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rUv 3314c8db8d feat(cog-pose-estimation): scaffold first Cog from this repo (ADR-100 + ADR-101) (#642) 
* 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.
2026-05-19 17:03:09 -04:00

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[workspace]
resolver = "2"
members = [
"crates/wifi-densepose-core",
"crates/wifi-densepose-signal",
"crates/wifi-densepose-nn",
# wifi-densepose-api / -db / -config: removed in #578.
# The crate names were reserved early for an envisioned REST/database/config
# split, but no implementation followed and no code referenced them. The
# functionality they would provide is covered today by:
# - REST/WS: `wifi-densepose-sensing-server` (Axum)
# - Config: per-crate config + CLI args in `wifi-densepose-sensing-server`
# and `wifi-densepose-desktop`
# - DB: no persistent state; system is real-time
# If we ever need any of these as a published surface, they can be
# reintroduced with a real implementation.
"crates/wifi-densepose-hardware",
"crates/wifi-densepose-wasm",
"crates/wifi-densepose-cli",
"crates/wifi-densepose-mat",
"crates/wifi-densepose-train",
"crates/wifi-densepose-sensing-server",
"crates/wifi-densepose-wifiscan",
"crates/wifi-densepose-vitals",
"crates/wifi-densepose-ruvector",
"crates/wifi-densepose-desktop",
"crates/wifi-densepose-pointcloud",
"crates/wifi-densepose-geo",
"crates/nvsim",
"crates/nvsim-server",
# ADR-100/ADR-101: Cognitum Cog packaging — first Cog from this repo.
# Ships the wifi-densepose pose-estimation model as a signed binary +
# JSONL manifest installable by the Cognitum V0 appliance (cognitum-v0,
# cognitum-cluster-*, ruvultra). The companion appliance-side crate
# lives in cognitum-one/v0-appliance as `cognitum-pose-estimation`.
"crates/cog-pose-estimation",
# rvCSI — edge RF sensing runtime (ADR-095 platform, ADR-096 FFI/crate layout):
# lives in its own repo (https://github.com/ruvnet/rvcsi), vendored here as
# `vendor/rvcsi` and published to crates.io as `rvcsi-*` 0.3.x. Depend on the
# published crates (or the submodule's `crates/rvcsi-*` paths) — not as v2
# workspace members, since `vendor/rvcsi/Cargo.toml` is its own workspace.
]
# ADR-040: WASM edge crate targets wasm32-unknown-unknown (no_std),
# excluded from workspace to avoid breaking `cargo test --workspace`.
# Build separately: cargo build -p wifi-densepose-wasm-edge --target wasm32-unknown-unknown --release
exclude = [
"crates/wifi-densepose-wasm-edge",
]
[workspace.package]
version = "0.3.0"
edition = "2021"
authors = ["rUv <ruv@ruv.net>", "WiFi-DensePose Contributors"]
license = "MIT OR Apache-2.0"
repository = "https://github.com/ruvnet/wifi-densepose"
documentation = "https://docs.rs/wifi-densepose"
keywords = ["wifi", "densepose", "csi", "pose-estimation", "rust"]
categories = ["science", "computer-vision", "wasm"]
[workspace.dependencies]
# Core utilities
thiserror = "2.0"
anyhow = "1.0"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
serde_yaml = "0.9"
tokio = { version = "1.35", features = ["full"] }
tracing = "0.1"
tracing-subscriber = { version = "0.3", features = ["env-filter", "json"] }
# Signal processing
ndarray = { version = "0.17", features = ["serde"] }
ndarray-linalg = { version = "0.18", features = ["openblas-static"] }
rustfft = "6.1"
num-complex = "0.4"
num-traits = "0.2"
# Neural network
tch = "0.24"
ort = { version = "2.0.0-rc.11" }
candle-core = "0.4"
candle-nn = "0.4"
# Web framework
axum = { version = "0.7", features = ["ws", "macros"] }
tower = { version = "0.4", features = ["full"] }
tower-http = { version = "0.6", features = ["cors", "trace", "compression-gzip"] }
hyper = { version = "1.1", features = ["full"] }
# Database
sqlx = { version = "0.7", features = ["runtime-tokio", "postgres", "sqlite", "uuid", "chrono", "json"] }
redis = { version = "0.24", features = ["tokio-comp", "connection-manager"] }
# Configuration
config = "0.14"
dotenvy = "0.15"
envy = "0.4"
# WASM
wasm-bindgen = "0.2"
wasm-bindgen-futures = "0.4"
js-sys = "0.3"
web-sys = { version = "0.3", features = ["console", "Window", "WebSocket"] }
getrandom = { version = "0.2", features = ["js"] }
# Hardware
serialport = "4.3"
pcap = "1.1"
# Graph algorithms (for min-cut assignment in metrics)
petgraph = "0.6"
# Data loading
ndarray-npy = "0.10"
walkdir = "2.4"
# Hashing (for proof)
sha2 = "0.10"
# CSV logging
csv = "1.3"
# Progress bars
indicatif = "0.17"
# CLI
clap = { version = "4.4", features = ["derive", "env"] }
# rvCSI: napi-rs (Rust -> Node bindings) + napi-c (C-shim build glue)
napi = { version = "2.16", default-features = false, features = ["napi8"] }
napi-derive = "2.16"
napi-build = "2.1"
cc = "1.0"
libc = "0.2"
# Testing
criterion = { version = "0.5", features = ["html_reports"] }
proptest = "1.4"
mockall = "0.12"
wiremock = "0.5"
# midstreamer integration (published on crates.io)
midstreamer-quic = "0.1.0"
midstreamer-scheduler = "0.1.0"
midstreamer-temporal-compare = "0.1.0"
midstreamer-attractor = "0.1.0"
# ruvector integration (published on crates.io)
# Vendored at v2.1.0 in vendor/ruvector; using crates.io versions until published.
ruvector-core = "2.2.0"
ruvector-mincut = "2.0.4"
ruvector-attn-mincut = "2.0.4"
ruvector-temporal-tensor = "2.0.6"
ruvector-solver = "2.0.4"
ruvector-attention = "2.0.4"
ruvector-crv = "0.1.1"
ruvector-gnn = { version = "2.0.5", default-features = false }
# Internal crates
wifi-densepose-core = { version = "0.3.0", path = "crates/wifi-densepose-core" }
wifi-densepose-signal = { version = "0.3.0", path = "crates/wifi-densepose-signal" }
wifi-densepose-nn = { version = "0.3.0", path = "crates/wifi-densepose-nn" }
wifi-densepose-api = { version = "0.3.0", path = "crates/wifi-densepose-api" }
wifi-densepose-db = { version = "0.3.0", path = "crates/wifi-densepose-db" }
wifi-densepose-config = { version = "0.3.0", path = "crates/wifi-densepose-config" }
wifi-densepose-hardware = { version = "0.3.0", path = "crates/wifi-densepose-hardware" }
wifi-densepose-wasm = { version = "0.3.0", path = "crates/wifi-densepose-wasm" }
wifi-densepose-mat = { version = "0.3.0", path = "crates/wifi-densepose-mat" }
wifi-densepose-ruvector = { version = "0.3.0", path = "crates/wifi-densepose-ruvector" }
[profile.release]
lto = true
codegen-units = 1
panic = "abort"
strip = true
opt-level = 3
[profile.release-with-debug]
inherits = "release"
debug = true
strip = false
[profile.bench]
inherits = "release"
debug = true
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