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6959a42312
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.
85 lines
2.6 KiB
Rust
85 lines
2.6 KiB
Rust
//! Smoke tests for cog-person-count.
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use cog_person_count::{
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fusion::{fuse_confidence_weighted, fuse_with_mincut_clip},
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inference::{
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CountPrediction, CsiWindow, InferenceEngine, SyntheticInput,
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COUNT_CLASSES, INPUT_SUBCARRIERS, INPUT_TIMESTEPS,
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},
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};
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#[test]
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fn synthetic_window_has_correct_shape() {
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let w = SyntheticInput::default().as_window();
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assert_eq!(w.data.len(), INPUT_SUBCARRIERS * INPUT_TIMESTEPS);
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}
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#[test]
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fn stub_engine_returns_finite_output() {
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let engine = InferenceEngine::with_weights(None).expect("stub engine");
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let pred = engine.infer(&SyntheticInput::default().as_window()).expect("infer");
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assert!(pred.is_finite());
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assert_eq!(pred.probs.len(), COUNT_CLASSES);
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let sum: f32 = pred.probs.iter().sum();
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assert!((sum - 1.0).abs() < 1e-5, "stub probs must sum to 1, got {}", sum);
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assert_eq!(pred.argmax(), 1, "stub default is 1-person");
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assert_eq!(pred.confidence, 0.0, "stub confidence is 0");
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}
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#[test]
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fn engine_rejects_wrong_shape_input() {
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let engine = InferenceEngine::with_weights(None).expect("stub engine");
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let bad = CsiWindow { data: vec![0.0; 10] };
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assert!(engine.infer(&bad).is_err());
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}
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#[test]
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fn stub_backend_string_is_stable() {
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let engine = InferenceEngine::with_weights(None).expect("stub engine");
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assert_eq!(engine.backend(), "stub");
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}
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#[test]
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fn p95_range_includes_mode() {
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// Sharp peak at 2
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let mut probs = [0.0_f32; COUNT_CLASSES];
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probs[2] = 0.85;
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probs[1] = 0.08;
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probs[3] = 0.07;
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let p = CountPrediction { probs, confidence: 0.9 };
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let (lo, hi) = p.p95_range();
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assert!(lo <= 2 && hi >= 2);
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}
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#[test]
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fn fusion_with_no_inputs_is_safe_default() {
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let p = fuse_confidence_weighted(&[]);
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assert_eq!(p.argmax(), 1);
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assert_eq!(p.confidence, 0.0);
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}
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#[test]
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fn fusion_passes_through_single_node() {
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// A single-node ESP32 deployment must produce the same output as the
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// raw inference — fusion is a no-op for N=1.
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let mut probs = [0.0_f32; COUNT_CLASSES];
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probs[3] = 1.0;
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let input = CountPrediction { probs, confidence: 0.6 };
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let out = fuse_confidence_weighted(&[input.clone()]);
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assert_eq!(out.argmax(), 3);
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assert!((out.confidence - 0.6).abs() < 1e-6);
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}
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#[test]
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fn mincut_clip_with_high_cap_is_noop() {
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let mut probs = [0.0_f32; COUNT_CLASSES];
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probs[2] = 0.5;
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probs[3] = 0.5;
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let input = CountPrediction { probs, confidence: 0.7 };
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let clipped = fuse_with_mincut_clip(&[input], 7);
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// No clip happened (cap == max class)
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assert!((clipped.probs[2] - 0.5).abs() < 1e-6);
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assert!((clipped.probs[3] - 0.5).abs() < 1e-6);
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}
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