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rUv 17471e93ff ADR-152: WiFi-Pose SOTA 2026 intake — WiFlow-STD benchmark, Rust integrations, ADR-153 802.11bf layer, efficiency frontier (#1008) 
* feat(calibration): NodeGeometry transceiver-geometry recording (ADR-152 §2.1.1)

PerceptAlign-motivated geometry capture at enrollment: per-node optional
records (position, antenna orientation, inter-node distances, acquisition
method) — recorded when known, never required. Event-sourced via
EnrollmentEvent::GeometryRecorded (latest recording wins); persisted on
SpecialistBank with serde defaults so pre-ADR-152 bank JSON loads cleanly
(fixture-proven, and geometry-free banks serialize byte-shape-identical
to the old schema); threaded through MultiNodeMixture as data only — the
learned geometry embeddings and algorithmic fusion use are §2.1.2,
deliberately deferred until the ADR-151 P6 LoRA heads exist.

Geometry recorded from now on means banks captured today remain usable
for layout-conditioned training later — you can't retroactively add
geometry to data you didn't record.

8 new tests (3 geometry, 2 anchor, 2 bank, 1 multistatic) + full-loop
extension (2-node geometry, one tape-measured + one unknown, surviving
the bank JSON round-trip the runtime loads from). 50/50 calibration
(both feature configs) + 23 CLI tests green.

Co-Authored-By: RuFlo <ruv@ruv.net>

* feat(training): two-checkerboard camera↔room calibration for ADR-079 labels (ADR-152 §2.1.3)

Defends the camera-supervised pipeline against PerceptAlign's
"coordinate overfitting": MediaPipe keypoints were emitted in raw camera
coordinates with no shared frame and no transceiver-geometry metadata —
the exact label shape that memorizes deployment layout and collapses
cross-layout.

- scripts/calibrate-camera-room.py + calibration_lib.py: OpenCV
  two-checkerboard calibration → versioned bundle JSON (intrinsics,
  camera→room extrinsics, checkerboard spec, transceiver geometry,
  sha256 calibration_id). Intrinsics resolve from file > cache >
  multi-view computation > loud-warning 2-view fallback.
- collect-ground-truth.py --calibration <bundle>: every sample gains
  keypoints_room (unit bearing rays from the camera center in the room
  frame — documented projective alignment; raw image coords preserved
  so training chooses), camera_origin_room, calibration_id, and the
  transceiver geometry stamp. Without the flag, output is byte-identical
  to before (tested) + a one-line ADR-152 warning.

Design finding (recorded for ADR-152): a single planar checkerboard's
corner grid is centrosymmetric — the reversed corner ordering fits a
ghost camera pose with IDENTICAL reprojection error, so per-board flip
disambiguation is mathematically ill-posed. solve_two_board_extrinsics
solves the joint wall+floor set over all 4 flip combinations, where the
minimum is unique — an independent reason the TWO-checkerboard method is
required, beyond what PerceptAlign states.

15 headless pytest tests green (synthetic corners: extrinsics recovery
incl. ghost resolution, bundle round-trip + hash stability, ray
transforms w/ distortion + cross-resolution, no-calibration byte
identity).

Co-Authored-By: RuFlo <ruv@ruv.net>

* feat(benchmarks): WiFlow-STD reproduction harness + measurement (a) results (ADR-152 §2.2)

Shipped checkpoint REFUTED (0.08% PCK@20, wrong keypoint normalization);
6 reproducibility defects documented (broken imports, corrupted dataset
tail with float32-max garbage that NaN-poisons fp16 BatchNorm, unreachable
test phase). After repairs, retraining with upstream defaults reproduces
96.09% PCK@20 full-test / 96.61% corruption-free (published 97.25%) on
RTX 5080. Claims graded MEASURED-EQUIVALENT; 2.23M params + ~0.055 GFLOPs
verified. Third-party code/weights/data stay out of tree (gitignored).

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: ADR-152 Rust integrations + ADR-153 802.11bf protocol model

- calibration: GeometryEmbedding — 32-slot permutation-invariant NodeGeometry
  featurization for future LoRA-head conditioning (ADR-152 §2.1.2); derived
  SpecialistBank::geometry_embedding() accessor; 59 tests
- train: MaePretrainConfig + patchify/random-mask with UNSW measured recipe
  (80% masking, (30,3) patches; ADR-152 §2.3, arXiv 2511.18792); strict
  no-truncate/no-NaN policy; proptest properties
- train: WiFlowStdModel — tch-gated port of the verified ~96%-PCK@20
  WiFlow-STD architecture (ADR-152 §2.2 beyond-SOTA); ungated param formula
  pinned to 2,225,042; 15/17-keypoint support; 239 crate tests
- hardware: ieee80211bf forward-compatibility protocol model (ADR-153):
  SpecProfile gates, SensingCapabilities negotiation, required ConsentMode,
  session FSM, SensingTransport + SimTransport + OpportunisticCsiBridge;
  full acceptance checklist covered; 156+4 tests
- deps: ruvector bumps per ADR-152 §2.6 survey (mincut/solver 2.0.6,
  attention 2.1.0, gnn 2.2.0); vendor/ruvector synced to a083bd77f
- docs: ADR-153 accepted; ADR-152 §2.2 status, §2.4 amendment, §2.6 added

Workspace: 162 test suites green (--no-default-features); Python proof PASS.
Known pre-existing flake: homecore-api env_empty_falls_back_to_defaults
(unserialized env-var mutation) — untouched, follow-up.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: CHANGELOG + CLAUDE.md entries for ADR-152 integrations and ADR-153

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(train): repair tch-backend bit-rot — gated path compiles and tests run again

Mechanical API refresh against current tch: Vec::from(Tensor) -> try_from
(+ explicit flatten), numel() usize cast, Rem/div ops -> remainder() /
divide_scalar_mode(floor) — the latter fixed a silent true-division bug in
heatmap argmax decoding; clamp(1.0, f64::MAX) -> clamp_min (torch 2.x scalar
overflow panic); petgraph EdgeRef import; missing EvalMetrics and
verify_checkpoint_dir APIs that tests documented. wiflow_std roundtrip test
uses safetensors (.pt _save_parameters roundtrip broken in torch 2.11
Windows). Gated: 349 passed (incl. all 20 wiflow_std); ungated: unchanged.
Known pre-existing: gaussian-heatmap convention mismatch (2 tests), proof
seed race under parallel threads — documented, deliberate follow-ups.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(train): WiFlow-STD PyTorch->tch weight import + numerical parity proof

export_to_safetensors.py maps the retrained checkpoint (295 tensors -> 248
mapped, param sum exactly 2,225,042; num_batches_tracked dropped) into a
tch-loadable safetensors plus a deterministic parity fixture. Gated #[ignore]
integration test loads it strictly and asserts forward-pass agreement:
max abs diff 1.192e-7 on the seed-42 fixture. dump_variable_names test makes
the tch name layout authoritative. Zero architecture discrepancies found.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: workflow-review findings — BN gamma init, ThresholdParams serde, init docs

Concurrent validation workflow (2 review lanes + adversarial verification,
13 agents): 5 confirmed findings, 3 refuted. Fixes:
- wiflow_std: pin BatchNorm gamma to 1.0 (tch default draws Uniform(0,1) —
  silently halves activations in from-scratch training; loaded checkpoints
  unaffected, parity re-verified after the change)
- wiflow_std: document the conv-init divergences vs the reference's
  effective kaiming_normal(fan_out) re-init (from-scratch dynamics only)
- ieee80211bf: ThresholdParams deserialization validates via try_from so
  the <=100 invariant holds for untrusted payloads (+ rejection test)

Benchmarks (release, ruvzen): GeometryEmbedding 1.84us/call (542k/s),
MAE tokenization 7.38us/window (135k/s), 802.11bf FSM 8.9M events/s —
nothing suspicious.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs(adr): ADR-152 §2.1.4 gate resolved — PerceptAlign repo MIT, dataset on HF

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(benchmarks): edge optimization measured + measurement (b) blocked + 92.9% retraction

Edge optimization (ADR-152 optimize track): ONNX Runtime fp32 is the CPU
latency win (3.2 ms/window, ~3.4x faster than torch, parity 2.4e-7); ORT
dynamic int8 reaches 2.44 MB (paper's ~2.2 MB claim plausible only via
conv-capable toolchains; -0.16pt PCK@20, +18% MPJPE, 2x slower); torch
dynamic quant converts 0% of this conv-only model; fp16 halves storage free
but is slower on CPU.

Measurement (b) BLOCKED-ON-DATA: only 1,077 paired ESP32 windows exist
(stop rule <2k). Forensic recheck of the surviving April holdout RETRACTS
the ADR-079 '92.9% PCK@20' figure: constant-output model, absolute (not
torso) threshold, 69 near-static frames — mean predictor scores 100% under
that protocol; torso-PCK@20 is 19.1%. Corroborates PR #535. Stale citations
removed from user-guide, readme-details, ADR-152 §2.1.3; no-citation rule
extended to ADR-079 accuracy claims. Unblock: >=2k-window multi-pose paired
session + torso-PCK re-baseline.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs(user-guide): corrected camera-supervised collection tutorial

Step 0 CSI-rate check + session-length math (window yield = frames/20 —
the May session's 8x under-delivery was a ~12 Hz CSI rate, not an aligner
bug); two-checkerboard calibration step (ADR-152 §2.1.3); pose-variety and
confidence guidance; torso-normalized PCK + temporal-split + pred-variance
eval protocol (lessons from the 92.9% retraction); scale presets re-keyed
to realistic window counts.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(benchmarks): static PTQ int8 (calibrated) results + overnight capture script

Conv-only static QDQ beats dynamic int8 on accuracy (PCK@20 96.61-96.63%
vs 96.52%, MPJPE +10% vs +18% over fp32) at ~equal size/latency; all-ops
QDQ strictly worse (int8 activations through attention glue). Entropy
calibration verified bit-identical to MinMax on this data. Deployment:
ONNX fp32 for speed (3.2ms), static conv-only QDQ for smallest (2.53MB).

Also: scripts/overnight-empty-capture.py — segmented UDP CSI recorder for
empty-room baselines (no glob collisions, detach-safe).

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(benchmarks): measurement (b) MEASURED — optimization transfer only, mean-pose baseline wins

WiFlow-STD fine-tuned on 2,046 fresh single-room ESP32 paired windows
(temporal 70/15/15, 70->540 adapter, K=17): pretrained-init 65% PCK@20 vs
scratch 0% (optimization transfer) but frozen-trunk ~0% (no feature
transfer), and NOTHING beats the mean-pose baseline (95.9% PCK@20 —
single subject, near-static normalized coords). Honesty gates held: pred
std 0.0113 (non-constant model) but mean-baseline dominance means no
citable CSI->pose capability from this data. ADR-152 open question 1
answered partially; definitive answer needs multi-subject/position data.

Two new aligner findings: heterogeneous csi_shape with silent zero-padding
(~20%), and extractCsiMatrix's transposed shape label (frame-major data,
[nSc, nFrames] label) — fixes pending.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(benchmarks): efficiency sweep MEASURED — half model dominates full reference

Compact WiFlow-STD variants on the same data/split/protocol: half (843,834
params, 0.38x) strictly dominates the 2.23M reference (PCK@20 96.62 vs
96.61, PCK@50 99.47 vs 99.11, MPJPE 0.00898 vs 0.0094) — the published
architecture is over-parameterized for its own benchmark. quarter (338k)
96.05%; tiny (56,290 params, 1/39.5) holds 94.11% — a ~220KB fp32 edge
candidate. In-domain caveats recorded; cross-domain untested.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(train): compact WiFlow-STD presets in Rust + tiny edge artifact (ADR-152)

WiFlowStdConfig gains half()/quarter()/tiny() mirroring the overnight sweep
exactly: TcnGroupsMode (Fixed/Gcd/Depthwise), input_pw_groups, derived
stride schedule and decoder-mid (all default to upstream behavior; legacy
serde JSON unaffected). Param formulas pin to trained ground truth first
try: 843,834 / 338,600 / 56,290; default 2,225,042 pin and 1.192e-7 parity
unchanged. 248 tests green.

Tiny edge artifact (tiny_edge_bench.py): ONNX fp32 = 295 KB, 0.66 ms/win
(~1,500/s CPU), 94.11% PCK@20 (matches sweep clean-test exactly; parity
1.49e-7). Static int8 is a bad trade at this scale (-1.43pt, +19% MPJPE,
-16% size, slower) — recorded as negative result. Export note: width-16
breaks AdaptiveAvgPool((15,1)) TorchScript export; replaced by exact
mean+matmul equivalent, proven by parity.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: resolve all 10 confirmed code-review findings (7-angle review, 20/20 verified)

wiflow_std: min_feature_width (default 15) replaces the keypoints->stride
coupling — for_keypoints(17) now provably builds the trained [2,2,2,2]
graph and pools 15->17, matching the validated Python protocol (pinned by
tests); param_count() total on invalid configs; random_mask returns Result
and rejects non-finite/out-of-range ratios; trainer checkpoints switched
to safetensors (.pt VarStore roundtrip broken on Windows torch 2.11).

ieee80211bf: SBP proxy now re-triggers instances and relays reports via
Action::RelaySbpReport -> SensingFrame::SbpReport (clients consume via
their existing path); missed_instances reset on success = consecutive
semantics; SessionTable gains a guarded SBP entry point + unknown-id drop
counter; initiator-role sessions reject inbound setup/SBP requests
(RejectedNotSupported) closing the idle hijack; StartSetup/StartSbp
outside Idle return InvalidStateForCommand; SBP validation unified
through evaluate_setup with a 1:1 SetupStatus->SbpStatus mapping.
events.rs split out to honor the 500-line cap.

calibration/cli: enrollment geometry now actually reaches trained banks —
both production call sites attach .with_geometry; --geometry flag on
train-room and POST /enroll/geometry + train-body geometry on
calibrate-serve give production a recording surface; geometry-free banks
log the ADR-152 §2.1.2 note.

benchmarks: corruption masks committed as ground truth (unregenerable
after in-place cleaning; verified bit-identical regeneration from the
pristine copy) + generate_corruption_masks.py producer; _bench_common.py
dedups the 5x-copied shim/evaluate/seed/remap (post-refactor PCK@20
re-verified equal to the last digit); remote scripts get the mmap patch;
tiny_edge --calib validated multiple-of-64; onnx_bench --help no longer
executes (and overwrote) the export — artifact restored byte-exact.

Workspace: 2,963 tests passed, 0 failed; Python proof PASS.

Co-Authored-By: claude-flow <ruv@ruv.net>

* ci: build workspace tests without debuginfo — runner disk exhaustion

The combined 38-crate debug target exceeds the GitHub runner's disk
('final link failed: No space left on device'); the same tree measured
151GB locally with full debuginfo. CARGO_PROFILE_{DEV,TEST}_DEBUG=0
shrinks the target ~5-10x; debuginfo serves no purpose in CI test runs.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-06-11 17:02:23 -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/wifi-densepose-worldgraph", # ADR-139 — WorldGraph environmental digital twin
"crates/wifi-densepose-engine", # ADR-135..146 integration/composition layer
"crates/wifi-densepose-calibration", # ADR-151 — per-room calibration & specialist training
"crates/nvsim",
"crates/nvsim-server",
"crates/homecore", # ADR-127 — HOMECORE state machine
"crates/homecore-plugins", # ADR-128 — HOMECORE-PLUGINS WASM runtime (P1 scaffold)
"crates/homecore-api", # ADR-130 — HOMECORE REST + WS API
"crates/homecore-automation", # ADR-129 — HOMECORE automation engine
"crates/homecore-recorder", # ADR-132 — HOMECORE state recorder
"crates/homecore-migrate", # ADR-134 — HOMECORE migration from Python HA
# 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",
# ADR-103: Learned multi-person counter (SOTA path) — replaces the
# PR #491 slot heuristic with a Candle network + Stoer-Wagner fusion.
# Motivated by #499 ghost-skeleton reports.
"crates/cog-person-count",
# ADR-116: Home Assistant + Matter Cognitum Seed cog. Wraps the
# ADR-115 MQTT publisher as a Seed-installable artifact with
# mDNS, embedded broker, RuVector thresholds, Ed25519 witness.
"crates/cog-ha-matter",
# ADR-118: BFLD — Beamforming Feedback Layer for Detection. The
# privacy/safety layer that measures and gates identity leakage from
# WiFi BFI captures. Sub-ADRs: 119 (frame), 120 (privacy class),
# 121 (identity risk), 122 (HA/Matter), 123 (capture path).
"crates/wifi-densepose-bfld",
# ADR-147: OccWorld thin-client bridge — WorldGraph PersonTrack history →
# OccWorld Python subprocess → TrajectoryPrior injection into pose tracker.
"crates/wifi-densepose-worldmodel",
# ADR-147 (Phase 5): OccWorld TransVQVAE ported to Candle — native Rust
# inference without Python/IPC overhead. Loaded alongside the Python bridge
# as a faster alternative once Phase-5 weights are available.
"crates/wifi-densepose-occworld-candle",
# 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.
"crates/homecore-hap", # ADR-125 — Apple Home HomeKit Accessory Protocol bridge
"crates/homecore-assist", # ADR-133 — HOMECORE voice assistant + ruflo bridge
"crates/homecore-server", # iter-9 — HOMECORE integration binary (all 8 crates wired together)
"crates/ruview-swarm", # ADR-148 — drone swarm control system
]
# 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
#
# ADR-128 P2: example WASM plugin — also wasm32-only (no_std, cdylib),
# excluded for the same reason. Build separately:
# cargo build --target wasm32-unknown-unknown --release -p homecore-plugin-example
exclude = [
"crates/wifi-densepose-wasm-edge",
"crates/homecore-plugin-example",
]
[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)
# 0.1.0 was yanked; upgrade to latest 0.3/0.2 releases which pull in
# quinn-proto >=0.11.14 (fixes RUSTSEC-2026-0037) and
# rustls-webpki >=0.103.13 (fixes RUSTSEC-2026-0049/0098/0099/0104).
midstreamer-quic = "0.3"
midstreamer-scheduler = "0.2"
midstreamer-temporal-compare = "0.2"
midstreamer-attractor = "0.2"
# ruvector integration (published on crates.io)
# Vendored at origin/main (a083bd77f) in vendor/ruvector; using crates.io versions
# until published. Bumps per ADR-152 §2.6 (2026-06-10 vendor sync survey).
ruvector-core = "2.2.0"
ruvector-mincut = "2.0.6"
ruvector-attn-mincut = "2.0.4"
ruvector-temporal-tensor = "2.0.6"
ruvector-solver = "2.0.6"
ruvector-attention = "2.1.0"
ruvector-crv = "0.1.1"
ruvector-gnn = { version = "2.2.0", 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" }
wifi-densepose-worldmodel = { version = "0.3.0", path = "crates/wifi-densepose-worldmodel" }
[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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