// Long-running inference loop.
Run {
/// Path to runtime config JSON. See `cog/config.schema.json`.
#[arg(long, value_name = "PATH")]
config: PathBuf,
},
}
fn main() -> std::process::ExitCode {
init_logging();
let cli = Cli::parse();
let result = match cli.command {
Cmd::Version => cmd_version(),
Cmd::Manifest => cmd_manifest(),
Cmd::Health => cmd_health(),
Cmd::Run { config } => cmd_run(config),
};
match result {
Ok(()) => std::process::ExitCode::SUCCESS,
Err(err) => {
eprintln!("{COG_ID}: {err}");
std::process::ExitCode::FAILURE
}
}
}
fn init_logging() {
let _ = tracing_subscriber::fmt()
.with_env_filter(
tracing_subscriber::EnvFilter::try_from_default_env()
.unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
)
.with_target(false)
.json()
.try_init();
}
fn cmd_version() -> Result<(), Box<dyn std::error::Error>> {
println!("{COG_ID} {COG_VERSION}");
Ok(())
}
fn cmd_manifest() -> Result<(), Box<dyn std::error::Error>> {
let spec = ManifestSpec::embedded(COG_ID, COG_VERSION);
println!("{}", serde_json::to_string_pretty(&spec)?);
Ok(())
}
fn cmd_health() -> Result<(), Box<dyn std::error::Error>> {
let engine = InferenceEngine::new()?;
let synthetic = SyntheticInput::default();
let out = engine.infer(&synthetic.as_window())?;
if out.is_finite() {
emit_event(&Event::health_ok(
COG_ID,
engine.backend(),
out.confidence,
));
Ok(())
} else {
Err("inference produced non-finite output".into())
}
}
fn cmd_run(config_path: PathBuf) -> Result<(), Box<dyn std::error::Error>> {
let cfg = CogConfig::load(&config_path)?;
emit_event(&Event::run_started(COG_ID, &cfg));
let engine = InferenceEngine::new()?;
let rt = tokio::runtime::Builder::new_multi_thread()
.enable_all()
.build()?;
rt.block_on(cog_pose_estimation::runtime::run_loop(cfg, engine))?;
Ok(())
}
v2/crates/cog-pose-estimation/src/manifest.rs
+37
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@@ -0,0 +1,37 @@
//! Cog manifest — see ADR-100 §"manifest.json schema".
//!
//! The `cog-pose-estimation manifest` subcommand emits the embedded spec
//! (no signature fields); the build pipeline post-processes it after
//! computing `binary_sha256` + `binary_signature`.
use serde::{Deserialize, Serialize};
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(deny_unknown_fields)]
pub struct ManifestSpec {
pub id: String,
pub version: String,
pub binary_url: Option<String>,
pub binary_bytes: Option<u64>,
pub binary_sha256: Option<String>,
pub binary_signature: Option<String>,
pub installed_at: Option<u64>,
pub status: Option<String>,
}
impl ManifestSpec {
/// The skeleton emitted by `cog-pose-estimation manifest` before the
/// release pipeline fills in the signature/hash/url fields.
pub fn embedded(id: &str, version: &str) -> Self {
Self {
id: id.to_string(),
version: version.to_string(),
binary_url: None,
binary_bytes: None,
binary_sha256: None,
binary_signature: None,
installed_at: None,
status: None,
}
}
}
v2/crates/cog-pose-estimation/src/publisher.rs
+70
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@@ -0,0 +1,70 @@
//! Structured JSON event publisher — one line per event on stdout.
//!
//! Format is the ADR-100 runtime contract: `{ts, level, event, fields}`.
use serde::Serialize;
use serde_json::Value;
use std::time::{SystemTime, UNIX_EPOCH};
#[derive(Debug, Serialize)]
pub struct Event<'a> {
pub ts: f64,
pub level: &'a str,
pub event: &'a str,
pub fields: Value,
}
impl<'a> Event<'a> {
pub fn health_ok(cog_id: &'a str, backend: &str, output_confidence: f32) -> Self {
Self {
ts: now_secs(),
level: "info",
event: "health.ok",
fields: serde_json::json!({
"cog": cog_id,
"backend": backend,
"synthetic_output_confidence": output_confidence,
}),
}
}
pub fn run_started(cog_id: &'a str, cfg: &crate::config::CogConfig) -> Self {
Self {
ts: now_secs(),
level: "info",
event: "run.started",
fields: serde_json::json!({
"cog": cog_id,
"sensing_url": cfg.sensing_url,
"model_path": cfg.model_path,
"poll_ms": cfg.poll_ms,
}),
}
}
pub fn pose_frame(tick: u64, n_persons: usize, persons: Value) -> Self {
Self {
ts: now_secs(),
level: "info",
event: "pose.frame",
fields: serde_json::json!({
"tick": tick,
"n_persons": n_persons,
"persons": persons,
}),
}
}
}
pub fn emit_event(ev: &Event<'_>) {
if let Ok(line) = serde_json::to_string(ev) {
println!("{line}");
}
}
fn now_secs() -> f64 {
SystemTime::now()
.duration_since(UNIX_EPOCH)
.map(|d| d.as_secs_f64())
.unwrap_or(0.0)
}
v2/crates/cog-pose-estimation/src/runtime.rs
+80
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//! Long-running inference loop. Polls the appliance's sensing-server,
//! runs a CSI window through the engine, emits `pose.frame` events.
use crate::config::CogConfig;
use crate::inference::{CsiWindow, InferenceEngine, INPUT_SUBCARRIERS, INPUT_TIMESTEPS};
use crate::publisher::{emit_event, Event};
use std::time::Duration;
use tokio::time::sleep;
pub async fn run_loop(
cfg: CogConfig,
engine: InferenceEngine,
) -> Result<(), Box<dyn std::error::Error>> {
let mut buffer: Vec<f32> = Vec::with_capacity(INPUT_SUBCARRIERS * INPUT_TIMESTEPS);
let mut tick: u64 = 0;
loop {
// Poll one frame from the sensing-server. On error, sleep and retry —
// we expect transient blips when the server restarts.
match fetch_frame(&cfg.sensing_url).await {
Ok(amplitudes) => {
tick += 1;
buffer.extend(amplitudes);
// Slide-window: keep only the most recent N*T values
let cap = INPUT_SUBCARRIERS * INPUT_TIMESTEPS;
if buffer.len() >= cap {
let window = CsiWindow {
data: buffer.split_off(buffer.len() - cap),
};
if let Ok(out) = engine.infer(&window) {
if out.confidence >= cfg.min_confidence {
// Flatten persons array (single-person v0.0.1)
let persons = serde_json::json!([{
"keypoints": chunk_pairs(&out.keypoints),
"confidence": out.confidence,
}]);
emit_event(&Event::pose_frame(tick, 1, persons));
}
}
}
}
Err(e) => {
tracing::warn!(error = %e, "sensing-server fetch failed");
}
}
sleep(Duration::from_millis(cfg.poll_ms)).await;
}
}
async fn fetch_frame(url: &str) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
// Synchronous ureq inside an async fn — we accept the blocking call
// here because the per-frame cost (~1 ms loopback) is dwarfed by the
// inference cost. Replace with a proper async client if we ever poll
// remote sensing-servers over the wire.
let url = url.to_string();
let body = tokio::task::spawn_blocking(move || -> Result<String, ureq::Error> {
Ok(ureq::get(&url).call()?.into_string()?)
})
.await??;
let json: serde_json::Value = serde_json::from_str(&body)?;
let snapshot = json.get("snapshot").unwrap_or(&json);
let nodes = snapshot
.get("nodes")
.and_then(|v| v.as_array())
.ok_or("missing nodes[]")?;
// Take node 0's amplitude vector — we'll add multi-node fusion later.
let amplitude = nodes
.first()
.and_then(|n| n.get("amplitude"))
.and_then(|v| v.as_array())
.ok_or("missing nodes[0].amplitude[]")?;
Ok(amplitude
.iter()
.filter_map(|v| v.as_f64().map(|f| f as f32))
.collect())
}
fn chunk_pairs(flat: &[f32]) -> Vec<[f32; 2]> {
flat.chunks_exact(2).map(|c| [c[0], c[1]]).collect()
}
v2/crates/cog-pose-estimation/tests/smoke.rs
+67
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@@ -0,0 +1,67 @@
//! Smoke tests for the cog-pose-estimation crate.
//!
//! These are deliberately tight — full inference integration tests
//! depend on a trained safetensors blob that doesn't live in-repo yet.
use cog_pose_estimation::{
inference::{InferenceEngine, SyntheticInput, INPUT_SUBCARRIERS, INPUT_TIMESTEPS, OUTPUT_KEYPOINTS},
manifest::ManifestSpec,
};
#[test]
fn synthetic_window_has_correct_shape() {
let syn = SyntheticInput::default();
let window = syn.as_window();
assert_eq!(window.data.len(), INPUT_SUBCARRIERS * INPUT_TIMESTEPS);
}
#[test]
fn engine_produces_finite_output_for_synthetic_input() {
let engine = InferenceEngine::new().expect("engine init");
let out = engine
.infer(&SyntheticInput::default().as_window())
.expect("infer");
assert!(out.is_finite(), "synthetic input must produce finite output");
assert_eq!(out.keypoints.len(), OUTPUT_KEYPOINTS * 2);
}
#[test]
fn engine_rejects_wrong_shape_input() {
let engine = InferenceEngine::new().expect("engine init");
let bad = cog_pose_estimation::inference::CsiWindow { data: vec![0.0; 10] };
assert!(engine.infer(&bad).is_err());
}
#[test]
fn real_weights_load_when_available() {
use cog_pose_estimation::inference::InferenceEngine;
let weights = std::path::Path::new("cog/artifacts/pose_v1.safetensors");
if !weights.exists() {
// Skip when running outside the repo (e.g. on a fresh appliance install).
eprintln!("(skipping — cog/artifacts/pose_v1.safetensors not present in cwd)");
return;
}
let engine = InferenceEngine::with_weights(Some(weights)).expect("load real weights");
assert!(
engine.backend().starts_with("candle-"),
"expected real Candle backend, got {}",
engine.backend()
);
let out = engine
.infer(&SyntheticInput::default().as_window())
.expect("infer");
assert!(out.is_finite());
// Real model emits the published validation PCK@50 as its self-reported
// confidence — stub returns 0.0. This is the key assertion that proves
// the cog isn't silently falling back to the stub.
assert!(out.confidence > 0.0, "real model should emit non-zero confidence");
}
#[test]
fn manifest_roundtrips() {
let spec = ManifestSpec::embedded("pose-estimation", "0.0.1");
let s = serde_json::to_string(&spec).unwrap();
let back: ManifestSpec = serde_json::from_str(&s).unwrap();
assert_eq!(back.id, "pose-estimation");
assert_eq!(back.version, "0.0.1");
}