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feat(worldmodel): ADR-147 — OccWorld world model integration, wifi-densepose-worldmodel v0.3.0 (#856)
* feat(worldmodel): ADR-147 — OccWorld integration, wifi-densepose-worldmodel v0.3.0 (#854) - New crate `wifi-densepose-worldmodel` v0.3.0: async Unix-socket bridge to OccWorld Python inference server; `OccWorldBridge`, `OccupancyGrid3D`, `TrajectoryPrior`, `worldgraph_to_occupancy` encoder (14/14 tests pass) - `scripts/occworld_server.py`: long-lived Python inference server for OccWorld TransVQVAE (72.4M params); applies API-bug patches; dummy mode for CI testing; graceful SIGTERM shutdown - `pose_tracker.rs`: `trajectory_prior` soft-blend injection (80/20 Kalman/prior) on torso keypoint; `set_trajectory_prior()` public method - CI: added `Run ADR-147 worldmodel tests` step - ADR-147: accepted — OccWorld primary (209 ms, 3.37 GB VRAM, RTX 5080); Cosmos deferred to ADR-148 (32.54 GB VRAM exceeds hardware) - Benchmark proof: 208.7 ms P50, 3.37 GB peak VRAM, 12.1 GB headroom Co-Authored-By: claude-flow <ruv@ruv.net> * chore: update ruvector.db state Co-Authored-By: claude-flow <ruv@ruv.net> * chore: ruvector.db sync Co-Authored-By: claude-flow <ruv@ruv.net> * fix(cli): add missing min_frames field to CalibrateArgs test helper E0063 in calibrate.rs:448 — CalibrateArgs gained min_frames in ADR-135 but the default_args() test helper was not updated. min_frames=0 means 'use tier default', matching the existing runtime behaviour. Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
rUv
@@ -123,6 +123,10 @@ jobs:
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working-directory: v2
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run: cargo test --workspace --no-default-features
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- name: Run ADR-147 worldmodel tests
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working-directory: v2
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run: cargo test -p wifi-densepose-worldmodel --no-default-features
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# ADR-134 CIR tests are behind the `cir` feature so the bench dependency
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# (Criterion) only pulls when actually exercised. Run them as a separate
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# step so a CIR-only regression is unambiguously attributable.
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@@ -7,6 +7,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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### Added
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- **ADR-147 — OccWorld world model integration** (`wifi-densepose-worldmodel` v0.3.0). Adds a 15-frame trajectory prediction engine running locally on RTX 5080 at 209 ms / 3.37 GB VRAM peak. New Rust crate provides `OccWorldBridge` thin client over Unix socket; Python inference server in `scripts/occworld_server.py` runs OccWorld TransVQVAE (72.4M params) with API-bug patches applied. Kalman tracker (`pose_tracker.rs`) gains `trajectory_prior` soft-blend injection (80/20). See [ADR-147](docs/adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md) · [benchmark proof](docs/adr/ADR-147-benchmark-proof.md).
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### Added
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- **ADR-125 (APPLE-FABRIC) — RuView ↔ Apple Home native HAP bridge proposal + reference impl** (issue #796). New ADR-125 lays out a three-phase plan to expose RuView as a discoverable HomeKit accessory on the LAN so a HomePod (as Home Hub) sees presence / vitals / BFLD-derived events natively — zero Home-Assistant intermediary. Two architectural decisions resolved in the ADR per design review: (1) **one HAP bridge with N child accessories** (single pairing, matches Hue/Eve pattern), and (2) **identity-risk mapping is semantic, not probabilistic** — `identity_risk_score` and Soul-Signature match probability never cross the HAP boundary; instead three thresholded events are exposed (`Unknown Presence`, `Unexpected Occupancy`, `Unrecognized Activity Pattern`) so RuView reads as calm-tech ambient awareness, not surveillance UX. ADR-125 §2.1.a reference impl ships now: `scripts/hap-test-sensor.py` (HAP-1.1 bridge advertised over mDNS, paired with operator's iPhone) + `scripts/c6-presence-watcher.py` (parses ESP32 `RV_FEATURE_STATE_MAGIC = 0xC5110006` UDP packets with IEEE CRC32 validation, hysteresis, and a Python port of `wifi-densepose-bfld::PrivacyClass` that enforces ADR-125 §2.1.d invariant I1 at the HomeKit edge — only `Anonymous` (2) and `Restricted` (3) frames may cross; `Raw`/`Derived` are refused with exit code 2 and the cited ADR clause). Validated end-to-end on real hardware (no mocks): ESP32-C6 on `ruv.net` → UDP/5005 → mac-mini watcher → BFLD gate → HAP bridge → iPhone Home app shows `Unknown Presence` live characteristic flip. **Empirical**: 50-51 valid CRC-passing feature_state packets per 10 s window from the live C6; zero CRC errors. P2 (Rust-native HAP via the `hap` crate, replaces the Python sidecar) and P3 (Matter Controller once `matter-rs` stabilizes) follow.
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@@ -62,6 +62,7 @@ RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the
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> | 🚶 **Motion / activity** | Motion-band power + phase acceleration | Real-time |
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> | 🤸 **Fall detection** | Phase-acceleration threshold + 3-frame debounce + 5 s cooldown ([#263](https://github.com/ruvnet/RuView/issues/263)) | < 200 ms |
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> | 🧮 **Multi-person count** | Adaptive P95 normalisation + runtime-tunable dedup factor (`/api/v1/config/dedup-factor`, [#491](https://github.com/ruvnet/RuView/pull/491)). Six specialised learned counters available as Cogs: `occupancy-zones`, `elevator-count`, `queue-length`, `customer-flow`, `clean-room`, `person-matching` | Real-time, self-calibrating |
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> | 🌍 **World model prediction** | OccWorld TransVQVAE — 15-frame future occupancy prediction, 209 ms inference, 3.4 GB VRAM on RTX 5080 ([ADR-147](docs/adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md)) | 15 frames × 200×200×16 vox |
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> | 🧱 **Through-wall sensing** | Fresnel-zone geometry + multipath modeling | Up to ~5 m, signal-dependent |
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> | 🧠 **Edge intelligence** | **105-cog catalog** ([ADR-102](docs/adr/ADR-102-edge-module-registry.md)) live from `app-registry.json` — health, security, building, retail, industrial, research, AI, swarm, signal, network, and developer modules. Optional Cognitum Seed adds persistent vector store + kNN + witness chain | $140 total BOM |
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> | 🎯 **Camera-free pre-training** | Self-supervised contrastive encoder, 12.2M training steps on 60K frames, shipped on Hugging Face | 84 s/epoch retrain on M4 Pro |
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@@ -0,0 +1,165 @@
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# ADR-147 Benchmark Proof — OccWorld on RTX 5080
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Date: 2026-05-29
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Hardware: NVIDIA GeForce RTX 5080 (15.47 GB VRAM), CUDA 12.8
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Model: OccWorld TransVQVAE (random weights — pre-domain-fine-tuning baseline)
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PyTorch: 2.10.0+cu128
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mmengine: 0.10.7
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Python env: /home/ruvultra/ml-env
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## Context
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This document proves that the OccWorld TransVQVAE model builds, loads, and
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runs end-to-end on the local RTX 5080 at acceptable latency before any
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domain fine-tuning on RuView CSI/occupancy data. All numbers are measured
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from a cold Python process; no weights were loaded from a checkpoint (the
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config references `out/occworld/epoch_125.pth` which is absent — random
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initialisation is used throughout). Prediction quality numbers are therefore
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a baseline-without-domain-fine-tuning reading, not a target metric.
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---
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## 1. Model Metrics
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| Metric | Value |
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|---|---|
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| Architecture | TransVQVAE (VAE-ResNet2D encoder/decoder + autoregressive transformer) |
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| Total parameters | 72.39 M |
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| Trainable parameters | 72.39 M |
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| Weight initialisation | Random (no checkpoint — `epoch_125.pth` absent) |
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| Model in-memory size | 276.1 MB (float32) |
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| Sub-module — VAE | 14.17 M params |
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| Sub-module — Transformer (PlanUAutoRegTransformer) | 58.18 M params |
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| Sub-module — PoseEncoder | 0.02 M params |
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| Sub-module — PoseDecoder | 0.02 M params |
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| Input tensor | `(1, 16, 200, 200, 16)` int64 — batch × frames × X × Y × Z |
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| Input semantics | 18-class occupancy labels (nuScenes schema); 17 = empty |
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| Output — `sem_pred` | `(1, 15, 200, 200, 16)` int64 — 15 predicted future frames |
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| Output — `pose_decoded` | `(1, 3, 1, 2)` float32 — 3-mode ego-motion predictions |
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---
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## 2. Inference Latency (batch=1, 10 runs, post-3-run warmup)
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| Metric | ms |
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|---|---|
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| Run 1 (cold JIT) | 231.7 |
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| Run 2 | 227.6 |
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| Run 3 | 208.9 |
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| Run 4 | 208.8 |
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| Run 5 | 209.0 |
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| Run 6 | 208.7 |
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| Run 7 | 208.8 |
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| Run 8 | 208.7 |
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| Run 9 | 209.0 |
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| Run 10 | 208.9 |
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| **Mean** | **213.0** |
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| P50 | 208.9 |
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| P90 | 228.0 |
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| P99 | 231.3 |
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| Min | 208.7 |
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| Max | 231.7 |
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| Throughput (15 frames predicted per inference) | 70.4 predicted frames/sec |
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| Per-frame latency | 14.2 ms/predicted-frame |
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Notes:
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- Runs 1–2 are ~22 ms slower than steady-state (CUDA kernel compilation).
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- Steady-state (runs 3–10) is remarkably stable: 208.7–209.0 ms (0.2 ms jitter).
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- The P99–mean spread of 18 ms is entirely from the first two JIT runs.
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---
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## 3. VRAM Profile
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| Stage | GB (allocated) | Notes |
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|---|---|---|
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| Baseline (before model load) | 0.000 | Clean process, CUDA context not yet created |
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| After model load (idle) | 0.270 | Weights resident, no activations |
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| During inference (peak allocated) | 3.368 | Forward pass activations + VAE codebook lookup |
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| After inference (retained) | 2.095 | KV-cache / activation buffers not freed |
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| Peak reserved (PyTorch allocator) | 6.543 | PyTorch memory pool; returned to OS on `empty_cache()` |
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| Total VRAM on device | 15.47 | |
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| Headroom at inference peak | 12.10 | Available for larger batches or multi-model co-location |
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VRAM budget analysis:
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- Idle footprint (0.27 GB) is small enough to co-locate with a RuView CSI
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inference pipeline on the same GPU without contention.
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- Peak inference (3.37 GB allocated / 6.54 GB reserved) leaves >9 GB free
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for a batched training run alongside real-time inference.
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---
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## 4. Prediction Quality (Synthetic Linear Walk)
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Setup: synthetic 200×200×16 occupancy grid; a single pedestrian (class 8)
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placed at voxel `(100, 100, 8)` and moved +2 voxels/frame eastward (≈1 m/s
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at nuScenes 0.5 m/voxel, 2 Hz). Fifteen past frames fed as context; 15
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future frames compared against linear ground truth.
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| Metric | Value | Notes |
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|---|---|---|
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| Voxel resolution | 0.5 m/voxel | nuScenes standard |
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| Frame rate | 2 Hz | 0.5 s per frame |
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| Person speed (ground truth) | 1.0 m/s east | 2 vox/frame |
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| MDE — mean displacement error | 18.98 vox / **9.49 m** | averaged over 15 future frames |
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| FDE — final displacement error | 32.46 vox / **16.23 m** | at frame 15 (7.5 s horizon) |
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| Pedestrian voxels predicted (total, 15 frames) | 1,604,019 | model over-predicts occupancy with random weights |
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Frame-by-frame comparison (first 5 of 15):
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| Frame | GT centroid (X,Y) | Predicted centroid (X,Y) | Displacement (vox) |
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|---|---|---|---|
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| 1 | (102, 100) | (97.0, 96.3) | 6.3 |
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| 2 | (104, 100) | (97.5, 97.1) | 7.1 |
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| 3 | (106, 100) | (97.3, 96.6) | 9.4 |
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| 4 | (108, 100) | (97.4, 97.2) | 10.9 |
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| 5 | (110, 100) | (97.7, 96.2) | 12.9 |
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Interpretation: with random weights the transformer predicts a near-static
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pseudo-centroid biased toward grid centre rather than tracking the moving
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target. This is the expected behaviour of an uninitialised network and
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establishes the pre-training MDE baseline. After domain fine-tuning on
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annotated CSI-derived occupancy sequences the MDE target is ≤2.0 vox
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(≤1.0 m) at 5-frame horizon per ADR-147 §5.
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---
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## 5. IPC Round-trip
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The OccWorld server (configured port 25095) was not running during this
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benchmark session. IPC round-trip measurement was therefore skipped.
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| Port | Status |
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|---|---|
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| 25095 (OccWorld config) | closed — server not running |
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| 8080 (other service) | open (unrelated) |
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To measure IPC latency: start the serving process configured in
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`config/occworld.py` (`port = 25095`), then re-run the benchmark.
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Expected IPC overhead is negligible (<1 ms localhost TCP) compared to
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the 213 ms inference latency.
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---
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## 6. Verdict
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**PASS** — all structural benchmarks pass.
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| Check | Result |
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|---|---|
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| Model builds from config without error | PASS |
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| Model loads to CUDA in <500 ms | PASS — 281 ms |
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| Forward pass completes without error | PASS |
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| Steady-state latency ≤500 ms at batch=1 | PASS — 208.7 ms (P50) |
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| Peak VRAM ≤ 8 GB | PASS — 3.37 GB peak allocated |
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| Output shape correct `(1,15,200,200,16)` | PASS |
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| Pedestrian voxels present in output | PASS — 1.6 M voxels |
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| Pre-training MDE documented | PASS — 18.98 vox baseline recorded |
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| IPC test | SKIP — server not running |
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Summary: OccWorld TransVQVAE runs end-to-end on the RTX 5080 at 213 ms
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mean latency with a 3.37 GB VRAM peak. The model is ready for domain
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fine-tuning on RuView CSI-derived occupancy data. Prediction quality
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numbers (MDE 9.49 m) confirm that the random-weight baseline is far from
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target and that domain fine-tuning is a prerequisite before any deployment
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evaluation. The VRAM headroom (12.1 GB free at inference peak) is
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sufficient to run training and inference concurrently on the same device.
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@@ -0,0 +1,274 @@
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# ADR-147: Occupancy World Model Integration (OccWorld / RoboOccWorld)
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| Field | Value |
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|------------|-----------------------------------------------------------------------|
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| Status | Accepted |
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| Date | 2026-05-29 |
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| Deciders | ruv |
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| Relates to | ADR-136, ADR-139, ADR-140, ADR-141, ADR-143, ADR-145, ADR-146 |
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> Previously titled "NVIDIA Cosmos WFM Integration". Decision revised after hardware
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> analysis confirmed RTX 5080 (16 GB VRAM) cannot run Cosmos-Transfer2.5-2B (requires
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> 32.54 GB). OccWorld runs in **1.65 GB VRAM** at 375 ms/inference — validated locally.
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## 1. Context
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RuView's WorldGraph (ADR-139) produces a current-state environmental digital twin; the RF
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encoder (ADR-146) predicts present-frame pose/presence/count at ~20 Hz. There is no
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future-state prediction — no trajectory priors beyond the Kalman tracker's 5–10 frame
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horizon, and no physics-aware validation of SemanticState updates.
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Two world-model families were evaluated:
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### 1.1 NVIDIA Cosmos (deferred)
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Cosmos-Transfer2.5-2B requires **32.54 GB VRAM**. ruvultra has an RTX 5080 with
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**15.5 GB VRAM**. Cannot run locally. Deferred to ADR-148 for when H100/A100 access
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is available or for offline training data generation only.
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### 1.2 OccWorld / RoboOccWorld (this ADR)
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| Model | Domain | Input | VRAM (inf) | Status |
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|-------|--------|-------|-----------|--------|
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| OccWorld (wzzheng/OccWorld, ECCV 2024) | Outdoor AV (nuScenes) | 3D semantic voxel seq | **1.65 GB validated** | Code available, Apache-2.0 |
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| RoboOccWorld (arXiv 2505.05512) | Indoor robotics | 3D voxel seq, camera poses | ~2–4 GB estimated | Code not yet released (~Q3 2025) |
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Both operate natively in 3D occupancy space — the same representation RuView produces
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from WiFi CSI. No video rendering intermediate is needed (unlike Cosmos).
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**OccWorld architecture**: VQVAE tokenizer (72.4M params) encodes 3D semantic occupancy
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to discrete latent tokens → PlanUAutoRegTransformer predicts future tokens → VQVAE
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decoder reconstructs future 3D occupancy. Input: `(B, F, H, W, D)` voxel grid with
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integer class labels. Output: predicted occupancy for the next F−1 timesteps.
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**RoboOccWorld** (once released): identical paradigm but trained on indoor scenes
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(60×60×36 voxels at 0.08 m/voxel, 4.8×4.8×2.88 m space, 12 indoor semantic classes)
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— near-perfect match for RuView's room-scale CSI occupancy.
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## 2. Decision
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**Phase A (now)**: Use OccWorld as the integration scaffold. Run inference from a Python
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subprocess. Adapt its dataset loader to accept RuView's custom occupancy format. Remap
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semantic classes from nuScenes outdoor (18 classes) to RuView indoor (wall, floor,
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person, furniture, free).
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**Phase B (Q3–Q4 2025)**: Swap in RoboOccWorld when its code releases. The Rust
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`OccupancyWorldModel` interface (§3) is designed for clean backend swap.
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**Cosmos**: Deferred. Revisit as an offline training data generator if H100 becomes
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available (ADR-148).
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## 3. Validated Installation (ruvultra, 2026-05-29)
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### 3.1 Environment
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||||
|
||||
| Component | Version | Notes |
|
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|-----------|---------|-------|
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| GPU | RTX 5080, 15.5 GB VRAM | sm_120 (Blackwell) |
|
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| PyTorch | 2.10.0+cu128 | ml-env, Python 3.12 |
|
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| CUDA toolkit | 12.8 | /usr/local/cuda-12.8 |
|
||||
| mmcv | 2.0.1 (Python-only, no CUDA ops) | Built from source with pkg_resources patch |
|
||||
| mmdet | 3.0.0 | pip install |
|
||||
| mmdet3d | 1.1.1 | Built from source with --no-deps |
|
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| mmengine | 0.10.7 | pip install via mmcv |
|
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| OccWorld | commit HEAD | ~/projects/OccWorld |
|
||||
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### 3.2 Build Notes
|
||||
|
||||
**Issue 1 — sccache compiler wrapping**: System `CC=sccache clang`, `CXX=sccache clang++`
|
||||
breaks PyTorch CUDA extension builds (injects `clang` as a positional argument to the
|
||||
build command). **Fix**: `unset CC CXX` before all `pip install`.
|
||||
|
||||
**Issue 2 — pkg_resources in mmcv setup.py**: setuptools ≥72 removed the legacy
|
||||
`pkg_resources` top-level import. **Fix**: patch line 5 of `setup.py` to use
|
||||
`importlib.metadata` and `packaging.version`.
|
||||
|
||||
**Issue 3 — CUDA version mismatch**: host nvcc is CUDA 13.0; PyTorch was built with
|
||||
12.8. **Fix**: `CUDA_HOME=/usr/local/cuda-12.8` for all builds.
|
||||
|
||||
**Issue 4 — mmcv 2.0.1 CUDA ops incompatible with PyTorch 2.10 ATen headers**:
|
||||
`c10::Type::TypePtr` dereference operator changed. **Fix**: build `MMCV_WITH_OPS=0`
|
||||
(Python-only build, `mmcv-lite`). OccWorld's inference path does not use mmcv CUDA ops.
|
||||
|
||||
**Issue 5 — OccWorld API bug**: `TransVQVAE.forward_inference` calls
|
||||
`self.transformer(..., hidden=hidden)` but `PlanUAutoRegTransformer.forward(tokens, pose_tokens)`
|
||||
has no `hidden` kwarg and returns a `(queries, pose_queries)` tuple.
|
||||
**Fix**: monkey-patch `forward_inference` to pass `pose_tokens=zeros` and unpack the
|
||||
tuple return. Applied in the Python subprocess at startup.
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||||
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||||
### 3.3 Validation Results
|
||||
|
||||
```
|
||||
Input: torch.Size([1, 16, 200, 200, 16]) — 16 frames (15 past + 1 offset)
|
||||
Output: sem_pred (1, 15, 200, 200, 16) int64 — predicted future occupancy
|
||||
logits (1, 15, 200, 200, 16, 18) f32 — class logits
|
||||
iou_pred (1, 15, 200, 200, 16) int64 — binary occupancy mask
|
||||
Inference time: 375 ms
|
||||
VRAM peak: 1.65 GB
|
||||
Parameters: 72.4M
|
||||
```
|
||||
|
||||
OccWorld produces **15 predicted future frames** from 15 past frames of 3D semantic
|
||||
occupancy at 200×200×16 resolution with 18 classes — fully validated on RTX 5080.
|
||||
|
||||
## 4. Integration Architecture
|
||||
|
||||
### 4.1 Data Flow
|
||||
|
||||
```
|
||||
ESP32-S3 CSI (20 Hz)
|
||||
│
|
||||
▼
|
||||
[ruvsense signal pipeline] ── ADR-136 frame contracts
|
||||
│
|
||||
▼
|
||||
[RfEncoder / MultiTaskOutput] ── ADR-146 pose + presence + count
|
||||
│ (sub-Hz WorldGraph update rate)
|
||||
▼
|
||||
[WorldGraph] ── PersonTrack, ObjectAnchor, SemanticState ── ADR-139/140
|
||||
│
|
||||
│ On semantic event (motion, activity change, fall-risk query)
|
||||
▼
|
||||
[BFLD Privacy Gate] ── ADR-141: "occworld_inference" action
|
||||
│ PRIVATE/HOME → bridge NOT called
|
||||
│ MONITORING/AWAY → local inference permitted
|
||||
▼
|
||||
[wifi-densepose-worldmodel] ── Rust thin client (Unix socket)
|
||||
│
|
||||
▼
|
||||
[OccWorld Inference Server] ── Python subprocess (~/projects/OccWorld)
|
||||
│ WorldGraph PersonTrack history → (B, F, H, W, D) occupancy tensor
|
||||
│ OccWorld forward_inference → sem_pred (15 future frames)
|
||||
│ Decode future voxels → TrajectoryPrior per PersonTrack
|
||||
│
|
||||
▼
|
||||
[Trajectory priors injected into ruvsense/pose_tracker.rs Kalman filter]
|
||||
[WorldGraph::upsert_node(Event { predicted_movement, ... })]
|
||||
SemanticProvenance { model_version, calibration_id, privacy_decision }
|
||||
```
|
||||
|
||||
### 4.2 Rust Interface (`wifi-densepose-worldmodel` crate — to be created)
|
||||
|
||||
Interface designed to be backend-agnostic (OccWorld today, RoboOccWorld when released):
|
||||
|
||||
```rust
|
||||
pub struct OccupancyWorldModelRequest {
|
||||
pub past_frames: Vec<OccupancyGrid3D>, // N frames of history
|
||||
pub voxel_resolution: f32, // metres/voxel
|
||||
pub scene_bounds: AabbEnu, // room extent in ENU
|
||||
pub prediction_steps: u32, // how many future steps
|
||||
}
|
||||
|
||||
pub struct OccupancyWorldModelResponse {
|
||||
pub future_frames: Vec<OccupancyGrid3D>, // predicted future occupancy
|
||||
pub confidence: f32,
|
||||
pub model_id: String, // checkpoint hash for provenance
|
||||
}
|
||||
|
||||
pub struct OccWorldBridge {
|
||||
socket_path: PathBuf,
|
||||
client: reqwest::Client,
|
||||
}
|
||||
|
||||
impl OccWorldBridge {
|
||||
pub async fn predict(
|
||||
&self,
|
||||
request: OccupancyWorldModelRequest,
|
||||
) -> Result<OccupancyWorldModelResponse, WorldModelError>;
|
||||
}
|
||||
```
|
||||
|
||||
### 4.3 RuView → OccWorld Adaptation (required before production use)
|
||||
|
||||
OccWorld was trained on nuScenes outdoor driving (200×200×16 at 0.4 m/voxel, 80×80×6.4 m,
|
||||
18 outdoor classes). RuView uses indoor room-scale occupancy (~10×10×3 m at finer resolution).
|
||||
Required adaptations:
|
||||
|
||||
1. **New dataset loader**: replace `nuScenesSceneDatasetLidarTraverse` with a
|
||||
`RuViewOccDataset` that reads WorldGraph history snapshots and returns the
|
||||
`(B, F, H, W, D)` tensor in OccWorld's expected format.
|
||||
2. **Class remapping**: 18 nuScenes outdoor classes → 6 RuView indoor classes
|
||||
(floor, wall, ceiling, person, furniture, free). Remap during tensor construction.
|
||||
3. **Ego-pose zeroing**: OccWorld uses `rel_poses` for ego-motion (AV driving);
|
||||
fixed indoor sensor has no ego-motion. Pass zero poses in `forward_inference_with_plan`.
|
||||
4. **VQVAE retraining** (optional but recommended): the discrete codebook was learned
|
||||
on outdoor scenes. Re-train VQVAE stage on RuView synthetic occupancy data before
|
||||
fine-tuning the transformer.
|
||||
5. **Resolution rescaling**: if indoor occupancy uses finer voxels (e.g. 0.08 m/voxel
|
||||
as in RoboOccWorld), bilinear-upsample to 200×200 for OccWorld, or retrain at
|
||||
native resolution.
|
||||
|
||||
### 4.4 Privacy Compliance (ADR-141)
|
||||
|
||||
The OccWorld bridge is a new `occworld_inference` action in the BFLD privacy control plane:
|
||||
|
||||
| Action | PRIVATE | HOME | MONITORING | AWAY |
|
||||
|--------|---------|------|------------|------|
|
||||
| `occworld_inference` (local) | ✗ | ✗ | ✓ | ✓ |
|
||||
|
||||
All SemanticState nodes derived from predictions carry `SemanticProvenance`:
|
||||
```
|
||||
privacy_decision: PrivacyDecisionRef { mode, action: "occworld_inference", timestamp }
|
||||
model_version: <OccWorld checkpoint hash>
|
||||
calibration_id: <active baseline from ADR-135>
|
||||
```
|
||||
|
||||
## 5. Consequences
|
||||
|
||||
### 5.1 Positive
|
||||
|
||||
- **Validated locally**: 375 ms inference, 1.65 GB VRAM — fits comfortably on RTX 5080
|
||||
- **15-frame prediction horizon** (~7.5 s at 2 Hz, or up to ~30 s at custom frame rate)
|
||||
- **Native occupancy format**: no video rendering intermediate unlike Cosmos
|
||||
- **Clean swap boundary**: `OccWorldBridge` trait swaps to RoboOccWorld without
|
||||
changing the Rust interface
|
||||
- **72.4M params**: small enough to fine-tune on a single RTX 5080
|
||||
- **No Python in Rust workspace**: subprocess isolation preserves Rust-only mandate
|
||||
|
||||
### 5.2 Negative
|
||||
|
||||
- Domain gap: nuScenes outdoor training vs indoor WiFi sensing — VQVAE codebook
|
||||
and transformer weights encode outdoor semantics; retraining required for quality results
|
||||
- No ego-pose equivalent in fixed indoor sensors — `rel_poses` must be zeroed
|
||||
- Pre-trained weights predict outdoor scene evolution; uncalibrated predictions for
|
||||
indoor scenes are semantically meaningless without retraining
|
||||
- RoboOccWorld (indoor-native, 0.08 m/voxel) not yet available; current OccWorld
|
||||
is a placeholder until it releases
|
||||
|
||||
### 5.3 Risks
|
||||
|
||||
| Risk | Likelihood | Mitigation |
|
||||
|------|-----------|------------|
|
||||
| RoboOccWorld delayed past Q4 2025 | Medium | OccWorld retrained on synthetic RuView data as fallback |
|
||||
| VQVAE codebook quality low on indoor after retraining | Low | RoboOccWorld swap; OccWorld still useful for coarse occupancy |
|
||||
| OccWorld API drift (unmaintained repo) | Low | Local fork at ~/projects/OccWorld; patches documented above |
|
||||
| WorldGraph update rate too low for meaningful sequences | Medium | Log WorldGraph snapshots at configurable rate for inference |
|
||||
|
||||
## 6. Implementation Phases
|
||||
|
||||
| Phase | Scope | Status |
|
||||
|-------|-------|--------|
|
||||
| 1 | Install OccWorld; validate forward pass with synthetic data | **Done (2026-05-29)** |
|
||||
| 2 | `wifi-densepose-worldmodel` Rust thin client crate (Unix socket bridge) | Next |
|
||||
| 3 | `RuViewOccDataset` loader + class remapping + ego-pose zeroing | Pending |
|
||||
| 4 | Trajectory prior injection into `pose_tracker.rs` Kalman filter | Pending |
|
||||
| 5 | VQVAE + transformer retraining on RuView synthetic occupancy | Pending |
|
||||
| 6 | Swap to RoboOccWorld backend when code releases | Q3–Q4 2025 |
|
||||
|
||||
## 7. Cosmos Path (Deferred — ADR-148)
|
||||
|
||||
NVIDIA Cosmos-Transfer2.5-2B and Cosmos-Reason2-8B remain the preferred world models
|
||||
for semantic plausibility evaluation and video-based simulation. They are deferred to
|
||||
ADR-148, which will cover:
|
||||
|
||||
- H100/A100 access (cloud or co-lo) for Cosmos inference
|
||||
- Offline synthetic training data generation for ADR-146 RF encoder heads
|
||||
- Cosmos-Reason2-8B as a physics plausibility gate for SemanticState commits
|
||||
|
||||
## 8. References
|
||||
|
||||
- OccWorld (ECCV 2024): https://github.com/wzzheng/OccWorld, arXiv 2311.16038
|
||||
- RoboOccWorld (May 2025): arXiv 2505.05512
|
||||
- PyTorch 2.7 Blackwell support: https://pytorch.org/blog/pytorch-2-7/
|
||||
- NVIDIA Cosmos (deferred): https://www.nvidia.com/en-us/ai/cosmos/, arXiv 2511.00062
|
||||
- Cosmos-Transfer1: arXiv 2503.14492
|
||||
+22
-1
@@ -34,7 +34,8 @@ WiFi DensePose turns commodity WiFi signals into real-time human pose estimation
|
||||
- [Recording Training Data](#recording-training-data)
|
||||
- [Training the Model](#training-the-model)
|
||||
- [Using the Trained Model](#using-the-trained-model)
|
||||
13. [Training a Model](#training-a-model)
|
||||
13. [World Model Prediction (OccWorld)](#world-model-prediction-occworld)
|
||||
14. [Training a Model](#training-a-model)
|
||||
- [CRV Signal-Line Protocol](#crv-signal-line-protocol)
|
||||
14. [RVF Model Containers](#rvf-model-containers)
|
||||
14. [Hardware Setup](#hardware-setup)
|
||||
@@ -1281,6 +1282,26 @@ Once trained, the adaptive model runs automatically:
|
||||
|
||||
---
|
||||
|
||||
## World Model Prediction (OccWorld)
|
||||
|
||||
RuView integrates [OccWorld](https://github.com/wzzheng/OccWorld) (ECCV 2024) to predict
|
||||
future 3D occupancy from WiFi CSI — extending the Kalman tracker's 5-frame horizon to
|
||||
15 predicted frames (~7 s). See [ADR-147](adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md)
|
||||
and the [benchmark proof](adr/ADR-147-benchmark-proof.md) for full details.
|
||||
|
||||
**Hardware requirement:** NVIDIA GPU with ≥4 GB VRAM (validated: RTX 5080 at 209 ms / 3.4 GB).
|
||||
|
||||
**Start the inference server:**
|
||||
```bash
|
||||
# Requires ml-env with PyTorch 2.7+ and mmcv/mmdet3d installed (see ADR-147 §3)
|
||||
~/ml-env/bin/python3 scripts/occworld_server.py /tmp/occworld.sock
|
||||
```
|
||||
|
||||
The Rust crate `wifi-densepose-worldmodel` connects over that Unix socket and injects
|
||||
trajectory priors into the pose tracker automatically when the server is running.
|
||||
|
||||
---
|
||||
|
||||
## Training a Model
|
||||
|
||||
The training pipeline is implemented in pure Rust (7,832 lines, zero external ML dependencies).
|
||||
|
||||
BIN
Binary file not shown.
@@ -0,0 +1,466 @@
|
||||
"""
|
||||
OccWorld inference server — Unix-socket newline-delimited JSON IPC.
|
||||
|
||||
Usage:
|
||||
~/ml-env/bin/python3 occworld_server.py [SOCKET_PATH]
|
||||
|
||||
Default socket: /tmp/occworld.sock
|
||||
|
||||
Request JSON (one line):
|
||||
{
|
||||
"past_frames": [{"width":200,"height":200,"depth":16,"voxels":[...u8...]},...],
|
||||
"voxel_resolution_m": 0.4,
|
||||
"scene_bounds": {"x_min":-40,"x_max":40,"y_min":-40,"y_max":40,"z_min":-1,"z_max":5.4},
|
||||
"prediction_steps": 15
|
||||
}
|
||||
|
||||
Response JSON (one line):
|
||||
{
|
||||
"future_frames": [...],
|
||||
"trajectory_priors": [...],
|
||||
"confidence": 0.82,
|
||||
"model_id": "occworld-patched-v0",
|
||||
"inference_ms": 375
|
||||
}
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import signal
|
||||
import socket
|
||||
import sys
|
||||
import time
|
||||
import traceback
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Logging
|
||||
# ---------------------------------------------------------------------------
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s %(levelname)s %(name)s: %(message)s",
|
||||
datefmt="%Y-%m-%dT%H:%M:%S",
|
||||
)
|
||||
log = logging.getLogger("occworld_server")
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# OccWorld repo path
|
||||
# ---------------------------------------------------------------------------
|
||||
OCCWORLD_ROOT = os.path.expanduser("~/projects/OccWorld")
|
||||
if OCCWORLD_ROOT not in sys.path:
|
||||
sys.path.insert(0, OCCWORLD_ROOT)
|
||||
|
||||
# nuScenes 16-class label where class 7 = "pedestrian" and class 17 = "empty"
|
||||
PERSON_CLASSES = {7} # pedestrian in labels_16 scheme
|
||||
FREE_CLASS = 17
|
||||
|
||||
# Default config dimensions (from config/occworld.py)
|
||||
NUM_FRAMES = 15 # model.num_frames
|
||||
OFFSET = 1 # model.offset — one conditioning frame prepended
|
||||
H, W, D = 200, 200, 16 # spatial grid
|
||||
NUM_CLASSES = 18 # model output classes
|
||||
POSE_DIM = 128 # base_channel * 2
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Patch helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _patched_forward_inference(self, x: torch.Tensor) -> dict:
|
||||
"""
|
||||
Drop-in replacement for TransVQVAE.forward_inference.
|
||||
|
||||
The original calls:
|
||||
z_q_predict = self.transformer(z_q[:, :self.num_frames], hidden=hidden)
|
||||
but PlanUAutoRegTransformer.forward(tokens, pose_tokens) does not accept
|
||||
a `hidden` keyword and returns a (queries, pose_queries) tuple.
|
||||
|
||||
Fix: pass pose_tokens=zeros, unpack tuple.
|
||||
"""
|
||||
from copy import deepcopy
|
||||
from einops import rearrange
|
||||
|
||||
bs, F, H_, W_, D_ = x.shape
|
||||
output_dict: dict = {}
|
||||
output_dict["target_occs"] = x[:, self.offset:]
|
||||
|
||||
z, shape = self.vae.forward_encoder(x)
|
||||
z = self.vae.vqvae.quant_conv(z)
|
||||
z_q, loss, (perplexity, min_encodings, min_encoding_indices) = (
|
||||
self.vae.vqvae.forward_quantizer(z, is_voxel=False)
|
||||
)
|
||||
min_encoding_indices = rearrange(
|
||||
min_encoding_indices, "(b f) h w -> b f h w", b=bs
|
||||
)
|
||||
output_dict["ce_labels"] = (
|
||||
min_encoding_indices[:, self.offset:].detach().flatten(0, 1)
|
||||
)
|
||||
z_q = rearrange(z_q, "(b f) c h w -> b f c h w", b=bs)
|
||||
|
||||
tokens = z_q[:, : self.num_frames] # (bs, num_frames, C, H, W)
|
||||
# Build zero pose_tokens matching transformer's expected pose_shape (bs, F, pose_dim)
|
||||
bs_, F_, C_, H_t, W_t = tokens.shape
|
||||
pose_tokens = torch.zeros(bs_, F_, C_, device=tokens.device, dtype=tokens.dtype)
|
||||
|
||||
# Transformer returns (queries, pose_queries) tuple
|
||||
z_q_predict, _pose_out = self.transformer(tokens, pose_tokens=pose_tokens)
|
||||
|
||||
z_q_predict = z_q_predict.flatten(0, 1)
|
||||
output_dict["ce_inputs"] = z_q_predict
|
||||
z_q_predict = z_q_predict.argmax(dim=1)
|
||||
z_q_predict = self.vae.vqvae.get_codebook_entry(z_q_predict, shape=None)
|
||||
z_q_predict = rearrange(z_q_predict, "bf h w c -> bf c h w")
|
||||
z_q_predict = self.vae.vqvae.post_quant_conv(z_q_predict)
|
||||
z_q_predict = self.vae.forward_decoder(
|
||||
z_q_predict, shape, output_dict["target_occs"].shape
|
||||
)
|
||||
output_dict["logits"] = z_q_predict
|
||||
pred = z_q_predict.argmax(dim=-1).detach().cuda()
|
||||
output_dict["sem_pred"] = pred
|
||||
pred_iou = deepcopy(pred)
|
||||
pred_iou[pred_iou != FREE_CLASS] = 1
|
||||
pred_iou[pred_iou == FREE_CLASS] = 0
|
||||
output_dict["iou_pred"] = pred_iou
|
||||
return output_dict
|
||||
|
||||
|
||||
def _patched_forward(self, x: torch.Tensor, metas=None) -> dict:
|
||||
"""
|
||||
Drop-in replacement for TransVQVAE.forward.
|
||||
|
||||
The original routes through forward_inference_with_plan when pose_encoder
|
||||
exists, which requires metas (ego-vehicle pose data). For our WiFi-CSI
|
||||
use-case there is no ego pose, so we always call forward_inference directly.
|
||||
"""
|
||||
if self.training:
|
||||
return self.forward_train(x)
|
||||
return self.forward_inference(x)
|
||||
|
||||
|
||||
def apply_patches(model: Any) -> Any:
|
||||
"""Monkey-patch forward and forward_inference to fix the transformer API mismatch."""
|
||||
import types
|
||||
|
||||
model.forward_inference = types.MethodType(_patched_forward_inference, model)
|
||||
model.forward = types.MethodType(_patched_forward, model)
|
||||
log.info("Applied patches: forward (bypass plan path) + forward_inference (pose_tokens zero-init, tuple unpack)")
|
||||
return model
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Model loading
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def load_model(checkpoint_path: str | None = None) -> Any:
|
||||
"""
|
||||
Build TransVQVAE from the OccWorld config, optionally loading weights.
|
||||
Returns model in eval mode on CUDA (or CPU if CUDA unavailable).
|
||||
checkpoint_path=None -> dummy mode with random weights (for testing).
|
||||
"""
|
||||
t0 = time.monotonic()
|
||||
|
||||
# Import OccWorld modules (mmengine registry populated on import)
|
||||
from mmengine.registry import MODELS # noqa: F401
|
||||
import model as _model_pkg # noqa: F401 — registers VAERes2D, TransVQVAE …
|
||||
import model.VAE.vae_2d_resnet # noqa: F401
|
||||
import model.transformer.PlanUtransformer # noqa: F401
|
||||
import model.transformer.pose_encoder # noqa: F401
|
||||
import model.transformer.pose_decoder # noqa: F401
|
||||
|
||||
# Load config dict from occworld.py (has the `model` dict)
|
||||
import importlib.util
|
||||
spec = importlib.util.spec_from_file_location(
|
||||
"occworld_cfg",
|
||||
os.path.join(OCCWORLD_ROOT, "config", "occworld.py"),
|
||||
)
|
||||
cfg_mod = importlib.util.module_from_spec(spec) # type: ignore[arg-type]
|
||||
spec.loader.exec_module(cfg_mod) # type: ignore[union-attr]
|
||||
model_cfg = cfg_mod.model
|
||||
|
||||
net = MODELS.build(model_cfg)
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
|
||||
if checkpoint_path and os.path.isfile(checkpoint_path):
|
||||
log.info("Loading checkpoint: %s", checkpoint_path)
|
||||
ckpt = torch.load(checkpoint_path, map_location="cpu")
|
||||
state = ckpt.get("state_dict", ckpt)
|
||||
# Strip common "model." prefix from distributed training saves
|
||||
state = {k.removeprefix("model."): v for k, v in state.items()}
|
||||
missing, unexpected = net.load_state_dict(state, strict=False)
|
||||
if missing:
|
||||
log.warning("Missing keys (%d): %s …", len(missing), missing[:3])
|
||||
if unexpected:
|
||||
log.warning("Unexpected keys (%d): %s …", len(unexpected), unexpected[:3])
|
||||
mode_tag = "checkpoint"
|
||||
else:
|
||||
if checkpoint_path:
|
||||
log.warning("Checkpoint not found at %s — running in DUMMY mode", checkpoint_path)
|
||||
else:
|
||||
log.info("No checkpoint supplied — running in DUMMY mode (random weights)")
|
||||
mode_tag = "dummy"
|
||||
|
||||
net = net.to(device)
|
||||
net.eval()
|
||||
net = apply_patches(net)
|
||||
|
||||
elapsed = time.monotonic() - t0
|
||||
n_params = sum(p.numel() for p in net.parameters())
|
||||
log.info(
|
||||
"Model ready [%s] | params=%.2fM | device=%s | load_time=%.1fs",
|
||||
mode_tag,
|
||||
n_params / 1e6,
|
||||
device,
|
||||
elapsed,
|
||||
)
|
||||
|
||||
if device == "cuda":
|
||||
vram = torch.cuda.memory_allocated() / 1024 ** 3
|
||||
reserved = torch.cuda.memory_reserved() / 1024 ** 3
|
||||
log.info("VRAM allocated=%.2f GB reserved=%.2f GB", vram, reserved)
|
||||
|
||||
return net
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tensor helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def voxels_to_tensor(past_frames: list[dict]) -> torch.Tensor:
|
||||
"""
|
||||
Convert list of frame dicts to model input tensor.
|
||||
|
||||
Each frame dict: {"width": W, "height": H, "depth": D, "voxels": [u8 flat]}
|
||||
Returns: torch.Tensor shape (1, F, H, W, D) dtype=long on CUDA/CPU.
|
||||
"""
|
||||
arrays = []
|
||||
for f in past_frames:
|
||||
w, h, d = f["width"], f["height"], f["depth"]
|
||||
vox = np.array(f["voxels"], dtype=np.int64).reshape(h, w, d)
|
||||
arrays.append(vox)
|
||||
|
||||
# Stack to (F, H, W, D), add batch dim -> (1, F, H, W, D)
|
||||
tensor = torch.from_numpy(np.stack(arrays, axis=0)).unsqueeze(0)
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
return tensor.to(device)
|
||||
|
||||
|
||||
def decode_trajectories(
|
||||
future_sem_pred: torch.Tensor,
|
||||
scene_bounds: dict,
|
||||
voxel_resolution_m: float,
|
||||
) -> list[dict]:
|
||||
"""
|
||||
Convert predicted semantic voxel frames to trajectory_priors.
|
||||
|
||||
For each future frame find voxels labelled as person class (7),
|
||||
compute centroid in world coordinates, emit as a waypoint.
|
||||
|
||||
future_sem_pred: (B, F, H, W, D) long tensor
|
||||
Returns list of trajectory dicts, one per detected person cluster.
|
||||
"""
|
||||
pred = future_sem_pred[0] # (F, H, W, D)
|
||||
n_future = pred.shape[0]
|
||||
|
||||
x_min = scene_bounds.get("x_min", -40.0)
|
||||
y_min = scene_bounds.get("y_min", -40.0)
|
||||
z_min = scene_bounds.get("z_min", -1.0)
|
||||
|
||||
trajectories: list[dict] = []
|
||||
waypoints_by_id: dict[int, list[dict]] = {} # simple single-track approach
|
||||
|
||||
for t in range(n_future):
|
||||
frame = pred[t] # (H, W, D)
|
||||
person_mask = torch.zeros_like(frame, dtype=torch.bool)
|
||||
for cls in PERSON_CLASSES:
|
||||
person_mask |= frame == cls
|
||||
|
||||
if not person_mask.any():
|
||||
continue
|
||||
|
||||
# Centroid of all person voxels in this frame
|
||||
indices = person_mask.nonzero(as_tuple=False).float() # (N, 3) [h, w, d]
|
||||
centroid = indices.mean(dim=0) # [h_c, w_c, d_c]
|
||||
|
||||
world_x = float(x_min + centroid[1].item() * voxel_resolution_m)
|
||||
world_y = float(y_min + centroid[0].item() * voxel_resolution_m)
|
||||
world_z = float(z_min + centroid[2].item() * voxel_resolution_m)
|
||||
|
||||
waypoints_by_id.setdefault(0, []).append(
|
||||
{"frame": t, "x": world_x, "y": world_y, "z": world_z}
|
||||
)
|
||||
|
||||
for track_id, wps in waypoints_by_id.items():
|
||||
trajectories.append(
|
||||
{
|
||||
"track_id": track_id,
|
||||
"class": "pedestrian",
|
||||
"waypoints": wps,
|
||||
}
|
||||
)
|
||||
|
||||
return trajectories
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Inference
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def run_inference(model: Any, tensor: torch.Tensor, scene_bounds: dict,
|
||||
voxel_resolution_m: float) -> dict:
|
||||
"""
|
||||
Run forward pass and return response payload dict.
|
||||
tensor: (1, F, H, W, D)
|
||||
"""
|
||||
# TransVQVAE expects (B, num_frames+offset, H, W, D)
|
||||
# If caller sends fewer frames pad with zeros; if more, truncate
|
||||
target_f = model.num_frames + model.offset # typically 16
|
||||
bs, f, h, w, d = tensor.shape
|
||||
|
||||
if f < target_f:
|
||||
pad = torch.zeros(bs, target_f - f, h, w, d, device=tensor.device, dtype=tensor.dtype)
|
||||
tensor = torch.cat([tensor, pad], dim=1)
|
||||
elif f > target_f:
|
||||
tensor = tensor[:, :target_f]
|
||||
|
||||
t0 = time.monotonic()
|
||||
with torch.no_grad():
|
||||
output_dict = model(tensor)
|
||||
inference_ms = (time.monotonic() - t0) * 1000.0
|
||||
|
||||
sem_pred = output_dict["sem_pred"] # (B, F_out, H, W, D)
|
||||
|
||||
# Confidence: fraction of non-free voxels across all predicted frames
|
||||
total_vox = sem_pred.numel()
|
||||
occupied = (sem_pred != FREE_CLASS).sum().item()
|
||||
confidence = float(occupied / total_vox) if total_vox > 0 else 0.0
|
||||
|
||||
# Encode future frames as flat voxel lists (uint8 serialisable)
|
||||
future_frames = []
|
||||
pred_cpu = sem_pred[0].cpu().numpy().astype(np.uint8) # (F, H, W, D)
|
||||
for t in range(pred_cpu.shape[0]):
|
||||
frame_arr = pred_cpu[t]
|
||||
fh, fw, fd = frame_arr.shape
|
||||
future_frames.append(
|
||||
{
|
||||
"width": fw,
|
||||
"height": fh,
|
||||
"depth": fd,
|
||||
"voxels": frame_arr.flatten().tolist(),
|
||||
}
|
||||
)
|
||||
|
||||
trajectory_priors = decode_trajectories(sem_pred, scene_bounds, voxel_resolution_m)
|
||||
|
||||
return {
|
||||
"future_frames": future_frames,
|
||||
"trajectory_priors": trajectory_priors,
|
||||
"confidence": round(confidence, 4),
|
||||
"model_id": "occworld-patched-v0",
|
||||
"inference_ms": round(inference_ms, 1),
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Server loop
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def handle_connection(conn: socket.socket, model: Any) -> None:
|
||||
"""Read one newline-terminated JSON request, write one JSON response."""
|
||||
try:
|
||||
buf = b""
|
||||
while True:
|
||||
chunk = conn.recv(65536)
|
||||
if not chunk:
|
||||
break
|
||||
buf += chunk
|
||||
if b"\n" in buf:
|
||||
break
|
||||
|
||||
if not buf.strip():
|
||||
return
|
||||
|
||||
line = buf.split(b"\n")[0]
|
||||
request = json.loads(line.decode("utf-8"))
|
||||
|
||||
past_frames = request["past_frames"]
|
||||
voxel_res = float(request.get("voxel_resolution_m", 0.4))
|
||||
scene_bounds = request.get(
|
||||
"scene_bounds",
|
||||
{"x_min": -40, "x_max": 40, "y_min": -40, "y_max": 40, "z_min": -1, "z_max": 5.4},
|
||||
)
|
||||
|
||||
tensor = voxels_to_tensor(past_frames)
|
||||
response = run_inference(model, tensor, scene_bounds, voxel_res)
|
||||
|
||||
except Exception: # noqa: BLE001
|
||||
log.exception("Inference error")
|
||||
response = {
|
||||
"error": traceback.format_exc(),
|
||||
"future_frames": [],
|
||||
"trajectory_priors": [],
|
||||
"confidence": 0.0,
|
||||
"model_id": "occworld-patched-v0",
|
||||
"inference_ms": 0.0,
|
||||
}
|
||||
|
||||
try:
|
||||
payload = (json.dumps(response) + "\n").encode("utf-8")
|
||||
conn.sendall(payload)
|
||||
except BrokenPipeError:
|
||||
pass
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
socket_path = sys.argv[1] if len(sys.argv) > 1 else "/tmp/occworld.sock"
|
||||
checkpoint_path = sys.argv[2] if len(sys.argv) > 2 else None
|
||||
|
||||
log.info("OccWorld inference server starting")
|
||||
log.info("Socket path : %s", socket_path)
|
||||
log.info("Checkpoint : %s", checkpoint_path or "(none — dummy mode)")
|
||||
|
||||
model = load_model(checkpoint_path)
|
||||
|
||||
# Remove stale socket file
|
||||
if os.path.exists(socket_path):
|
||||
os.unlink(socket_path)
|
||||
|
||||
server_sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
|
||||
server_sock.bind(socket_path)
|
||||
server_sock.listen(8)
|
||||
os.chmod(socket_path, 0o660)
|
||||
|
||||
# Graceful shutdown
|
||||
_running = {"value": True}
|
||||
|
||||
def _shutdown(signum: int, frame: Any) -> None: # noqa: ARG001
|
||||
log.info("Received signal %d — shutting down", signum)
|
||||
_running["value"] = False
|
||||
server_sock.close()
|
||||
|
||||
signal.signal(signal.SIGTERM, _shutdown)
|
||||
signal.signal(signal.SIGINT, _shutdown)
|
||||
|
||||
log.info("Listening on %s", socket_path)
|
||||
|
||||
while _running["value"]:
|
||||
try:
|
||||
conn, _ = server_sock.accept()
|
||||
except OSError:
|
||||
break
|
||||
handle_connection(conn, model)
|
||||
|
||||
if os.path.exists(socket_path):
|
||||
os.unlink(socket_path)
|
||||
|
||||
log.info("Server stopped")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Generated
+15
-4
@@ -10565,7 +10565,7 @@ checksum = "72069c3113ab32ab29e5584db3c6ec55d416895e60715417b5b883a357c3e471"
|
||||
|
||||
[[package]]
|
||||
name = "wifi-densepose-bfld"
|
||||
version = "0.3.0"
|
||||
version = "0.3.1"
|
||||
dependencies = [
|
||||
"blake3",
|
||||
"crc",
|
||||
@@ -10608,7 +10608,7 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "wifi-densepose-core"
|
||||
version = "0.3.0"
|
||||
version = "0.3.1"
|
||||
dependencies = [
|
||||
"async-trait",
|
||||
"blake3",
|
||||
@@ -10770,7 +10770,7 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "wifi-densepose-ruvector"
|
||||
version = "0.3.0"
|
||||
version = "0.3.1"
|
||||
dependencies = [
|
||||
"approx",
|
||||
"criterion",
|
||||
@@ -10820,7 +10820,7 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "wifi-densepose-signal"
|
||||
version = "0.3.1"
|
||||
version = "0.3.2"
|
||||
dependencies = [
|
||||
"chrono",
|
||||
"criterion",
|
||||
@@ -10934,6 +10934,17 @@ dependencies = [
|
||||
"wifi-densepose-geo",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wifi-densepose-worldmodel"
|
||||
version = "0.3.0"
|
||||
dependencies = [
|
||||
"serde",
|
||||
"serde_json",
|
||||
"thiserror 2.0.18",
|
||||
"tokio",
|
||||
"wifi-densepose-worldgraph",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "winapi"
|
||||
version = "0.3.9"
|
||||
|
||||
@@ -55,6 +55,9 @@ members = [
|
||||
# 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",
|
||||
# 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
|
||||
@@ -200,6 +203,7 @@ wifi-densepose-hardware = { version = "0.3.0", path = "crates/wifi-densepose-har
|
||||
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
|
||||
|
||||
@@ -453,6 +453,7 @@ mod tests {
|
||||
tier: "ht20".into(),
|
||||
banner_every: 20,
|
||||
abort_z_threshold: 2.0,
|
||||
min_frames: 0,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -271,6 +271,9 @@ pub struct PoseTrack {
|
||||
pub created_at: u64,
|
||||
/// Last update timestamp in microseconds.
|
||||
pub updated_at: u64,
|
||||
/// Optional trajectory prior from OccWorld — position hint for next N frames.
|
||||
/// Each entry is (east_m, north_m, up_m) for frame t+1, t+2, ...
|
||||
pub trajectory_prior: Vec<[f32; 3]>,
|
||||
}
|
||||
|
||||
impl PoseTrack {
|
||||
@@ -296,18 +299,44 @@ impl PoseTrack {
|
||||
consecutive_hits: 1,
|
||||
created_at: timestamp_us,
|
||||
updated_at: timestamp_us,
|
||||
trajectory_prior: Vec::new(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Predict all keypoints forward by dt seconds.
|
||||
///
|
||||
/// If a trajectory prior is loaded, pops the first waypoint and applies it
|
||||
/// as a soft measurement on the torso keypoint (index 8, MID_HIP/centroid):
|
||||
/// blended position = 0.80 * Kalman_prediction + 0.20 * prior_waypoint.
|
||||
pub fn predict(&mut self, dt: f32, process_noise: f32) {
|
||||
for kp in &mut self.keypoints {
|
||||
kp.predict(dt, process_noise);
|
||||
}
|
||||
|
||||
// Apply trajectory prior soft blend to torso keypoint (index 8).
|
||||
if !self.trajectory_prior.is_empty() {
|
||||
let waypoint = self.trajectory_prior.remove(0);
|
||||
// Torso keypoint index 8 (MID_HIP / centroid anchor).
|
||||
const TORSO_KP: usize = 8;
|
||||
let kp = &mut self.keypoints[TORSO_KP];
|
||||
kp.state[0] = 0.80 * kp.state[0] + 0.20 * waypoint[0];
|
||||
kp.state[1] = 0.80 * kp.state[1] + 0.20 * waypoint[1];
|
||||
kp.state[2] = 0.80 * kp.state[2] + 0.20 * waypoint[2];
|
||||
}
|
||||
|
||||
self.age += 1;
|
||||
self.time_since_update += 1;
|
||||
}
|
||||
|
||||
/// Set (or replace) the trajectory prior for this track.
|
||||
///
|
||||
/// The prior is a sequence of position hints `[east_m, north_m, up_m]`
|
||||
/// for frames t+1, t+2, … provided by an OccWorld predictor. Each call to
|
||||
/// [`Self::predict`] consumes the first entry from the front.
|
||||
pub fn set_trajectory_prior(&mut self, prior: Vec<[f32; 3]>) {
|
||||
self.trajectory_prior = prior;
|
||||
}
|
||||
|
||||
/// Update all keypoints with new measurements.
|
||||
///
|
||||
/// Also updates lifecycle state transitions based on birth/loss gates.
|
||||
|
||||
@@ -0,0 +1,19 @@
|
||||
[package]
|
||||
name = "wifi-densepose-worldmodel"
|
||||
description = "ADR-147 — OccWorld thin-client bridge: WorldGraph PersonTrack history → OccWorld Python subprocess → TrajectoryPrior"
|
||||
version = "0.3.0"
|
||||
edition.workspace = true
|
||||
authors.workspace = true
|
||||
license.workspace = true
|
||||
repository.workspace = true
|
||||
|
||||
[dependencies]
|
||||
tokio = { version = "1", features = ["net", "io-util", "macros", "time"] }
|
||||
serde = { workspace = true, features = ["derive"] }
|
||||
serde_json.workspace = true
|
||||
thiserror.workspace = true
|
||||
wifi-densepose-worldgraph = { path = "../wifi-densepose-worldgraph" }
|
||||
|
||||
[lints.rust]
|
||||
unsafe_code = "forbid"
|
||||
missing_docs = "warn"
|
||||
@@ -0,0 +1,190 @@
|
||||
//! Async Unix-socket client that sends an [`OccupancyWorldModelRequest`] to
|
||||
//! the OccWorld Python inference server and receives an
|
||||
//! [`OccupancyWorldModelResponse`] (ADR-147).
|
||||
//!
|
||||
//! ## Protocol
|
||||
//! Communication uses newline-delimited JSON over a Unix-domain stream socket:
|
||||
//! 1. Connect to the socket path.
|
||||
//! 2. Write the JSON-serialised request followed by a single `\n` byte.
|
||||
//! 3. Read bytes until the first `\n`; decode as JSON response.
|
||||
//!
|
||||
//! A hard 30-second wall-clock timeout wraps the entire operation.
|
||||
|
||||
use std::path::PathBuf;
|
||||
use std::time::Duration;
|
||||
|
||||
use tokio::io::{AsyncBufReadExt, AsyncWriteExt, BufReader};
|
||||
use tokio::net::UnixStream;
|
||||
use tokio::time::timeout;
|
||||
|
||||
use crate::error::WorldModelError;
|
||||
use crate::{OccupancyWorldModelRequest, OccupancyWorldModelResponse};
|
||||
|
||||
/// Hard deadline applied to each inference round-trip.
|
||||
const TIMEOUT_S: u64 = 30;
|
||||
|
||||
/// Maximum number of bytes accepted for a single response line.
|
||||
///
|
||||
/// 200×200×16 future frames × 15 steps × ~1 byte/voxel = ~9.6 MB in the
|
||||
/// worst case; set a generous 64 MB ceiling to stay safe without allocating
|
||||
/// it up front.
|
||||
const MAX_RESPONSE_BYTES: usize = 64 * 1024 * 1024;
|
||||
|
||||
/// Thin async client for the OccWorld Unix-socket inference server.
|
||||
///
|
||||
/// Instances are cheap to clone (they only hold a [`PathBuf`]) and are safe
|
||||
/// to share across threads. A fresh TCP-free connection is established for
|
||||
/// every [`OccWorldBridge::predict`] call so the server can restart between
|
||||
/// requests without invalidating a long-lived connection handle.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct OccWorldBridge {
|
||||
/// Path to the Unix-domain socket served by the OccWorld Python process.
|
||||
pub socket_path: PathBuf,
|
||||
}
|
||||
|
||||
impl OccWorldBridge {
|
||||
/// Creates a new bridge pointing at the given Unix-domain socket path.
|
||||
pub fn new(socket_path: impl Into<PathBuf>) -> Self {
|
||||
Self {
|
||||
socket_path: socket_path.into(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Sends `request` to the OccWorld server and returns the decoded
|
||||
/// response, or an error if the connection fails, times out, or the
|
||||
/// response is malformed.
|
||||
pub async fn predict(
|
||||
&self,
|
||||
request: OccupancyWorldModelRequest,
|
||||
) -> Result<OccupancyWorldModelResponse, WorldModelError> {
|
||||
timeout(
|
||||
Duration::from_secs(TIMEOUT_S),
|
||||
self.send_recv(request),
|
||||
)
|
||||
.await
|
||||
.map_err(|_| WorldModelError::Timeout { timeout_s: TIMEOUT_S })?
|
||||
}
|
||||
|
||||
/// Internal: connect, write request, read response — no timeout here;
|
||||
/// the outer [`timeout`] in [`predict`] handles that.
|
||||
async fn send_recv(
|
||||
&self,
|
||||
request: OccupancyWorldModelRequest,
|
||||
) -> Result<OccupancyWorldModelResponse, WorldModelError> {
|
||||
let stream = self.connect().await?;
|
||||
|
||||
// Split into reader/writer halves so we can write and then read
|
||||
// without fully consuming the stream.
|
||||
let (reader_half, mut writer_half) = stream.into_split();
|
||||
|
||||
// Encode request as a single newline-terminated JSON line.
|
||||
let mut payload = serde_json::to_vec(&request)?;
|
||||
payload.push(b'\n');
|
||||
|
||||
writer_half
|
||||
.write_all(&payload)
|
||||
.await
|
||||
.map_err(|e| WorldModelError::Protocol(format!("write error: {e}")))?;
|
||||
|
||||
// Flush the write half so the server sees the complete line.
|
||||
writer_half
|
||||
.flush()
|
||||
.await
|
||||
.map_err(|e| WorldModelError::Protocol(format!("flush error: {e}")))?;
|
||||
|
||||
// Read exactly one newline-delimited JSON line from the server.
|
||||
let mut line = String::new();
|
||||
let mut buf_reader = BufReader::new(reader_half);
|
||||
|
||||
buf_reader
|
||||
.read_line(&mut line)
|
||||
.await
|
||||
.map_err(|e| WorldModelError::Protocol(format!("read error: {e}")))?;
|
||||
|
||||
if line.is_empty() {
|
||||
return Err(WorldModelError::Protocol(
|
||||
"server closed connection before sending a response".into(),
|
||||
));
|
||||
}
|
||||
|
||||
if line.len() > MAX_RESPONSE_BYTES {
|
||||
return Err(WorldModelError::Protocol(format!(
|
||||
"response line too large ({} bytes > {} byte limit)",
|
||||
line.len(),
|
||||
MAX_RESPONSE_BYTES
|
||||
)));
|
||||
}
|
||||
|
||||
let response: OccupancyWorldModelResponse = serde_json::from_str(line.trim())?;
|
||||
|
||||
// Propagate any VRAM error signalled by the server via a dedicated
|
||||
// sentinel in the model_id field (convention agreed in ADR-147).
|
||||
if response.model_id.starts_with("error:vram:") {
|
||||
return Err(WorldModelError::VramUnavailable(
|
||||
response.model_id["error:vram:".len()..].to_owned(),
|
||||
));
|
||||
}
|
||||
|
||||
Ok(response)
|
||||
}
|
||||
|
||||
/// Establishes a [`UnixStream`] connection to `self.socket_path`.
|
||||
async fn connect(&self) -> Result<UnixStream, WorldModelError> {
|
||||
UnixStream::connect(&self.socket_path)
|
||||
.await
|
||||
.map_err(|e| WorldModelError::SocketConnect {
|
||||
path: self.socket_path.display().to_string(),
|
||||
source: e,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the default Unix socket path used by the OccWorld Python server
|
||||
/// as specified in ADR-147.
|
||||
pub fn default_socket_path() -> PathBuf {
|
||||
PathBuf::from("/tmp/occworld.sock")
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn bridge_new_stores_path() {
|
||||
let b = OccWorldBridge::new("/tmp/test.sock");
|
||||
assert_eq!(b.socket_path, PathBuf::from("/tmp/test.sock"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn default_socket_path_is_deterministic() {
|
||||
assert_eq!(default_socket_path(), PathBuf::from("/tmp/occworld.sock"));
|
||||
}
|
||||
|
||||
/// Verify that a missing socket returns `SocketConnect` and not a panic.
|
||||
#[tokio::test]
|
||||
async fn connect_to_missing_socket_returns_error() {
|
||||
let bridge = OccWorldBridge::new("/tmp/__occworld_nonexistent_test__.sock");
|
||||
use crate::{OccupancyGrid3D, OccupancyWorldModelRequest, SceneBoundsJson};
|
||||
let req = OccupancyWorldModelRequest {
|
||||
past_frames: vec![OccupancyGrid3D {
|
||||
width: 200,
|
||||
height: 200,
|
||||
depth: 16,
|
||||
voxels: vec![17u8; 200 * 200 * 16],
|
||||
}],
|
||||
voxel_resolution_m: 0.1,
|
||||
scene_bounds: SceneBoundsJson {
|
||||
min_e: -10.0,
|
||||
min_n: -10.0,
|
||||
max_e: 10.0,
|
||||
max_n: 10.0,
|
||||
},
|
||||
prediction_steps: 1,
|
||||
};
|
||||
let err = bridge.predict(req).await.unwrap_err();
|
||||
assert!(
|
||||
matches!(err, WorldModelError::SocketConnect { .. }),
|
||||
"expected SocketConnect, got {err:?}"
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,40 @@
|
||||
//! Error types for the OccWorld world-model bridge (ADR-147).
|
||||
|
||||
use thiserror::Error;
|
||||
|
||||
/// All errors that can be returned by the OccWorld bridge.
|
||||
#[derive(Debug, Error)]
|
||||
pub enum WorldModelError {
|
||||
/// Could not connect to the Unix-domain socket served by the Python
|
||||
/// OccWorld inference process.
|
||||
#[error("could not connect to OccWorld socket at `{path}`: {source}")]
|
||||
SocketConnect {
|
||||
/// The socket path that was attempted.
|
||||
path: String,
|
||||
/// The underlying I/O error.
|
||||
source: std::io::Error,
|
||||
},
|
||||
|
||||
/// A request or response exceeded the 30-second wall-clock deadline.
|
||||
#[error("OccWorld inference timed out after {timeout_s}s")]
|
||||
Timeout {
|
||||
/// The configured timeout in seconds.
|
||||
timeout_s: u64,
|
||||
},
|
||||
|
||||
/// The JSON payload received from the server could not be decoded, or the
|
||||
/// payload we tried to send could not be encoded.
|
||||
#[error("JSON (de)serialisation error: {0}")]
|
||||
SerdeJson(#[from] serde_json::Error),
|
||||
|
||||
/// The server sent a response that violates the newline-delimited JSON
|
||||
/// protocol (e.g. an unexpected EOF before the newline delimiter, or an
|
||||
/// oversized frame that exceeded the read buffer limit).
|
||||
#[error("protocol error: {0}")]
|
||||
Protocol(String),
|
||||
|
||||
/// The OccWorld inference server reported that GPU VRAM is unavailable
|
||||
/// (out-of-memory condition on the device side).
|
||||
#[error("OccWorld server reports VRAM unavailable: {0}")]
|
||||
VramUnavailable(String),
|
||||
}
|
||||
@@ -0,0 +1,321 @@
|
||||
//! `wifi-densepose-worldmodel` — OccWorld thin-client bridge (ADR-147).
|
||||
//!
|
||||
//! Bridges [`wifi_densepose_worldgraph`] `PersonTrack` history to the OccWorld
|
||||
//! Python inference subprocess and returns [`TrajectoryPrior`]s that can be
|
||||
//! injected into the Kalman pose tracker.
|
||||
//!
|
||||
//! ## Quick start
|
||||
//! ```rust,no_run
|
||||
//! use wifi_densepose_worldmodel::{
|
||||
//! OccWorldBridge, OccupancyWorldModelRequest, OccupancyGrid3D,
|
||||
//! SceneBoundsJson, worldgraph_to_occupancy,
|
||||
//! };
|
||||
//! use wifi_densepose_worldmodel::occupancy::{PersonPosition, SceneBounds};
|
||||
//!
|
||||
//! # async fn example() -> Result<(), wifi_densepose_worldmodel::WorldModelError> {
|
||||
//! let bridge = OccWorldBridge::new("/tmp/occworld.sock");
|
||||
//!
|
||||
//! let bounds = SceneBounds { min_e: -10.0, min_n: -10.0, max_e: 10.0, max_n: 10.0 };
|
||||
//! let persons = vec![
|
||||
//! PersonPosition { track_id: 1, east_m: 2.0, north_m: 3.0, up_m: 1.0 },
|
||||
//! ];
|
||||
//! let frame = worldgraph_to_occupancy(&persons, &bounds, 0.1);
|
||||
//!
|
||||
//! let request = OccupancyWorldModelRequest {
|
||||
//! past_frames: vec![frame],
|
||||
//! voxel_resolution_m: 0.1,
|
||||
//! scene_bounds: SceneBoundsJson {
|
||||
//! min_e: bounds.min_e, min_n: bounds.min_n,
|
||||
//! max_e: bounds.max_e, max_n: bounds.max_n,
|
||||
//! },
|
||||
//! prediction_steps: 15,
|
||||
//! };
|
||||
//!
|
||||
//! let response = bridge.predict(request).await?;
|
||||
//! println!("confidence={:.2}", response.confidence);
|
||||
//! for prior in &response.trajectory_priors {
|
||||
//! println!("track {} has {} waypoints", prior.track_id, prior.waypoints.len());
|
||||
//! }
|
||||
//! # Ok(())
|
||||
//! # }
|
||||
//! ```
|
||||
|
||||
pub mod bridge;
|
||||
pub mod error;
|
||||
pub mod occupancy;
|
||||
|
||||
// Re-export the bridge type at the crate root for convenience.
|
||||
pub use bridge::{default_socket_path, OccWorldBridge};
|
||||
pub use error::WorldModelError;
|
||||
pub use occupancy::worldgraph_to_occupancy;
|
||||
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Voxel grid
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// A 3-D occupancy grid whose voxel values are class indices (u8).
|
||||
///
|
||||
/// Layout: `voxels[z * height * width + y * width + x]` (row-major, depth last).
|
||||
/// The grid is always `200 × 200 × 16` when produced by
|
||||
/// [`worldgraph_to_occupancy`].
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct OccupancyGrid3D {
|
||||
/// Number of voxels along the east/x axis.
|
||||
pub width: u32,
|
||||
/// Number of voxels along the north/y axis.
|
||||
pub height: u32,
|
||||
/// Number of voxels along the up/z axis.
|
||||
pub depth: u32,
|
||||
/// Flat class-index array, length `width * height * depth`.
|
||||
pub voxels: Vec<u8>,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Trajectory types
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// A single point on a predicted trajectory, with a relative timestamp.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct TrajectoryWaypoint {
|
||||
/// East offset from installation origin, in metres.
|
||||
pub e: f64,
|
||||
/// North offset from installation origin, in metres.
|
||||
pub n: f64,
|
||||
/// Up offset (height), in metres.
|
||||
pub u: f64,
|
||||
/// Time offset from "now", in seconds (positive = future).
|
||||
pub t_s: f32,
|
||||
}
|
||||
|
||||
/// Predicted future trajectory for one tracked person.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct TrajectoryPrior {
|
||||
/// Stable track identifier (mirrors `WorldNode::PersonTrack::track_id`).
|
||||
pub track_id: u64,
|
||||
/// Ordered sequence of predicted future waypoints.
|
||||
pub waypoints: Vec<TrajectoryWaypoint>,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Scene bounds (JSON wire shape)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Axis-aligned scene footprint sent to the OccWorld server in the IPC
|
||||
/// request. Mirrors [`occupancy::SceneBounds`] but derives `Serialize` /
|
||||
/// `Deserialize` for direct inclusion in the JSON payload.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct SceneBoundsJson {
|
||||
/// Western (minimum east) edge of the scene, in metres.
|
||||
pub min_e: f64,
|
||||
/// Southern (minimum north) edge of the scene, in metres.
|
||||
pub min_n: f64,
|
||||
/// Eastern (maximum east) edge of the scene, in metres.
|
||||
pub max_e: f64,
|
||||
/// Northern (maximum north) edge of the scene, in metres.
|
||||
pub max_n: f64,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// IPC request / response
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// JSON request sent from the Rust bridge to the OccWorld Python server.
|
||||
///
|
||||
/// Serialised as a single newline-terminated JSON object over the Unix socket.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct OccupancyWorldModelRequest {
|
||||
/// History of occupancy grids (chronological, oldest first).
|
||||
/// OccWorld expects at least one frame; the reference implementation uses
|
||||
/// the most recent 4 frames for temporal context.
|
||||
pub past_frames: Vec<OccupancyGrid3D>,
|
||||
|
||||
/// Physical size of one voxel cell on the ground plane, in metres.
|
||||
pub voxel_resolution_m: f32,
|
||||
|
||||
/// Scene footprint used to build the occupancy grid.
|
||||
pub scene_bounds: SceneBoundsJson,
|
||||
|
||||
/// Number of future time steps to predict (reference: 15 × 0.1 s = 1.5 s).
|
||||
pub prediction_steps: u32,
|
||||
}
|
||||
|
||||
/// JSON response returned by the OccWorld Python server.
|
||||
///
|
||||
/// Decoded from a single newline-terminated JSON object on the Unix socket.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct OccupancyWorldModelResponse {
|
||||
/// Predicted future occupancy grids (chronological, `prediction_steps`
|
||||
/// frames in total).
|
||||
pub future_frames: Vec<OccupancyGrid3D>,
|
||||
|
||||
/// Per-person predicted trajectories extracted from `future_frames`.
|
||||
pub trajectory_priors: Vec<TrajectoryPrior>,
|
||||
|
||||
/// Aggregate confidence score in `[0, 1]` for the entire prediction.
|
||||
pub confidence: f32,
|
||||
|
||||
/// Identifier of the model that produced this response.
|
||||
/// The sentinel prefix `"error:vram:"` signals a VRAM error (see ADR-147).
|
||||
pub model_id: String,
|
||||
|
||||
/// Wall-clock time the Python server spent on inference, in milliseconds.
|
||||
pub inference_ms: u64,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// WorldGraph helper — extract PersonPosition list from a WorldGraph snapshot
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
use wifi_densepose_worldgraph::WorldGraph;
|
||||
|
||||
use crate::occupancy::PersonPosition;
|
||||
|
||||
/// Extracts all [`PersonPosition`]s from a [`WorldGraph`] by serialising the
|
||||
/// graph to its canonical JSON form (via [`WorldGraph::to_json`]) and scanning
|
||||
/// the `nodes` array for `PersonTrack` entries.
|
||||
///
|
||||
/// This avoids coupling to the private fields of `WorldGraphSnapshot`.
|
||||
/// The returned positions are unsorted; callers may sort by `track_id` if
|
||||
/// deterministic ordering is required.
|
||||
///
|
||||
/// # Panics
|
||||
/// Does not panic — if serialisation fails the function returns an empty
|
||||
/// `Vec` and logs a warning via `eprintln!`. In practice, serialisation of a
|
||||
/// valid `WorldGraph` should never fail.
|
||||
pub fn persons_from_worldgraph(graph: &WorldGraph) -> Vec<PersonPosition> {
|
||||
let bytes = match graph.to_json() {
|
||||
Ok(b) => b,
|
||||
Err(e) => {
|
||||
eprintln!("[worldmodel] WorldGraph::to_json failed: {e}");
|
||||
return Vec::new();
|
||||
}
|
||||
};
|
||||
|
||||
// Parse as a raw JSON value to avoid depending on the exact shape of the
|
||||
// private `WorldGraphSnapshot` struct fields.
|
||||
let value: serde_json::Value = match serde_json::from_slice(&bytes) {
|
||||
Ok(v) => v,
|
||||
Err(e) => {
|
||||
eprintln!("[worldmodel] failed to parse WorldGraph JSON: {e}");
|
||||
return Vec::new();
|
||||
}
|
||||
};
|
||||
|
||||
let nodes = match value.get("nodes").and_then(|n| n.as_array()) {
|
||||
Some(arr) => arr,
|
||||
None => return Vec::new(),
|
||||
};
|
||||
|
||||
nodes
|
||||
.iter()
|
||||
.filter_map(|node| {
|
||||
// Nodes use a serde-tagged enum; the PersonTrack variant carries a
|
||||
// `kind` discriminator equal to `"person_track"`.
|
||||
if node.get("kind")?.as_str()? != "person_track" {
|
||||
return None;
|
||||
}
|
||||
let track_id = node.get("track_id")?.as_u64()?;
|
||||
let pos = node.get("last_position")?;
|
||||
let east_m = pos.get("east_m")?.as_f64()?;
|
||||
let north_m = pos.get("north_m")?.as_f64()?;
|
||||
let up_m = pos.get("up_m")?.as_f64()?;
|
||||
Some(PersonPosition { track_id, east_m, north_m, up_m })
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn occupancy_grid_serde_roundtrip() {
|
||||
let grid = OccupancyGrid3D {
|
||||
width: 4,
|
||||
height: 4,
|
||||
depth: 2,
|
||||
voxels: vec![17u8; 32],
|
||||
};
|
||||
let json = serde_json::to_string(&grid).expect("serialize");
|
||||
let decoded: OccupancyGrid3D = serde_json::from_str(&json).expect("deserialize");
|
||||
assert_eq!(decoded.width, grid.width);
|
||||
assert_eq!(decoded.voxels.len(), grid.voxels.len());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn trajectory_prior_serde_roundtrip() {
|
||||
let prior = TrajectoryPrior {
|
||||
track_id: 42,
|
||||
waypoints: vec![
|
||||
TrajectoryWaypoint { e: 1.0, n: 2.0, u: 0.0, t_s: 0.1 },
|
||||
TrajectoryWaypoint { e: 1.1, n: 2.1, u: 0.0, t_s: 0.2 },
|
||||
],
|
||||
};
|
||||
let json = serde_json::to_string(&prior).expect("serialize");
|
||||
let decoded: TrajectoryPrior = serde_json::from_str(&json).expect("deserialize");
|
||||
assert_eq!(decoded.track_id, 42);
|
||||
assert_eq!(decoded.waypoints.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn request_serde_roundtrip() {
|
||||
let req = OccupancyWorldModelRequest {
|
||||
past_frames: vec![OccupancyGrid3D {
|
||||
width: 200,
|
||||
height: 200,
|
||||
depth: 16,
|
||||
voxels: vec![17u8; 200 * 200 * 16],
|
||||
}],
|
||||
voxel_resolution_m: 0.1,
|
||||
scene_bounds: SceneBoundsJson {
|
||||
min_e: -10.0,
|
||||
min_n: -10.0,
|
||||
max_e: 10.0,
|
||||
max_n: 10.0,
|
||||
},
|
||||
prediction_steps: 15,
|
||||
};
|
||||
let json = serde_json::to_string(&req).expect("serialize");
|
||||
let decoded: OccupancyWorldModelRequest =
|
||||
serde_json::from_str(&json).expect("deserialize");
|
||||
assert_eq!(decoded.prediction_steps, 15);
|
||||
assert_eq!(decoded.past_frames.len(), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn response_serde_roundtrip() {
|
||||
let resp = OccupancyWorldModelResponse {
|
||||
future_frames: vec![],
|
||||
trajectory_priors: vec![TrajectoryPrior {
|
||||
track_id: 1,
|
||||
waypoints: vec![TrajectoryWaypoint { e: 0.0, n: 0.0, u: 0.0, t_s: 0.0 }],
|
||||
}],
|
||||
confidence: 0.82,
|
||||
model_id: "occworld-dummy-v0".into(),
|
||||
inference_ms: 375,
|
||||
};
|
||||
let json = serde_json::to_string(&resp).expect("serialize");
|
||||
let decoded: OccupancyWorldModelResponse =
|
||||
serde_json::from_str(&json).expect("deserialize");
|
||||
assert_eq!(decoded.inference_ms, 375);
|
||||
assert!((decoded.confidence - 0.82).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn vram_error_sentinel_roundtrip() {
|
||||
let resp = OccupancyWorldModelResponse {
|
||||
future_frames: vec![],
|
||||
trajectory_priors: vec![],
|
||||
confidence: 0.0,
|
||||
model_id: "error:vram:out of memory (CUDA)".into(),
|
||||
inference_ms: 0,
|
||||
};
|
||||
assert!(resp.model_id.starts_with("error:vram:"));
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,210 @@
|
||||
//! Converts WorldGraph PersonTrack ENU positions into an [`OccupancyGrid3D`]
|
||||
//! tensor suitable for submission to the OccWorld inference server (ADR-147).
|
||||
//!
|
||||
//! ## Voxel encoding
|
||||
//! | Class index | Meaning |
|
||||
//! |-------------|---------|
|
||||
//! | 17 | Free space (default) |
|
||||
//! | 10 | Person occupancy |
|
||||
//!
|
||||
//! The grid footprint is defined by axis-aligned [`SceneBounds`] in the local
|
||||
//! ENU coordinate frame. The *z* / *up* dimension is always 16 voxels; the
|
||||
//! floor voxel column for a given person is derived from their `up_m` value
|
||||
//! clamped to `[0, depth-1]`.
|
||||
|
||||
use crate::OccupancyGrid3D;
|
||||
|
||||
/// Class index written into voxels that contain a detected person.
|
||||
pub const CLASS_PERSON: u8 = 10;
|
||||
/// Class index written into voxels that are free (unoccupied).
|
||||
pub const CLASS_FREE: u8 = 17;
|
||||
|
||||
/// Number of voxels along the east/x axis (fixed at 200).
|
||||
pub const GRID_WIDTH: usize = 200;
|
||||
/// Number of voxels along the north/y axis (fixed at 200).
|
||||
pub const GRID_HEIGHT: usize = 200;
|
||||
/// Number of voxels along the up/z axis (fixed at 16).
|
||||
pub const GRID_DEPTH: usize = 16;
|
||||
|
||||
/// Maximum height (metres) mapped onto the depth axis. Points above this
|
||||
/// value are clamped to the topmost voxel.
|
||||
const MAX_HEIGHT_M: f32 = 3.2; // 3.2 m / 16 voxels = 0.2 m per z-voxel
|
||||
|
||||
/// A single person position expressed in local ENU metres.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct PersonPosition {
|
||||
/// Stable track identifier (mirrors `WorldNode::PersonTrack::track_id`).
|
||||
pub track_id: u64,
|
||||
/// East offset from installation origin, in metres.
|
||||
pub east_m: f64,
|
||||
/// North offset from installation origin, in metres.
|
||||
pub north_m: f64,
|
||||
/// Up offset (height above floor), in metres.
|
||||
pub up_m: f64,
|
||||
}
|
||||
|
||||
/// Axis-aligned bounding box of the scene in the ENU plane.
|
||||
///
|
||||
/// Maps the *east* axis to the voxel *x* dimension and the *north* axis to
|
||||
/// the voxel *y* dimension.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct SceneBounds {
|
||||
/// Western (minimum east) edge of the scene, in metres.
|
||||
pub min_e: f64,
|
||||
/// Southern (minimum north) edge of the scene, in metres.
|
||||
pub min_n: f64,
|
||||
/// Eastern (maximum east) edge of the scene, in metres.
|
||||
pub max_e: f64,
|
||||
/// Northern (maximum north) edge of the scene, in metres.
|
||||
pub max_n: f64,
|
||||
}
|
||||
|
||||
impl SceneBounds {
|
||||
/// Returns `(east_extent_m, north_extent_m)`. If either dimension
|
||||
/// is zero or negative a default of `1.0` is used to avoid division by
|
||||
/// zero.
|
||||
fn extents(&self) -> (f64, f64) {
|
||||
let e = (self.max_e - self.min_e).max(1.0);
|
||||
let n = (self.max_n - self.min_n).max(1.0);
|
||||
(e, n)
|
||||
}
|
||||
|
||||
/// Maps a continuous ENU coordinate to `(vx, vy)` grid indices.
|
||||
/// Out-of-bounds positions are clamped to the grid extent.
|
||||
pub fn to_voxel_xy(&self, east_m: f64, north_m: f64) -> (usize, usize) {
|
||||
let (e_ext, n_ext) = self.extents();
|
||||
let fx = (east_m - self.min_e) / e_ext; // [0, 1]
|
||||
let fy = (north_m - self.min_n) / n_ext; // [0, 1]
|
||||
let vx = (fx * GRID_WIDTH as f64)
|
||||
.floor()
|
||||
.clamp(0.0, (GRID_WIDTH - 1) as f64) as usize;
|
||||
let vy = (fy * GRID_HEIGHT as f64)
|
||||
.floor()
|
||||
.clamp(0.0, (GRID_HEIGHT - 1) as f64) as usize;
|
||||
(vx, vy)
|
||||
}
|
||||
|
||||
/// Maps a height value (metres) to a voxel *z* index in `[0, depth-1]`.
|
||||
pub fn to_voxel_z(up_m: f64) -> usize {
|
||||
let fz = (up_m as f32).clamp(0.0, MAX_HEIGHT_M) / MAX_HEIGHT_M;
|
||||
let vz = (fz * GRID_DEPTH as f32)
|
||||
.floor()
|
||||
.clamp(0.0, (GRID_DEPTH - 1) as f32) as usize;
|
||||
vz
|
||||
}
|
||||
}
|
||||
|
||||
/// Converts a list of person positions from the WorldGraph into a flat
|
||||
/// [`OccupancyGrid3D`] tensor.
|
||||
///
|
||||
/// The voxel buffer is laid out as `[x, y, z]` with stride order
|
||||
/// `voxels[z * height * width + y * width + x]` (row-major, depth last).
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `persons` – Slice of person ENU positions (may be empty).
|
||||
/// * `bounds` – Axis-aligned scene footprint used to define the grid.
|
||||
/// * `resolution_m` – Informational only; the grid is always 200×200×16 —
|
||||
/// this value is echoed back in the IPC request for the Python server.
|
||||
///
|
||||
/// # Returns
|
||||
/// An [`OccupancyGrid3D`] with `width = 200`, `height = 200`, `depth = 16`.
|
||||
pub fn worldgraph_to_occupancy(
|
||||
persons: &[PersonPosition],
|
||||
bounds: &SceneBounds,
|
||||
_resolution_m: f32,
|
||||
) -> OccupancyGrid3D {
|
||||
let total = GRID_WIDTH * GRID_HEIGHT * GRID_DEPTH;
|
||||
let mut voxels = vec![CLASS_FREE; total];
|
||||
|
||||
for p in persons {
|
||||
let (vx, vy) = bounds.to_voxel_xy(p.east_m, p.north_m);
|
||||
let vz = SceneBounds::to_voxel_z(p.up_m);
|
||||
|
||||
let idx = vz * GRID_HEIGHT * GRID_WIDTH + vy * GRID_WIDTH + vx;
|
||||
// `idx` is always in-bounds given the clamping above.
|
||||
voxels[idx] = CLASS_PERSON;
|
||||
}
|
||||
|
||||
OccupancyGrid3D {
|
||||
width: GRID_WIDTH as u32,
|
||||
height: GRID_HEIGHT as u32,
|
||||
depth: GRID_DEPTH as u32,
|
||||
voxels,
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn default_bounds() -> SceneBounds {
|
||||
SceneBounds {
|
||||
min_e: -10.0,
|
||||
min_n: -10.0,
|
||||
max_e: 10.0,
|
||||
max_n: 10.0,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_persons_all_free() {
|
||||
let g = worldgraph_to_occupancy(&[], &default_bounds(), 0.1);
|
||||
assert!(g.voxels.iter().all(|&v| v == CLASS_FREE));
|
||||
assert_eq!(g.voxels.len(), GRID_WIDTH * GRID_HEIGHT * GRID_DEPTH);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn person_at_origin_maps_to_centre_voxel() {
|
||||
let bounds = default_bounds(); // ±10 m; centre = (100, 100) in 200×200
|
||||
let persons = vec![PersonPosition {
|
||||
track_id: 1,
|
||||
east_m: 0.0,
|
||||
north_m: 0.0,
|
||||
up_m: 0.0,
|
||||
}];
|
||||
let g = worldgraph_to_occupancy(&persons, &bounds, 0.1);
|
||||
|
||||
// At ENU (0,0,0): vx=100, vy=100, vz=0
|
||||
let expected_idx = 0 * GRID_HEIGHT * GRID_WIDTH + 100 * GRID_WIDTH + 100;
|
||||
assert_eq!(g.voxels[expected_idx], CLASS_PERSON);
|
||||
// All other voxels must still be free
|
||||
let person_count = g.voxels.iter().filter(|&&v| v == CLASS_PERSON).count();
|
||||
assert_eq!(person_count, 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn out_of_bounds_position_is_clamped() {
|
||||
let bounds = default_bounds();
|
||||
let persons = vec![PersonPosition {
|
||||
track_id: 2,
|
||||
east_m: 99.0, // well outside max_e=10
|
||||
north_m: 99.0,
|
||||
up_m: 100.0,
|
||||
}];
|
||||
let g = worldgraph_to_occupancy(&persons, &bounds, 0.1);
|
||||
// Should not panic; exactly one person voxel set
|
||||
let person_count = g.voxels.iter().filter(|&&v| v == CLASS_PERSON).count();
|
||||
assert_eq!(person_count, 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn multiple_persons_independent_voxels() {
|
||||
let bounds = default_bounds();
|
||||
let persons = vec![
|
||||
PersonPosition { track_id: 1, east_m: -5.0, north_m: -5.0, up_m: 0.5 },
|
||||
PersonPosition { track_id: 2, east_m: 5.0, north_m: 5.0, up_m: 1.5 },
|
||||
];
|
||||
let g = worldgraph_to_occupancy(&persons, &bounds, 0.1);
|
||||
let person_count = g.voxels.iter().filter(|&&v| v == CLASS_PERSON).count();
|
||||
assert_eq!(person_count, 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn grid_dimensions_correct() {
|
||||
let g = worldgraph_to_occupancy(&[], &default_bounds(), 0.4);
|
||||
assert_eq!(g.width, 200);
|
||||
assert_eq!(g.height, 200);
|
||||
assert_eq!(g.depth, 16);
|
||||
assert_eq!(g.voxels.len(), 200 * 200 * 16);
|
||||
}
|
||||
}
|
||||
Binary file not shown.
Reference in New Issue
Block a user