feat(cog-person-count): v0.0.1 scaffold + tests + fusion math + bench (ADR-103)#694
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First implementation PR for ADR-103. Same incremental shape that
ADR-101 used: scaffold the cog crate, ship a stub-backend release
that satisfies the runtime contract + 15 tests + measured cold-start,
then follow up with the trained count_v1.safetensors in a separate PR.
What ships:
* v2/crates/cog-person-count/ — new workspace member.
- Cargo.toml: candle-core/candle-nn 0.9 (cpu default, cuda feature
opt-in), safetensors, ureq, sha2 — same dep shape as the pose cog
but minus wifi-densepose-train (this cog has no training-side
consumer, so the dep tree is materially smaller → 2.36 MB
binary vs the pose cog's 4.5 MB).
- src/inference.rs: CountNet (Conv1d 56→64→128→128 encoder + count
head Linear(128→64→8)+softmax + confidence head
Linear(128→32→1)+sigmoid). Stub backend returns
`{1-person, 0-confidence}` honestly when no safetensors present.
- src/fusion.rs: fuse_confidence_weighted() — Bayesian product of
per-node distributions with confidence-weighted log-sum, plus
fuse_with_mincut_clip() hook for the v0.2.0 Stoer-Wagner
upper-bound (`ruvector-mincut` dep lands when min-cut graph
builder is ready). Confidences floored at 1e-3 and probs floored
at 1e-9 before logs — no NaN propagation.
- src/publisher.rs: emits {count, confidence, count_p95_low,
count_p95_high, n_nodes, probs} per ADR-103 §"Output".
- src/main.rs: full ADR-100 four-verb CLI (version|manifest|health
|run). The `run` subcommand explicitly returns "wiring pending
v0.0.1" so the in-process library API is the v0.0.1-clean
integration path.
- tests/smoke.rs (8 tests) + fusion::tests (7 tests, in-lib) — 15
total, all green. Cover stub-backend behaviour, wrong-shape
rejection, fusion math (empty / single / agreement / high-conf
override / normalisation), p95-range correctness, and min-cut
clip semantics.
- cog/{manifest.template.json, config.schema.json, README.md} +
cog/artifacts/ placeholder dir.
* v2/Cargo.toml: registers the new workspace member.
Verified locally:
cargo check -p cog-person-count --no-default-features → clean
cargo test -p cog-person-count --no-default-features → 8/8 pass
cargo test -p cog-person-count --lib → 7/7 pass
cargo build -p cog-person-count --release → 2.36 MB binary
./cog-person-count version → "person-count 0.3.0"
./cog-person-count manifest → JSON skeleton
./cog-person-count health → backend:stub,
count:1, conf:0,
p95:[1,1]
Cold-start: 30 sequential `health` invocations → 53.3 ms/invocation
(vs cog-pose-estimation's 76.2 ms — smaller dep tree)
cog/README.md adds:
* Security section — six-row threat table covering safetensor mmap
trust, non-finite outputs, sensing fetch failures, fusion
divide-by-zero / log-of-zero, min-cut degenerate cases, and stdout
spoofing.
* Performance / optimization section — binary size, release profile
(already opt-level=3 / lto=fat / codegen-units=1 / strip=true at
workspace level), cold-start comparison table, projected warm-path
latency budget.
Still pending (separate PRs, ADR-103 §"Migration"):
* Train count_v1.safetensors on the existing 1,077 paired samples
with `n_persons` labels (Candle on RTX 5080, same script that
produced pose_v1.safetensors yesterday).
* `run` subcommand wiring (long-running polling loop, same shape as
cog-pose-estimation::runtime).
* Cross-compile + sign + GCS upload (mirror of cog-pose-estimation
release pipeline).
* Server-side `csi.rs::score_to_person_count` call-site rewire to
consume this cog when installed; falls back to PR #491's heuristic
when not.
ruvnet
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May 21, 2026
…al (#697) Phase 4 of ADR-103. Adds the long-running polling loop so the cog's fourth verb (`run`) does real work, completing the ADR-100 runtime contract end-to-end: cog-person-count version → "person-count 0.3.0" cog-person-count manifest → JSON skeleton cog-person-count health → loads weights + 1-shot infer + emit cog-person-count run --config → long-running per-frame emit ← THIS What ships: * src/runtime.rs (new) — `run_loop` polls sensing_url every poll_ms, slides a [56, 20] CSI window, runs InferenceEngine::infer, emits publisher::person_count events. Same shape as cog-pose-estimation::runtime — fetch_frame extracts amplitudes from `snapshot.nodes[0].amplitude[]`, fails open on connect errors with a WARN log rather than crashing. * src/lib.rs — registers the runtime module. * src/main.rs — cmd_run now loads RunConfig from a JSON file, builds the InferenceEngine (with weights if cfg.model_path is set, otherwise auto-discover), emits a run.started event, and hands off to the Tokio multi-thread runtime's block_on(run_loop). Single-node fusion is a no-op for N=1 today; v0.2.0 will append predictions from sibling nodes and call fusion::fuse_confidence_weighted before emit. Verified locally: cargo check -p cog-person-count --no-default-features → clean cargo test -p cog-person-count → 15/15 pass (no regressions) cargo build -p cog-person-count --release → 2.36 MB unchanged ./cog-person-count run --config bad-config.json: line 1: {"event":"run.started","fields":{"cog":"person-count", "sensing_url":"http://127.0.0.1:9999/...",poll_ms:100, "model_path":"(auto-discover)"}} line 2: WARN sensing-server fetch failed error=Connection Failed: Connect error: actively refused (loop alive — exits cleanly on SIGTERM, no crash, no NaN) Also adds a "Relationship to the in-process score_to_person_count heuristic" section to cog/README.md explaining the dual-emitter design (sensing-server keeps emitting the PR #491 slot heuristic; the cog runs out-of-process and emits person.count events from the learned model). Operators choose by installing the cog or not — no sensing-server rebuild required. ADR-103 §"Migration" status: 1. Land ADR + scaffold ........... done (#693, #694) 2. Train count_v1 ................ done (#695) 3. Cross-compile + sign + GCS .... done (#696) 4. Server-side wiring ............ done — out-of-process design means no rewire needed; this cog is the wiring. 5. v0.2.0 multi-room + LoRA ...... data-bound (#645)
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First implementation PR for ADR-103. Same incremental shape that ADR-101 used: scaffold the cog crate, ship a stub-backend release that satisfies the runtime contract + 15 tests + measured cold-start, then follow up with the trained
count_v1.safetensorsin a separate PR.What lands
v2/crates/cog-person-count/with Candle inference (Conv1d encoder + count softmax head + confidence sigmoid head), Bayesian-product fusion, structured-JSON publisher, ADR-100 four-verb CLI.cog-pose-estimation's 4.5 MB because we don't pullwifi-densepose-train).cog/README.mdincludes a 6-row security threat table + perf/optimization notes.Verified locally
Still pending (separate PRs)
count_v1.safetensorson the existing 1,077 paired samples (Candle on RTX 5080, same script that producedpose_v1yesterday).runsubcommand wiring (long-running polling loop).csi.rs::score_to_person_countcall-site rewire.🤖 Generated with claude-flow