diff --git a/README.md b/README.md index 6f05b5c0..59d202a3 100644 --- a/README.md +++ b/README.md @@ -75,7 +75,7 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest |----------|-------------| | [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training | | [Build Guide](docs/build-guide.md) | Building from source (Rust and Python) | -| [Architecture Decisions](docs/adr/README.md) | 48 ADRs — why each technical choice was made, organized by domain (hardware, signal processing, ML, platform, infrastructure) | +| [Architecture Decisions](docs/adr/README.md) | 49 ADRs — why each technical choice was made, organized by domain (hardware, signal processing, ML, platform, infrastructure) | | [Domain Models](docs/ddd/README.md) | 7 DDD models (RuvSense, Signal Processing, Training Pipeline, Hardware Platform, Sensing Server, WiFi-Mat, CHCI) — bounded contexts, aggregates, domain events, and ubiquitous language | | [Desktop App](rust-port/wifi-densepose-rs/crates/wifi-densepose-desktop/README.md) | **WIP** — Tauri v2 desktop app for node management, OTA updates, WASM deployment, and mesh visualization | @@ -89,8 +89,12 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables
▶ Live Observatory Demo +  |  + ▶ Dual-Modal Pose Fusion Demo > The [server](#-quick-start) is optional for visualization and aggregation — the ESP32 [runs independently](#esp32-s3-hardware-pipeline) for presence detection, vital signs, and fall alerts. +> +> **Live ESP32 pipeline**: Connect an ESP32-S3 node → run the [sensing server](#sensing-server) → open the [pose fusion demo](https://ruvnet.github.io/RuView/pose-fusion.html) for real-time dual-modal pose estimation (webcam + WiFi CSI). See [ADR-059](docs/adr/ADR-059-live-esp32-csi-pipeline.md). ## 🚀 Key Features diff --git a/docs/adr/ADR-058-ruvector-wasm-browser-pose-example.md b/docs/adr/ADR-058-ruvector-wasm-browser-pose-example.md new file mode 100644 index 00000000..1e25c81d --- /dev/null +++ b/docs/adr/ADR-058-ruvector-wasm-browser-pose-example.md @@ -0,0 +1,392 @@ +# ADR-058: Dual-Modal WASM Browser Pose Estimation — Live Video + WiFi CSI Fusion + +- **Status**: Proposed +- **Date**: 2026-03-12 +- **Deciders**: ruv +- **Tags**: wasm, browser, cnn, pose-estimation, ruvector, video, multimodal, fusion + +## Context + +WiFi-DensePose estimates human poses from WiFi CSI (Channel State Information). +The `ruvector-cnn` crate provides a pure Rust CNN (MobileNet-V3) with WASM bindings. +Both modalities exist independently — what's missing is **fusing live webcam video +with WiFi CSI** in a single browser demo to achieve robust pose estimation that +works even when one modality degrades (occlusion, signal noise, poor lighting). + +Existing assets: + +1. **`wifi-densepose-wasm`** — CSI signal processing compiled to WASM +2. **`wifi-densepose-sensing-server`** — Axum server streaming live CSI via WebSocket +3. **`ruvector-cnn`** — Pure Rust CNN with MobileNet-V3 backbones, SIMD, contrastive learning +4. **`ruvector-cnn-wasm`** — wasm-bindgen bindings: `WasmCnnEmbedder`, `SimdOps`, `LayerOps`, contrastive losses +5. **`vendor/ruvector/examples/wasm-vanilla/`** — Reference vanilla JS WASM example + +Research shows multi-modal fusion (camera + WiFi) significantly outperforms either alone: +- Camera fails under occlusion, poor lighting, privacy constraints +- WiFi CSI fails with signal noise, multipath, low spatial resolution +- Fusion compensates: WiFi provides through-wall coverage, camera provides fine-grained detail + +## Decision + +Build a **dual-modal browser demo** at `examples/wasm-browser-pose/` that: + +1. Captures **live webcam video** via `getUserMedia` API +2. Receives **live WiFi CSI** via WebSocket from the sensing server +3. Processes **both streams** through separate CNN pipelines in `ruvector-cnn-wasm` +4. **Fuses embeddings** with learned attention weights for combined pose estimation +5. Renders **video overlay** with skeleton + WiFi confidence heatmap on Canvas +6. Runs entirely in the browser — all inference client-side via WASM + +### Architecture + +``` +┌──────────────────────────────────────────────────────────────────┐ +│ Browser │ +│ │ +│ ┌────────────┐ ┌────────────────┐ ┌───────────────────┐ │ +│ │ getUserMedia│───▶│ Video Frame │───▶│ CNN WASM │ │ +│ │ (Webcam) │ │ Capture │ │ (Visual Embedder) │ │ +│ └────────────┘ │ 224×224 RGB │ │ → 512-dim │ │ +│ └────────────────┘ └────────┬──────────┘ │ +│ │ │ +│ visual_embedding │ +│ │ │ +│ ┌──────▼──────┐ │ +│ ┌────────────┐ ┌────────────────┐ │ │ │ +│ │ WebSocket │───▶│ CSI WASM │ │ Attention │ │ +│ │ Client │ │ (densepose- │ │ Fusion │ │ +│ │ │ │ wasm) │ │ Module │ │ +│ └────────────┘ └───────┬────────┘ │ │ │ +│ │ └──────┬──────┘ │ +│ ┌───────▼────────┐ │ │ +│ │ CNN WASM │ fused_embedding │ +│ │ (CSI Embedder) │ │ │ +│ │ → 512-dim │ ┌──────▼──────┐ │ +│ └───────┬────────┘ │ Pose │ │ +│ │ │ Decoder │ │ +│ csi_embedding │ → 17 kpts │ │ +│ │ └──────┬──────┘ │ +│ └──────────────────────┘ │ +│ │ │ +│ ┌──────────────┐ ┌─────▼──────┐ │ +│ │ Video Canvas │◀────────│ Overlay │ │ +│ │ + Skeleton │ │ Renderer │ │ +│ │ + Heatmap │ └────────────┘ │ +│ └──────────────┘ │ +│ │ +└──────────────────────────────────────────────────────────────────┘ + ▲ ▲ + │ getUserMedia │ WebSocket + │ (camera) │ (ws://host:3030/ws/csi) + │ │ + ┌────┴────┐ ┌───────┴─────────┐ + │ Webcam │ │ Sensing Server │ + └─────────┘ └─────────────────┘ +``` + +### Dual Pipeline Design + +Two parallel CNN pipelines run on each frame tick (~30 FPS): + +| Pipeline | Input | Preprocessing | CNN Config | Output | +|----------|-------|---------------|------------|--------| +| **Visual** | Webcam frame (640×480) | Resize to 224×224 RGB, ImageNet normalize | MobileNet-V3 Small, 512-dim | Visual embedding | +| **CSI** | CSI frame (ADR-018 binary) | Amplitude/phase/delta → 224×224 pseudo-RGB | MobileNet-V3 Small, 512-dim | CSI embedding | + +Both use the same `WasmCnnEmbedder` but with separate instances and weight sets. + +### Fusion Strategy + +**Learned attention-weighted fusion** combines the two 512-dim embeddings: + +```javascript +// Attention fusion: learn which modality to trust per-dimension +// α ∈ [0,1]^512 — attention weights (shipped as JSON, trained offline) +// visual_emb, csi_emb ∈ R^512 + +function fuseEmbeddings(visual_emb, csi_emb, attention_weights) { + const fused = new Float32Array(512); + for (let i = 0; i < 512; i++) { + const α = attention_weights[i]; + fused[i] = α * visual_emb[i] + (1 - α) * csi_emb[i]; + } + return fused; +} +``` + +**Dynamic confidence gating** adjusts fusion based on signal quality: + +| Condition | Behavior | +|-----------|----------| +| Good video + good CSI | Balanced fusion (α ≈ 0.5) | +| Poor lighting / occlusion | CSI-dominant (α → 0, WiFi takes over) | +| CSI noise / no ESP32 | Video-dominant (α → 1, camera only) | +| Video-only mode (no WiFi) | α = 1.0, pure visual CNN pose estimation | +| CSI-only mode (no camera) | α = 0.0, pure WiFi pose estimation | + +Quality detection: +- **Video quality**: Frame brightness variance (dark = low quality), motion blur score +- **CSI quality**: Signal-to-noise ratio from `wifi-densepose-wasm`, coherence gate output + +### CSI-to-Image Encoding + +CSI data encoded as 3-channel pseudo-image for the CSI CNN pipeline: + +| Channel | Data | Normalization | +|---------|------|---------------| +| R | CSI amplitude (subcarrier × time window) | Min-max to [0, 255] | +| G | CSI phase (unwrapped, subcarrier × time window) | Min-max to [0, 255] | +| B | Temporal difference (frame-to-frame Δ amplitude) | Abs, min-max to [0, 255] | + +### Video Processing + +Webcam frames processed through standard ImageNet pipeline: + +```javascript +// Capture frame from video element +const frame = captureVideoFrame(videoElement, 224, 224); // Returns Uint8Array RGB + +// ImageNet normalization happens inside WasmCnnEmbedder.extract() +const visual_embedding = visual_embedder.extract(frame, 224, 224); +``` + +### Pose Keypoint Mapping + +17 COCO-format keypoints decoded from the fused 512-dim embedding: + +``` + 0: nose 1: left_eye 2: right_eye + 3: left_ear 4: right_ear 5: left_shoulder + 6: right_shoulder 7: left_elbow 8: right_elbow + 9: left_wrist 10: right_wrist 11: left_hip +12: right_hip 13: left_knee 14: right_knee +15: left_ankle 16: right_ankle +``` + +Each keypoint decoded as (x, y, confidence) = 51 values from the 512-dim embedding +via a learned linear projection. + +### Operating Modes + +The demo supports three modes, selectable in the UI: + +| Mode | Video | CSI | Fusion | Use Case | +|------|-------|-----|--------|----------| +| **Dual (default)** | ✅ | ✅ | Attention-weighted | Best accuracy, full demo | +| **Video Only** | ✅ | ❌ | α = 1.0 | No ESP32 available, quick demo | +| **CSI Only** | ❌ | ✅ | α = 0.0 | Privacy mode, through-wall sensing | + +**Video Only mode works without any hardware** — just a webcam — making the demo +instantly accessible for anyone wanting to try it. + +### File Layout + +``` +examples/wasm-browser-pose/ +├── index.html # Single-page app (vanilla JS, no bundler) +├── js/ +│ ├── app.js # Main entry, mode selection, orchestration +│ ├── video-capture.js # getUserMedia, frame extraction, quality detection +│ ├── csi-processor.js # WebSocket CSI client, frame parsing, pseudo-image encoding +│ ├── fusion.js # Attention-weighted embedding fusion, confidence gating +│ ├── pose-decoder.js # Fused embedding → 17 keypoints +│ └── canvas-renderer.js # Video overlay, skeleton, CSI heatmap, confidence bars +├── data/ +│ ├── visual-weights.json # Visual CNN → embedding projection (placeholder until trained) +│ ├── csi-weights.json # CSI CNN → embedding projection (placeholder until trained) +│ ├── fusion-weights.json # Attention fusion α weights (512 values) +│ └── pose-weights.json # Fused embedding → keypoint projection +├── css/ +│ └── style.css # Dark theme UI styling +├── pkg/ # Built WASM packages (gitignored, built by script) +│ ├── wifi_densepose_wasm/ +│ └── ruvector_cnn_wasm/ +├── build.sh # wasm-pack build script for both packages +└── README.md # Setup and usage instructions +``` + +### Build Pipeline + +```bash +#!/bin/bash +# build.sh — builds both WASM packages into pkg/ + +set -e + +# Build wifi-densepose-wasm (CSI processing) +wasm-pack build ../../rust-port/wifi-densepose-rs/crates/wifi-densepose-wasm \ + --target web --out-dir "$(pwd)/pkg/wifi_densepose_wasm" --no-typescript + +# Build ruvector-cnn-wasm (CNN inference for both video and CSI) +wasm-pack build ../../vendor/ruvector/crates/ruvector-cnn-wasm \ + --target web --out-dir "$(pwd)/pkg/ruvector_cnn_wasm" --no-typescript + +echo "Build complete. Serve with: python3 -m http.server 8080" +``` + +### UI Layout + +``` +┌─────────────────────────────────────────────────────────┐ +│ WiFi-DensePose — Live Dual-Modal Pose Estimation │ +│ [Dual Mode ▼] [⚙ Settings] FPS: 28 ◉ Live │ +├───────────────────────────┬─────────────────────────────┤ +│ │ │ +│ ┌───────────────────┐ │ ┌───────────────────┐ │ +│ │ │ │ │ │ │ +│ │ Video + Skeleton │ │ │ CSI Heatmap │ │ +│ │ Overlay │ │ │ (amplitude × │ │ +│ │ (main canvas) │ │ │ subcarrier) │ │ +│ │ │ │ │ │ │ +│ └───────────────────┘ │ └───────────────────┘ │ +│ │ │ +├───────────────────────────┴─────────────────────────────┤ +│ Fusion Confidence: ████████░░ 78% │ +│ Video: ██████████ 95% │ CSI: ██████░░░░ 61% │ +├─────────────────────────────────────────────────────────┤ +│ ┌─────────────────────────────────────────────────┐ │ +│ │ Embedding Space (2D projection) │ │ +│ │ · · · │ │ +│ │ · · · · · · (color = pose cluster) │ │ +│ │ · · · · │ │ +│ └─────────────────────────────────────────────────┘ │ +├─────────────────────────────────────────────────────────┤ +│ Latency: Video 12ms │ CSI 8ms │ Fusion 1ms │ Total 21ms│ +│ [▶ Record] [📷 Snapshot] [Confidence: ████ 0.6] │ +└─────────────────────────────────────────────────────────┘ +``` + +### WASM Module Structure + +| Package | Source Crate | Provides | Size (est.) | +|---------|-------------|----------|-------------| +| `wifi_densepose_wasm` | `wifi-densepose-wasm` | CSI frame parsing, signal processing, feature extraction | ~200KB | +| `ruvector_cnn_wasm` | `ruvector-cnn-wasm` | `WasmCnnEmbedder` (×2 instances), `SimdOps`, `LayerOps`, contrastive losses | ~150KB | + +Two `WasmCnnEmbedder` instances are created — one for video frames, one for CSI pseudo-images. +They share the same WASM module but have independent state. + +### Browser API Requirements + +| API | Purpose | Required | Fallback | +|-----|---------|----------|----------| +| `getUserMedia` | Webcam capture | For video mode | CSI-only mode | +| WebAssembly | CNN inference | Yes | None (hard requirement) | +| WASM SIMD128 | Accelerated inference | No | Scalar fallback (~2× slower) | +| WebSocket | CSI data stream | For CSI mode | Video-only mode | +| Canvas 2D | Rendering | Yes | None | +| `requestAnimationFrame` | Render loop | Yes | `setTimeout` fallback | +| ES Modules | Code organization | Yes | None | + +Target: Chrome 89+, Firefox 89+, Safari 15+, Edge 89+ + +### Performance Budget + +| Stage | Target Latency | Notes | +|-------|---------------|-------| +| Video frame capture + resize | <3ms | `drawImage` to offscreen canvas | +| Video CNN embedding | <15ms | 224×224 RGB → 512-dim | +| CSI receive + parse | <2ms | Binary WebSocket message | +| CSI pseudo-image encoding | <3ms | Amplitude/phase/delta channels | +| CSI CNN embedding | <15ms | 224×224 pseudo-RGB → 512-dim | +| Attention fusion | <1ms | Element-wise weighted sum | +| Pose decoding | <1ms | Linear projection | +| Canvas overlay render | <3ms | Video + skeleton + heatmap | +| **Total (dual mode)** | **<33ms** | **30 FPS capable** | +| **Total (video only)** | **<22ms** | **45 FPS capable** | + +Note: Video and CSI CNN pipelines can run in parallel using Web Workers, +reducing dual-mode latency to ~max(15, 15) + 5 = ~20ms (50 FPS). + +### Contrastive Learning Integration + +The demo optionally shows real-time contrastive learning in the browser: + +- **InfoNCE loss** (`WasmInfoNCELoss`): Compare video vs CSI embeddings for the same pose — trains cross-modal alignment +- **Triplet loss** (`WasmTripletLoss`): Push apart different poses, pull together same pose across modalities +- **SimdOps**: Accelerated dot products for real-time similarity computation +- **Embedding space panel**: Live 2D projection shows video and CSI embeddings converging when viewing the same person + +### Relationship to Existing Crates + +| Existing Crate | Role in This Demo | +|---------------|-------------------| +| `ruvector-cnn-wasm` | CNN inference for **both** video frames and CSI pseudo-images | +| `wifi-densepose-wasm` | CSI frame parsing and signal processing | +| `wifi-densepose-sensing-server` | WebSocket CSI data source | +| `wifi-densepose-core` | ADR-018 frame format definitions | +| `ruvector-cnn` | Underlying MobileNet-V3, layers, contrastive learning | + +No new Rust crates are needed. The example is pure HTML/JS consuming existing WASM packages. + +## Consequences + +### Positive + +- **Instant demo**: Video-only mode works with just a webcam — no ESP32 needed +- **Multi-modal showcase**: Demonstrates camera + WiFi fusion, the core innovation of the project +- **Graceful degradation**: Works with video-only, CSI-only, or both +- **Through-wall capability**: CSI mode shows pose estimation where cameras cannot reach +- **Zero-install**: Anyone with a browser can try it +- **Training data collection**: Can record paired (video, CSI) data for offline model training +- **Reusable**: JS modules embed directly in the Tauri desktop app's webview + +### Negative + +- **Model weights**: Requires offline-trained weights for visual CNN, CSI CNN, fusion, and pose decoder (~200KB total JSON) +- **WASM size**: Two WASM modules total ~350KB (acceptable) +- **No GPU**: CPU-only WASM inference; adequate at 224×224 but limits resolution scaling +- **Camera privacy**: Video mode requires camera permission (mitigated: CSI-only mode available) +- **Two CNN instances**: Memory footprint doubles vs single-modal (~10MB total, acceptable for desktop browsers) + +### Risks + +- **Cross-modal alignment**: Video and CSI embeddings must be trained jointly for fusion to work; + without proper training, fusion may be worse than either modality alone +- **Latency on mobile**: Dual CNN on mobile browsers may exceed 33ms; implement automatic quality reduction +- **WebSocket drops**: Network jitter → CSI frame gaps; buffer last 3 frames, interpolate missing data + +## Implementation Plan + +1. **Phase 1 — Scaffold**: File layout, build.sh, index.html shell, mode selector UI +2. **Phase 2 — Video pipeline**: getUserMedia → frame capture → CNN embedding → basic pose display +3. **Phase 3 — CSI pipeline**: WebSocket client → CSI parsing → pseudo-image → CNN embedding +4. **Phase 4 — Fusion**: Attention-weighted combination, confidence gating, mode switching +5. **Phase 5 — Pose decoder**: Linear projection with placeholder weights → 17 keypoints +6. **Phase 6 — Overlay renderer**: Video canvas with skeleton overlay, CSI heatmap panel +7. **Phase 7 — Training**: Use `wifi-densepose-train` to generate real weights for both CNNs + fusion + decoder +8. **Phase 8 — Contrastive demo**: Embedding space visualization, cross-modal similarity display +9. **Phase 9 — Web Workers**: Move CNN inference to workers for parallel video + CSI processing +10. **Phase 10 — Polish**: Recording, snapshots, adaptive quality, mobile optimization + +## Alternatives Considered + +### 1. CSI-Only (No Video) +Rejected: Misses the opportunity to show multi-modal fusion and makes the demo less +accessible (requires ESP32 hardware). Video-only mode as a fallback is strictly better. + +### 2. Server-Side Video Inference +Rejected: Adds latency, requires webcam stream upload (privacy concern), and defeats +the WASM-first architecture. All inference must be client-side. + +### 3. TensorFlow.js for Video, ruvector-cnn-wasm for CSI +Rejected: Would require two different ML frameworks. Using `ruvector-cnn-wasm` for both +keeps a single WASM module, unified embedding space, and simpler fusion. + +### 4. Pre-recorded Video Demo +Rejected: Live webcam input is far more compelling for demonstrations. +Pre-recorded mode can be added as a secondary option. + +### 5. React/Vue Framework +Rejected: Adds build tooling. Vanilla JS + ES modules keeps the demo self-contained. + +## References + +- [ADR-018: Binary CSI Frame Format](ADR-018-binary-csi-frame-format.md) +- [ADR-024: Contrastive CSI Embedding / AETHER](ADR-024-contrastive-csi-embedding.md) +- [ADR-055: Integrated Sensing Server](ADR-055-integrated-sensing-server.md) +- `vendor/ruvector/crates/ruvector-cnn/src/lib.rs` — CNN embedder implementation +- `vendor/ruvector/crates/ruvector-cnn-wasm/src/lib.rs` — WASM bindings +- `vendor/ruvector/examples/wasm-vanilla/index.html` — Reference vanilla JS WASM pattern +- Person-in-WiFi: Fine-grained Person Perception using WiFi (ICCV 2019) — camera+WiFi fusion precedent +- WiPose: Multi-Person WiFi Pose Estimation (TMC 2022) — cross-modal embedding approach diff --git a/docs/adr/ADR-059-live-esp32-csi-pipeline.md b/docs/adr/ADR-059-live-esp32-csi-pipeline.md new file mode 100644 index 00000000..a08ecc0b --- /dev/null +++ b/docs/adr/ADR-059-live-esp32-csi-pipeline.md @@ -0,0 +1,83 @@ +# ADR-059: Live ESP32 CSI Pipeline Integration + +## Status + +Accepted + +## Date + +2026-03-12 + +## Context + +ADR-058 established a dual-modal browser demo combining webcam video and WiFi CSI for pose estimation. However, it used simulated CSI data. To demonstrate real-world capability, we need an end-to-end pipeline from physical ESP32 hardware through to the browser visualization. + +The ESP32-S3 firmware (`firmware/esp32-csi-node/`) already supports CSI collection and UDP streaming (ADR-018). The sensing server (`wifi-densepose-sensing-server`) already supports UDP ingestion and WebSocket bridging. The missing piece was connecting these components and enabling the browser demo to consume live data. + +## Decision + +Implement a complete live CSI pipeline: + +``` +ESP32-S3 (CSI capture) → UDP:5005 → sensing-server (Rust/Axum) → WS:8765 → browser demo +``` + +### Components + +1. **ESP32 Firmware** — Rebuilt with native Windows ESP-IDF v5.4.0 toolchain (no Docker). Configured for target network and PC IP via `sdkconfig`. Helper scripts added: + - `build_firmware.ps1` — Sets up IDF environment, cleans, builds, and flashes + - `read_serial.ps1` — Serial monitor with DTR/RTS reset capability + +2. **Sensing Server** — `wifi-densepose-sensing-server` started with: + - `--source esp32` — Expect real ESP32 UDP frames + - `--bind-addr 0.0.0.0` — Accept connections from any interface + - `--ui-path ` — Serve the demo UI via HTTP + +3. **Browser Demo** — `main.js` updated to auto-connect to `ws://localhost:8765/ws/sensing` on page load. Falls back to simulated CSI if the WebSocket is unavailable (GitHub Pages). + +### Network Configuration + +The ESP32 sends UDP packets to a configured target IP. If the PC's IP doesn't match the firmware's compiled target, a secondary IP alias can be added: + +```powershell +# PowerShell (Admin) +New-NetIPAddress -IPAddress 192.168.1.100 -PrefixLength 24 -InterfaceAlias "Wi-Fi" +``` + +### Data Flow + +| Stage | Protocol | Format | Rate | +|-------|----------|--------|------| +| ESP32 → Server | UDP | ADR-018 binary frame (magic `0xC5110001`, I/Q pairs) | ~100 Hz | +| Server → Browser | WebSocket | ADR-018 binary frame (forwarded) | ~10 Hz (tick-ms=100) | +| Browser decode | JavaScript | Float32 amplitude/phase arrays | Per frame | + +### Build Environment (Windows) + +ESP-IDF v5.4.0 on Windows requires: +- IDF_PATH pointing to the ESP-IDF framework +- IDF_TOOLS_PATH pointing to toolchain binaries +- MSYS/MinGW environment variables removed (ESP-IDF rejects them) +- Python venv from ESP-IDF tools for `idf.py` execution + +The `build_firmware.ps1` script handles all of this automatically. + +## Consequences + +### Positive +- First end-to-end demonstration of real WiFi CSI → pose estimation in a browser +- No Docker required for firmware builds on Windows +- Demo gracefully degrades to simulated CSI when no server is available +- Same demo works on GitHub Pages (simulated) and locally (live ESP32) + +### Negative +- ESP32 target IP is compiled into firmware; changing it requires a rebuild or NVS override +- Windows firewall may block UDP:5005; user must allow it +- Mixed content restrictions prevent HTTPS pages from connecting to ws:// (local only) + +## Related + +- [ADR-018](ADR-018-esp32-dev-implementation.md) — ESP32 CSI frame format and UDP streaming +- [ADR-058](ADR-058-ruvector-wasm-browser-pose-example.md) — Dual-modal WASM browser pose demo +- [ADR-039](ADR-039-edge-intelligence-framework.md) — Edge intelligence on ESP32 +- Issue [#245](https://github.com/ruvnet/RuView/issues/245) — Tracking issue diff --git a/firmware/esp32-csi-node/build_firmware.ps1 b/firmware/esp32-csi-node/build_firmware.ps1 new file mode 100644 index 00000000..9bfb5afc --- /dev/null +++ b/firmware/esp32-csi-node/build_firmware.ps1 @@ -0,0 +1,31 @@ +# Remove MSYS environment variables that trigger ESP-IDF's MinGW rejection +Remove-Item env:MSYSTEM -ErrorAction SilentlyContinue +Remove-Item env:MSYSTEM_CARCH -ErrorAction SilentlyContinue +Remove-Item env:MSYSTEM_CHOST -ErrorAction SilentlyContinue +Remove-Item env:MSYSTEM_PREFIX -ErrorAction SilentlyContinue +Remove-Item env:MINGW_CHOST -ErrorAction SilentlyContinue +Remove-Item env:MINGW_PACKAGE_PREFIX -ErrorAction SilentlyContinue +Remove-Item env:MINGW_PREFIX -ErrorAction SilentlyContinue + +$env:IDF_PATH = "C:\Users\ruv\esp\v5.4\esp-idf" +$env:IDF_TOOLS_PATH = "C:\Espressif\tools" +$env:IDF_PYTHON_ENV_PATH = "C:\Espressif\tools\python\v5.4\venv" +$env:PATH = "C:\Espressif\tools\xtensa-esp-elf\esp-14.2.0_20241119\xtensa-esp-elf\bin;C:\Espressif\tools\cmake\3.30.2\cmake-3.30.2-windows-x86_64\bin;C:\Espressif\tools\ninja\1.12.1;C:\Espressif\tools\ccache\4.10.2\ccache-4.10.2-windows-x86_64;C:\Espressif\tools\idf-exe\1.0.3;C:\Espressif\tools\python\v5.4\venv\Scripts;$env:PATH" + +Set-Location "C:\Users\ruv\Projects\wifi-densepose\firmware\esp32-csi-node" + +$python = "$env:IDF_PYTHON_ENV_PATH\Scripts\python.exe" +$idf = "$env:IDF_PATH\tools\idf.py" + +Write-Host "=== Cleaning stale build cache ===" +& $python $idf fullclean + +Write-Host "=== Building firmware (SSID=ruv.net, target=192.168.1.20:5005) ===" +& $python $idf build + +if ($LASTEXITCODE -eq 0) { + Write-Host "=== Build succeeded! Flashing to COM7 ===" + & $python $idf -p COM7 flash +} else { + Write-Host "=== Build failed with exit code $LASTEXITCODE ===" +} diff --git a/firmware/esp32-csi-node/read_serial.ps1 b/firmware/esp32-csi-node/read_serial.ps1 new file mode 100644 index 00000000..7c001227 --- /dev/null +++ b/firmware/esp32-csi-node/read_serial.ps1 @@ -0,0 +1,14 @@ +$p = New-Object System.IO.Ports.SerialPort('COM7', 115200) +$p.ReadTimeout = 5000 +$p.Open() +Start-Sleep -Milliseconds 200 + +for ($i = 0; $i -lt 60; $i++) { + try { + $line = $p.ReadLine() + Write-Host $line + } catch { + break + } +} +$p.Close() diff --git a/ui/index.html b/ui/index.html index 59b4671e..a68dc799 100644 --- a/ui/index.html +++ b/ui/index.html @@ -29,6 +29,7 @@ + Pose Fusion Observatory diff --git a/ui/pose-fusion.html b/ui/pose-fusion.html new file mode 100644 index 00000000..326da3ce --- /dev/null +++ b/ui/pose-fusion.html @@ -0,0 +1,201 @@ + + + + + + WiFi-DensePose — Dual-Modal Pose Estimation + + + + + +
+
+ +
Dual-Modal Pose Estimation — Live Video + WiFi CSI Fusion
+
+
+ +
+ + READY +
+ -- FPS + ← Dashboard + Observatory → +
+
+ + +
+ + +
+ + +
DUAL FUSION
+ +
+
DUAL FUSION
+

Enable your webcam for live video pose estimation.
+ Or switch to CSI Only mode for WiFi-based sensing.

+ +
+
+ + +
+ + +
+
◆ Fusion Confidence
+
+
+ Video +
+ 0% +
+
+ CSI +
+ 0% +
+
+ Fused +
+ 0% +
+
+
+ Cross-modal: 0.000 +
+
+ + +
+
◆ CSI Amplitude Heatmap
+
+ +
+
+ + +
+
◆ RSSI Signal Strength
+
+
+
+
+
+
+ -- dBm + -- +
+
+ +
+
+ + +
+
◆ Embedding Space (2D Projection)
+
+ +
+
+ + +
+
◆ RuVector WASM Attention Pipeline
+
+
Flash
+
→
+
MHA
+
→
+
Hyper
+
→
+
Linear
+
→
+
MoE
+
→
+
L+G
+
+
+ Energy: -- + Refinement: -- + Pose Impact: -- +
+
+ + +
+
◆ Pipeline Latency
+
+
+
--
+
Video CNN
+
+
+
--
+
CSI CNN
+
+
+
--
+
Fusion
+
+
+
--
+
Total
+
+
+
+ + +
+
◆ Controls
+
+ +
+ +
+ + + 0.30 +
+ +
+
◆ Live CSI Source
+
+ + +
+
+
+ +
+ + +
+
+ WiFi-DensePose · Dual-Modal Pose Estimation · + Architecture: Conv2D → RuVector 6-Stage Attention (Flash+MHA+Hyperbolic+Linear+MoE+L/G) → Fusion → 26-Keypoint Pose +
+
+ GitHub · + CNN: ruvector-cnn (loading…) · + Observatory +
+
+ +
+ + + + diff --git a/ui/pose-fusion/build.sh b/ui/pose-fusion/build.sh new file mode 100644 index 00000000..4d76eba2 --- /dev/null +++ b/ui/pose-fusion/build.sh @@ -0,0 +1,30 @@ +#!/bin/bash +# Build WASM packages for the dual-modal pose estimation demo. +# Requires: wasm-pack (cargo install wasm-pack) +# +# Usage: ./build.sh +# +# Output: pkg/ruvector_cnn_wasm/ — WASM CNN embedder for browser + +set -e + +SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" +VENDOR_DIR="$SCRIPT_DIR/../../vendor/ruvector" +OUT_DIR="$SCRIPT_DIR/pkg/ruvector_cnn_wasm" + +echo "Building ruvector-cnn-wasm..." +wasm-pack build "$VENDOR_DIR/crates/ruvector-cnn-wasm" \ + --target web \ + --out-dir "$OUT_DIR" \ + --no-typescript + +# Remove .gitignore so we can commit the build output for GitHub Pages +rm -f "$OUT_DIR/.gitignore" + +echo "" +echo "Build complete!" +echo " WASM: $(du -sh "$OUT_DIR/ruvector_cnn_wasm_bg.wasm" | cut -f1)" +echo " JS: $(du -sh "$OUT_DIR/ruvector_cnn_wasm.js" | cut -f1)" +echo "" +echo "Serve the demo: cd $SCRIPT_DIR/.. && python3 -m http.server 8080" +echo "Open: http://localhost:8080/pose-fusion.html" diff --git a/ui/pose-fusion/css/style.css b/ui/pose-fusion/css/style.css new file mode 100644 index 00000000..ba4315ea --- /dev/null +++ b/ui/pose-fusion/css/style.css @@ -0,0 +1,535 @@ +/* WiFi-DensePose — Dual-Modal Pose Fusion Demo + Dark theme matching Observatory */ + +@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&family=JetBrains+Mono:wght@400;600&display=swap'); + +:root { + --bg-deep: #080c14; + --bg-panel: rgba(8, 16, 28, 0.92); + --bg-panel-border: rgba(0, 210, 120, 0.25); + --green-glow: #00d878; + --green-bright:#3eff8a; + --green-dim: #0a6b3a; + --amber: #ffb020; + --amber-dim: #a06800; + --blue-signal: #2090ff; + --blue-dim: #0a3060; + --red-alert: #ff3040; + --cyan: #00e5ff; + --text-primary: #e8ece0; + --text-secondary: rgba(232,236,224, 0.55); + --text-label: rgba(232,236,224, 0.35); + --radius: 8px; +} + +* { margin: 0; padding: 0; box-sizing: border-box; } + +body { + background: var(--bg-deep); + font-family: 'Inter', -apple-system, sans-serif; + color: var(--text-primary); + -webkit-font-smoothing: antialiased; + overflow-x: hidden; + min-height: 100vh; +} + +/* === Header === */ +.header { + display: flex; + align-items: center; + justify-content: space-between; + padding: 16px 24px; + border-bottom: 1px solid var(--bg-panel-border); + background: var(--bg-panel); + backdrop-filter: blur(12px); +} + +.header-left { + display: flex; + align-items: center; + gap: 16px; +} + +.logo { + font-weight: 700; + font-size: 24px; + color: var(--green-glow); +} + +.logo .pi { font-style: normal; } + +.header-title { + font-size: 14px; + color: var(--text-secondary); + font-weight: 300; +} + +.header-right { + display: flex; + align-items: center; + gap: 16px; +} + +.mode-select { + background: rgba(0,210,120,0.1); + border: 1px solid var(--bg-panel-border); + color: var(--text-primary); + padding: 6px 12px; + border-radius: var(--radius); + font-family: inherit; + font-size: 13px; + cursor: pointer; +} + +.mode-select option { background: #0c1420; } + +.status-badge { + display: flex; + align-items: center; + gap: 6px; + font-family: 'JetBrains Mono', monospace; + font-size: 12px; + padding: 4px 10px; + border-radius: 12px; + background: rgba(0,210,120,0.1); + border: 1px solid var(--bg-panel-border); +} + +.status-dot { + width: 8px; height: 8px; + border-radius: 50%; + background: var(--green-glow); + box-shadow: 0 0 8px var(--green-glow); + animation: pulse-dot 2s ease infinite; +} + +.status-dot.offline { background: #555; box-shadow: none; animation: none; } +.status-dot.warning { background: var(--amber); box-shadow: 0 0 8px var(--amber); } + +@keyframes pulse-dot { + 0%, 100% { opacity: 1; } + 50% { opacity: 0.5; } +} + +.fps-badge { + font-family: 'JetBrains Mono', monospace; + font-size: 12px; + color: var(--green-glow); +} + +.back-link { + color: var(--text-secondary); + text-decoration: none; + font-size: 13px; + transition: color 0.2s; +} +.back-link:hover { color: var(--green-glow); } + +/* === Main Layout === */ +.main-grid { + display: grid; + grid-template-columns: 1fr 360px; + grid-template-rows: 1fr auto; + gap: 16px; + padding: 16px 24px; + height: calc(100vh - 72px); + overflow: hidden; +} + +.video-panel { + grid-row: 1; +} + +.side-panels { + grid-row: 1; +} + +/* === Video Panel === */ +.video-panel { + position: relative; + background: #000; + border-radius: var(--radius); + border: 1px solid var(--bg-panel-border); + overflow: hidden; + min-height: 0; +} + +.video-panel video { + width: 100%; + height: 100%; + object-fit: cover; + transform: scaleX(-1); +} + +.video-panel canvas { + position: absolute; + top: 0; left: 0; + width: 100%; + height: 100%; + transform: scaleX(-1); +} + +.video-overlay-label { + position: absolute; + top: 12px; left: 12px; + font-family: 'JetBrains Mono', monospace; + font-size: 11px; + padding: 4px 8px; + background: rgba(0,0,0,0.7); + border-radius: 4px; + color: var(--green-glow); + z-index: 5; + transform: scaleX(-1); +} + +.camera-prompt { + position: absolute; + top: 0; left: 0; right: 0; bottom: 0; + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + text-align: center; + color: var(--text-secondary); + padding: 24px; + z-index: 6; +} + +.camera-prompt button { + margin-top: 16px; + padding: 10px 24px; + background: var(--green-glow); + color: #000; + border: none; + border-radius: var(--radius); + font-family: inherit; + font-weight: 600; + font-size: 14px; + cursor: pointer; + transition: background 0.2s; +} + +.camera-prompt button:hover { background: var(--green-bright); } + +.camera-prompt-label { + font-family: 'JetBrains Mono', monospace; + font-size: 14px; + font-weight: 600; + letter-spacing: 2px; + color: var(--green-glow); + text-shadow: 0 0 12px rgba(0,216,120,0.4); + margin-bottom: 12px; +} + +/* === Side Panels === */ +.side-panels { + display: flex; + flex-direction: column; + gap: 8px; + overflow-y: auto; + min-height: 0; + max-height: 100%; + scrollbar-width: thin; + scrollbar-color: var(--green-dim) transparent; +} + +.panel { + background: var(--bg-panel); + border: 1px solid var(--bg-panel-border); + border-radius: var(--radius); + padding: 10px 14px; + flex-shrink: 0; +} + +.panel-title { + font-size: 11px; + text-transform: uppercase; + letter-spacing: 1.2px; + color: var(--text-label); + margin-bottom: 10px; + display: flex; + align-items: center; + gap: 6px; +} + +/* === CSI Heatmap === */ +.csi-canvas-wrapper { + position: relative; + border-radius: 4px; + overflow: hidden; + background: #000; +} + +.csi-canvas-wrapper canvas { + width: 100%; + display: block; +} + +/* === Fusion Bars === */ +.fusion-bars { + display: flex; + flex-direction: column; + gap: 8px; +} + +.bar-row { + display: flex; + align-items: center; + gap: 8px; +} + +.bar-label { + font-family: 'JetBrains Mono', monospace; + font-size: 11px; + color: var(--text-secondary); + width: 55px; + text-align: right; +} + +.bar-track { + flex: 1; + height: 6px; + background: rgba(255,255,255,0.06); + border-radius: 3px; + overflow: hidden; +} + +.bar-fill { + height: 100%; + border-radius: 3px; + transition: width 0.3s ease; +} + +.bar-fill.video { background: var(--cyan); } +.bar-fill.csi { background: var(--amber); } +.bar-fill.fused { background: var(--green-glow); box-shadow: 0 0 8px var(--green-glow); } + +.bar-value { + font-family: 'JetBrains Mono', monospace; + font-size: 11px; + color: var(--text-primary); + width: 36px; +} + +/* === Embedding Space === */ +.embedding-canvas-wrapper { + position: relative; + background: #000; + border-radius: 4px; + overflow: hidden; +} +.embedding-canvas-wrapper canvas { + width: 100%; + display: block; +} + +/* === RuVector Pipeline === */ +.rv-pipeline { + display: flex; + align-items: center; + gap: 2px; + margin-bottom: 8px; + flex-wrap: wrap; +} + +.rv-stage { + font-family: 'JetBrains Mono', monospace; + font-size: 10px; + padding: 3px 6px; + border-radius: 3px; + background: rgba(0,210,120,0.12); + border: 1px solid rgba(0,210,120,0.3); + color: var(--green-glow); + transition: all 0.3s; +} + +.rv-stage.active { + background: rgba(0,210,120,0.25); + box-shadow: 0 0 6px rgba(0,210,120,0.3); +} + +.rv-arrow { + font-size: 10px; + color: var(--text-label); +} + +.rv-stats { + display: flex; + gap: 12px; + font-family: 'JetBrains Mono', monospace; + font-size: 10px; + color: var(--text-secondary); +} + +/* === Latency Panel === */ +.latency-grid { + display: grid; + grid-template-columns: repeat(4, 1fr); + gap: 6px; +} + +.latency-item { + text-align: center; + padding: 6px 0; +} + +.latency-value { + font-family: 'JetBrains Mono', monospace; + font-size: 16px; + font-weight: 600; + color: var(--green-glow); +} + +.latency-label { + font-size: 10px; + color: var(--text-label); + margin-top: 2px; +} + +/* === Controls === */ +.controls-row { + display: flex; + gap: 8px; + flex-wrap: wrap; +} + +.btn { + padding: 6px 14px; + border: 1px solid var(--bg-panel-border); + background: rgba(0,210,120,0.08); + color: var(--text-primary); + border-radius: var(--radius); + font-family: inherit; + font-size: 12px; + cursor: pointer; + transition: all 0.2s; +} +.btn:hover { background: rgba(0,210,120,0.2); } +.btn.active { background: var(--green-glow); color: #000; font-weight: 600; } + +.slider-row { + display: flex; + align-items: center; + gap: 8px; + margin-top: 8px; +} + +.slider-row label { + font-size: 11px; + color: var(--text-secondary); + white-space: nowrap; +} + +.slider-row input[type=range] { + flex: 1; + accent-color: var(--green-glow); +} + +.slider-row .slider-val { + font-family: 'JetBrains Mono', monospace; + font-size: 11px; + width: 32px; + color: var(--green-glow); +} + +/* === Bottom Bar === */ +.bottom-bar { + grid-column: 1 / -1; + display: flex; + align-items: center; + justify-content: space-between; + padding: 10px 16px; + background: var(--bg-panel); + border: 1px solid var(--bg-panel-border); + border-radius: var(--radius); + font-family: 'JetBrains Mono', monospace; + font-size: 11px; + color: var(--text-secondary); +} + +.bottom-bar a { + color: var(--green-glow); + text-decoration: none; +} + +/* === RSSI Signal Strength === */ +.rssi-row { + display: flex; + align-items: center; + gap: 12px; +} + +.rssi-gauge { flex: 1; } + +.rssi-bar-track { + height: 8px; + background: rgba(255,255,255,0.06); + border-radius: 4px; + overflow: hidden; + position: relative; +} + +.rssi-bar-fill { + height: 100%; + border-radius: 4px; + background: linear-gradient(90deg, var(--red-alert), var(--amber), var(--green-glow)); + transition: width 0.4s ease; + position: relative; + box-shadow: 0 0 6px rgba(0,210,120,0.3); +} + +.rssi-bar-fill::after { + content: ''; + position: absolute; + top: 0; left: 0; right: 0; bottom: 0; + background: linear-gradient(90deg, transparent 0%, rgba(255,255,255,0.2) 50%, transparent 100%); + animation: rssi-shimmer 2s ease-in-out infinite; +} + +@keyframes rssi-shimmer { + 0% { transform: translateX(-100%); } + 100% { transform: translateX(100%); } +} + +.rssi-values { + display: flex; + justify-content: space-between; + margin-top: 4px; +} + +.rssi-dbm { + font-family: 'JetBrains Mono', monospace; + font-size: 14px; + font-weight: 600; + color: var(--green-glow); +} + +.rssi-quality { + font-family: 'JetBrains Mono', monospace; + font-size: 11px; + color: var(--text-secondary); + text-transform: uppercase; +} + +#rssi-sparkline { + flex-shrink: 0; + border-radius: 4px; + background: rgba(0,0,0,0.3); +} + +/* === Skeleton colors === */ +.skeleton-joint { fill: var(--green-glow); } +.skeleton-limb { stroke: var(--green-bright); } +.skeleton-joint-csi { fill: var(--amber); } +.skeleton-limb-csi { stroke: var(--amber); } + +/* === Responsive === */ +@media (max-width: 900px) { + .main-grid { + grid-template-columns: 1fr; + height: auto; + overflow: auto; + } + .video-panel { aspect-ratio: 16/9; max-height: 50vh; } + .side-panels { max-height: none; overflow: visible; } +} diff --git a/ui/pose-fusion/js/canvas-renderer.js b/ui/pose-fusion/js/canvas-renderer.js new file mode 100644 index 00000000..b2452b84 --- /dev/null +++ b/ui/pose-fusion/js/canvas-renderer.js @@ -0,0 +1,307 @@ +/** + * CanvasRenderer — Renders skeleton overlay on video, CSI heatmap, + * embedding space visualization, and fusion confidence bars. + */ + +import { SKELETON_CONNECTIONS } from './pose-decoder.js'; + +export class CanvasRenderer { + constructor() { + this.colors = { + joint: '#00d878', + jointGlow: 'rgba(0, 216, 120, 0.4)', + limb: '#3eff8a', + limbGlow: 'rgba(62, 255, 138, 0.15)', + csiJoint: '#ffb020', + csiLimb: '#ffc850', + fused: '#00e5ff', + confidence: 'rgba(255,255,255,0.3)', + videoEmb: '#00e5ff', + csiEmb: '#ffb020', + fusedEmb: '#00d878', + }; + } + + /** + * Draw skeleton overlay on the video canvas + * @param {CanvasRenderingContext2D} ctx + * @param {Array<{x,y,confidence}>} keypoints - Normalized [0,1] coordinates + * @param {number} width - Canvas width + * @param {number} height - Canvas height + * @param {object} opts + */ + drawSkeleton(ctx, keypoints, width, height, opts = {}) { + const minConf = opts.minConfidence || 0.3; + const color = opts.color || 'green'; + const jointColor = color === 'amber' ? this.colors.csiJoint : this.colors.joint; + const limbColor = color === 'amber' ? this.colors.csiLimb : this.colors.limb; + const glowColor = color === 'amber' ? 'rgba(255,176,32,0.4)' : this.colors.jointGlow; + + // Extended keypoint styling + const fingerColor = '#ff6ef0'; // Magenta for finger tips + const fingerGlow = 'rgba(255,110,240,0.4)'; + const fingerLimb = 'rgba(255,110,240,0.5)'; + const toeColor = '#6ef0ff'; // Cyan for toes + const neckColor = '#ffffff'; // White for neck + + ctx.clearRect(0, 0, width, height); + + if (!keypoints || keypoints.length === 0) return; + + // Draw limbs first (behind joints) + ctx.lineCap = 'round'; + + for (const [i, j] of SKELETON_CONNECTIONS) { + const kpA = keypoints[i]; + const kpB = keypoints[j]; + if (!kpA || !kpB || kpA.confidence < minConf || kpB.confidence < minConf) continue; + + const ax = kpA.x * width, ay = kpA.y * height; + const bx = kpB.x * width, by = kpB.y * height; + const avgConf = (kpA.confidence + kpB.confidence) / 2; + + // Is this a hand/finger connection? (indices 17-22) + const isFingerLink = i >= 17 && i <= 22 || j >= 17 && j <= 22; + const isToeLink = i >= 23 && i <= 24 || j >= 23 && j <= 24; + + // Glow + ctx.strokeStyle = isFingerLink ? fingerLimb : this.colors.limbGlow; + ctx.lineWidth = isFingerLink ? 4 : 8; + ctx.globalAlpha = avgConf * (isFingerLink ? 0.3 : 0.4); + ctx.beginPath(); + ctx.moveTo(ax, ay); + ctx.lineTo(bx, by); + ctx.stroke(); + + // Main line + ctx.strokeStyle = isFingerLink ? fingerColor : isToeLink ? toeColor : limbColor; + ctx.lineWidth = isFingerLink || isToeLink ? 1.5 : 2.5; + ctx.globalAlpha = avgConf; + ctx.beginPath(); + ctx.moveTo(ax, ay); + ctx.lineTo(bx, by); + ctx.stroke(); + } + + // Draw joints + ctx.globalAlpha = 1; + for (let idx = 0; idx < keypoints.length; idx++) { + const kp = keypoints[idx]; + if (!kp || kp.confidence < minConf) continue; + + const x = kp.x * width; + const y = kp.y * height; + const isFinger = idx >= 17 && idx <= 22; + const isToe = idx >= 23 && idx <= 24; + const isNeck = idx === 25; + const r = isFinger ? 2 + kp.confidence * 2 : isToe ? 2 : 3 + kp.confidence * 3; + const jColor = isFinger ? fingerColor : isToe ? toeColor : isNeck ? neckColor : jointColor; + const gColor = isFinger ? fingerGlow : glowColor; + + // Glow + ctx.beginPath(); + ctx.arc(x, y, r + (isFinger ? 3 : 4), 0, Math.PI * 2); + ctx.fillStyle = gColor; + ctx.globalAlpha = kp.confidence * (isFinger ? 0.5 : 0.6); + ctx.fill(); + + // Joint dot + ctx.beginPath(); + ctx.arc(x, y, r, 0, Math.PI * 2); + ctx.fillStyle = jColor; + ctx.globalAlpha = kp.confidence; + ctx.fill(); + + // White center (body joints only) + if (!isFinger && !isToe) { + ctx.beginPath(); + ctx.arc(x, y, r * 0.4, 0, Math.PI * 2); + ctx.fillStyle = '#fff'; + ctx.globalAlpha = kp.confidence * 0.8; + ctx.fill(); + } + } + + ctx.globalAlpha = 1; + + // Confidence label + keypoint count + if (opts.label) { + const visCount = keypoints.filter(kp => kp && kp.confidence >= minConf).length; + ctx.font = '11px "JetBrains Mono", monospace'; + ctx.fillStyle = jointColor; + ctx.globalAlpha = 0.8; + ctx.fillText(`${opts.label} · ${visCount} joints`, 8, height - 8); + ctx.globalAlpha = 1; + } + } + + /** + * Draw CSI amplitude heatmap + * @param {CanvasRenderingContext2D} ctx + * @param {{ data: Float32Array, width: number, height: number }} heatmap + * @param {number} canvasW + * @param {number} canvasH + */ + drawCsiHeatmap(ctx, heatmap, canvasW, canvasH) { + ctx.clearRect(0, 0, canvasW, canvasH); + + if (!heatmap || !heatmap.data || heatmap.height < 2) { + ctx.fillStyle = '#0a0e18'; + ctx.fillRect(0, 0, canvasW, canvasH); + ctx.font = '11px "JetBrains Mono", monospace'; + ctx.fillStyle = 'rgba(255,255,255,0.3)'; + ctx.fillText('Waiting for CSI data...', 8, canvasH / 2); + return; + } + + const { data, width: dw, height: dh } = heatmap; + const cellW = canvasW / dw; + const cellH = canvasH / dh; + + for (let y = 0; y < dh; y++) { + for (let x = 0; x < dw; x++) { + const val = Math.min(1, Math.max(0, data[y * dw + x])); + ctx.fillStyle = this._heatmapColor(val); + ctx.fillRect(x * cellW, y * cellH, cellW + 0.5, cellH + 0.5); + } + } + + // Axis labels + ctx.font = '9px "JetBrains Mono", monospace'; + ctx.fillStyle = 'rgba(255,255,255,0.4)'; + ctx.fillText('Subcarrier →', 4, canvasH - 4); + ctx.save(); + ctx.translate(canvasW - 4, canvasH - 4); + ctx.rotate(-Math.PI / 2); + ctx.fillText('Time ↑', 0, 0); + ctx.restore(); + } + + /** + * Draw embedding space 2D projection + * @param {CanvasRenderingContext2D} ctx + * @param {{ video: Array, csi: Array, fused: Array }} points + * @param {number} w + * @param {number} h + */ + drawEmbeddingSpace(ctx, points, w, h) { + ctx.fillStyle = '#050810'; + ctx.fillRect(0, 0, w, h); + + // Grid + ctx.strokeStyle = 'rgba(255,255,255,0.05)'; + ctx.lineWidth = 0.5; + for (let i = 0; i <= 4; i++) { + const x = (i / 4) * w; + ctx.beginPath(); ctx.moveTo(x, 0); ctx.lineTo(x, h); ctx.stroke(); + const y = (i / 4) * h; + ctx.beginPath(); ctx.moveTo(0, y); ctx.lineTo(w, y); ctx.stroke(); + } + + // Axes + ctx.strokeStyle = 'rgba(255,255,255,0.1)'; + ctx.lineWidth = 1; + ctx.beginPath(); ctx.moveTo(w / 2, 0); ctx.lineTo(w / 2, h); ctx.stroke(); + ctx.beginPath(); ctx.moveTo(0, h / 2); ctx.lineTo(w, h / 2); ctx.stroke(); + + // Auto-scale: find max extent across all point sets + let maxExtent = 0.01; + for (const pts of [points.video, points.csi, points.fused]) { + if (!pts) continue; + for (const p of pts) { + if (!p) continue; + maxExtent = Math.max(maxExtent, Math.abs(p[0]), Math.abs(p[1])); + } + } + const scale = 0.42 / maxExtent; // Fill ~84% of half-width + + const drawPoints = (pts, color, size) => { + if (!pts || pts.length === 0) return; + const len = pts.length; + + // Draw trail line connecting recent points + if (len >= 2) { + ctx.beginPath(); + let started = false; + for (let i = 0; i < len; i++) { + const p = pts[i]; + if (!p) continue; + const px = w / 2 + p[0] * scale * w; + const py = h / 2 + p[1] * scale * h; + if (px < -10 || px > w + 10 || py < -10 || py > h + 10) continue; + if (!started) { ctx.moveTo(px, py); started = true; } + else ctx.lineTo(px, py); + } + ctx.strokeStyle = color; + ctx.globalAlpha = 0.2; + ctx.lineWidth = 1; + ctx.stroke(); + } + + // Draw dots with glow on newest + for (let i = 0; i < len; i++) { + const p = pts[i]; + if (!p) continue; + const age = 1 - (i / len) * 0.7; + const px = w / 2 + p[0] * scale * w; + const py = h / 2 + p[1] * scale * h; + + if (px < -10 || px > w + 10 || py < -10 || py > h + 10) continue; + + // Glow on newest point + if (i === len - 1) { + ctx.beginPath(); + ctx.arc(px, py, size + 4, 0, Math.PI * 2); + ctx.fillStyle = color; + ctx.globalAlpha = 0.3; + ctx.fill(); + } + + ctx.beginPath(); + ctx.arc(px, py, i === len - 1 ? size + 1 : size, 0, Math.PI * 2); + ctx.fillStyle = color; + ctx.globalAlpha = age * 0.8; + ctx.fill(); + } + }; + + drawPoints(points.video, this.colors.videoEmb, 3); + drawPoints(points.csi, this.colors.csiEmb, 3); + drawPoints(points.fused, this.colors.fusedEmb, 4); + ctx.globalAlpha = 1; + + // Legend + ctx.font = '9px "JetBrains Mono", monospace'; + const legends = [ + { color: this.colors.videoEmb, label: 'Video' }, + { color: this.colors.csiEmb, label: 'CSI' }, + { color: this.colors.fusedEmb, label: 'Fused' }, + ]; + legends.forEach((l, i) => { + const ly = 12 + i * 14; + ctx.fillStyle = l.color; + ctx.beginPath(); + ctx.arc(10, ly - 3, 3, 0, Math.PI * 2); + ctx.fill(); + ctx.fillStyle = 'rgba(255,255,255,0.5)'; + ctx.fillText(l.label, 18, ly); + }); + } + + _heatmapColor(val) { + // Dark blue → cyan → green → yellow → red + if (val < 0.25) { + const t = val / 0.25; + return `rgb(${Math.floor(t * 20)}, ${Math.floor(20 + t * 60)}, ${Math.floor(60 + t * 100)})`; + } else if (val < 0.5) { + const t = (val - 0.25) / 0.25; + return `rgb(${Math.floor(20 + t * 20)}, ${Math.floor(80 + t * 100)}, ${Math.floor(160 - t * 60)})`; + } else if (val < 0.75) { + const t = (val - 0.5) / 0.25; + return `rgb(${Math.floor(40 + t * 180)}, ${Math.floor(180 + t * 75)}, ${Math.floor(100 - t * 80)})`; + } else { + const t = (val - 0.75) / 0.25; + return `rgb(${Math.floor(220 + t * 35)}, ${Math.floor(255 - t * 120)}, ${Math.floor(20 - t * 20)})`; + } + } +} diff --git a/ui/pose-fusion/js/cnn-embedder.js b/ui/pose-fusion/js/cnn-embedder.js new file mode 100644 index 00000000..10039319 --- /dev/null +++ b/ui/pose-fusion/js/cnn-embedder.js @@ -0,0 +1,443 @@ +/** + * CNN Embedder — RuVector Attention-powered feature extractor. + * + * Uses the real ruvector-attention-wasm WASM module for Multi-Head Attention + * and Flash Attention on CSI/video data. Falls back to a JS Conv2D pipeline + * when WASM is not available. + * + * Pipeline: Conv2D → BatchNorm → ReLU → Pool → RuVector Attention → Project → L2 Normalize + * Two instances are created: one for video frames, one for CSI pseudo-images. + */ + +// Seeded PRNG for deterministic weight initialization +function mulberry32(seed) { + return function() { + let t = (seed += 0x6D2B79F5); + t = Math.imul(t ^ (t >>> 15), t | 1); + t ^= t + Math.imul(t ^ (t >>> 7), t | 61); + return ((t ^ (t >>> 14)) >>> 0) / 4294967296; + }; +} + +export class CnnEmbedder { + /** + * @param {object} opts + * @param {number} opts.inputSize - Square input dimension (default 56 for speed) + * @param {number} opts.embeddingDim - Output embedding dimension (default 128) + * @param {boolean} opts.normalize - L2 normalize output + * @param {number} opts.seed - PRNG seed for weight init + */ + constructor(opts = {}) { + this.inputSize = opts.inputSize || 56; + this.embeddingDim = opts.embeddingDim || 128; + this.normalize = opts.normalize !== false; + this.wasmEmbedder = null; + this.rvAttention = null; // RuVector Multi-Head Attention (WASM) + this.rvFlash = null; // RuVector Flash Attention (WASM) + this.rvHyperbolic = null; // RuVector Hyperbolic Attention (hierarchical body) + this.rvMoE = null; // RuVector Mixture-of-Experts (body-region routing) + this.rvLinear = null; // RuVector Linear Attention (O(n) fast hand refinement) + this.rvLocalGlobal = null; // RuVector Local-Global Attention (detail + context) + this.rvModule = null; // RuVector WASM module reference + this.useRuVector = false; + + // Initialize weights with deterministic PRNG + const rng = mulberry32(opts.seed || 42); + const randRange = (lo, hi) => lo + rng() * (hi - lo); + + // Conv 3x3: 3 input channels → 16 output channels + this.convWeights = new Float32Array(3 * 3 * 3 * 16); + for (let i = 0; i < this.convWeights.length; i++) { + this.convWeights[i] = randRange(-0.15, 0.15); + } + + // BatchNorm params (16 channels) + this.bnGamma = new Float32Array(16).fill(1.0); + this.bnBeta = new Float32Array(16).fill(0.0); + this.bnMean = new Float32Array(16).fill(0.0); + this.bnVar = new Float32Array(16).fill(1.0); + + // Projection: 16 → embeddingDim (used when RuVector not available) + this.projWeights = new Float32Array(16 * this.embeddingDim); + for (let i = 0; i < this.projWeights.length; i++) { + this.projWeights[i] = randRange(-0.1, 0.1); + } + + // Attention projection: attention_dim → embeddingDim + this.attnProjWeights = new Float32Array(16 * this.embeddingDim); + for (let i = 0; i < this.attnProjWeights.length; i++) { + this.attnProjWeights[i] = randRange(-0.08, 0.08); + } + } + + /** + * Try to load RuVector attention WASM, then fall back to ruvector-cnn-wasm + * @param {string} wasmPath - Path to the WASM package directory + */ + async tryLoadWasm(wasmPath) { + // First try: RuVector Attention WASM (the real thing — browser ESM build) + try { + const attnBase = new URL('../pkg/ruvector-attention/ruvector_attention_browser.js', import.meta.url).href; + const mod = await import(attnBase); + await mod.default(); // async WASM init via fetch + mod.init(); + + // Create all 6 attention mechanisms + this.rvAttention = new mod.WasmMultiHeadAttention(16, 4); + this.rvFlash = new mod.WasmFlashAttention(16, 8); + this.rvHyperbolic = new mod.WasmHyperbolicAttention(16, -1.0); + this.rvMoE = new mod.WasmMoEAttention(16, 3, 2); + this.rvLinear = new mod.WasmLinearAttention(16, 16); + this.rvLocalGlobal = new mod.WasmLocalGlobalAttention(16, 4, 2); + this.rvModule = mod; + this.useRuVector = true; + + // Log available mechanisms + const mechs = mod.available_mechanisms(); + console.log(`[CNN] RuVector WASM v${mod.version()} — all 6 attention mechanisms active`, mechs); + return true; + } catch (e) { + console.log('[CNN] RuVector Attention WASM not available:', e.message); + } + + // Second try: ruvector-cnn-wasm (legacy path) + try { + const mod = await import(`${wasmPath}/ruvector_cnn_wasm.js`); + await mod.default(); + const config = new mod.EmbedderConfig(); + config.input_size = this.inputSize; + config.embedding_dim = this.embeddingDim; + config.normalize = this.normalize; + this.wasmEmbedder = new mod.WasmCnnEmbedder(config); + console.log('[CNN] WASM CNN embedder loaded successfully'); + return true; + } catch (e) { + console.log('[CNN] WASM CNN not available, using JS fallback:', e.message); + return false; + } + } + + /** + * Extract embedding from RGB image data + * @param {Uint8Array} rgbData - RGB pixel data (H*W*3) + * @param {number} width + * @param {number} height + * @returns {Float32Array} embedding vector + */ + extract(rgbData, width, height) { + if (this.wasmEmbedder) { + try { + const result = this.wasmEmbedder.extract(rgbData, width, height); + return new Float32Array(result); + } catch (_) { /* fallback to JS */ } + } + return this._extractJS(rgbData, width, height); + } + + _extractJS(rgbData, width, height) { + // 1. Resize to inputSize × inputSize if needed + const sz = this.inputSize; + let input; + if (width === sz && height === sz) { + input = new Float32Array(rgbData.length); + for (let i = 0; i < rgbData.length; i++) input[i] = rgbData[i] / 255.0; + } else { + input = this._resize(rgbData, width, height, sz, sz); + } + + // 2. ImageNet normalization + const mean = [0.485, 0.456, 0.406]; + const std = [0.229, 0.224, 0.225]; + const pixels = sz * sz; + for (let i = 0; i < pixels; i++) { + input[i * 3] = (input[i * 3] - mean[0]) / std[0]; + input[i * 3 + 1] = (input[i * 3 + 1] - mean[1]) / std[1]; + input[i * 3 + 2] = (input[i * 3 + 2] - mean[2]) / std[2]; + } + + // 3. Conv2D 3x3 (3 → 16 channels) + const convOut = this._conv2d3x3(input, sz, sz, 3, 16); + + // 4. BatchNorm + this._batchNorm(convOut, 16); + + // 5. ReLU + for (let i = 0; i < convOut.length; i++) { + if (convOut[i] < 0) convOut[i] = 0; + } + + // 6. Global average pooling → spatial tokens (each 16-dim) + const outH = sz - 2, outW = sz - 2; + const spatial = outH * outW; + + // 7. RuVector Attention (if loaded) — apply attention over spatial tokens + if (this.useRuVector && this.rvAttention) { + return this._extractWithAttention(convOut, spatial, 16); + } + + // Fallback: simple global average pool + linear projection + const pooled = new Float32Array(16); + for (let i = 0; i < spatial; i++) { + for (let c = 0; c < 16; c++) { + pooled[c] += convOut[i * 16 + c]; + } + } + for (let c = 0; c < 16; c++) pooled[c] /= spatial; + + // Linear projection → embeddingDim + const emb = new Float32Array(this.embeddingDim); + for (let o = 0; o < this.embeddingDim; o++) { + let sum = 0; + for (let i = 0; i < 16; i++) { + sum += pooled[i] * this.projWeights[i * this.embeddingDim + o]; + } + emb[o] = sum; + } + + // L2 normalize + if (this.normalize) { + let norm = 0; + for (let i = 0; i < emb.length; i++) norm += emb[i] * emb[i]; + norm = Math.sqrt(norm); + if (norm > 1e-8) { + for (let i = 0; i < emb.length; i++) emb[i] /= norm; + } + } + + return emb; + } + + /** + * Full 6-stage RuVector WASM attention pipeline: + * 1. Flash Attention (efficient O(n) pre-screening of spatial tokens) + * 2. Multi-Head Attention (global spatial reasoning) + * 3. Hyperbolic Attention (hierarchical body-part structure, Poincaré ball) + * 4. Linear Attention (O(n) refinement for fine detail — hands/extremities) + * 5. MoE Attention (body-region specialized expert routing) + * 6. Local-Global Attention (local detail + global context fusion) + * → Weighted blend + batch_normalize + project + L2 normalize + */ + _extractWithAttention(convOut, numTokens, channels) { + const mod = this.rvModule; + + // Subsample spatial tokens for attention (max 64 for speed) + const maxTokens = 64; + const step = numTokens > maxTokens ? Math.floor(numTokens / maxTokens) : 1; + const tokens = []; + for (let i = 0; i < numTokens && tokens.length < maxTokens; i += step) { + const token = new Float32Array(channels); + for (let c = 0; c < channels; c++) { + token[c] = convOut[i * channels + c]; + } + tokens.push(token); + } + + const numQueries = Math.min(4, tokens.length); + const queryStride = Math.floor(tokens.length / numQueries); + + // === Stage 1: Flash Attention (efficient pre-screening) === + const flashOut = new Float32Array(channels); + try { + // Flash attention with block size 8 for efficient O(n) screening + const result = this.rvFlash.compute(tokens[0], tokens, tokens); + for (let c = 0; c < channels; c++) flashOut[c] = result[c]; + } catch (_) { + flashOut.set(tokens[0]); + } + + // === Stage 2: Multi-Head Attention (global spatial reasoning) === + const mhaOut = new Float32Array(channels); + for (let q = 0; q < numQueries; q++) { + const queryToken = tokens[q * queryStride]; + try { + const result = this.rvAttention.compute(queryToken, tokens, tokens); + for (let c = 0; c < channels; c++) mhaOut[c] += result[c] / numQueries; + } catch (_) { + for (let c = 0; c < channels; c++) mhaOut[c] += queryToken[c] / numQueries; + } + } + + // === Stage 3: Hyperbolic Attention (hierarchical body structure) === + const hyOut = new Float32Array(channels); + try { + const result = this.rvHyperbolic.compute(mhaOut, tokens, tokens); + for (let c = 0; c < channels; c++) hyOut[c] = result[c]; + } catch (_) { + hyOut.set(mhaOut); + } + + // === Stage 4: Linear Attention (O(n) fast refinement for extremities) === + const linOut = new Float32Array(channels); + try { + const result = this.rvLinear.compute(hyOut, tokens, tokens); + for (let c = 0; c < channels; c++) linOut[c] = result[c]; + } catch (_) { + linOut.set(hyOut); + } + + // === Stage 5: MoE Attention (body-region expert routing) === + const moeOut = new Float32Array(channels); + try { + const result = this.rvMoE.compute(linOut, tokens, tokens); + for (let c = 0; c < channels; c++) moeOut[c] = result[c]; + } catch (_) { + moeOut.set(linOut); + } + + // === Stage 6: Local-Global Attention (detail + context) === + const lgOut = new Float32Array(channels); + try { + const result = this.rvLocalGlobal.compute(moeOut, tokens, tokens); + for (let c = 0; c < channels; c++) lgOut[c] = result[c]; + } catch (_) { + lgOut.set(moeOut); + } + + // === Blend all 6 outputs === + // Use WASM softmax on log-energy scores for dynamic stage weighting + const blended = new Float32Array(channels); + const stages = [flashOut, mhaOut, hyOut, linOut, moeOut, lgOut]; + // Use log-energy to prevent exp() overflow in softmax + const logEnergies = new Float32Array(6); + for (let s = 0; s < 6; s++) { + const e = this._energy(stages[s]); + logEnergies[s] = e > 1e-10 ? Math.log(e) : -20; + } + try { mod.softmax(logEnergies); } catch (_) { + let max = -Infinity; + for (let i = 0; i < 6; i++) max = Math.max(max, logEnergies[i]); + let sum = 0; + for (let i = 0; i < 6; i++) { logEnergies[i] = Math.exp(logEnergies[i] - max); sum += logEnergies[i]; } + for (let i = 0; i < 6; i++) logEnergies[i] /= sum; + } + for (let c = 0; c < channels; c++) { + for (let s = 0; s < 6; s++) { + blended[c] += logEnergies[s] * stages[s][c]; + } + } + + // Batch normalize only when we have enough diversity (skip for single vectors) + // Single-vector batch norm collapses to zeros, killing embedding space + let normed = blended; + + // Project to embeddingDim + const emb = new Float32Array(this.embeddingDim); + for (let o = 0; o < this.embeddingDim; o++) { + let sum = 0; + for (let i = 0; i < channels; i++) { + sum += normed[i] * this.attnProjWeights[i * this.embeddingDim + o]; + } + emb[o] = sum; + } + + // L2 normalize using RuVector WASM + if (this.normalize) { + try { mod.normalize(emb); } catch (_) { + let norm = 0; + for (let i = 0; i < emb.length; i++) norm += emb[i] * emb[i]; + norm = Math.sqrt(norm); + if (norm > 1e-8) for (let i = 0; i < emb.length; i++) emb[i] /= norm; + } + } + + return emb; + } + + /** Compute vector energy (L2 norm squared) for attention weighting */ + _energy(vec) { + let e = 0; + for (let i = 0; i < vec.length; i++) e += vec[i] * vec[i]; + return e; + } + + _conv2d3x3(input, H, W, Cin, Cout) { + const outH = H - 2, outW = W - 2; + const output = new Float32Array(outH * outW * Cout); + for (let y = 0; y < outH; y++) { + for (let x = 0; x < outW; x++) { + for (let co = 0; co < Cout; co++) { + let sum = 0; + for (let ky = 0; ky < 3; ky++) { + for (let kx = 0; kx < 3; kx++) { + for (let ci = 0; ci < Cin; ci++) { + const px = ((y + ky) * W + (x + kx)) * Cin + ci; + const wt = (((ky * 3 + kx) * Cin) + ci) * Cout + co; + sum += input[px] * this.convWeights[wt]; + } + } + } + output[(y * outW + x) * Cout + co] = sum; + } + } + } + return output; + } + + _batchNorm(data, channels) { + const spatial = data.length / channels; + for (let i = 0; i < spatial; i++) { + for (let c = 0; c < channels; c++) { + const idx = i * channels + c; + data[idx] = this.bnGamma[c] * (data[idx] - this.bnMean[c]) / Math.sqrt(this.bnVar[c] + 1e-5) + this.bnBeta[c]; + } + } + } + + _resize(rgbData, srcW, srcH, dstW, dstH) { + const output = new Float32Array(dstW * dstH * 3); + const xRatio = srcW / dstW; + const yRatio = srcH / dstH; + for (let y = 0; y < dstH; y++) { + for (let x = 0; x < dstW; x++) { + const sx = Math.min(Math.floor(x * xRatio), srcW - 1); + const sy = Math.min(Math.floor(y * yRatio), srcH - 1); + const srcIdx = (sy * srcW + sx) * 3; + const dstIdx = (y * dstW + x) * 3; + output[dstIdx] = rgbData[srcIdx] / 255.0; + output[dstIdx + 1] = rgbData[srcIdx + 1] / 255.0; + output[dstIdx + 2] = rgbData[srcIdx + 2] / 255.0; + } + } + return output; + } + + /** Cosine similarity using WASM when available, JS fallback */ + cosineSim(a, b) { + if (this.rvModule) { + try { return this.rvModule.cosine_similarity(a, b); } catch (_) { /* fallback */ } + } + return CnnEmbedder.cosineSimilarity(a, b); + } + + /** L2 norm using WASM when available */ + l2Norm(vec) { + if (this.rvModule) { + try { return this.rvModule.l2_norm(vec); } catch (_) { /* fallback */ } + } + let norm = 0; + for (let i = 0; i < vec.length; i++) norm += vec[i] * vec[i]; + return Math.sqrt(norm); + } + + /** Pairwise distance matrix using WASM (for skeleton validation) */ + pairwiseDistances(vectors) { + if (this.rvModule) { + try { return this.rvModule.pairwise_distances(vectors); } catch (_) { /* fallback */ } + } + return null; + } + + /** Static JS fallback for cosine similarity */ + static cosineSimilarity(a, b) { + let dot = 0, normA = 0, normB = 0; + for (let i = 0; i < a.length; i++) { + dot += a[i] * b[i]; + normA += a[i] * a[i]; + normB += b[i] * b[i]; + } + normA = Math.sqrt(normA); + normB = Math.sqrt(normB); + if (normA < 1e-8 || normB < 1e-8) return 0; + return dot / (normA * normB); + } +} diff --git a/ui/pose-fusion/js/csi-simulator.js b/ui/pose-fusion/js/csi-simulator.js new file mode 100644 index 00000000..fe1e48b1 --- /dev/null +++ b/ui/pose-fusion/js/csi-simulator.js @@ -0,0 +1,357 @@ +/** + * CSI Simulator — Generates realistic WiFi Channel State Information data. + * + * In live mode, connects to the sensing server via WebSocket. + * In demo mode, generates synthetic CSI that correlates with detected motion. + * + * Outputs: 3-channel pseudo-image (amplitude, phase, temporal diff) + * matching the ADR-018 frame format expectations. + */ + +export class CsiSimulator { + static VERSION = 'v4-drift'; // Cache-bust verification + + constructor(opts = {}) { + this.subcarriers = opts.subcarriers || 52; // 802.11n HT20 + this.timeWindow = opts.timeWindow || 56; // frames in sliding window + this.mode = 'demo'; // 'demo' | 'live' + this.ws = null; + + // Circular buffer for CSI frames + this.amplitudeBuffer = []; + this.phaseBuffer = []; + this.frameCount = 0; + + // Noise parameters + this._rng = this._mulberry32(opts.seed || 7); + this._noiseState = new Float32Array(this.subcarriers); + this._baseAmplitude = new Float32Array(this.subcarriers); + this._basePhase = new Float32Array(this.subcarriers); + + // Initialize base CSI profile (empty room) + for (let i = 0; i < this.subcarriers; i++) { + this._baseAmplitude[i] = 0.5 + 0.3 * Math.sin(i * 0.12); + this._basePhase[i] = (i / this.subcarriers) * Math.PI * 2; + } + + // RSSI tracking + this.rssiDbm = -70; // default mid-range + this._rssiTarget = -70; + + // Person influence (updated from video motion) + this.personPresence = 0; + this.personX = 0.5; + this.personY = 0.5; + this.personMotion = 0; + } + + /** + * Connect to live sensing server WebSocket + * @param {string} url - WebSocket URL (e.g. ws://localhost:3030/ws/csi) + */ + async connectLive(url) { + return new Promise((resolve) => { + try { + this.ws = new WebSocket(url); + this.ws.binaryType = 'arraybuffer'; + this.ws.onmessage = (evt) => this._handleLiveFrame(evt.data); + this.ws.onopen = () => { this.mode = 'live'; resolve(true); }; + this.ws.onerror = () => resolve(false); + this.ws.onclose = () => { this.mode = 'demo'; }; + // Timeout after 3s + setTimeout(() => { if (this.mode !== 'live') resolve(false); }, 3000); + } catch { + resolve(false); + } + }); + } + + disconnect() { + if (this.ws) { this.ws.close(); this.ws = null; } + this.mode = 'demo'; + } + + get isLive() { return this.mode === 'live'; } + + /** + * Update person state from video detection (for correlated demo data). + * When person exits frame, CSI maintains presence with slow decay + * (simulating through-wall sensing capability). + */ + updatePersonState(presence, x, y, motion) { + // Don't override real CSI sensing with synthetic video-derived state + if (this.mode === 'live') return; + + if (presence > 0.1) { + // Person detected in video — update CSI state directly + this.personPresence = presence; + this.personX = x; + this.personY = y; + this.personMotion = motion; + this._lastSeenTime = performance.now(); + this._lastSeenX = x; + this._lastSeenY = y; + } else if (this._lastSeenTime) { + // Person NOT in video — CSI "through-wall" persistence + const elapsed = (performance.now() - this._lastSeenTime) / 1000; + // CSI can sense through walls for ~10 seconds with decaying confidence + const decayRate = 0.15; // Lose ~15% per second + this.personPresence = Math.max(0, 1.0 - elapsed * decayRate); + // Position slowly drifts (person walking behind wall) + this.personX = this._lastSeenX; + this.personY = this._lastSeenY; + this.personMotion = Math.max(0, motion * 0.5 + this.personPresence * 0.2); + + if (this.personPresence < 0.05) { + this._lastSeenTime = null; + } + } else { + this.personPresence = 0; + this.personMotion = 0; + } + } + + /** + * Generate next CSI frame (demo mode) or return latest live frame + * @param {number} elapsed - Time in seconds + * @returns {{ amplitude: Float32Array, phase: Float32Array, snr: number }} + */ + nextFrame(elapsed) { + const amp = new Float32Array(this.subcarriers); + const phase = new Float32Array(this.subcarriers); + + if (this.mode === 'live' && this._liveAmplitude) { + amp.set(this._liveAmplitude); + phase.set(this._livePhase); + } else { + this._generateDemoFrame(amp, phase, elapsed); + } + + // Push to circular buffer + this.amplitudeBuffer.push(new Float32Array(amp)); + this.phaseBuffer.push(new Float32Array(phase)); + if (this.amplitudeBuffer.length > this.timeWindow) { + this.amplitudeBuffer.shift(); + this.phaseBuffer.shift(); + } + + // RSSI: smooth toward target (demo mode generates synthetic RSSI) + if (this.mode === 'demo') { + // Simulate RSSI based on person presence and slow drift + this._rssiTarget = -55 - 25 * (1 - this.personPresence) + Math.sin(elapsed * 0.3) * 3; + } + this.rssiDbm += (this._rssiTarget - this.rssiDbm) * 0.1; + + // SNR estimate + let signalPower = 0, noisePower = 0; + for (let i = 0; i < this.subcarriers; i++) { + signalPower += amp[i] * amp[i]; + noisePower += this._noiseState[i] * this._noiseState[i]; + } + const snr = noisePower > 0 ? 10 * Math.log10(signalPower / noisePower) : 30; + + this.frameCount++; + return { amplitude: amp, phase, snr: Math.max(0, Math.min(40, snr)) }; + } + + /** + * Build 3-channel pseudo-image for CNN input + * @param {number} targetSize - Output image dimension (square) + * @returns {Uint8Array} RGB data (targetSize * targetSize * 3) + */ + buildPseudoImage(targetSize = 56) { + const buf = this.amplitudeBuffer; + const pBuf = this.phaseBuffer; + const frames = buf.length; + if (frames < 2) { + return new Uint8Array(targetSize * targetSize * 3); + } + + const rgb = new Uint8Array(targetSize * targetSize * 3); + + for (let y = 0; y < targetSize; y++) { + const fi = Math.min(Math.floor(y / targetSize * frames), frames - 1); + for (let x = 0; x < targetSize; x++) { + const si = Math.min(Math.floor(x / targetSize * this.subcarriers), this.subcarriers - 1); + const idx = (y * targetSize + x) * 3; + + // R: Amplitude (normalized to 0-255) + const ampVal = buf[fi][si]; + rgb[idx] = Math.min(255, Math.max(0, Math.floor(ampVal * 255))); + + // G: Phase (wrapped to 0-255) + const phaseVal = (pBuf[fi][si] % (2 * Math.PI) + 2 * Math.PI) % (2 * Math.PI); + rgb[idx + 1] = Math.floor(phaseVal / (2 * Math.PI) * 255); + + // B: Temporal difference + if (fi > 0) { + const diff = Math.abs(buf[fi][si] - buf[fi - 1][si]); + rgb[idx + 2] = Math.min(255, Math.floor(diff * 500)); + } + } + } + + return rgb; + } + + /** + * Get heatmap data for visualization + * @returns {{ data: Float32Array, width: number, height: number }} + */ + getHeatmapData() { + const frames = this.amplitudeBuffer.length; + const w = this.subcarriers; + const h = Math.min(frames, this.timeWindow); + const data = new Float32Array(w * h); + for (let y = 0; y < h; y++) { + const fi = frames - h + y; + if (fi >= 0 && fi < frames) { + for (let x = 0; x < w; x++) { + data[y * w + x] = this.amplitudeBuffer[fi][x]; + } + } + } + return { data, width: w, height: h }; + } + + // === Private === + + _generateDemoFrame(amp, phase, elapsed) { + const rng = this._rng; + const presence = this.personPresence; + const motion = this.personMotion; + const px = this.personX; + + for (let i = 0; i < this.subcarriers; i++) { + // Base CSI profile (frequency-selective channel) + let a = this._baseAmplitude[i]; + let p = this._basePhase[i] + elapsed * 0.05; + + // Environmental noise (correlated across subcarriers) + this._noiseState[i] = 0.95 * this._noiseState[i] + 0.05 * (rng() * 2 - 1) * 0.03; + a += this._noiseState[i]; + + // Ambient temporal drift (multipath fading even in empty room) + a += 0.06 * Math.sin(elapsed * 0.7 + i * 0.25) + + 0.04 * Math.sin(elapsed * 1.3 - i * 0.18) + + 0.03 * Math.cos(elapsed * 2.1 + i * 0.4); + + // Person-induced CSI perturbation + if (presence > 0.1) { + // Subcarrier-dependent body reflection (Fresnel zone model) + const freqOffset = (i - this.subcarriers * px) / (this.subcarriers * 0.3); + const bodyReflection = presence * 0.25 * Math.exp(-freqOffset * freqOffset); + + // Motion causes amplitude fluctuation + const motionEffect = motion * 0.15 * Math.sin(elapsed * 3.5 + i * 0.3); + + // Breathing modulation (0.2-0.3 Hz) + const breathing = presence * 0.02 * Math.sin(elapsed * 1.5 + i * 0.05); + + a += bodyReflection + motionEffect + breathing; + p += presence * 0.4 * Math.sin(elapsed * 2.1 + i * 0.15); + } + + amp[i] = Math.max(0, Math.min(1, a)); + phase[i] = p; + } + } + + _handleLiveFrame(data) { + // Handle JSON text frames from the sensing server + if (typeof data === 'string') { + try { + const msg = JSON.parse(data); + this._handleJsonFrame(msg); + } catch (_) { /* ignore malformed JSON */ } + return; + } + + // Handle binary ArrayBuffer frames (ADR-018 format) + if (!(data instanceof ArrayBuffer)) return; + const view = new DataView(data); + // Check ADR-018 magic: 0xC5110001 + if (data.byteLength < 20) return; + const magic = view.getUint32(0, true); + if (magic !== 0xC5110001) return; + + const numSub = Math.min(view.getUint16(8, true), this.subcarriers); + this._liveAmplitude = new Float32Array(this.subcarriers); + this._livePhase = new Float32Array(this.subcarriers); + + const headerSize = 20; + for (let i = 0; i < numSub && (headerSize + i * 4 + 3) < data.byteLength; i++) { + const real = view.getInt16(headerSize + i * 4, true); + const imag = view.getInt16(headerSize + i * 4 + 2, true); + this._liveAmplitude[i] = Math.sqrt(real * real + imag * imag) / 2048; + this._livePhase[i] = Math.atan2(imag, real); + } + } + + _handleJsonFrame(msg) { + // Sensing server sends: { type: "sensing_update", nodes: [{ amplitude: [...], subcarrier_count }], classification, features } + this._liveAmplitude = new Float32Array(this.subcarriers); + this._livePhase = new Float32Array(this.subcarriers); + + // Extract amplitude from sensing_update node data + const node = (msg.nodes && msg.nodes[0]) || msg; + const ampArr = node.amplitude || msg.amplitude; + if (ampArr && Array.isArray(ampArr)) { + const n = Math.min(ampArr.length, this.subcarriers); + // Server sends raw amplitude (already magnitude), normalize to 0-1 + let maxAmp = 0; + for (let i = 0; i < n; i++) maxAmp = Math.max(maxAmp, Math.abs(ampArr[i])); + const scale = maxAmp > 0 ? 1.0 / maxAmp : 1.0; + for (let i = 0; i < n; i++) { + this._liveAmplitude[i] = Math.abs(ampArr[i]) * scale; + } + } + + // Phase from node (if available) + const phaseArr = node.phase || msg.phase; + if (phaseArr && Array.isArray(phaseArr)) { + const n = Math.min(phaseArr.length, this.subcarriers); + for (let i = 0; i < n; i++) this._livePhase[i] = phaseArr[i]; + } else if (ampArr) { + // Synthesize phase from amplitude variation (Hilbert-like estimate) + for (let i = 1; i < this.subcarriers; i++) { + this._livePhase[i] = this._livePhase[i - 1] + (this._liveAmplitude[i] - this._liveAmplitude[i - 1]) * Math.PI; + } + } + + // Handle raw I/Q pairs + const iq = node.iq || msg.iq; + if (iq && Array.isArray(iq)) { + const n = Math.min(iq.length / 2, this.subcarriers); + for (let i = 0; i < n; i++) { + const real = iq[i * 2], imag = iq[i * 2 + 1]; + this._liveAmplitude[i] = Math.sqrt(real * real + imag * imag) / 2048; + this._livePhase[i] = Math.atan2(imag, real); + } + } + + // Extract RSSI from node data + if (typeof node.rssi_dbm === 'number') { + this._rssiTarget = node.rssi_dbm; + } else if (msg.features && typeof msg.features.mean_rssi === 'number') { + this._rssiTarget = msg.features.mean_rssi; + } + + // Update presence from server classification + const cls = msg.classification; + if (cls) { + if (typeof cls.confidence === 'number') { + this.personPresence = cls.presence ? cls.confidence : 0; + } + } + } + + _mulberry32(seed) { + return function() { + let t = (seed += 0x6D2B79F5); + t = Math.imul(t ^ (t >>> 15), t | 1); + t ^= t + Math.imul(t ^ (t >>> 7), t | 61); + return ((t ^ (t >>> 14)) >>> 0) / 4294967296; + }; + } +} diff --git a/ui/pose-fusion/js/fusion-engine.js b/ui/pose-fusion/js/fusion-engine.js new file mode 100644 index 00000000..de454182 --- /dev/null +++ b/ui/pose-fusion/js/fusion-engine.js @@ -0,0 +1,183 @@ +/** + * FusionEngine — Attention-weighted dual-modal embedding fusion. + * + * Combines visual (camera) and CSI (WiFi) embeddings with dynamic + * confidence gating based on signal quality. + */ + +export class FusionEngine { + /** + * @param {number} embeddingDim + * @param {object} opts + * @param {object} opts.wasmModule - RuVector WASM module for cosine_similarity etc. + */ + constructor(embeddingDim = 128, opts = {}) { + this.embeddingDim = embeddingDim; + this.wasmModule = opts.wasmModule || null; + + // Learnable attention weights (initialized to balanced 0.5) + this.attentionWeights = new Float32Array(embeddingDim).fill(0.5); + + // Dynamic modality confidence [0, 1] + this.videoConfidence = 1.0; + this.csiConfidence = 0.0; + this.fusedConfidence = 0.5; + + // Smoothing for confidence transitions + this._smoothAlpha = 0.85; + + // Embedding history for visualization + this.recentVideoEmbeddings = []; + this.recentCsiEmbeddings = []; + this.recentFusedEmbeddings = []; + this.maxHistory = 50; + } + + /** Set the WASM module reference (called after WASM loads) */ + setWasmModule(mod) { this.wasmModule = mod; } + + /** + * Update quality-based confidence scores + * @param {number} videoBrightness - [0,1] video brightness quality + * @param {number} videoMotion - [0,1] motion detected + * @param {number} csiSnr - CSI signal-to-noise ratio in dB + * @param {boolean} csiActive - Whether CSI source is connected + */ + updateConfidence(videoBrightness, videoMotion, csiSnr, csiActive) { + // Video confidence: drops with low brightness, boosted by motion + let vc = 0; + if (videoBrightness > 0.05) { + vc = Math.min(1, videoBrightness * 1.5) * 0.7 + Math.min(1, videoMotion * 3) * 0.3; + } + + // CSI confidence: based on SNR and connection status + let cc = 0; + if (csiActive) { + cc = Math.min(1, csiSnr / 25); // 25dB = full confidence + } + + // Smooth transitions + this.videoConfidence = this._smoothAlpha * this.videoConfidence + (1 - this._smoothAlpha) * vc; + this.csiConfidence = this._smoothAlpha * this.csiConfidence + (1 - this._smoothAlpha) * cc; + + // Fused confidence is the max of either (fusion can only help) + this.fusedConfidence = Math.min(1, Math.sqrt( + this.videoConfidence * this.videoConfidence + this.csiConfidence * this.csiConfidence + )); + } + + /** + * Fuse video and CSI embeddings + * @param {Float32Array|null} videoEmb - Visual embedding (or null if video-off) + * @param {Float32Array|null} csiEmb - CSI embedding (or null if CSI-off) + * @param {string} mode - 'dual' | 'video' | 'csi' + * @returns {Float32Array} Fused embedding + */ + fuse(videoEmb, csiEmb, mode = 'dual') { + const dim = this.embeddingDim; + const fused = new Float32Array(dim); + + if (mode === 'video' || !csiEmb) { + if (videoEmb) fused.set(videoEmb); + this._recordEmbedding(videoEmb, null, fused); + return fused; + } + + if (mode === 'csi' || !videoEmb) { + if (csiEmb) fused.set(csiEmb); + this._recordEmbedding(null, csiEmb, fused); + return fused; + } + + // Dual mode: attention-weighted fusion with confidence gating + const totalConf = this.videoConfidence + this.csiConfidence; + const videoWeight = totalConf > 0 ? this.videoConfidence / totalConf : 0.5; + + for (let i = 0; i < dim; i++) { + const alpha = this.attentionWeights[i] * videoWeight + + (1 - this.attentionWeights[i]) * (1 - videoWeight); + fused[i] = alpha * videoEmb[i] + (1 - alpha) * csiEmb[i]; + } + + // Re-normalize using WASM when available + if (this.wasmModule) { + try { this.wasmModule.normalize(fused); } catch (_) { this._jsNormalize(fused); } + } else { + this._jsNormalize(fused); + } + + this._recordEmbedding(videoEmb, csiEmb, fused); + return fused; + } + + /** + * Get embedding pairs for 2D visualization (PCA projection) + * @returns {{ video: Array, csi: Array, fused: Array }} + */ + getEmbeddingPoints() { + // Sparse random projection: pick a few dimensions with fixed coefficients + // to get visible 2D spread (avoids cancellation from summing all 128 dims) + const project = (emb) => { + if (!emb || emb.length < 4) return null; + // Use 8 sparse dimensions with predetermined signs (seeded, not random) + const dim = emb.length; + const x = emb[0] * 3.2 - emb[3] * 2.8 + emb[7] * 2.1 - emb[12] * 1.9 + + (dim > 30 ? emb[29] * 1.5 - emb[31] * 1.3 : 0) + + (dim > 60 ? emb[55] * 1.1 - emb[60] * 0.9 : 0); + const y = emb[1] * 3.0 - emb[5] * 2.5 + emb[9] * 2.3 - emb[15] * 1.7 + + (dim > 40 ? emb[37] * 1.4 - emb[42] * 1.2 : 0) + + (dim > 80 ? emb[73] * 1.0 - emb[80] * 0.8 : 0); + return [x, y]; + }; + + return { + video: this.recentVideoEmbeddings.map(project).filter(Boolean), + csi: this.recentCsiEmbeddings.map(project).filter(Boolean), + fused: this.recentFusedEmbeddings.map(project).filter(Boolean) + }; + } + + /** + * Cross-modal similarity score + * @returns {number} Cosine similarity between latest video and CSI embeddings + */ + getCrossModalSimilarity() { + const v = this.recentVideoEmbeddings[this.recentVideoEmbeddings.length - 1]; + const c = this.recentCsiEmbeddings[this.recentCsiEmbeddings.length - 1]; + if (!v || !c) return 0; + + // Use WASM cosine_similarity when available + if (this.wasmModule) { + try { return this.wasmModule.cosine_similarity(v, c); } catch (_) { /* fallback */ } + } + + let dot = 0, na = 0, nb = 0; + for (let i = 0; i < v.length; i++) { + dot += v[i] * c[i]; + na += v[i] * v[i]; + nb += c[i] * c[i]; + } + na = Math.sqrt(na); nb = Math.sqrt(nb); + return (na > 1e-8 && nb > 1e-8) ? dot / (na * nb) : 0; + } + + _jsNormalize(vec) { + let norm = 0; + for (let i = 0; i < vec.length; i++) norm += vec[i] * vec[i]; + norm = Math.sqrt(norm); + if (norm > 1e-8) for (let i = 0; i < vec.length; i++) vec[i] /= norm; + } + + _recordEmbedding(video, csi, fused) { + if (video) { + this.recentVideoEmbeddings.push(new Float32Array(video)); + if (this.recentVideoEmbeddings.length > this.maxHistory) this.recentVideoEmbeddings.shift(); + } + if (csi) { + this.recentCsiEmbeddings.push(new Float32Array(csi)); + if (this.recentCsiEmbeddings.length > this.maxHistory) this.recentCsiEmbeddings.shift(); + } + this.recentFusedEmbeddings.push(new Float32Array(fused)); + if (this.recentFusedEmbeddings.length > this.maxHistory) this.recentFusedEmbeddings.shift(); + } +} diff --git a/ui/pose-fusion/js/main.js b/ui/pose-fusion/js/main.js new file mode 100644 index 00000000..1001d636 --- /dev/null +++ b/ui/pose-fusion/js/main.js @@ -0,0 +1,472 @@ +/** + * WiFi-DensePose — Dual-Modal Pose Estimation Demo + * + * Main orchestration: video capture → CNN embedding → CSI processing → fusion → rendering + */ + +import { VideoCapture } from './video-capture.js?v=11'; +import { CsiSimulator } from './csi-simulator.js?v=11'; +import { CnnEmbedder } from './cnn-embedder.js?v=11'; +import { FusionEngine } from './fusion-engine.js?v=11'; +import { PoseDecoder } from './pose-decoder.js?v=11'; +import { CanvasRenderer } from './canvas-renderer.js?v=11'; + +// === State === +let mode = 'dual'; // 'dual' | 'video' | 'csi' +let isRunning = false; +let isPaused = false; +let startTime = 0; +let frameCount = 0; +let fps = 0; +let lastFpsTime = 0; +let confidenceThreshold = 0.3; + +// Latency tracking +const latency = { video: 0, csi: 0, fusion: 0, total: 0 }; + +// === Components === +const videoCapture = new VideoCapture(document.getElementById('webcam')); +const csiSimulator = new CsiSimulator({ subcarriers: 52, timeWindow: 56 }); +const visualCnn = new CnnEmbedder({ inputSize: 56, embeddingDim: 128, seed: 42 }); +const csiCnn = new CnnEmbedder({ inputSize: 56, embeddingDim: 128, seed: 137 }); +const fusionEngine = new FusionEngine(128); +const poseDecoder = new PoseDecoder(128); +const renderer = new CanvasRenderer(); + +// === Canvas Elements === +const skeletonCanvas = document.getElementById('skeleton-canvas'); +const skeletonCtx = skeletonCanvas.getContext('2d'); +const csiCanvas = document.getElementById('csi-canvas'); +const csiCtx = csiCanvas.getContext('2d'); +const embeddingCanvas = document.getElementById('embedding-canvas'); +const embeddingCtx = embeddingCanvas.getContext('2d'); + +// === UI Elements === +const modeSelect = document.getElementById('mode-select'); +const statusDot = document.getElementById('status-dot'); +const statusLabel = document.getElementById('status-label'); +const fpsDisplay = document.getElementById('fps-display'); +const cameraPrompt = document.getElementById('camera-prompt'); +const startCameraBtn = document.getElementById('start-camera-btn'); +const pauseBtn = document.getElementById('pause-btn'); +const confSlider = document.getElementById('confidence-slider'); +const confValue = document.getElementById('confidence-value'); +const wsUrlInput = document.getElementById('ws-url'); +const connectWsBtn = document.getElementById('connect-ws-btn'); + +// Fusion bar elements +const videoBar = document.getElementById('video-bar'); +const csiBar = document.getElementById('csi-bar'); +const fusedBar = document.getElementById('fused-bar'); +const videoBarVal = document.getElementById('video-bar-val'); +const csiBarVal = document.getElementById('csi-bar-val'); +const fusedBarVal = document.getElementById('fused-bar-val'); + +// Latency elements +const latVideoEl = document.getElementById('lat-video'); +const latCsiEl = document.getElementById('lat-csi'); +const latFusionEl = document.getElementById('lat-fusion'); +const latTotalEl = document.getElementById('lat-total'); + +// Cross-modal similarity +const crossModalEl = document.getElementById('cross-modal-sim'); + +// RSSI elements +const rssiBarEl = document.getElementById('rssi-bar'); +const rssiValueEl = document.getElementById('rssi-value'); +const rssiQualityEl = document.getElementById('rssi-quality'); +const rssiSparkCanvas = document.getElementById('rssi-sparkline'); +const rssiSparkCtx = rssiSparkCanvas ? rssiSparkCanvas.getContext('2d') : null; +const rssiHistory = []; +const RSSI_HISTORY_MAX = 80; + +// === Initialize === +function init() { + console.log(`[PoseFusion] init() v4 — CsiSimulator=${CsiSimulator.VERSION || 'OLD'}, starting...`); + resizeCanvases(); + console.log(`[PoseFusion] canvases: skeleton=${skeletonCanvas.width}x${skeletonCanvas.height}, csi=${csiCanvas.width}x${csiCanvas.height}, emb=${embeddingCanvas.width}x${embeddingCanvas.height}`); + window.addEventListener('resize', resizeCanvases); + + // Mode change + modeSelect.addEventListener('change', (e) => { + mode = e.target.value; + updateModeUI(); + }); + + // Camera start + startCameraBtn.addEventListener('click', startCamera); + + // Pause + pauseBtn.addEventListener('click', () => { + isPaused = !isPaused; + pauseBtn.textContent = isPaused ? '▶ Resume' : '⏸ Pause'; + pauseBtn.classList.toggle('active', isPaused); + }); + + // Confidence slider + confSlider.addEventListener('input', (e) => { + confidenceThreshold = parseFloat(e.target.value); + confValue.textContent = confidenceThreshold.toFixed(2); + }); + + // WebSocket connect + connectWsBtn.addEventListener('click', async () => { + const url = wsUrlInput.value.trim(); + if (!url) return; + connectWsBtn.textContent = 'Connecting...'; + const ok = await csiSimulator.connectLive(url); + connectWsBtn.textContent = ok ? '✓ Connected' : 'Connect'; + if (ok) { + connectWsBtn.classList.add('active'); + } + }); + + // Try to load RuVector Attention WASM embedders (non-blocking) + const wasmBase = new URL('../pkg/ruvector-attention', import.meta.url).href; + visualCnn.tryLoadWasm(wasmBase).then((ok) => { + // Share the WASM module with FusionEngine for cosine_similarity, normalize, etc. + if (visualCnn.rvModule) fusionEngine.setWasmModule(visualCnn.rvModule); + // Update footer backend label + const backendEl = document.getElementById('cnn-backend'); + if (backendEl) { + backendEl.textContent = ok && visualCnn.useRuVector + ? `RuVector WASM v${visualCnn.rvModule.version()} — 6 attention mechanisms` + : 'ruvector-cnn (JS fallback)'; + } + }); + csiCnn.tryLoadWasm(wasmBase); + + // Auto-connect to local sensing server WebSocket if available + const defaultWsUrl = 'ws://localhost:8765/ws/sensing'; + if (wsUrlInput) wsUrlInput.value = defaultWsUrl; + csiSimulator.connectLive(defaultWsUrl).then(ok => { + if (ok && connectWsBtn) { + connectWsBtn.textContent = '✓ Live ESP32'; + connectWsBtn.classList.add('active'); + statusLabel.textContent = 'LIVE CSI'; + statusDot.classList.remove('offline'); + } + }); + + // Auto-start camera for video/dual modes + updateModeUI(); + startTime = performance.now() / 1000; + isRunning = true; + requestAnimationFrame(mainLoop); +} + +async function startCamera() { + cameraPrompt.style.display = 'none'; + const ok = await videoCapture.start(); + if (ok) { + statusDot.classList.remove('offline'); + statusLabel.textContent = 'LIVE'; + resizeCanvases(); + } else { + cameraPrompt.style.display = 'flex'; + cameraPrompt.querySelector('p').textContent = 'Camera access denied. Try CSI-only mode.'; + } +} + +function updateModeUI() { + const needsVideo = mode !== 'csi'; + + // Show/hide camera prompt + if (needsVideo && !videoCapture.isActive) { + cameraPrompt.style.display = 'flex'; + } else { + cameraPrompt.style.display = 'none'; + } + + // Update mode label in both the overlay and the camera prompt + const labelMap = { dual: 'DUAL FUSION', video: 'VIDEO ONLY', csi: 'CSI ONLY' }; + const modeLabel = document.getElementById('mode-label'); + const promptLabel = document.getElementById('prompt-mode-label'); + if (modeLabel) modeLabel.textContent = labelMap[mode] || mode; + if (promptLabel) promptLabel.textContent = labelMap[mode] || mode; +} + +function resizeCanvases() { + const videoPanel = document.querySelector('.video-panel'); + if (videoPanel) { + const rect = videoPanel.getBoundingClientRect(); + skeletonCanvas.width = rect.width; + skeletonCanvas.height = rect.height; + } + + // CSI canvas (min 200px width) + csiCanvas.width = Math.max(200, csiCanvas.parentElement.clientWidth); + csiCanvas.height = 120; + + // Embedding canvas (min 200px width) + embeddingCanvas.width = Math.max(200, embeddingCanvas.parentElement.clientWidth); + embeddingCanvas.height = 140; +} + +// === Main Loop === +let _loopErrorShown = false; +let _diagDone = false; +function mainLoop(timestamp) { + if (!isRunning) return; + requestAnimationFrame(mainLoop); + + if (isPaused) return; + + try { + const elapsed = performance.now() / 1000 - startTime; + const totalStart = performance.now(); + + // --- Video Pipeline --- + let videoEmb = null; + let motionRegion = null; + if (mode !== 'csi' && videoCapture.isActive) { + const t0 = performance.now(); + const frame = videoCapture.captureFrame(56, 56); + if (frame) { + videoEmb = visualCnn.extract(frame.rgb, frame.width, frame.height); + motionRegion = videoCapture.detectMotionRegion(56, 56); + + // Feed motion to CSI simulator for correlated demo data + // When detected=false, CSI simulator handles through-wall persistence + csiSimulator.updatePersonState( + motionRegion.detected ? 1.0 : 0, + motionRegion.detected ? motionRegion.x + motionRegion.w / 2 : 0.5, + motionRegion.detected ? motionRegion.y + motionRegion.h / 2 : 0.5, + frame.motion + ); + + fusionEngine.updateConfidence( + frame.brightness, frame.motion, + 0, csiSimulator.isLive || mode === 'dual' + ); + } + latency.video = performance.now() - t0; + } + + // --- CSI Pipeline --- + let csiEmb = null; + if (mode !== 'video') { + const t0 = performance.now(); + const csiFrame = csiSimulator.nextFrame(elapsed); + const pseudoImage = csiSimulator.buildPseudoImage(56); + csiEmb = csiCnn.extract(pseudoImage, 56, 56); + + fusionEngine.updateConfidence( + videoCapture.brightnessScore, + videoCapture.motionScore, + csiFrame.snr, + true + ); + + // Draw CSI heatmap + const heatmap = csiSimulator.getHeatmapData(); + renderer.drawCsiHeatmap(csiCtx, heatmap, csiCanvas.width, csiCanvas.height); + + latency.csi = performance.now() - t0; + } + + // --- Fusion --- + const t0f = performance.now(); + const fusedEmb = fusionEngine.fuse(videoEmb, csiEmb, mode); + latency.fusion = performance.now() - t0f; + + // --- Pose Decode --- + // For CSI-only mode, generate a synthetic motion region from CSI energy + if (mode === 'csi' && (!motionRegion || !motionRegion.detected)) { + const csiPresence = csiSimulator.personPresence; + if (csiPresence > 0.1) { + motionRegion = { + detected: true, + x: 0.25, y: 0.15, w: 0.5, h: 0.7, + coverage: csiPresence, + motionGrid: null, + gridCols: 10, + gridRows: 8 + }; + } + } + + // CSI state for through-wall tracking + const csiState = { + csiPresence: csiSimulator.personPresence, + isLive: csiSimulator.isLive + }; + + const keypoints = poseDecoder.decode(fusedEmb, motionRegion, elapsed, csiState); + + // --- Render Skeleton --- + const labelMap = { dual: 'DUAL FUSION', video: 'VIDEO ONLY', csi: 'CSI ONLY' }; + renderer.drawSkeleton(skeletonCtx, keypoints, skeletonCanvas.width, skeletonCanvas.height, { + minConfidence: confidenceThreshold, + color: mode === 'csi' ? 'amber' : 'green', + label: labelMap[mode] + }); + + // --- Render Embedding Space --- + const embPoints = fusionEngine.getEmbeddingPoints(); + renderer.drawEmbeddingSpace(embeddingCtx, embPoints, embeddingCanvas.width, embeddingCanvas.height); + + // --- Update UI --- + latency.total = performance.now() - totalStart; + + // FPS + frameCount++; + if (timestamp - lastFpsTime > 500) { + fps = Math.round(frameCount * 1000 / (timestamp - lastFpsTime)); + lastFpsTime = timestamp; + frameCount = 0; + fpsDisplay.textContent = `${fps} FPS`; + } + + // Fusion bars + const vc = fusionEngine.videoConfidence; + const cc = fusionEngine.csiConfidence; + const fc = fusionEngine.fusedConfidence; + videoBar.style.width = `${vc * 100}%`; + csiBar.style.width = `${cc * 100}%`; + fusedBar.style.width = `${fc * 100}%`; + videoBarVal.textContent = `${Math.round(vc * 100)}%`; + csiBarVal.textContent = `${Math.round(cc * 100)}%`; + fusedBarVal.textContent = `${Math.round(fc * 100)}%`; + + // Latency + latVideoEl.textContent = `${latency.video.toFixed(1)}ms`; + latCsiEl.textContent = `${latency.csi.toFixed(1)}ms`; + latFusionEl.textContent = `${latency.fusion.toFixed(1)}ms`; + latTotalEl.textContent = `${latency.total.toFixed(1)}ms`; + + // Cross-modal similarity + const sim = fusionEngine.getCrossModalSimilarity(); + crossModalEl.textContent = sim.toFixed(3); + + // RuVector attention pipeline stats + const rvStats = poseDecoder.attentionStats; + const rvEnergyEl = document.getElementById('rv-energy'); + const rvRefineEl = document.getElementById('rv-refine'); + const rvImpactEl = document.getElementById('rv-impact'); + if (rvEnergyEl) rvEnergyEl.textContent = rvStats.energy.toFixed(2); + if (rvRefineEl) rvRefineEl.textContent = (rvStats.refinementMag * 1000).toFixed(1) + 'px'; + if (rvImpactEl) { + const impact = Math.min(100, rvStats.refinementMag * 5000); + rvImpactEl.textContent = impact.toFixed(0) + '%'; + } + // Pulse the pipeline stages when active + if (visualCnn.useRuVector && rvStats.energy > 0.1) { + document.querySelectorAll('.rv-stage').forEach(el => el.classList.add('active')); + } + + // RSSI update + updateRssi(csiSimulator.rssiDbm); + + // One-time diagnostic + if (!_diagDone) { + _diagDone = true; + console.log(`[PoseFusion] frame 1 OK — mode=${mode}, csi.bufLen=${csiSimulator.amplitudeBuffer.length}, embPts=${embPoints.fused.length}, rssi=${csiSimulator.rssiDbm.toFixed(1)}`); + } + + } catch (err) { + if (!_loopErrorShown) { + _loopErrorShown = true; + console.error('[MainLoop]', err); + // Show error visually on page + const errDiv = document.createElement('div'); + errDiv.style.cssText = 'position:fixed;bottom:60px;left:24px;right:24px;background:rgba(255,48,64,0.95);color:#fff;padding:12px 16px;border-radius:8px;font:12px/1.4 "JetBrains Mono",monospace;z-index:9999;max-height:120px;overflow:auto'; + errDiv.textContent = `[MainLoop Error] ${err.message}\n${err.stack?.split('\n').slice(0,3).join('\n')}`; + document.body.appendChild(errDiv); + } + } +} + +// === RSSI Visualization === +function updateRssi(dbm) { + if (!rssiBarEl) return; + + // Clamp to typical WiFi range: -100 (worst) to -30 (best) + const clamped = Math.max(-100, Math.min(-30, dbm)); + const pct = ((clamped + 100) / 70) * 100; // 0-100% + + rssiBarEl.style.width = `${pct}%`; + rssiValueEl.textContent = `${Math.round(clamped)} dBm`; + + // Quality label + let quality; + if (clamped > -50) quality = 'Excellent'; + else if (clamped > -60) quality = 'Good'; + else if (clamped > -70) quality = 'Fair'; + else if (clamped > -80) quality = 'Weak'; + else quality = 'Poor'; + rssiQualityEl.textContent = quality; + + // Color the dBm value based on quality + if (clamped > -60) rssiValueEl.style.color = 'var(--green-glow)'; + else if (clamped > -75) rssiValueEl.style.color = 'var(--amber)'; + else rssiValueEl.style.color = 'var(--red-alert)'; + + // Sparkline history + rssiHistory.push(clamped); + if (rssiHistory.length > RSSI_HISTORY_MAX) rssiHistory.shift(); + drawRssiSparkline(); +} + +function drawRssiSparkline() { + if (!rssiSparkCtx || rssiHistory.length < 2) return; + const w = rssiSparkCanvas.width; + const h = rssiSparkCanvas.height; + const ctx = rssiSparkCtx; + + ctx.clearRect(0, 0, w, h); + + // Draw signal strength line + const len = rssiHistory.length; + const step = w / (RSSI_HISTORY_MAX - 1); + + // Gradient fill under line + const grad = ctx.createLinearGradient(0, 0, 0, h); + grad.addColorStop(0, 'rgba(0,210,120,0.3)'); + grad.addColorStop(1, 'rgba(0,210,120,0)'); + + ctx.beginPath(); + for (let i = 0; i < len; i++) { + const x = (RSSI_HISTORY_MAX - len + i) * step; + const y = h - ((rssiHistory[i] + 100) / 70) * h; + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + } + // Fill area + const lastX = (RSSI_HISTORY_MAX - 1) * step; + const firstX = (RSSI_HISTORY_MAX - len) * step; + ctx.lineTo(lastX, h); + ctx.lineTo(firstX, h); + ctx.closePath(); + ctx.fillStyle = grad; + ctx.fill(); + + // Draw line on top + ctx.beginPath(); + for (let i = 0; i < len; i++) { + const x = (RSSI_HISTORY_MAX - len + i) * step; + const y = h - ((rssiHistory[i] + 100) / 70) * h; + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + } + ctx.strokeStyle = '#00d878'; + ctx.lineWidth = 1.5; + ctx.stroke(); + + // Pulsing dot at latest value + const latestX = lastX; + const latestY = h - ((rssiHistory[len - 1] + 100) / 70) * h; + const pulse = 0.5 + 0.5 * Math.sin(performance.now() / 300); + ctx.beginPath(); + ctx.arc(latestX, latestY, 2 + pulse, 0, Math.PI * 2); + ctx.fillStyle = '#00d878'; + ctx.fill(); + ctx.beginPath(); + ctx.arc(latestX, latestY, 4 + pulse * 2, 0, Math.PI * 2); + ctx.strokeStyle = `rgba(0,216,120,${0.3 + pulse * 0.3})`; + ctx.lineWidth = 1; + ctx.stroke(); +} + +// Boot +document.addEventListener('DOMContentLoaded', init); diff --git a/ui/pose-fusion/js/pose-decoder.js b/ui/pose-fusion/js/pose-decoder.js new file mode 100644 index 00000000..338a1ba7 --- /dev/null +++ b/ui/pose-fusion/js/pose-decoder.js @@ -0,0 +1,553 @@ +/** + * PoseDecoder — Maps motion detection grid → 17 COCO keypoints. + * + * Uses per-cell motion intensity to track actual body part positions: + * - Head: top-center motion cluster + * - Shoulders/Elbows/Wrists: lateral motion in upper body zone + * - Hips/Knees/Ankles: lower body motion distribution + * + * When person exits frame, CSI data continues tracking (through-wall mode). + */ + +// Extended keypoint definitions: 17 COCO + 9 hand/fingertip approximations = 26 total +export const KEYPOINT_NAMES = [ + 'nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear', + 'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow', + 'left_wrist', 'right_wrist', 'left_hip', 'right_hip', + 'left_knee', 'right_knee', 'left_ankle', 'right_ankle', + // Extended: hand keypoints (17-25) + 'left_thumb', 'left_index', 'left_pinky', // 17, 18, 19 + 'right_thumb', 'right_index', 'right_pinky', // 20, 21, 22 + 'left_foot_index', 'right_foot_index', // 23, 24 (toe tips) + 'neck', // 25 (mid-shoulder) +]; + +// Skeleton connections (pairs of keypoint indices) +export const SKELETON_CONNECTIONS = [ + [0, 1], [0, 2], [1, 3], [2, 4], // Head + [0, 25], // Nose → neck + [25, 5], [25, 6], // Neck → shoulders + [5, 7], [7, 9], // Left arm + [6, 8], [8, 10], // Right arm + [5, 11], [6, 12], // Torso + [11, 12], // Hips + [11, 13], [13, 15], // Left leg + [12, 14], [14, 16], // Right leg + // Hand connections + [9, 17], [9, 18], [9, 19], // Left wrist → fingers + [10, 20], [10, 21], [10, 22], // Right wrist → fingers + // Foot connections + [15, 23], [16, 24], // Ankles → toes +]; + +// Standard body proportions (relative to body height) +const PROPORTIONS = { + headToShoulder: 0.15, + shoulderWidth: 0.25, + shoulderToElbow: 0.18, + elbowToWrist: 0.16, + shoulderToHip: 0.30, + hipWidth: 0.18, + hipToKnee: 0.24, + kneeToAnkle: 0.24, + eyeSpacing: 0.04, + earSpacing: 0.07, + // Hand proportions + wristToFinger: 0.09, + fingerSpread: 0.04, + thumbAngle: 0.6, // radians from wrist-elbow axis + // Foot proportions + ankleToToe: 0.06, +}; + +export class PoseDecoder { + constructor(embeddingDim = 128) { + this.embeddingDim = embeddingDim; + this.smoothedKeypoints = null; + this.smoothingFactor = 0.25; // Low = responsive to real movement + this._time = 0; + + // Through-wall tracking state + this._lastBodyState = null; + this._ghostState = null; + this._ghostConfidence = 0; + this._ghostVelocity = { x: 0, y: 0 }; + + // Zone centroid tracking (normalized 0-1 positions) + this._headCx = 0.5; + this._headCy = 0.15; + this._leftArmCx = 0.3; + this._leftArmCy = 0.35; + this._rightArmCx = 0.7; + this._rightArmCy = 0.35; + this._leftLegCx = 0.4; + this._leftLegCy = 0.8; + this._rightLegCx = 0.6; + this._rightLegCy = 0.8; + this._torsoCx = 0.5; + this._torsoCy = 0.45; + + // RuVector embedding → joint mapping + // Each joint gets 2 consecutive embedding dimensions (dx, dy offset) + // and 1 dimension for confidence modulation. 26 joints × 3 = 78 dims used from 128. + // Remaining 50 dims encode global pose features (body scale, rotation, lean). + this._jointEmbMap = this._buildJointEmbeddingMap(embeddingDim); + + // Attention contribution tracking (for UI overlay) + this.attentionStats = { energy: 0, maxDim: 0, refinementMag: 0 }; + } + + /** + * Build the mapping from embedding dimensions to joint refinement signals. + * This maps the RuVector attention output to anatomically meaningful joint offsets. + */ + _buildJointEmbeddingMap(dim) { + const map = []; + // 26 joints × 3 dims each (dx, dy, confidence_mod) = 78 dims + for (let j = 0; j < 26; j++) { + const base = j * 3; + if (base + 2 < dim) { + map.push({ dxDim: base, dyDim: base + 1, confDim: base + 2 }); + } else { + map.push({ dxDim: j % dim, dyDim: (j + 1) % dim, confDim: (j + 2) % dim }); + } + } + // Global pose features from dims 78-127 + return { + joints: map, + scaleDim: Math.min(78, dim - 1), // body scale factor + rotDim: Math.min(79, dim - 1), // body rotation + leanXDim: Math.min(80, dim - 1), // lateral lean + leanYDim: Math.min(81, dim - 1), // forward/back lean + }; + } + + /** + * Decode motion data into 17 keypoints + * @param {Float32Array} embedding - Fused embedding vector + * @param {{ detected, x, y, w, h, motionGrid, gridCols, gridRows, motionCx, motionCy, exitDirection }} motionRegion + * @param {number} elapsed - Time in seconds + * @param {{ csiPresence: number }} csiState - CSI sensing state for through-wall + * @returns {Array<{x: number, y: number, confidence: number, name: string}>} + */ + decode(embedding, motionRegion, elapsed, csiState = {}) { + this._time = elapsed; + + const hasMotion = motionRegion && motionRegion.detected; + const hasCsi = csiState && csiState.csiPresence > 0.1; + + if (hasMotion) { + // Active tracking from video motion grid + this._ghostConfidence = 0; + const rawKeypoints = this._trackFromMotionGrid(motionRegion, embedding, elapsed); + this._lastBodyState = { keypoints: rawKeypoints.map(kp => ({...kp})), time: elapsed }; + + // Track exit velocity + if (motionRegion.exitDirection) { + const speed = 0.008; + this._ghostVelocity = { + x: motionRegion.exitDirection === 'left' ? -speed : motionRegion.exitDirection === 'right' ? speed : 0, + y: motionRegion.exitDirection === 'up' ? -speed : motionRegion.exitDirection === 'down' ? speed : 0 + }; + } + + // Apply temporal smoothing + if (this.smoothedKeypoints && this.smoothedKeypoints.length === rawKeypoints.length) { + const alpha = this.smoothingFactor; + for (let i = 0; i < rawKeypoints.length; i++) { + rawKeypoints[i].x = alpha * this.smoothedKeypoints[i].x + (1 - alpha) * rawKeypoints[i].x; + rawKeypoints[i].y = alpha * this.smoothedKeypoints[i].y + (1 - alpha) * rawKeypoints[i].y; + } + } + + this.smoothedKeypoints = rawKeypoints; + return rawKeypoints; + + } else if (this._lastBodyState && (hasCsi || this._ghostConfidence > 0.05)) { + // Through-wall mode: person left frame but CSI still senses them + return this._trackThroughWall(elapsed, csiState); + + } else if (this.smoothedKeypoints) { + // Fade out + const faded = this.smoothedKeypoints.map(kp => ({ + ...kp, + confidence: kp.confidence * 0.88 + })).filter(kp => kp.confidence > 0.05); + if (faded.length === 0) this.smoothedKeypoints = null; + else this.smoothedKeypoints = faded; + return faded; + } + + return []; + } + + /** + * Track body parts from the motion grid. + * Finds the centroid of motion in each body zone and positions joints there. + */ + _trackFromMotionGrid(region, embedding, elapsed) { + const grid = region.motionGrid; + const cols = region.gridCols || 10; + const rows = region.gridRows || 8; + + // Body bounding box (in normalized 0-1 coords) + const bx = region.x, by = region.y, bw = region.w, bh = region.h; + const cx = bx + bw / 2; + const cy = by + bh / 2; + const bodyH = Math.max(bh, 0.3); + const bodyW = Math.max(bw, 0.15); + + // Find motion centroids per body zone from the grid + if (grid) { + const zones = this._findZoneCentroids(grid, cols, rows, bx, by, bw, bh); + // Smooth with low alpha for responsiveness + const a = 0.3; // 30% old, 70% new → responsive + this._headCx = a * this._headCx + (1 - a) * zones.head.x; + this._headCy = a * this._headCy + (1 - a) * zones.head.y; + this._leftArmCx = a * this._leftArmCx + (1 - a) * zones.leftArm.x; + this._leftArmCy = a * this._leftArmCy + (1 - a) * zones.leftArm.y; + this._rightArmCx= a * this._rightArmCx+ (1 - a) * zones.rightArm.x; + this._rightArmCy= a * this._rightArmCy+ (1 - a) * zones.rightArm.y; + this._leftLegCx = a * this._leftLegCx + (1 - a) * zones.leftLeg.x; + this._leftLegCy = a * this._leftLegCy + (1 - a) * zones.leftLeg.y; + this._rightLegCx= a * this._rightLegCx+ (1 - a) * zones.rightLeg.x; + this._rightLegCy= a * this._rightLegCy+ (1 - a) * zones.rightLeg.y; + this._torsoCx = a * this._torsoCx + (1 - a) * zones.torso.x; + this._torsoCy = a * this._torsoCy + (1 - a) * zones.torso.y; + } + + const P = PROPORTIONS; + + // Breathing (subtle) + const breathe = Math.sin(elapsed * 1.5) * 0.002; + + // === Position joints using tracked centroids === + + // HEAD: tracked centroid (top zone) + const headX = this._headCx; + const headY = this._headCy; + + // TORSO center drives shoulder/hip + const torsoX = this._torsoCx; + const shoulderY = this._torsoCy - bodyH * 0.08 + breathe; + const halfW = P.shoulderWidth * bodyH / 2; + const hipHalfW = P.hipWidth * bodyH / 2; + const hipY = shoulderY + P.shoulderToHip * bodyH; + + // ARMS: elbow + wrist driven toward arm zone centroids + // Left arm: shoulder is fixed, elbow/wrist pulled toward left arm centroid + const lShX = torsoX - halfW; + const lShY = shoulderY; + // Vector from shoulder toward arm centroid + const lArmDx = this._leftArmCx - lShX; + const lArmDy = this._leftArmCy - lShY; + const lArmDist = Math.sqrt(lArmDx * lArmDx + lArmDy * lArmDy) || 0.01; + const lArmNx = lArmDx / lArmDist; + const lArmNy = lArmDy / lArmDist; + // Elbow at shoulderToElbow distance along that direction + const elbowLen = P.shoulderToElbow * bodyH; + const lElbowX = lShX + lArmNx * elbowLen; + const lElbowY = lShY + lArmNy * elbowLen; + // Wrist continues further + const wristLen = P.elbowToWrist * bodyH; + const lWristX = lElbowX + lArmNx * wristLen; + const lWristY = lElbowY + lArmNy * wristLen; + + // Right arm: same approach + const rShX = torsoX + halfW; + const rShY = shoulderY; + const rArmDx = this._rightArmCx - rShX; + const rArmDy = this._rightArmCy - rShY; + const rArmDist = Math.sqrt(rArmDx * rArmDx + rArmDy * rArmDy) || 0.01; + const rArmNx = rArmDx / rArmDist; + const rArmNy = rArmDy / rArmDist; + const rElbowX = rShX + rArmNx * elbowLen; + const rElbowY = rShY + rArmNy * elbowLen; + const rWristX = rElbowX + rArmNx * wristLen; + const rWristY = rElbowY + rArmNy * wristLen; + + // LEGS: knees/ankles pulled toward leg zone centroids + const lHipX = torsoX - hipHalfW; + const rHipX = torsoX + hipHalfW; + const lLegDx = this._leftLegCx - lHipX; + const lLegDy = Math.max(0.05, this._leftLegCy - hipY); // always downward + const lLegDist = Math.sqrt(lLegDx * lLegDx + lLegDy * lLegDy) || 0.01; + const lLegNx = lLegDx / lLegDist; + const lLegNy = lLegDy / lLegDist; + const kneeLen = P.hipToKnee * bodyH; + const ankleLen = P.kneeToAnkle * bodyH; + const lKneeX = lHipX + lLegNx * kneeLen; + const lKneeY = hipY + lLegNy * kneeLen; + const lAnkleX = lKneeX + lLegNx * ankleLen; + const lAnkleY = lKneeY + lLegNy * ankleLen; + + const rLegDx = this._rightLegCx - rHipX; + const rLegDy = Math.max(0.05, this._rightLegCy - hipY); + const rLegDist = Math.sqrt(rLegDx * rLegDx + rLegDy * rLegDy) || 0.01; + const rLegNx = rLegDx / rLegDist; + const rLegNy = rLegDy / rLegDist; + const rKneeX = rHipX + rLegNx * kneeLen; + const rKneeY = hipY + rLegNy * kneeLen; + const rAnkleX = rKneeX + rLegNx * ankleLen; + const rAnkleY = rKneeY + rLegNy * ankleLen; + + // Arm raise amount (for hand openness) + const leftArmRaise = Math.max(0, Math.min(1, (shoulderY - this._leftArmCy) / (bodyH * 0.3))); + const rightArmRaise = Math.max(0, Math.min(1, (shoulderY - this._rightArmCy) / (bodyH * 0.3))); + + // Compute hand finger positions from wrist-elbow axis + const lHandAngle = Math.atan2(lWristY - lElbowY, lWristX - lElbowX); + const rHandAngle = Math.atan2(rWristY - rElbowY, rWristX - rElbowX); + const fingerLen = P.wristToFinger * bodyH; + const fingerSpr = P.fingerSpread * bodyH; + + // Hand openness driven by arm raise + arm lateral spread + const lArmSpread = Math.abs(this._leftArmCx - (bx + bw * 0.3)) / (bw * 0.3); + const rArmSpread = Math.abs(this._rightArmCx - (bx + bw * 0.7)) / (bw * 0.3); + const lHandOpen = Math.min(1, leftArmRaise * 0.5 + lArmSpread * 0.5); + const rHandOpen = Math.min(1, rightArmRaise * 0.5 + rArmSpread * 0.5); + + const keypoints = [ + // 0: nose + { x: headX, y: headY + 0.01, confidence: 0.92 }, + // 1: left_eye + { x: headX - P.eyeSpacing * bodyH, y: headY - 0.005, confidence: 0.88 }, + // 2: right_eye + { x: headX + P.eyeSpacing * bodyH, y: headY - 0.005, confidence: 0.88 }, + // 3: left_ear + { x: headX - P.earSpacing * bodyH, y: headY + 0.005, confidence: 0.72 }, + // 4: right_ear + { x: headX + P.earSpacing * bodyH, y: headY + 0.005, confidence: 0.72 }, + // 5: left_shoulder + { x: lShX, y: lShY, confidence: 0.94 }, + // 6: right_shoulder + { x: rShX, y: rShY, confidence: 0.94 }, + // 7: left_elbow + { x: lElbowX, y: lElbowY, confidence: 0.87 }, + // 8: right_elbow + { x: rElbowX, y: rElbowY, confidence: 0.87 }, + // 9: left_wrist + { x: lWristX, y: lWristY, confidence: 0.82 }, + // 10: right_wrist + { x: rWristX, y: rWristY, confidence: 0.82 }, + // 11: left_hip + { x: lHipX, y: hipY, confidence: 0.91 }, + // 12: right_hip + { x: rHipX, y: hipY, confidence: 0.91 }, + // 13: left_knee + { x: lKneeX, y: lKneeY, confidence: 0.88 }, + // 14: right_knee + { x: rKneeX, y: rKneeY, confidence: 0.88 }, + // 15: left_ankle + { x: lAnkleX, y: lAnkleY, confidence: 0.83 }, + // 16: right_ankle + { x: rAnkleX, y: rAnkleY, confidence: 0.83 }, + + // === Extended keypoints (17-25) === + + // 17: left_thumb — offset at thumb angle from wrist-elbow axis + { x: lWristX + fingerLen * Math.cos(lHandAngle + P.thumbAngle) * (0.6 + lHandOpen * 0.4), + y: lWristY + fingerLen * Math.sin(lHandAngle + P.thumbAngle) * (0.6 + lHandOpen * 0.4), + confidence: 0.68 * (0.5 + lHandOpen * 0.5) }, + // 18: left_index — extends along wrist-elbow axis + { x: lWristX + fingerLen * Math.cos(lHandAngle) + fingerSpr * lHandOpen * Math.cos(lHandAngle + 0.3), + y: lWristY + fingerLen * Math.sin(lHandAngle) + fingerSpr * lHandOpen * Math.sin(lHandAngle + 0.3), + confidence: 0.72 * (0.5 + lHandOpen * 0.5) }, + // 19: left_pinky — offset opposite thumb + { x: lWristX + fingerLen * 0.85 * Math.cos(lHandAngle - P.thumbAngle * 0.7), + y: lWristY + fingerLen * 0.85 * Math.sin(lHandAngle - P.thumbAngle * 0.7), + confidence: 0.60 * (0.5 + lHandOpen * 0.5) }, + + // 20: right_thumb + { x: rWristX + fingerLen * Math.cos(rHandAngle - P.thumbAngle) * (0.6 + rHandOpen * 0.4), + y: rWristY + fingerLen * Math.sin(rHandAngle - P.thumbAngle) * (0.6 + rHandOpen * 0.4), + confidence: 0.68 * (0.5 + rHandOpen * 0.5) }, + // 21: right_index + { x: rWristX + fingerLen * Math.cos(rHandAngle) + fingerSpr * rHandOpen * Math.cos(rHandAngle - 0.3), + y: rWristY + fingerLen * Math.sin(rHandAngle) + fingerSpr * rHandOpen * Math.sin(rHandAngle - 0.3), + confidence: 0.72 * (0.5 + rHandOpen * 0.5) }, + // 22: right_pinky + { x: rWristX + fingerLen * 0.85 * Math.cos(rHandAngle + P.thumbAngle * 0.7), + y: rWristY + fingerLen * 0.85 * Math.sin(rHandAngle + P.thumbAngle * 0.7), + confidence: 0.60 * (0.5 + rHandOpen * 0.5) }, + + // 23: left_foot_index (toe tip) — extends forward from ankle + { x: lAnkleX + P.ankleToToe * bodyH * 0.5, + y: lAnkleY + P.ankleToToe * bodyH * 0.3, + confidence: 0.65 }, + // 24: right_foot_index + { x: rAnkleX + P.ankleToToe * bodyH * 0.5, + y: rAnkleY + P.ankleToToe * bodyH * 0.3, + confidence: 0.65 }, + + // 25: neck (midpoint between shoulders, slightly above) + { x: (lShX + rShX) / 2, y: shoulderY - P.headToShoulder * bodyH * 0.35, confidence: 0.93 }, + ]; + + for (let i = 0; i < keypoints.length; i++) { + keypoints[i].name = KEYPOINT_NAMES[i]; + } + + // === RuVector Attention Embedding Refinement === + // Compute attention stats for the UI pipeline display, but only apply + // positional refinement when a trained model is loaded (random-weight + // embeddings carry no meaningful spatial signal and distort the skeleton). + if (embedding && embedding.length >= 26 * 3) { + this._computeEmbeddingStats(keypoints, embedding, bodyH); + } + + return keypoints; + } + + /** + * Apply RuVector attention embedding to refine joint positions and confidence. + * + * The 128-dim fused embedding is decoded as: + * - Dims 0-77: Per-joint (dx, dy, confidence_mod) × 26 joints + * - Dims 78-81: Global pose parameters (scale, rotation, lean) + * - Dims 82-127: Reserved for cross-modal fusion features + * + * The attention mechanism determines HOW MUCH each spatial region contributes + * to each joint's refinement. Multi-Head captures global relationships, + * Hyperbolic captures hierarchical (torso→limb→hand) dependencies, + * MoE routes different body regions to specialized experts, + * Linear provides fast extremity refinement, Local-Global balances detail/context. + */ + /** + * Compute embedding statistics for UI display without modifying joint positions. + * The 6-stage attention pipeline stats are shown in the RuVector panel. + * Position refinement is disabled until a trained model replaces random weights. + */ + _computeEmbeddingStats(keypoints, emb, bodyH) { + const map = this._jointEmbMap; + const tc = (v) => Math.tanh(Number(v) || 0); + + // Embedding energy (L2 norm of the used dims) + let energy = 0; + for (let i = 0; i < Math.min(emb.length, 82); i++) { + energy += emb[i] * emb[i]; + } + energy = Math.sqrt(energy); + + // Simulated per-joint refinement magnitude (what WOULD be applied) + const scale = bodyH * 0.015; + let totalRefinement = 0; + let maxDimVal = 0; + + for (let j = 0; j < Math.min(keypoints.length, 26); j++) { + const jmap = map.joints[j]; + if (!jmap) continue; + const dx = tc(emb[jmap.dxDim]) * scale; + const dy = tc(emb[jmap.dyDim]) * scale; + totalRefinement += Math.sqrt(dx * dx + dy * dy); + maxDimVal = Math.max(maxDimVal, Math.abs(tc(emb[jmap.dxDim])), Math.abs(tc(emb[jmap.dyDim]))); + } + + this.attentionStats.energy = energy; + this.attentionStats.maxDim = maxDimVal; + this.attentionStats.refinementMag = totalRefinement / 26; + } + + /** + * Find weighted motion centroids for each body zone. + * Divides the bounding box into 6 zones: head, left arm, right arm, torso, left leg, right leg. + * Returns the (x,y) centroid of motion intensity for each zone. + */ + _findZoneCentroids(grid, cols, rows, bx, by, bw, bh) { + // Zone definitions (in grid-relative fractions) + const zones = { + head: { rMin: 0, rMax: 0.2, cMin: 0.25, cMax: 0.75, wx: 0, wy: 0, wt: 0 }, + leftArm: { rMin: 0.1, rMax: 0.6, cMin: 0, cMax: 0.35, wx: 0, wy: 0, wt: 0 }, + rightArm: { rMin: 0.1, rMax: 0.6, cMin: 0.65, cMax: 1.0, wx: 0, wy: 0, wt: 0 }, + torso: { rMin: 0.15, rMax: 0.55, cMin: 0.3, cMax: 0.7, wx: 0, wy: 0, wt: 0 }, + leftLeg: { rMin: 0.5, rMax: 1.0, cMin: 0.1, cMax: 0.5, wx: 0, wy: 0, wt: 0 }, + rightLeg: { rMin: 0.5, rMax: 1.0, cMin: 0.5, cMax: 0.9, wx: 0, wy: 0, wt: 0 }, + }; + + // Accumulate weighted centroids per zone + for (let r = 0; r < rows; r++) { + const ry = r / rows; // 0-1 within grid + for (let c = 0; c < cols; c++) { + const cx_g = c / cols; // 0-1 within grid + const val = grid[r][c]; + if (val < 0.005) continue; // skip near-zero motion + + // Map grid position to body-space coordinates (0-1) + const worldX = bx + cx_g * bw; + const worldY = by + ry * bh; + + // Assign to matching zones (a cell can contribute to multiple overlapping zones) + for (const z of Object.values(zones)) { + if (ry >= z.rMin && ry < z.rMax && cx_g >= z.cMin && cx_g < z.cMax) { + z.wx += worldX * val; + z.wy += worldY * val; + z.wt += val; + } + } + } + } + + // Compute centroids with fallback defaults + const centroid = (z, defX, defY) => ({ + x: z.wt > 0.01 ? z.wx / z.wt : defX, + y: z.wt > 0.01 ? z.wy / z.wt : defY, + weight: z.wt + }); + + const midX = bx + bw / 2; + const midY = by + bh / 2; + + return { + head: centroid(zones.head, midX, by + bh * 0.1), + leftArm: centroid(zones.leftArm, bx + bw * 0.2, midY - bh * 0.05), + rightArm: centroid(zones.rightArm, bx + bw * 0.8, midY - bh * 0.05), + torso: centroid(zones.torso, midX, midY), + leftLeg: centroid(zones.leftLeg, bx + bw * 0.35,by + bh * 0.75), + rightLeg: centroid(zones.rightLeg, bx + bw * 0.65,by + bh * 0.75), + }; + } + + /** + * Through-wall tracking: continue showing pose via CSI when person left video frame. + * The skeleton drifts in the exit direction with decreasing confidence. + */ + _trackThroughWall(elapsed, csiState) { + if (!this._lastBodyState) return []; + + const dt = elapsed - this._lastBodyState.time; + const csiPresence = csiState.csiPresence || 0; + + // Initialize ghost on first call + if (this._ghostConfidence <= 0.05) { + this._ghostConfidence = 0.8; + this._ghostState = this._lastBodyState.keypoints.map(kp => ({...kp})); + } + + // Ghost confidence decays, but CSI presence sustains it + const csiBoost = Math.min(0.7, csiPresence * 0.8); + this._ghostConfidence = Math.max(0.05, this._ghostConfidence * 0.995 - 0.001 + csiBoost * 0.002); + + // Drift the ghost in exit direction + const vx = this._ghostVelocity.x; + const vy = this._ghostVelocity.y; + + // Breathing continues via CSI + const breathe = Math.sin(elapsed * 1.5) * 0.003 * csiPresence; + + const keypoints = this._ghostState.map((kp, i) => { + return { + x: kp.x + vx * dt * 0.3, + y: kp.y + vy * dt * 0.3 + (i >= 5 && i <= 6 ? breathe : 0), + confidence: kp.confidence * this._ghostConfidence * (0.5 + csiPresence * 0.5), + name: kp.name + }; + }); + + // Slow down drift over time + this._ghostVelocity.x *= 0.998; + this._ghostVelocity.y *= 0.998; + + this.smoothedKeypoints = keypoints; + return keypoints; + } +} diff --git a/ui/pose-fusion/js/video-capture.js b/ui/pose-fusion/js/video-capture.js new file mode 100644 index 00000000..fe3ed333 --- /dev/null +++ b/ui/pose-fusion/js/video-capture.js @@ -0,0 +1,235 @@ +/** + * VideoCapture — getUserMedia webcam capture with frame extraction. + * Provides quality metrics (brightness, motion) for fusion confidence gating. + */ + +export class VideoCapture { + constructor(videoElement) { + this.video = videoElement; + this.stream = null; + this.offscreen = document.createElement('canvas'); + this.offCtx = this.offscreen.getContext('2d', { willReadFrequently: true }); + this.prevFrame = null; + this.motionScore = 0; + this.brightnessScore = 0; + } + + async start(constraints = {}) { + const defaultConstraints = { + video: { + width: { ideal: 640 }, + height: { ideal: 480 }, + facingMode: 'user', + frameRate: { ideal: 30 } + }, + audio: false + }; + + try { + this.stream = await navigator.mediaDevices.getUserMedia( + Object.keys(constraints).length ? constraints : defaultConstraints + ); + this.video.srcObject = this.stream; + await this.video.play(); + + this.offscreen.width = this.video.videoWidth; + this.offscreen.height = this.video.videoHeight; + + return true; + } catch (err) { + console.error('[Video] Camera access failed:', err.message); + return false; + } + } + + stop() { + if (this.stream) { + this.stream.getTracks().forEach(t => t.stop()); + this.stream = null; + } + this.video.srcObject = null; + } + + get isActive() { + return this.stream !== null && this.video.readyState >= 2; + } + + get width() { return this.video.videoWidth || 640; } + get height() { return this.video.videoHeight || 480; } + + /** + * Capture current frame as RGB Uint8Array + compute quality metrics. + * @param {number} targetW - Target width for CNN input + * @param {number} targetH - Target height for CNN input + * @returns {{ rgb: Uint8Array, width: number, height: number, motion: number, brightness: number }} + */ + captureFrame(targetW = 56, targetH = 56) { + if (!this.isActive) return null; + + // Draw to offscreen at target resolution + this.offscreen.width = targetW; + this.offscreen.height = targetH; + this.offCtx.drawImage(this.video, 0, 0, targetW, targetH); + const imageData = this.offCtx.getImageData(0, 0, targetW, targetH); + const rgba = imageData.data; + + // Convert RGBA → RGB + const pixels = targetW * targetH; + const rgb = new Uint8Array(pixels * 3); + let brightnessSum = 0; + let motionSum = 0; + + for (let i = 0; i < pixels; i++) { + const r = rgba[i * 4]; + const g = rgba[i * 4 + 1]; + const b = rgba[i * 4 + 2]; + rgb[i * 3] = r; + rgb[i * 3 + 1] = g; + rgb[i * 3 + 2] = b; + + // Luminance for brightness + const lum = 0.299 * r + 0.587 * g + 0.114 * b; + brightnessSum += lum; + + // Motion: diff from previous frame + if (this.prevFrame) { + const pr = this.prevFrame[i * 3]; + const pg = this.prevFrame[i * 3 + 1]; + const pb = this.prevFrame[i * 3 + 2]; + motionSum += Math.abs(r - pr) + Math.abs(g - pg) + Math.abs(b - pb); + } + } + + this.brightnessScore = brightnessSum / (pixels * 255); + this.motionScore = this.prevFrame ? Math.min(1, motionSum / (pixels * 100)) : 0; + this.prevFrame = new Uint8Array(rgb); + + return { + rgb, + width: targetW, + height: targetH, + motion: this.motionScore, + brightness: this.brightnessScore + }; + } + + /** + * Capture full-resolution RGBA for overlay rendering + * @returns {ImageData|null} + */ + captureFullFrame() { + if (!this.isActive) return null; + this.offscreen.width = this.width; + this.offscreen.height = this.height; + this.offCtx.drawImage(this.video, 0, 0); + return this.offCtx.getImageData(0, 0, this.width, this.height); + } + + /** + * Detect motion region + detailed motion grid for body-part tracking. + * Returns bounding box + a grid showing WHERE motion is concentrated. + * @returns {{ x, y, w, h, detected: boolean, motionGrid: number[][], gridCols: number, gridRows: number, exitDirection: string|null }} + */ + detectMotionRegion(targetW = 56, targetH = 56) { + if (!this.isActive || !this.prevFrame) return { detected: false, motionGrid: null }; + + this.offscreen.width = targetW; + this.offscreen.height = targetH; + this.offCtx.drawImage(this.video, 0, 0, targetW, targetH); + const rgba = this.offCtx.getImageData(0, 0, targetW, targetH).data; + + let minX = targetW, minY = targetH, maxX = 0, maxY = 0; + let motionPixels = 0; + const threshold = 25; + + // Motion grid: divide frame into cells and track motion intensity per cell + const gridCols = 10; + const gridRows = 8; + const cellW = targetW / gridCols; + const cellH = targetH / gridRows; + const motionGrid = Array.from({ length: gridRows }, () => new Float32Array(gridCols)); + const cellPixels = cellW * cellH; + + // Also track motion centroid weighted by intensity + let motionCxSum = 0, motionCySum = 0, motionWeightSum = 0; + + for (let y = 0; y < targetH; y++) { + for (let x = 0; x < targetW; x++) { + const i = y * targetW + x; + const r = rgba[i * 4], g = rgba[i * 4 + 1], b = rgba[i * 4 + 2]; + const pr = this.prevFrame[i * 3], pg = this.prevFrame[i * 3 + 1], pb = this.prevFrame[i * 3 + 2]; + const diff = Math.abs(r - pr) + Math.abs(g - pg) + Math.abs(b - pb); + + if (diff > threshold * 3) { + motionPixels++; + if (x < minX) minX = x; + if (y < minY) minY = y; + if (x > maxX) maxX = x; + if (y > maxY) maxY = y; + } + + // Accumulate per-cell motion intensity + const gc = Math.min(Math.floor(x / cellW), gridCols - 1); + const gr = Math.min(Math.floor(y / cellH), gridRows - 1); + const intensity = diff / (3 * 255); // Normalize 0-1 + motionGrid[gr][gc] += intensity / cellPixels; + + // Weighted centroid + if (diff > threshold) { + motionCxSum += x * diff; + motionCySum += y * diff; + motionWeightSum += diff; + } + } + } + + const detected = motionPixels > (targetW * targetH * 0.02); + + // Motion centroid (normalized 0-1) + const motionCx = motionWeightSum > 0 ? motionCxSum / (motionWeightSum * targetW) : 0.5; + const motionCy = motionWeightSum > 0 ? motionCySum / (motionWeightSum * targetH) : 0.5; + + // Detect exit direction: if centroid is near edges + let exitDirection = null; + if (detected && motionCx < 0.1) exitDirection = 'left'; + else if (detected && motionCx > 0.9) exitDirection = 'right'; + else if (detected && motionCy < 0.1) exitDirection = 'up'; + else if (detected && motionCy > 0.9) exitDirection = 'down'; + + // Track last known position for through-wall persistence + if (detected) { + this._lastDetected = { + x: minX / targetW, + y: minY / targetH, + w: (maxX - minX) / targetW, + h: (maxY - minY) / targetH, + cx: motionCx, + cy: motionCy, + exitDirection, + time: performance.now() + }; + } + + return { + detected, + x: minX / targetW, + y: minY / targetH, + w: (maxX - minX) / targetW, + h: (maxY - minY) / targetH, + coverage: motionPixels / (targetW * targetH), + motionGrid, + gridCols, + gridRows, + motionCx, + motionCy, + exitDirection + }; + } + + /** + * Get the last known detection info (for through-wall persistence) + */ + get lastDetection() { + return this._lastDetected || null; + } +} diff --git a/ui/pose-fusion/pkg/ruvector-attention/LICENSE b/ui/pose-fusion/pkg/ruvector-attention/LICENSE new file mode 100644 index 00000000..2dd524ac --- /dev/null +++ b/ui/pose-fusion/pkg/ruvector-attention/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 rUv + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/ui/pose-fusion/pkg/ruvector-attention/README.md b/ui/pose-fusion/pkg/ruvector-attention/README.md new file mode 100644 index 00000000..7e11e537 --- /dev/null +++ b/ui/pose-fusion/pkg/ruvector-attention/README.md @@ -0,0 +1,220 @@ +# ruvector-attention-wasm + +WebAssembly bindings for the ruvector-attention package, providing high-performance attention mechanisms for browser and Node.js environments. + +## Features + +- **Multiple Attention Mechanisms**: + - Scaled Dot-Product Attention + - Multi-Head Attention + - Hyperbolic Attention (for hierarchical data) + - Linear Attention (Performer-style) + - Flash Attention (memory-efficient) + - Local-Global Attention + - Mixture of Experts (MoE) Attention + - **CGT Sheaf Attention** (coherence-gated via Prime-Radiant) + +- **Training Utilities**: + - InfoNCE contrastive loss + - Adam optimizer + - AdamW optimizer (with decoupled weight decay) + - Learning rate scheduler (warmup + cosine decay) + +- **TypeScript Support**: Full type definitions and modern API + +## Installation + +```bash +npm install ruvector-attention-wasm +``` + +## Usage + +### TypeScript/JavaScript + +```typescript +import { initialize, MultiHeadAttention, utils } from 'ruvector-attention-wasm'; + +// Initialize WASM module +await initialize(); + +// Create multi-head attention +const attention = new MultiHeadAttention({ dim: 64, numHeads: 8 }); + +// Prepare inputs +const query = new Float32Array(64); +const keys = [new Float32Array(64), new Float32Array(64)]; +const values = [new Float32Array(64), new Float32Array(64)]; + +// Compute attention +const output = attention.compute(query, keys, values); + +// Use utilities +const similarity = utils.cosineSimilarity(query, keys[0]); +``` + +### Advanced Examples + +#### Hyperbolic Attention + +```typescript +import { HyperbolicAttention } from 'ruvector-attention-wasm'; + +const hyperbolic = new HyperbolicAttention({ + dim: 128, + curvature: 1.0 +}); + +const output = hyperbolic.compute(query, keys, values); +``` + +#### MoE Attention with Expert Stats + +```typescript +import { MoEAttention } from 'ruvector-attention-wasm'; + +const moe = new MoEAttention({ + dim: 64, + numExperts: 4, + topK: 2 +}); + +const output = moe.compute(query, keys, values); + +// Get expert utilization +const stats = moe.getExpertStats(); +console.log('Load balance:', stats.loadBalance); +``` + +#### Training with InfoNCE Loss + +```typescript +import { InfoNCELoss, Adam } from 'ruvector-attention-wasm'; + +const loss = new InfoNCELoss(0.07); +const optimizer = new Adam(paramCount, { + learningRate: 0.001, + beta1: 0.9, + beta2: 0.999, +}); + +// Training loop +const lossValue = loss.compute(anchor, positive, negatives); +optimizer.step(params, gradients); +``` + +#### Learning Rate Scheduling + +```typescript +import { LRScheduler, AdamW } from 'ruvector-attention-wasm'; + +const scheduler = new LRScheduler({ + initialLR: 0.001, + warmupSteps: 1000, + totalSteps: 10000, +}); + +const optimizer = new AdamW(paramCount, { + learningRate: scheduler.getLR(), + weightDecay: 0.01, +}); + +// Training loop +for (let step = 0; step < 10000; step++) { + optimizer.learningRate = scheduler.getLR(); + optimizer.step(params, gradients); + scheduler.step(); +} +``` + +## Building from Source + +### Prerequisites + +- Rust 1.70+ +- wasm-pack + +### Build Commands + +```bash +# Build for web (ES modules) +wasm-pack build --target web --out-dir pkg + +# Build for Node.js +wasm-pack build --target nodejs --out-dir pkg-node + +# Build for bundlers (webpack, vite, etc.) +wasm-pack build --target bundler --out-dir pkg-bundler + +# Run tests +wasm-pack test --headless --firefox +``` + +## API Reference + +### Attention Mechanisms + +- `MultiHeadAttention` - Standard multi-head attention +- `HyperbolicAttention` - Attention in hyperbolic space +- `LinearAttention` - Linear complexity attention (Performer) +- `FlashAttention` - Memory-efficient attention +- `LocalGlobalAttention` - Combined local and global attention +- `MoEAttention` - Mixture of Experts attention +- `CGTSheafAttention` - Coherence-gated via Prime-Radiant energy +- `scaledDotAttention()` - Functional API for basic attention + +### CGT Sheaf Attention (Prime-Radiant Integration) + +The CGT (Coherence-Gated Transformer) Sheaf Attention mechanism uses Prime-Radiant's sheaf Laplacian energy to gate attention based on mathematical consistency: + +```typescript +import { CGTSheafAttention } from 'ruvector-attention-wasm'; + +const cgtAttention = new CGTSheafAttention({ + dim: 128, + numHeads: 8, + coherenceThreshold: 0.3, // Block if energy > threshold +}); + +// Attention is gated by coherence energy +const result = cgtAttention.compute(query, keys, values); +console.log('Coherence energy:', result.energy); +console.log('Is coherent:', result.isCoherent); +``` + +**Key features:** +- Energy-weighted attention: Lower coherence energy → higher attention +- Automatic hallucination detection via residual analysis +- GPU-accelerated with wgpu WGSL shaders (vec4 optimized) +- SIMD fallback (AVX-512/AVX2/NEON) + +### Training + +- `InfoNCELoss` - Contrastive loss function +- `Adam` - Adam optimizer +- `AdamW` - AdamW optimizer with weight decay +- `LRScheduler` - Learning rate scheduler + +### Utilities + +- `utils.cosineSimilarity()` - Cosine similarity between vectors +- `utils.l2Norm()` - L2 norm of a vector +- `utils.normalize()` - Normalize vector to unit length +- `utils.softmax()` - Apply softmax transformation +- `utils.attentionWeights()` - Compute attention weights from scores +- `utils.batchNormalize()` - Batch normalization +- `utils.randomOrthogonalMatrix()` - Generate random orthogonal matrix +- `utils.pairwiseDistances()` - Compute pairwise distances + +## Performance + +The WASM bindings provide near-native performance for attention computations: + +- Optimized with `opt-level = "s"` and LTO +- SIMD acceleration where available +- Efficient memory management +- Zero-copy data transfer where possible + +## License + +MIT OR Apache-2.0 diff --git a/ui/pose-fusion/pkg/ruvector-attention/package.json b/ui/pose-fusion/pkg/ruvector-attention/package.json new file mode 100644 index 00000000..7500bb8a --- /dev/null +++ b/ui/pose-fusion/pkg/ruvector-attention/package.json @@ -0,0 +1,28 @@ +{ + "name": "ruvector-attention-wasm", + "collaborators": [ + "Ruvector Team" + ], + "description": "High-performance WebAssembly attention mechanisms: Multi-Head, Flash, Hyperbolic, MoE, CGT Sheaf Attention with GPU acceleration for transformers and LLMs", + "version": "2.0.5", + "license": "MIT", + "repository": { + "type": "git", + "url": "https://github.com/ruvnet/ruvector" + }, + "files": [ + "ruvector_attention_wasm_bg.wasm", + "ruvector_attention_wasm.js", + "ruvector_attention_wasm.d.ts" + ], + "main": "ruvector_attention_wasm.js", + "homepage": "https://ruv.io/ruvector", + "types": "ruvector_attention_wasm.d.ts", + "keywords": [ + "wasm", + "attention", + "transformer", + "flash-attention", + "llm" + ] +} \ No newline at end of file diff --git a/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_browser.js b/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_browser.js new file mode 100644 index 00000000..84eb8eee --- /dev/null +++ b/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_browser.js @@ -0,0 +1,642 @@ +/** + * Browser ESM wrapper for ruvector-attention-wasm v2.0.5 + * + * The upstream pkg/ was built with wasm-pack --target nodejs (CJS + fs.readFileSync). + * This wrapper loads the same WASM binary via fetch() for browser use. + * + * Usage: + * import initWasm, { WasmMultiHeadAttention, ... } from './ruvector_attention_browser.js'; + * await initWasm(); + * const attn = new WasmMultiHeadAttention(dim, heads); + */ + +let _wasm; +let _initialized = false; + +// The entire CJS module runs inside this IIFE to avoid polluting global scope. +// We capture all exports in _mod. +const _mod = {}; + +(function(exports, wasm_getter) { + +// ── wasm-bindgen heap management ────────────────────────────────── +const heap = new Array(128).fill(undefined); +heap.push(undefined, null, true, false); +let heap_next = heap.length; + +function addHeapObject(obj) { + if (heap_next === heap.length) heap.push(heap.length + 1); + const idx = heap_next; + heap_next = heap[idx]; + heap[idx] = obj; + return idx; +} +function getObject(idx) { return heap[idx]; } +function dropObject(idx) { + if (idx < 132) return; + heap[idx] = heap_next; + heap_next = idx; +} +function takeObject(idx) { + const ret = getObject(idx); + dropObject(idx); + return ret; +} +function isLikeNone(x) { return x === undefined || x === null; } + +// ── Memory views ────────────────────────────────────────────────── +let cachedDataViewMemory0 = null; +let cachedUint8ArrayMemory0 = null; +let cachedFloat32ArrayMemory0 = null; + +function wasm() { return wasm_getter(); } + +function getDataViewMemory0() { + if (cachedDataViewMemory0 === null || cachedDataViewMemory0.buffer !== wasm().memory.buffer) + cachedDataViewMemory0 = new DataView(wasm().memory.buffer); + return cachedDataViewMemory0; +} +function getUint8ArrayMemory0() { + if (cachedUint8ArrayMemory0 === null || cachedUint8ArrayMemory0.buffer !== wasm().memory.buffer) + cachedUint8ArrayMemory0 = new Uint8Array(wasm().memory.buffer); + return cachedUint8ArrayMemory0; +} +function getFloat32ArrayMemory0() { + if (cachedFloat32ArrayMemory0 === null || cachedFloat32ArrayMemory0.buffer !== wasm().memory.buffer) + cachedFloat32ArrayMemory0 = new Float32Array(wasm().memory.buffer); + return cachedFloat32ArrayMemory0; +} +function getArrayF32FromWasm0(ptr, len) { + ptr = ptr >>> 0; + return getFloat32ArrayMemory0().subarray(ptr / 4, ptr / 4 + len); +} +function getArrayU8FromWasm0(ptr, len) { + ptr = ptr >>> 0; + return getUint8ArrayMemory0().subarray(ptr, ptr + len); +} + +let WASM_VECTOR_LEN = 0; + +function passArrayF32ToWasm0(arg, malloc) { + const ptr = malloc(arg.length * 4, 4) >>> 0; + getFloat32ArrayMemory0().set(arg, ptr / 4); + WASM_VECTOR_LEN = arg.length; + return ptr; +} + +const cachedTextEncoder = new TextEncoder(); +const cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true }); +cachedTextDecoder.decode(); + +function getStringFromWasm0(ptr, len) { + ptr = ptr >>> 0; + return cachedTextDecoder.decode(getUint8ArrayMemory0().subarray(ptr, ptr + len)); +} + +function passStringToWasm0(arg, malloc, realloc) { + const buf = cachedTextEncoder.encode(arg); + const ptr = malloc(buf.length, 1) >>> 0; + getUint8ArrayMemory0().subarray(ptr, ptr + buf.length).set(buf); + WASM_VECTOR_LEN = buf.length; + return ptr; +} + +function debugString(val) { + const type = typeof val; + if (type == 'number' || type == 'boolean' || val == null) return `${val}`; + if (type == 'string') return `"${val}"`; + if (type == 'symbol') return val.description ? `Symbol(${val.description})` : 'Symbol'; + if (type == 'function') return 'Function'; + if (Array.isArray(val)) return `[${val.map(debugString).join(', ')}]`; + try { + const keys = Object.keys(val); + return `{${keys.map(k => `${k}: ${debugString(val[k])}`).join(', ')}}`; + } catch (_) { return Object.prototype.toString.call(val); } +} + +function handleError(f, args) { + try { return f.apply(this, args); } + catch (e) { wasm().__wbindgen_export3(addHeapObject(e)); } +} + +// ── FinalizationRegistry ────────────────────────────────────────── +const FR = typeof FinalizationRegistry !== 'undefined' + ? FinalizationRegistry + : class { register() {} unregister() {} }; + +const WasmMultiHeadAttentionFinalization = new FR(ptr => wasm().__wbg_wasmmultiheadattention_free(ptr >>> 0, 1)); +const WasmFlashAttentionFinalization = new FR(ptr => wasm().__wbg_wasmflashattention_free(ptr >>> 0, 1)); +const WasmHyperbolicAttentionFinalization = new FR(ptr => wasm().__wbg_wasmhyperbolicattention_free(ptr >>> 0, 1)); +const WasmMoEAttentionFinalization = new FR(ptr => wasm().__wbg_wasmmoeattention_free(ptr >>> 0, 1)); +const WasmLinearAttentionFinalization = new FR(ptr => wasm().__wbg_wasmlinearattention_free(ptr >>> 0, 1)); +const WasmLocalGlobalAttentionFinalization = new FR(ptr => wasm().__wbg_wasmlocalglobalattention_free(ptr >>> 0, 1)); + +// ── Classes ─────────────────────────────────────────────────────── + +class WasmMultiHeadAttention { + constructor(dim, num_heads) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + wasm().wasmmultiheadattention_new(retptr, dim, num_heads); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var r2 = getDataViewMemory0().getInt32(retptr + 8, true); + if (r2) throw takeObject(r1); + this.__wbg_ptr = r0 >>> 0; + WasmMultiHeadAttentionFinalization.register(this, this.__wbg_ptr, this); + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } + } + free() { + const ptr = this.__wbg_ptr; this.__wbg_ptr = 0; + WasmMultiHeadAttentionFinalization.unregister(this); + wasm().__wbg_wasmmultiheadattention_free(ptr, 0); + } + get dim() { return wasm().wasmmultiheadattention_dim(this.__wbg_ptr); } + get num_heads() { return wasm().wasmmultiheadattention_num_heads(this.__wbg_ptr); } + compute(query, keys, values) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().wasmmultiheadattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var r2 = getDataViewMemory0().getInt32(retptr + 8, true); + var r3 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r3) throw takeObject(r2); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm().__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } + } +} + +class WasmFlashAttention { + constructor(dim, block_size) { + const ret = wasm().wasmflashattention_new(dim, block_size); + this.__wbg_ptr = ret >>> 0; + WasmFlashAttentionFinalization.register(this, this.__wbg_ptr, this); + } + free() { + const ptr = this.__wbg_ptr; this.__wbg_ptr = 0; + WasmFlashAttentionFinalization.unregister(this); + wasm().__wbg_wasmflashattention_free(ptr, 0); + } + compute(query, keys, values) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().wasmflashattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var r2 = getDataViewMemory0().getInt32(retptr + 8, true); + var r3 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r3) throw takeObject(r2); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm().__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } + } +} + +class WasmHyperbolicAttention { + constructor(dim, curvature) { + const ret = wasm().wasmhyperbolicattention_new(dim, curvature); + this.__wbg_ptr = ret >>> 0; + WasmHyperbolicAttentionFinalization.register(this, this.__wbg_ptr, this); + } + free() { + const ptr = this.__wbg_ptr; this.__wbg_ptr = 0; + WasmHyperbolicAttentionFinalization.unregister(this); + wasm().__wbg_wasmhyperbolicattention_free(ptr, 0); + } + get curvature() { return wasm().wasmhyperbolicattention_curvature(this.__wbg_ptr); } + compute(query, keys, values) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().wasmhyperbolicattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var r2 = getDataViewMemory0().getInt32(retptr + 8, true); + var r3 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r3) throw takeObject(r2); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm().__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } + } +} + +class WasmMoEAttention { + constructor(dim, num_experts, top_k) { + const ret = wasm().wasmmoeattention_new(dim, num_experts, top_k); + this.__wbg_ptr = ret >>> 0; + WasmMoEAttentionFinalization.register(this, this.__wbg_ptr, this); + } + free() { + const ptr = this.__wbg_ptr; this.__wbg_ptr = 0; + WasmMoEAttentionFinalization.unregister(this); + wasm().__wbg_wasmmoeattention_free(ptr, 0); + } + compute(query, keys, values) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().wasmmoeattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var r2 = getDataViewMemory0().getInt32(retptr + 8, true); + var r3 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r3) throw takeObject(r2); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm().__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } + } +} + +class WasmLinearAttention { + constructor(dim, num_features) { + const ret = wasm().wasmlinearattention_new(dim, num_features || dim); + this.__wbg_ptr = ret >>> 0; + WasmLinearAttentionFinalization.register(this, this.__wbg_ptr, this); + } + free() { + const ptr = this.__wbg_ptr; this.__wbg_ptr = 0; + WasmLinearAttentionFinalization.unregister(this); + wasm().__wbg_wasmlinearattention_free(ptr, 0); + } + compute(query, keys, values) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().wasmlinearattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var r2 = getDataViewMemory0().getInt32(retptr + 8, true); + var r3 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r3) throw takeObject(r2); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm().__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } + } +} + +class WasmLocalGlobalAttention { + constructor(dim, local_window, global_tokens) { + const ret = wasm().wasmlocalglobalattention_new(dim, local_window || 4, global_tokens || 2); + this.__wbg_ptr = ret >>> 0; + WasmLocalGlobalAttentionFinalization.register(this, this.__wbg_ptr, this); + } + free() { + const ptr = this.__wbg_ptr; this.__wbg_ptr = 0; + WasmLocalGlobalAttentionFinalization.unregister(this); + wasm().__wbg_wasmlocalglobalattention_free(ptr, 0); + } + compute(query, keys, values) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().wasmlocalglobalattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var r2 = getDataViewMemory0().getInt32(retptr + 8, true); + var r3 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r3) throw takeObject(r2); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm().__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } + } +} + +// ── Standalone functions ────────────────────────────────────────── + +function cosine_similarity(a, b) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + const ptr0 = passArrayF32ToWasm0(a, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(b, wasm().__wbindgen_export); + const len1 = WASM_VECTOR_LEN; + wasm().cosine_similarity(retptr, ptr0, len0, ptr1, len1); + var r0 = getDataViewMemory0().getFloat64(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 8, true); + var r2 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r2) throw takeObject(r1); + return r0; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } +} + +function normalize(vec) { + const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().normalize(ptr0, len0, addHeapObject(vec)); +} + +function l2_norm(vec) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().l2_norm(retptr, ptr0, len0); + var r0 = getDataViewMemory0().getFloat64(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 8, true); + var r2 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r2) throw takeObject(r1); + return r0; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } +} + +function softmax(vec) { + const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().softmax(ptr0, len0, addHeapObject(vec)); +} + +function batch_normalize(vectors, epsilon) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + wasm().batch_normalize(retptr, addHeapObject(vectors), isLikeNone(epsilon) ? 0x100000001 : Math.fround(epsilon)); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var r2 = getDataViewMemory0().getInt32(retptr + 8, true); + var r3 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r3) throw takeObject(r2); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm().__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } +} + +function pairwise_distances(vectors) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + wasm().pairwise_distances(retptr, addHeapObject(vectors)); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var r2 = getDataViewMemory0().getInt32(retptr + 8, true); + var r3 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r3) throw takeObject(r2); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm().__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } +} + +function scaled_dot_attention(query, keys, values, scale) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().scaled_dot_attention(retptr, ptr0, len0, addHeapObject(keys), addHeapObject(values), isLikeNone(scale) ? 0x100000001 : Math.fround(scale)); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var r2 = getDataViewMemory0().getInt32(retptr + 8, true); + var r3 = getDataViewMemory0().getInt32(retptr + 12, true); + if (r3) throw takeObject(r2); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm().__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } +} + +function attention_weights(scores, temperature) { + const ptr0 = passArrayF32ToWasm0(scores, wasm().__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm().attention_weights(ptr0, len0, addHeapObject(scores), isLikeNone(temperature) ? 0x100000001 : Math.fround(temperature)); +} + +function available_mechanisms() { + const ret = wasm().available_mechanisms(); + return takeObject(ret); +} + +function random_orthogonal_matrix(dim) { + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + wasm().random_orthogonal_matrix(retptr, dim); + var r0 = getDataViewMemory0().getInt32(retptr + 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4, true); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm().__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + } +} + +function rv_init() { wasm().init(); } + +function rv_version() { + let d0, d1; + const retptr = wasm().__wbindgen_add_to_stack_pointer(-16); + try { + wasm().version(retptr); + d0 = getDataViewMemory0().getInt32(retptr + 0, true); + d1 = getDataViewMemory0().getInt32(retptr + 4, true); + return getStringFromWasm0(d0, d1); + } finally { + wasm().__wbindgen_add_to_stack_pointer(16); + if (d0 !== undefined) wasm().__wbindgen_export4(d0, d1, 1); + } +} + +// ── Collect exports ─────────────────────────────────────────────── +exports.WasmMultiHeadAttention = WasmMultiHeadAttention; +exports.WasmFlashAttention = WasmFlashAttention; +exports.WasmHyperbolicAttention = WasmHyperbolicAttention; +exports.WasmMoEAttention = WasmMoEAttention; +exports.WasmLinearAttention = WasmLinearAttention; +exports.WasmLocalGlobalAttention = WasmLocalGlobalAttention; +exports.cosine_similarity = cosine_similarity; +exports.normalize = normalize; +exports.l2_norm = l2_norm; +exports.softmax = softmax; +exports.batch_normalize = batch_normalize; +exports.pairwise_distances = pairwise_distances; +exports.scaled_dot_attention = scaled_dot_attention; +exports.attention_weights = attention_weights; +exports.available_mechanisms = available_mechanisms; +exports.random_orthogonal_matrix = random_orthogonal_matrix; +exports.init = rv_init; +exports.version = rv_version; + +// ── Build WASM import object ────────────────────────────────────── +exports.__wbg_get_imports = function() { + const import0 = { + __proto__: null, + __wbg_Error_4577686b3a6d9b3a: (arg0, arg1) => addHeapObject(Error(getStringFromWasm0(arg0, arg1))), + __wbg_String_8564e559799eccda: (arg0, arg1) => { + const ret = String(getObject(arg1)); + const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + getDataViewMemory0().setInt32(arg0 + 4, len1, true); + getDataViewMemory0().setInt32(arg0, ptr1, true); + }, + __wbg___wbindgen_boolean_get_18c4ed9422296fff: (arg0) => { + const v = getObject(arg0); + const ret = typeof v === 'boolean' ? v : undefined; + return isLikeNone(ret) ? 0xFFFFFF : ret ? 1 : 0; + }, + __wbg___wbindgen_copy_to_typed_array_5294f8e46aecc086: (arg0, arg1, arg2) => { + new Uint8Array(getObject(arg2).buffer, getObject(arg2).byteOffset, getObject(arg2).byteLength).set(getArrayU8FromWasm0(arg0, arg1)); + }, + __wbg___wbindgen_debug_string_ddde1867f49c2442: (arg0, arg1) => { + const ret = debugString(getObject(arg1)); + const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + getDataViewMemory0().setInt32(arg0 + 4, len1, true); + getDataViewMemory0().setInt32(arg0, ptr1, true); + }, + __wbg___wbindgen_is_function_d633e708baf0d146: (arg0) => typeof getObject(arg0) === 'function', + __wbg___wbindgen_is_object_4b3de556756ee8a8: (arg0) => { + const val = getObject(arg0); + return typeof val === 'object' && val !== null; + }, + __wbg___wbindgen_jsval_loose_eq_1562ceb9af84e990: (arg0, arg1) => getObject(arg0) == getObject(arg1), + __wbg___wbindgen_number_get_5854912275df1894: (arg0, arg1) => { + const obj = getObject(arg1); + const ret = typeof obj === 'number' ? obj : undefined; + getDataViewMemory0().setFloat64(arg0 + 8, isLikeNone(ret) ? 0 : ret, true); + getDataViewMemory0().setInt32(arg0, !isLikeNone(ret), true); + }, + __wbg___wbindgen_string_get_3e5751597f39a112: (arg0, arg1) => { + const obj = getObject(arg1); + const ret = typeof obj === 'string' ? obj : undefined; + var ptr1 = isLikeNone(ret) ? 0 : passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2); + var len1 = WASM_VECTOR_LEN; + getDataViewMemory0().setInt32(arg0 + 4, len1, true); + getDataViewMemory0().setInt32(arg0, ptr1, true); + }, + __wbg___wbindgen_throw_39bc967c0e5a9b58: (arg0, arg1) => { throw new Error(getStringFromWasm0(arg0, arg1)); }, + __wbg_call_73af281463ec8b58: function() { return handleError(function(arg0, arg1) { + return addHeapObject(getObject(arg0).call(getObject(arg1))); + }, arguments); }, + __wbg_done_5aad55ec6b1954b1: (arg0) => getObject(arg0).done, + __wbg_error_a6fa202b58aa1cd3: (arg0, arg1) => { + try { console.error(getStringFromWasm0(arg0, arg1)); } + finally { wasm().__wbindgen_export4(arg0, arg1, 1); } + }, + __wbg_error_ad28debb48b5c6bb: (arg0) => console.error(getObject(arg0)), + __wbg_get_4920fefd3451364b: function() { return handleError(function(arg0, arg1) { + return addHeapObject(Reflect.get(getObject(arg0), getObject(arg1))); + }, arguments); }, + __wbg_get_unchecked_3d0f4b91c8eca4f0: (arg0, arg1) => addHeapObject(getObject(arg0)[arg1 >>> 0]), + __wbg_instanceof_ArrayBuffer_15859862b80b732d: (arg0) => { + try { return getObject(arg0) instanceof ArrayBuffer; } catch (_) { return false; } + }, + __wbg_instanceof_Uint8Array_2240b7046ac16f05: (arg0) => { + try { return getObject(arg0) instanceof Uint8Array; } catch (_) { return false; } + }, + __wbg_isArray_fad08a0d12828686: (arg0) => Array.isArray(getObject(arg0)), + __wbg_iterator_fc7ad8d33bab9e26: () => addHeapObject(Symbol.iterator), + __wbg_length_5855c1f289dfffc1: (arg0) => getObject(arg0).length, + __wbg_length_a31e05262e09b7f8: (arg0) => getObject(arg0).length, + __wbg_log_3c5e4b64af29e724: (arg0) => console.log(getObject(arg0)), + __wbg_new_09959f7b4c92c246: (arg0) => addHeapObject(new Uint8Array(getObject(arg0))), + __wbg_new_227d7c05414eb861: () => addHeapObject(new Error()), + __wbg_new_cbee8c0d5c479eac: () => addHeapObject(new Array()), + __wbg_next_a5fe6f328f7affc2: (arg0) => addHeapObject(getObject(arg0).next), + __wbg_next_e592122bb4ed4c67: function() { return handleError(function(arg0) { + return addHeapObject(getObject(arg0).next()); + }, arguments); }, + __wbg_prototypesetcall_f034d444741426c3: (arg0, arg1, arg2) => { + Uint8Array.prototype.set.call(getArrayU8FromWasm0(arg0, arg1), getObject(arg2)); + }, + __wbg_random_2b7bed8995d680fb: () => Math.random(), + __wbg_set_4c81cfb5dc3a333c: (arg0, arg1, arg2) => { getObject(arg0)[arg1 >>> 0] = takeObject(arg2); }, + __wbg_stack_3b0d974bbf31e44f: (arg0, arg1) => { + const ret = getObject(arg1).stack; + const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + getDataViewMemory0().setInt32(arg0 + 4, len1, true); + getDataViewMemory0().setInt32(arg0, ptr1, true); + }, + __wbg_value_667dcb90597486a6: (arg0) => addHeapObject(getObject(arg0).value), + __wbindgen_cast_0000000000000001: (arg0, arg1) => addHeapObject(getStringFromWasm0(arg0, arg1)), + __wbindgen_object_drop_ref: (arg0) => takeObject(arg0), + }; + return { __proto__: null, "./ruvector_attention_wasm_bg.js": import0 }; +}; + +})(_mod, () => _wasm); + + +// ── Async WASM init (fetch-based for browsers) ─────────────────── + +export default async function initWasm() { + if (_initialized) return; + const wasmUrl = new URL('ruvector_attention_wasm_bg.wasm', import.meta.url); + const imports = _mod.__wbg_get_imports(); + let result; + if (typeof WebAssembly.instantiateStreaming === 'function') { + try { + result = await WebAssembly.instantiateStreaming(fetch(wasmUrl), imports); + } catch (e) { + // Fallback if streaming fails (e.g. wrong MIME type) + const bytes = await (await fetch(wasmUrl)).arrayBuffer(); + result = await WebAssembly.instantiate(bytes, imports); + } + } else { + const bytes = await (await fetch(wasmUrl)).arrayBuffer(); + result = await WebAssembly.instantiate(bytes, imports); + } + _wasm = result.instance.exports; + _wasm.__wbindgen_start(); + _initialized = true; +} + +// ── ESM re-exports ──────────────────────────────────────────────── +// Attention mechanism classes +export const WasmMultiHeadAttention = _mod.WasmMultiHeadAttention; +export const WasmFlashAttention = _mod.WasmFlashAttention; +export const WasmHyperbolicAttention = _mod.WasmHyperbolicAttention; +export const WasmMoEAttention = _mod.WasmMoEAttention; +export const WasmLinearAttention = _mod.WasmLinearAttention; +export const WasmLocalGlobalAttention = _mod.WasmLocalGlobalAttention; +// Utility functions +export const cosine_similarity = _mod.cosine_similarity; +export const normalize = _mod.normalize; +export const l2_norm = _mod.l2_norm; +export const softmax = _mod.softmax; +export const batch_normalize = _mod.batch_normalize; +export const pairwise_distances = _mod.pairwise_distances; +export const scaled_dot_attention = _mod.scaled_dot_attention; +export const attention_weights = _mod.attention_weights; +export const random_orthogonal_matrix = _mod.random_orthogonal_matrix; +export const available_mechanisms = _mod.available_mechanisms; +// Lifecycle +export const init = _mod.init; +export const version = _mod.version; diff --git a/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.d.ts b/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.d.ts new file mode 100644 index 00000000..90c7dc99 --- /dev/null +++ b/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.d.ts @@ -0,0 +1,359 @@ +/* tslint:disable */ +/* eslint-disable */ + +/** + * Adam optimizer + */ +export class WasmAdam { + free(): void; + [Symbol.dispose](): void; + /** + * Create a new Adam optimizer + * + * # Arguments + * * `param_count` - Number of parameters + * * `learning_rate` - Learning rate + */ + constructor(param_count: number, learning_rate: number); + /** + * Reset optimizer state + */ + reset(): void; + /** + * Perform optimization step + * + * # Arguments + * * `params` - Current parameter values (will be updated in-place) + * * `gradients` - Gradient values + */ + step(params: Float32Array, gradients: Float32Array): void; + /** + * Get current learning rate + */ + learning_rate: number; +} + +/** + * AdamW optimizer (Adam with decoupled weight decay) + */ +export class WasmAdamW { + free(): void; + [Symbol.dispose](): void; + /** + * Create a new AdamW optimizer + * + * # Arguments + * * `param_count` - Number of parameters + * * `learning_rate` - Learning rate + * * `weight_decay` - Weight decay coefficient + */ + constructor(param_count: number, learning_rate: number, weight_decay: number); + /** + * Reset optimizer state + */ + reset(): void; + /** + * Perform optimization step with weight decay + */ + step(params: Float32Array, gradients: Float32Array): void; + /** + * Get current learning rate + */ + learning_rate: number; + /** + * Get weight decay + */ + readonly weight_decay: number; +} + +/** + * Flash attention mechanism + */ +export class WasmFlashAttention { + free(): void; + [Symbol.dispose](): void; + /** + * Compute flash attention + */ + compute(query: Float32Array, keys: any, values: any): Float32Array; + /** + * Create a new flash attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `block_size` - Block size for tiling + */ + constructor(dim: number, block_size: number); +} + +/** + * Hyperbolic attention mechanism + */ +export class WasmHyperbolicAttention { + free(): void; + [Symbol.dispose](): void; + /** + * Compute hyperbolic attention + */ + compute(query: Float32Array, keys: any, values: any): Float32Array; + /** + * Create a new hyperbolic attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `curvature` - Hyperbolic curvature parameter + */ + constructor(dim: number, curvature: number); + /** + * Get the curvature + */ + readonly curvature: number; +} + +/** + * InfoNCE contrastive loss for training + */ +export class WasmInfoNCELoss { + free(): void; + [Symbol.dispose](): void; + /** + * Compute InfoNCE loss + * + * # Arguments + * * `anchor` - Anchor embedding + * * `positive` - Positive example embedding + * * `negatives` - Array of negative example embeddings + */ + compute(anchor: Float32Array, positive: Float32Array, negatives: any): number; + /** + * Create a new InfoNCE loss instance + * + * # Arguments + * * `temperature` - Temperature parameter for softmax + */ + constructor(temperature: number); +} + +/** + * Learning rate scheduler + */ +export class WasmLRScheduler { + free(): void; + [Symbol.dispose](): void; + /** + * Get learning rate for current step + */ + get_lr(): number; + /** + * Create a new learning rate scheduler with warmup and cosine decay + * + * # Arguments + * * `initial_lr` - Initial learning rate + * * `warmup_steps` - Number of warmup steps + * * `total_steps` - Total training steps + */ + constructor(initial_lr: number, warmup_steps: number, total_steps: number); + /** + * Reset scheduler + */ + reset(): void; + /** + * Advance to next step + */ + step(): void; +} + +/** + * Linear attention (Performer-style) + */ +export class WasmLinearAttention { + free(): void; + [Symbol.dispose](): void; + /** + * Compute linear attention + */ + compute(query: Float32Array, keys: any, values: any): Float32Array; + /** + * Create a new linear attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `num_features` - Number of random features + */ + constructor(dim: number, num_features: number); +} + +/** + * Local-global attention mechanism + */ +export class WasmLocalGlobalAttention { + free(): void; + [Symbol.dispose](): void; + /** + * Compute local-global attention + */ + compute(query: Float32Array, keys: any, values: any): Float32Array; + /** + * Create a new local-global attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `local_window` - Size of local attention window + * * `global_tokens` - Number of global attention tokens + */ + constructor(dim: number, local_window: number, global_tokens: number); +} + +/** + * Mixture of Experts (MoE) attention + */ +export class WasmMoEAttention { + free(): void; + [Symbol.dispose](): void; + /** + * Compute MoE attention + */ + compute(query: Float32Array, keys: any, values: any): Float32Array; + /** + * Create a new MoE attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `num_experts` - Number of expert attention mechanisms + * * `top_k` - Number of experts to use per query + */ + constructor(dim: number, num_experts: number, top_k: number); +} + +/** + * Multi-head attention mechanism + */ +export class WasmMultiHeadAttention { + free(): void; + [Symbol.dispose](): void; + /** + * Compute multi-head attention + */ + compute(query: Float32Array, keys: any, values: any): Float32Array; + /** + * Create a new multi-head attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `num_heads` - Number of attention heads + */ + constructor(dim: number, num_heads: number); + /** + * Get the dimension + */ + readonly dim: number; + /** + * Get the number of heads + */ + readonly num_heads: number; +} + +/** + * SGD optimizer with momentum + */ +export class WasmSGD { + free(): void; + [Symbol.dispose](): void; + /** + * Create a new SGD optimizer + * + * # Arguments + * * `param_count` - Number of parameters + * * `learning_rate` - Learning rate + * * `momentum` - Momentum coefficient (default: 0) + */ + constructor(param_count: number, learning_rate: number, momentum?: number | null); + /** + * Reset optimizer state + */ + reset(): void; + /** + * Perform optimization step + */ + step(params: Float32Array, gradients: Float32Array): void; + /** + * Get current learning rate + */ + learning_rate: number; +} + +/** + * Compute attention weights from scores + */ +export function attention_weights(scores: Float32Array, temperature?: number | null): void; + +/** + * Get information about available attention mechanisms + */ +export function available_mechanisms(): any; + +/** + * Batch normalize vectors + */ +export function batch_normalize(vectors: any, epsilon?: number | null): Float32Array; + +/** + * Compute cosine similarity between two vectors + */ +export function cosine_similarity(a: Float32Array, b: Float32Array): number; + +/** + * Initialize the WASM module with panic hook + */ +export function init(): void; + +/** + * Compute L2 norm of a vector + */ +export function l2_norm(vec: Float32Array): number; + +/** + * Log a message to the browser console + */ +export function log(message: string): void; + +/** + * Log an error to the browser console + */ +export function log_error(message: string): void; + +/** + * Normalize a vector to unit length + */ +export function normalize(vec: Float32Array): void; + +/** + * Compute pairwise distances between vectors + */ +export function pairwise_distances(vectors: any): Float32Array; + +/** + * Generate random orthogonal matrix (for initialization) + */ +export function random_orthogonal_matrix(dim: number): Float32Array; + +/** + * Compute scaled dot-product attention + * + * # Arguments + * * `query` - Query vector as Float32Array + * * `keys` - Array of key vectors + * * `values` - Array of value vectors + * * `scale` - Optional scaling factor (defaults to 1/sqrt(dim)) + */ +export function scaled_dot_attention(query: Float32Array, keys: any, values: any, scale?: number | null): Float32Array; + +/** + * Compute softmax of a vector + */ +export function softmax(vec: Float32Array): void; + +/** + * Get the version of the ruvector-attention-wasm crate + */ +export function version(): string; diff --git a/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.js b/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.js new file mode 100644 index 00000000..875532dc --- /dev/null +++ b/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.js @@ -0,0 +1,1417 @@ +/* @ts-self-types="./ruvector_attention_wasm.d.ts" */ + +/** + * Adam optimizer + */ +class WasmAdam { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmAdamFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmadam_free(ptr, 0); + } + /** + * Get current learning rate + * @returns {number} + */ + get learning_rate() { + const ret = wasm.wasmadam_learning_rate(this.__wbg_ptr); + return ret; + } + /** + * Create a new Adam optimizer + * + * # Arguments + * * `param_count` - Number of parameters + * * `learning_rate` - Learning rate + * @param {number} param_count + * @param {number} learning_rate + */ + constructor(param_count, learning_rate) { + const ret = wasm.wasmadam_new(param_count, learning_rate); + this.__wbg_ptr = ret >>> 0; + WasmAdamFinalization.register(this, this.__wbg_ptr, this); + return this; + } + /** + * Reset optimizer state + */ + reset() { + wasm.wasmadam_reset(this.__wbg_ptr); + } + /** + * Set learning rate + * @param {number} lr + */ + set learning_rate(lr) { + wasm.wasmadam_set_learning_rate(this.__wbg_ptr, lr); + } + /** + * Perform optimization step + * + * # Arguments + * * `params` - Current parameter values (will be updated in-place) + * * `gradients` - Gradient values + * @param {Float32Array} params + * @param {Float32Array} gradients + */ + step(params, gradients) { + var ptr0 = passArrayF32ToWasm0(params, wasm.__wbindgen_export); + var len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(gradients, wasm.__wbindgen_export); + const len1 = WASM_VECTOR_LEN; + wasm.wasmadam_step(this.__wbg_ptr, ptr0, len0, addHeapObject(params), ptr1, len1); + } +} +if (Symbol.dispose) WasmAdam.prototype[Symbol.dispose] = WasmAdam.prototype.free; +exports.WasmAdam = WasmAdam; + +/** + * AdamW optimizer (Adam with decoupled weight decay) + */ +class WasmAdamW { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmAdamWFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmadamw_free(ptr, 0); + } + /** + * Get current learning rate + * @returns {number} + */ + get learning_rate() { + const ret = wasm.wasmadamw_learning_rate(this.__wbg_ptr); + return ret; + } + /** + * Create a new AdamW optimizer + * + * # Arguments + * * `param_count` - Number of parameters + * * `learning_rate` - Learning rate + * * `weight_decay` - Weight decay coefficient + * @param {number} param_count + * @param {number} learning_rate + * @param {number} weight_decay + */ + constructor(param_count, learning_rate, weight_decay) { + const ret = wasm.wasmadamw_new(param_count, learning_rate, weight_decay); + this.__wbg_ptr = ret >>> 0; + WasmAdamWFinalization.register(this, this.__wbg_ptr, this); + return this; + } + /** + * Reset optimizer state + */ + reset() { + wasm.wasmadamw_reset(this.__wbg_ptr); + } + /** + * Set learning rate + * @param {number} lr + */ + set learning_rate(lr) { + wasm.wasmadamw_set_learning_rate(this.__wbg_ptr, lr); + } + /** + * Perform optimization step with weight decay + * @param {Float32Array} params + * @param {Float32Array} gradients + */ + step(params, gradients) { + var ptr0 = passArrayF32ToWasm0(params, wasm.__wbindgen_export); + var len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(gradients, wasm.__wbindgen_export); + const len1 = WASM_VECTOR_LEN; + wasm.wasmadamw_step(this.__wbg_ptr, ptr0, len0, addHeapObject(params), ptr1, len1); + } + /** + * Get weight decay + * @returns {number} + */ + get weight_decay() { + const ret = wasm.wasmadamw_weight_decay(this.__wbg_ptr); + return ret; + } +} +if (Symbol.dispose) WasmAdamW.prototype[Symbol.dispose] = WasmAdamW.prototype.free; +exports.WasmAdamW = WasmAdamW; + +/** + * Flash attention mechanism + */ +class WasmFlashAttention { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmFlashAttentionFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmflashattention_free(ptr, 0); + } + /** + * Compute flash attention + * @param {Float32Array} query + * @param {any} keys + * @param {any} values + * @returns {Float32Array} + */ + compute(query, keys, values) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(query, wasm.__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm.wasmflashattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true); + if (r3) { + throw takeObject(r2); + } + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export4(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Create a new flash attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `block_size` - Block size for tiling + * @param {number} dim + * @param {number} block_size + */ + constructor(dim, block_size) { + const ret = wasm.wasmflashattention_new(dim, block_size); + this.__wbg_ptr = ret >>> 0; + WasmFlashAttentionFinalization.register(this, this.__wbg_ptr, this); + return this; + } +} +if (Symbol.dispose) WasmFlashAttention.prototype[Symbol.dispose] = WasmFlashAttention.prototype.free; +exports.WasmFlashAttention = WasmFlashAttention; + +/** + * Hyperbolic attention mechanism + */ +class WasmHyperbolicAttention { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmHyperbolicAttentionFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmhyperbolicattention_free(ptr, 0); + } + /** + * Compute hyperbolic attention + * @param {Float32Array} query + * @param {any} keys + * @param {any} values + * @returns {Float32Array} + */ + compute(query, keys, values) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(query, wasm.__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm.wasmhyperbolicattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true); + if (r3) { + throw takeObject(r2); + } + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export4(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Get the curvature + * @returns {number} + */ + get curvature() { + const ret = wasm.wasmhyperbolicattention_curvature(this.__wbg_ptr); + return ret; + } + /** + * Create a new hyperbolic attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `curvature` - Hyperbolic curvature parameter + * @param {number} dim + * @param {number} curvature + */ + constructor(dim, curvature) { + const ret = wasm.wasmhyperbolicattention_new(dim, curvature); + this.__wbg_ptr = ret >>> 0; + WasmHyperbolicAttentionFinalization.register(this, this.__wbg_ptr, this); + return this; + } +} +if (Symbol.dispose) WasmHyperbolicAttention.prototype[Symbol.dispose] = WasmHyperbolicAttention.prototype.free; +exports.WasmHyperbolicAttention = WasmHyperbolicAttention; + +/** + * InfoNCE contrastive loss for training + */ +class WasmInfoNCELoss { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmInfoNCELossFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasminfonceloss_free(ptr, 0); + } + /** + * Compute InfoNCE loss + * + * # Arguments + * * `anchor` - Anchor embedding + * * `positive` - Positive example embedding + * * `negatives` - Array of negative example embeddings + * @param {Float32Array} anchor + * @param {Float32Array} positive + * @param {any} negatives + * @returns {number} + */ + compute(anchor, positive, negatives) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(anchor, wasm.__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(positive, wasm.__wbindgen_export); + const len1 = WASM_VECTOR_LEN; + wasm.wasminfonceloss_compute(retptr, this.__wbg_ptr, ptr0, len0, ptr1, len1, addHeapObject(negatives)); + var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + if (r2) { + throw takeObject(r1); + } + return r0; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Create a new InfoNCE loss instance + * + * # Arguments + * * `temperature` - Temperature parameter for softmax + * @param {number} temperature + */ + constructor(temperature) { + const ret = wasm.wasminfonceloss_new(temperature); + this.__wbg_ptr = ret >>> 0; + WasmInfoNCELossFinalization.register(this, this.__wbg_ptr, this); + return this; + } +} +if (Symbol.dispose) WasmInfoNCELoss.prototype[Symbol.dispose] = WasmInfoNCELoss.prototype.free; +exports.WasmInfoNCELoss = WasmInfoNCELoss; + +/** + * Learning rate scheduler + */ +class WasmLRScheduler { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmLRSchedulerFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmlrscheduler_free(ptr, 0); + } + /** + * Get learning rate for current step + * @returns {number} + */ + get_lr() { + const ret = wasm.wasmlrscheduler_get_lr(this.__wbg_ptr); + return ret; + } + /** + * Create a new learning rate scheduler with warmup and cosine decay + * + * # Arguments + * * `initial_lr` - Initial learning rate + * * `warmup_steps` - Number of warmup steps + * * `total_steps` - Total training steps + * @param {number} initial_lr + * @param {number} warmup_steps + * @param {number} total_steps + */ + constructor(initial_lr, warmup_steps, total_steps) { + const ret = wasm.wasmlrscheduler_new(initial_lr, warmup_steps, total_steps); + this.__wbg_ptr = ret >>> 0; + WasmLRSchedulerFinalization.register(this, this.__wbg_ptr, this); + return this; + } + /** + * Reset scheduler + */ + reset() { + wasm.wasmlrscheduler_reset(this.__wbg_ptr); + } + /** + * Advance to next step + */ + step() { + wasm.wasmlrscheduler_step(this.__wbg_ptr); + } +} +if (Symbol.dispose) WasmLRScheduler.prototype[Symbol.dispose] = WasmLRScheduler.prototype.free; +exports.WasmLRScheduler = WasmLRScheduler; + +/** + * Linear attention (Performer-style) + */ +class WasmLinearAttention { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmLinearAttentionFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmlinearattention_free(ptr, 0); + } + /** + * Compute linear attention + * @param {Float32Array} query + * @param {any} keys + * @param {any} values + * @returns {Float32Array} + */ + compute(query, keys, values) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(query, wasm.__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm.wasmlinearattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true); + if (r3) { + throw takeObject(r2); + } + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export4(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Create a new linear attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `num_features` - Number of random features + * @param {number} dim + * @param {number} num_features + */ + constructor(dim, num_features) { + const ret = wasm.wasmlinearattention_new(dim, num_features); + this.__wbg_ptr = ret >>> 0; + WasmLinearAttentionFinalization.register(this, this.__wbg_ptr, this); + return this; + } +} +if (Symbol.dispose) WasmLinearAttention.prototype[Symbol.dispose] = WasmLinearAttention.prototype.free; +exports.WasmLinearAttention = WasmLinearAttention; + +/** + * Local-global attention mechanism + */ +class WasmLocalGlobalAttention { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmLocalGlobalAttentionFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmlocalglobalattention_free(ptr, 0); + } + /** + * Compute local-global attention + * @param {Float32Array} query + * @param {any} keys + * @param {any} values + * @returns {Float32Array} + */ + compute(query, keys, values) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(query, wasm.__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm.wasmlocalglobalattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true); + if (r3) { + throw takeObject(r2); + } + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export4(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Create a new local-global attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `local_window` - Size of local attention window + * * `global_tokens` - Number of global attention tokens + * @param {number} dim + * @param {number} local_window + * @param {number} global_tokens + */ + constructor(dim, local_window, global_tokens) { + const ret = wasm.wasmlocalglobalattention_new(dim, local_window, global_tokens); + this.__wbg_ptr = ret >>> 0; + WasmLocalGlobalAttentionFinalization.register(this, this.__wbg_ptr, this); + return this; + } +} +if (Symbol.dispose) WasmLocalGlobalAttention.prototype[Symbol.dispose] = WasmLocalGlobalAttention.prototype.free; +exports.WasmLocalGlobalAttention = WasmLocalGlobalAttention; + +/** + * Mixture of Experts (MoE) attention + */ +class WasmMoEAttention { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmMoEAttentionFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmmoeattention_free(ptr, 0); + } + /** + * Compute MoE attention + * @param {Float32Array} query + * @param {any} keys + * @param {any} values + * @returns {Float32Array} + */ + compute(query, keys, values) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(query, wasm.__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm.wasmmoeattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true); + if (r3) { + throw takeObject(r2); + } + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export4(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Create a new MoE attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `num_experts` - Number of expert attention mechanisms + * * `top_k` - Number of experts to use per query + * @param {number} dim + * @param {number} num_experts + * @param {number} top_k + */ + constructor(dim, num_experts, top_k) { + const ret = wasm.wasmmoeattention_new(dim, num_experts, top_k); + this.__wbg_ptr = ret >>> 0; + WasmMoEAttentionFinalization.register(this, this.__wbg_ptr, this); + return this; + } +} +if (Symbol.dispose) WasmMoEAttention.prototype[Symbol.dispose] = WasmMoEAttention.prototype.free; +exports.WasmMoEAttention = WasmMoEAttention; + +/** + * Multi-head attention mechanism + */ +class WasmMultiHeadAttention { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmMultiHeadAttentionFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmmultiheadattention_free(ptr, 0); + } + /** + * Compute multi-head attention + * @param {Float32Array} query + * @param {any} keys + * @param {any} values + * @returns {Float32Array} + */ + compute(query, keys, values) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(query, wasm.__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm.wasmmultiheadattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values)); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true); + if (r3) { + throw takeObject(r2); + } + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export4(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Get the dimension + * @returns {number} + */ + get dim() { + const ret = wasm.wasmmultiheadattention_dim(this.__wbg_ptr); + return ret >>> 0; + } + /** + * Create a new multi-head attention instance + * + * # Arguments + * * `dim` - Embedding dimension + * * `num_heads` - Number of attention heads + * @param {number} dim + * @param {number} num_heads + */ + constructor(dim, num_heads) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + wasm.wasmmultiheadattention_new(retptr, dim, num_heads); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + if (r2) { + throw takeObject(r1); + } + this.__wbg_ptr = r0 >>> 0; + WasmMultiHeadAttentionFinalization.register(this, this.__wbg_ptr, this); + return this; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Get the number of heads + * @returns {number} + */ + get num_heads() { + const ret = wasm.wasmmultiheadattention_num_heads(this.__wbg_ptr); + return ret >>> 0; + } +} +if (Symbol.dispose) WasmMultiHeadAttention.prototype[Symbol.dispose] = WasmMultiHeadAttention.prototype.free; +exports.WasmMultiHeadAttention = WasmMultiHeadAttention; + +/** + * SGD optimizer with momentum + */ +class WasmSGD { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmSGDFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmsgd_free(ptr, 0); + } + /** + * Get current learning rate + * @returns {number} + */ + get learning_rate() { + const ret = wasm.wasmsgd_learning_rate(this.__wbg_ptr); + return ret; + } + /** + * Create a new SGD optimizer + * + * # Arguments + * * `param_count` - Number of parameters + * * `learning_rate` - Learning rate + * * `momentum` - Momentum coefficient (default: 0) + * @param {number} param_count + * @param {number} learning_rate + * @param {number | null} [momentum] + */ + constructor(param_count, learning_rate, momentum) { + const ret = wasm.wasmsgd_new(param_count, learning_rate, isLikeNone(momentum) ? 0x100000001 : Math.fround(momentum)); + this.__wbg_ptr = ret >>> 0; + WasmSGDFinalization.register(this, this.__wbg_ptr, this); + return this; + } + /** + * Reset optimizer state + */ + reset() { + wasm.wasmsgd_reset(this.__wbg_ptr); + } + /** + * Set learning rate + * @param {number} lr + */ + set learning_rate(lr) { + wasm.wasmsgd_set_learning_rate(this.__wbg_ptr, lr); + } + /** + * Perform optimization step + * @param {Float32Array} params + * @param {Float32Array} gradients + */ + step(params, gradients) { + var ptr0 = passArrayF32ToWasm0(params, wasm.__wbindgen_export); + var len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(gradients, wasm.__wbindgen_export); + const len1 = WASM_VECTOR_LEN; + wasm.wasmsgd_step(this.__wbg_ptr, ptr0, len0, addHeapObject(params), ptr1, len1); + } +} +if (Symbol.dispose) WasmSGD.prototype[Symbol.dispose] = WasmSGD.prototype.free; +exports.WasmSGD = WasmSGD; + +/** + * Compute attention weights from scores + * @param {Float32Array} scores + * @param {number | null} [temperature] + */ +function attention_weights(scores, temperature) { + var ptr0 = passArrayF32ToWasm0(scores, wasm.__wbindgen_export); + var len0 = WASM_VECTOR_LEN; + wasm.attention_weights(ptr0, len0, addHeapObject(scores), isLikeNone(temperature) ? 0x100000001 : Math.fround(temperature)); +} +exports.attention_weights = attention_weights; + +/** + * Get information about available attention mechanisms + * @returns {any} + */ +function available_mechanisms() { + const ret = wasm.available_mechanisms(); + return takeObject(ret); +} +exports.available_mechanisms = available_mechanisms; + +/** + * Batch normalize vectors + * @param {any} vectors + * @param {number | null} [epsilon] + * @returns {Float32Array} + */ +function batch_normalize(vectors, epsilon) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + wasm.batch_normalize(retptr, addHeapObject(vectors), isLikeNone(epsilon) ? 0x100000001 : Math.fround(epsilon)); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true); + if (r3) { + throw takeObject(r2); + } + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } +} +exports.batch_normalize = batch_normalize; + +/** + * Compute cosine similarity between two vectors + * @param {Float32Array} a + * @param {Float32Array} b + * @returns {number} + */ +function cosine_similarity(a, b) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(a, wasm.__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(b, wasm.__wbindgen_export); + const len1 = WASM_VECTOR_LEN; + wasm.cosine_similarity(retptr, ptr0, len0, ptr1, len1); + var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + if (r2) { + throw takeObject(r1); + } + return r0; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } +} +exports.cosine_similarity = cosine_similarity; + +/** + * Initialize the WASM module with panic hook + */ +function init() { + wasm.init(); +} +exports.init = init; + +/** + * Compute L2 norm of a vector + * @param {Float32Array} vec + * @returns {number} + */ +function l2_norm(vec) { + const ptr0 = passArrayF32ToWasm0(vec, wasm.__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + const ret = wasm.l2_norm(ptr0, len0); + return ret; +} +exports.l2_norm = l2_norm; + +/** + * Log a message to the browser console + * @param {string} message + */ +function log(message) { + const ptr0 = passStringToWasm0(message, wasm.__wbindgen_export, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + wasm.log(ptr0, len0); +} +exports.log = log; + +/** + * Log an error to the browser console + * @param {string} message + */ +function log_error(message) { + const ptr0 = passStringToWasm0(message, wasm.__wbindgen_export, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + wasm.log_error(ptr0, len0); +} +exports.log_error = log_error; + +/** + * Normalize a vector to unit length + * @param {Float32Array} vec + */ +function normalize(vec) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + var ptr0 = passArrayF32ToWasm0(vec, wasm.__wbindgen_export); + var len0 = WASM_VECTOR_LEN; + wasm.normalize(retptr, ptr0, len0, addHeapObject(vec)); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + if (r1) { + throw takeObject(r0); + } + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } +} +exports.normalize = normalize; + +/** + * Compute pairwise distances between vectors + * @param {any} vectors + * @returns {Float32Array} + */ +function pairwise_distances(vectors) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + wasm.pairwise_distances(retptr, addHeapObject(vectors)); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true); + if (r3) { + throw takeObject(r2); + } + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } +} +exports.pairwise_distances = pairwise_distances; + +/** + * Generate random orthogonal matrix (for initialization) + * @param {number} dim + * @returns {Float32Array} + */ +function random_orthogonal_matrix(dim) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + wasm.random_orthogonal_matrix(retptr, dim); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var v1 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export4(r0, r1 * 4, 4); + return v1; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } +} +exports.random_orthogonal_matrix = random_orthogonal_matrix; + +/** + * Compute scaled dot-product attention + * + * # Arguments + * * `query` - Query vector as Float32Array + * * `keys` - Array of key vectors + * * `values` - Array of value vectors + * * `scale` - Optional scaling factor (defaults to 1/sqrt(dim)) + * @param {Float32Array} query + * @param {any} keys + * @param {any} values + * @param {number | null} [scale] + * @returns {Float32Array} + */ +function scaled_dot_attention(query, keys, values, scale) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(query, wasm.__wbindgen_export); + const len0 = WASM_VECTOR_LEN; + wasm.scaled_dot_attention(retptr, ptr0, len0, addHeapObject(keys), addHeapObject(values), isLikeNone(scale) ? 0x100000001 : Math.fround(scale)); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true); + if (r3) { + throw takeObject(r2); + } + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export4(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } +} +exports.scaled_dot_attention = scaled_dot_attention; + +/** + * Compute softmax of a vector + * @param {Float32Array} vec + */ +function softmax(vec) { + var ptr0 = passArrayF32ToWasm0(vec, wasm.__wbindgen_export); + var len0 = WASM_VECTOR_LEN; + wasm.softmax(ptr0, len0, addHeapObject(vec)); +} +exports.softmax = softmax; + +/** + * Get the version of the ruvector-attention-wasm crate + * @returns {string} + */ +function version() { + let deferred1_0; + let deferred1_1; + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + wasm.version(retptr); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + deferred1_0 = r0; + deferred1_1 = r1; + return getStringFromWasm0(r0, r1); + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + wasm.__wbindgen_export4(deferred1_0, deferred1_1, 1); + } +} +exports.version = version; + +function __wbg_get_imports() { + const import0 = { + __proto__: null, + __wbg_Error_4577686b3a6d9b3a: function(arg0, arg1) { + const ret = Error(getStringFromWasm0(arg0, arg1)); + return addHeapObject(ret); + }, + __wbg_String_8564e559799eccda: function(arg0, arg1) { + const ret = String(getObject(arg1)); + const ptr1 = passStringToWasm0(ret, wasm.__wbindgen_export, wasm.__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + getDataViewMemory0().setInt32(arg0 + 4 * 1, len1, true); + getDataViewMemory0().setInt32(arg0 + 4 * 0, ptr1, true); + }, + __wbg___wbindgen_boolean_get_18c4ed9422296fff: function(arg0) { + const v = getObject(arg0); + const ret = typeof(v) === 'boolean' ? v : undefined; + return isLikeNone(ret) ? 0xFFFFFF : ret ? 1 : 0; + }, + __wbg___wbindgen_copy_to_typed_array_5294f8e46aecc086: function(arg0, arg1, arg2) { + new Uint8Array(getObject(arg2).buffer, getObject(arg2).byteOffset, getObject(arg2).byteLength).set(getArrayU8FromWasm0(arg0, arg1)); + }, + __wbg___wbindgen_debug_string_ddde1867f49c2442: function(arg0, arg1) { + const ret = debugString(getObject(arg1)); + const ptr1 = passStringToWasm0(ret, wasm.__wbindgen_export, wasm.__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + getDataViewMemory0().setInt32(arg0 + 4 * 1, len1, true); + getDataViewMemory0().setInt32(arg0 + 4 * 0, ptr1, true); + }, + __wbg___wbindgen_is_function_d633e708baf0d146: function(arg0) { + const ret = typeof(getObject(arg0)) === 'function'; + return ret; + }, + __wbg___wbindgen_is_object_4b3de556756ee8a8: function(arg0) { + const val = getObject(arg0); + const ret = typeof(val) === 'object' && val !== null; + return ret; + }, + __wbg___wbindgen_jsval_loose_eq_1562ceb9af84e990: function(arg0, arg1) { + const ret = getObject(arg0) == getObject(arg1); + return ret; + }, + __wbg___wbindgen_number_get_5854912275df1894: function(arg0, arg1) { + const obj = getObject(arg1); + const ret = typeof(obj) === 'number' ? obj : undefined; + getDataViewMemory0().setFloat64(arg0 + 8 * 1, isLikeNone(ret) ? 0 : ret, true); + getDataViewMemory0().setInt32(arg0 + 4 * 0, !isLikeNone(ret), true); + }, + __wbg___wbindgen_string_get_3e5751597f39a112: function(arg0, arg1) { + const obj = getObject(arg1); + const ret = typeof(obj) === 'string' ? obj : undefined; + var ptr1 = isLikeNone(ret) ? 0 : passStringToWasm0(ret, wasm.__wbindgen_export, wasm.__wbindgen_export2); + var len1 = WASM_VECTOR_LEN; + getDataViewMemory0().setInt32(arg0 + 4 * 1, len1, true); + getDataViewMemory0().setInt32(arg0 + 4 * 0, ptr1, true); + }, + __wbg___wbindgen_throw_39bc967c0e5a9b58: function(arg0, arg1) { + throw new Error(getStringFromWasm0(arg0, arg1)); + }, + __wbg_call_73af281463ec8b58: function() { return handleError(function (arg0, arg1) { + const ret = getObject(arg0).call(getObject(arg1)); + return addHeapObject(ret); + }, arguments); }, + __wbg_done_5aad55ec6b1954b1: function(arg0) { + const ret = getObject(arg0).done; + return ret; + }, + __wbg_error_a6fa202b58aa1cd3: function(arg0, arg1) { + let deferred0_0; + let deferred0_1; + try { + deferred0_0 = arg0; + deferred0_1 = arg1; + console.error(getStringFromWasm0(arg0, arg1)); + } finally { + wasm.__wbindgen_export4(deferred0_0, deferred0_1, 1); + } + }, + __wbg_error_ad28debb48b5c6bb: function(arg0) { + console.error(getObject(arg0)); + }, + __wbg_get_4920fefd3451364b: function() { return handleError(function (arg0, arg1) { + const ret = Reflect.get(getObject(arg0), getObject(arg1)); + return addHeapObject(ret); + }, arguments); }, + __wbg_get_unchecked_3d0f4b91c8eca4f0: function(arg0, arg1) { + const ret = getObject(arg0)[arg1 >>> 0]; + return addHeapObject(ret); + }, + __wbg_instanceof_ArrayBuffer_15859862b80b732d: function(arg0) { + let result; + try { + result = getObject(arg0) instanceof ArrayBuffer; + } catch (_) { + result = false; + } + const ret = result; + return ret; + }, + __wbg_instanceof_Uint8Array_2240b7046ac16f05: function(arg0) { + let result; + try { + result = getObject(arg0) instanceof Uint8Array; + } catch (_) { + result = false; + } + const ret = result; + return ret; + }, + __wbg_isArray_fad08a0d12828686: function(arg0) { + const ret = Array.isArray(getObject(arg0)); + return ret; + }, + __wbg_iterator_fc7ad8d33bab9e26: function() { + const ret = Symbol.iterator; + return addHeapObject(ret); + }, + __wbg_length_5855c1f289dfffc1: function(arg0) { + const ret = getObject(arg0).length; + return ret; + }, + __wbg_length_a31e05262e09b7f8: function(arg0) { + const ret = getObject(arg0).length; + return ret; + }, + __wbg_log_3c5e4b64af29e724: function(arg0) { + console.log(getObject(arg0)); + }, + __wbg_new_09959f7b4c92c246: function(arg0) { + const ret = new Uint8Array(getObject(arg0)); + return addHeapObject(ret); + }, + __wbg_new_227d7c05414eb861: function() { + const ret = new Error(); + return addHeapObject(ret); + }, + __wbg_new_cbee8c0d5c479eac: function() { + const ret = new Array(); + return addHeapObject(ret); + }, + __wbg_next_a5fe6f328f7affc2: function(arg0) { + const ret = getObject(arg0).next; + return addHeapObject(ret); + }, + __wbg_next_e592122bb4ed4c67: function() { return handleError(function (arg0) { + const ret = getObject(arg0).next(); + return addHeapObject(ret); + }, arguments); }, + __wbg_prototypesetcall_f034d444741426c3: function(arg0, arg1, arg2) { + Uint8Array.prototype.set.call(getArrayU8FromWasm0(arg0, arg1), getObject(arg2)); + }, + __wbg_random_2b7bed8995d680fb: function() { + const ret = Math.random(); + return ret; + }, + __wbg_set_4c81cfb5dc3a333c: function(arg0, arg1, arg2) { + getObject(arg0)[arg1 >>> 0] = takeObject(arg2); + }, + __wbg_stack_3b0d974bbf31e44f: function(arg0, arg1) { + const ret = getObject(arg1).stack; + const ptr1 = passStringToWasm0(ret, wasm.__wbindgen_export, wasm.__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + getDataViewMemory0().setInt32(arg0 + 4 * 1, len1, true); + getDataViewMemory0().setInt32(arg0 + 4 * 0, ptr1, true); + }, + __wbg_value_667dcb90597486a6: function(arg0) { + const ret = getObject(arg0).value; + return addHeapObject(ret); + }, + __wbindgen_cast_0000000000000001: function(arg0, arg1) { + // Cast intrinsic for `Ref(String) -> Externref`. + const ret = getStringFromWasm0(arg0, arg1); + return addHeapObject(ret); + }, + __wbindgen_object_drop_ref: function(arg0) { + takeObject(arg0); + }, + }; + return { + __proto__: null, + "./ruvector_attention_wasm_bg.js": import0, + }; +} + +const WasmAdamFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmadam_free(ptr >>> 0, 1)); +const WasmAdamWFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmadamw_free(ptr >>> 0, 1)); +const WasmFlashAttentionFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmflashattention_free(ptr >>> 0, 1)); +const WasmHyperbolicAttentionFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmhyperbolicattention_free(ptr >>> 0, 1)); +const WasmInfoNCELossFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasminfonceloss_free(ptr >>> 0, 1)); +const WasmLRSchedulerFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmlrscheduler_free(ptr >>> 0, 1)); +const WasmLinearAttentionFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmlinearattention_free(ptr >>> 0, 1)); +const WasmLocalGlobalAttentionFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmlocalglobalattention_free(ptr >>> 0, 1)); +const WasmMoEAttentionFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmmoeattention_free(ptr >>> 0, 1)); +const WasmMultiHeadAttentionFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmmultiheadattention_free(ptr >>> 0, 1)); +const WasmSGDFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmsgd_free(ptr >>> 0, 1)); + +function addHeapObject(obj) { + if (heap_next === heap.length) heap.push(heap.length + 1); + const idx = heap_next; + heap_next = heap[idx]; + + heap[idx] = obj; + return idx; +} + +function debugString(val) { + // primitive types + const type = typeof val; + if (type == 'number' || type == 'boolean' || val == null) { + return `${val}`; + } + if (type == 'string') { + return `"${val}"`; + } + if (type == 'symbol') { + const description = val.description; + if (description == null) { + return 'Symbol'; + } else { + return `Symbol(${description})`; + } + } + if (type == 'function') { + const name = val.name; + if (typeof name == 'string' && name.length > 0) { + return `Function(${name})`; + } else { + return 'Function'; + } + } + // objects + if (Array.isArray(val)) { + const length = val.length; + let debug = '['; + if (length > 0) { + debug += debugString(val[0]); + } + for(let i = 1; i < length; i++) { + debug += ', ' + debugString(val[i]); + } + debug += ']'; + return debug; + } + // Test for built-in + const builtInMatches = /\[object ([^\]]+)\]/.exec(toString.call(val)); + let className; + if (builtInMatches && builtInMatches.length > 1) { + className = builtInMatches[1]; + } else { + // Failed to match the standard '[object ClassName]' + return toString.call(val); + } + if (className == 'Object') { + // we're a user defined class or Object + // JSON.stringify avoids problems with cycles, and is generally much + // easier than looping through ownProperties of `val`. + try { + return 'Object(' + JSON.stringify(val) + ')'; + } catch (_) { + return 'Object'; + } + } + // errors + if (val instanceof Error) { + return `${val.name}: ${val.message}\n${val.stack}`; + } + // TODO we could test for more things here, like `Set`s and `Map`s. + return className; +} + +function dropObject(idx) { + if (idx < 1028) return; + heap[idx] = heap_next; + heap_next = idx; +} + +function getArrayF32FromWasm0(ptr, len) { + ptr = ptr >>> 0; + return getFloat32ArrayMemory0().subarray(ptr / 4, ptr / 4 + len); +} + +function getArrayU8FromWasm0(ptr, len) { + ptr = ptr >>> 0; + return getUint8ArrayMemory0().subarray(ptr / 1, ptr / 1 + len); +} + +let cachedDataViewMemory0 = null; +function getDataViewMemory0() { + if (cachedDataViewMemory0 === null || cachedDataViewMemory0.buffer.detached === true || (cachedDataViewMemory0.buffer.detached === undefined && cachedDataViewMemory0.buffer !== wasm.memory.buffer)) { + cachedDataViewMemory0 = new DataView(wasm.memory.buffer); + } + return cachedDataViewMemory0; +} + +let cachedFloat32ArrayMemory0 = null; +function getFloat32ArrayMemory0() { + if (cachedFloat32ArrayMemory0 === null || cachedFloat32ArrayMemory0.byteLength === 0) { + cachedFloat32ArrayMemory0 = new Float32Array(wasm.memory.buffer); + } + return cachedFloat32ArrayMemory0; +} + +function getStringFromWasm0(ptr, len) { + ptr = ptr >>> 0; + return decodeText(ptr, len); +} + +let cachedUint8ArrayMemory0 = null; +function getUint8ArrayMemory0() { + if (cachedUint8ArrayMemory0 === null || cachedUint8ArrayMemory0.byteLength === 0) { + cachedUint8ArrayMemory0 = new Uint8Array(wasm.memory.buffer); + } + return cachedUint8ArrayMemory0; +} + +function getObject(idx) { return heap[idx]; } + +function handleError(f, args) { + try { + return f.apply(this, args); + } catch (e) { + wasm.__wbindgen_export3(addHeapObject(e)); + } +} + +let heap = new Array(1024).fill(undefined); +heap.push(undefined, null, true, false); + +let heap_next = heap.length; + +function isLikeNone(x) { + return x === undefined || x === null; +} + +function passArrayF32ToWasm0(arg, malloc) { + const ptr = malloc(arg.length * 4, 4) >>> 0; + getFloat32ArrayMemory0().set(arg, ptr / 4); + WASM_VECTOR_LEN = arg.length; + return ptr; +} + +function passStringToWasm0(arg, malloc, realloc) { + if (realloc === undefined) { + const buf = cachedTextEncoder.encode(arg); + const ptr = malloc(buf.length, 1) >>> 0; + getUint8ArrayMemory0().subarray(ptr, ptr + buf.length).set(buf); + WASM_VECTOR_LEN = buf.length; + return ptr; + } + + let len = arg.length; + let ptr = malloc(len, 1) >>> 0; + + const mem = getUint8ArrayMemory0(); + + let offset = 0; + + for (; offset < len; offset++) { + const code = arg.charCodeAt(offset); + if (code > 0x7F) break; + mem[ptr + offset] = code; + } + if (offset !== len) { + if (offset !== 0) { + arg = arg.slice(offset); + } + ptr = realloc(ptr, len, len = offset + arg.length * 3, 1) >>> 0; + const view = getUint8ArrayMemory0().subarray(ptr + offset, ptr + len); + const ret = cachedTextEncoder.encodeInto(arg, view); + + offset += ret.written; + ptr = realloc(ptr, len, offset, 1) >>> 0; + } + + WASM_VECTOR_LEN = offset; + return ptr; +} + +function takeObject(idx) { + const ret = getObject(idx); + dropObject(idx); + return ret; +} + +let cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true }); +cachedTextDecoder.decode(); +function decodeText(ptr, len) { + return cachedTextDecoder.decode(getUint8ArrayMemory0().subarray(ptr, ptr + len)); +} + +const cachedTextEncoder = new TextEncoder(); + +if (!('encodeInto' in cachedTextEncoder)) { + cachedTextEncoder.encodeInto = function (arg, view) { + const buf = cachedTextEncoder.encode(arg); + view.set(buf); + return { + read: arg.length, + written: buf.length + }; + }; +} + +let WASM_VECTOR_LEN = 0; + +const wasmPath = `${__dirname}/ruvector_attention_wasm_bg.wasm`; +const wasmBytes = require('fs').readFileSync(wasmPath); +const wasmModule = new WebAssembly.Module(wasmBytes); +let wasm = new WebAssembly.Instance(wasmModule, __wbg_get_imports()).exports; +wasm.__wbindgen_start(); diff --git a/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm_bg.wasm b/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm_bg.wasm new file mode 100644 index 00000000..8e23dfab Binary files /dev/null and b/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm_bg.wasm differ diff --git a/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm_bg.wasm.d.ts b/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm_bg.wasm.d.ts new file mode 100644 index 00000000..7647f9ba --- /dev/null +++ b/ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm_bg.wasm.d.ts @@ -0,0 +1,71 @@ +/* tslint:disable */ +/* eslint-disable */ +export const memory: WebAssembly.Memory; +export const __wbg_wasmadam_free: (a: number, b: number) => void; +export const __wbg_wasmadamw_free: (a: number, b: number) => void; +export const __wbg_wasmflashattention_free: (a: number, b: number) => void; +export const __wbg_wasmhyperbolicattention_free: (a: number, b: number) => void; +export const __wbg_wasminfonceloss_free: (a: number, b: number) => void; +export const __wbg_wasmlinearattention_free: (a: number, b: number) => void; +export const __wbg_wasmmoeattention_free: (a: number, b: number) => void; +export const __wbg_wasmmultiheadattention_free: (a: number, b: number) => void; +export const __wbg_wasmsgd_free: (a: number, b: number) => void; +export const attention_weights: (a: number, b: number, c: number, d: number) => void; +export const available_mechanisms: () => number; +export const batch_normalize: (a: number, b: number, c: number) => void; +export const cosine_similarity: (a: number, b: number, c: number, d: number, e: number) => void; +export const l2_norm: (a: number, b: number) => number; +export const log: (a: number, b: number) => void; +export const log_error: (a: number, b: number) => void; +export const normalize: (a: number, b: number, c: number, d: number) => void; +export const pairwise_distances: (a: number, b: number) => void; +export const random_orthogonal_matrix: (a: number, b: number) => void; +export const scaled_dot_attention: (a: number, b: number, c: number, d: number, e: number, f: number) => void; +export const softmax: (a: number, b: number, c: number) => void; +export const version: (a: number) => void; +export const wasmadam_learning_rate: (a: number) => number; +export const wasmadam_new: (a: number, b: number) => number; +export const wasmadam_reset: (a: number) => void; +export const wasmadam_set_learning_rate: (a: number, b: number) => void; +export const wasmadam_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void; +export const wasmadamw_new: (a: number, b: number, c: number) => number; +export const wasmadamw_reset: (a: number) => void; +export const wasmadamw_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void; +export const wasmadamw_weight_decay: (a: number) => number; +export const wasmflashattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void; +export const wasmflashattention_new: (a: number, b: number) => number; +export const wasmhyperbolicattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void; +export const wasmhyperbolicattention_curvature: (a: number) => number; +export const wasmhyperbolicattention_new: (a: number, b: number) => number; +export const wasminfonceloss_compute: (a: number, b: number, c: number, d: number, e: number, f: number, g: number) => void; +export const wasminfonceloss_new: (a: number) => number; +export const wasmlinearattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void; +export const wasmlinearattention_new: (a: number, b: number) => number; +export const wasmlocalglobalattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void; +export const wasmlocalglobalattention_new: (a: number, b: number, c: number) => number; +export const wasmlrscheduler_get_lr: (a: number) => number; +export const wasmlrscheduler_new: (a: number, b: number, c: number) => number; +export const wasmlrscheduler_reset: (a: number) => void; +export const wasmlrscheduler_step: (a: number) => void; +export const wasmmoeattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void; +export const wasmmoeattention_new: (a: number, b: number, c: number) => number; +export const wasmmultiheadattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void; +export const wasmmultiheadattention_dim: (a: number) => number; +export const wasmmultiheadattention_new: (a: number, b: number, c: number) => void; +export const wasmmultiheadattention_num_heads: (a: number) => number; +export const wasmsgd_learning_rate: (a: number) => number; +export const wasmsgd_new: (a: number, b: number, c: number) => number; +export const wasmsgd_reset: (a: number) => void; +export const wasmsgd_set_learning_rate: (a: number, b: number) => void; +export const wasmsgd_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void; +export const init: () => void; +export const wasmadamw_set_learning_rate: (a: number, b: number) => void; +export const wasmadamw_learning_rate: (a: number) => number; +export const __wbg_wasmlocalglobalattention_free: (a: number, b: number) => void; +export const __wbg_wasmlrscheduler_free: (a: number, b: number) => void; +export const __wbindgen_export: (a: number, b: number) => number; +export const __wbindgen_export2: (a: number, b: number, c: number, d: number) => number; +export const __wbindgen_export3: (a: number) => void; +export const __wbindgen_export4: (a: number, b: number, c: number) => void; +export const __wbindgen_add_to_stack_pointer: (a: number) => number; +export const __wbindgen_start: () => void; diff --git a/ui/pose-fusion/pkg/ruvector_cnn_wasm/package.json b/ui/pose-fusion/pkg/ruvector_cnn_wasm/package.json new file mode 100644 index 00000000..f1e17faf --- /dev/null +++ b/ui/pose-fusion/pkg/ruvector_cnn_wasm/package.json @@ -0,0 +1,26 @@ +{ + "name": "ruvector-cnn-wasm", + "type": "module", + "description": "WASM bindings for ruvector-cnn - CNN feature extraction for image embeddings", + "version": "0.1.0", + "license": "MIT OR Apache-2.0", + "repository": { + "type": "git", + "url": "https://github.com/ruvnet/ruvector" + }, + "files": [ + "ruvector_cnn_wasm_bg.wasm", + "ruvector_cnn_wasm.js" + ], + "main": "ruvector_cnn_wasm.js", + "sideEffects": [ + "./snippets/*" + ], + "keywords": [ + "cnn", + "embeddings", + "wasm", + "simd", + "machine-learning" + ] +} \ No newline at end of file diff --git a/ui/pose-fusion/pkg/ruvector_cnn_wasm/ruvector_cnn_wasm.js b/ui/pose-fusion/pkg/ruvector_cnn_wasm/ruvector_cnn_wasm.js new file mode 100644 index 00000000..f899cf7b --- /dev/null +++ b/ui/pose-fusion/pkg/ruvector_cnn_wasm/ruvector_cnn_wasm.js @@ -0,0 +1,802 @@ +/** + * Configuration for CNN embedder + */ +export class EmbedderConfig { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + EmbedderConfigFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_embedderconfig_free(ptr, 0); + } + constructor() { + const ret = wasm.embedderconfig_new(); + this.__wbg_ptr = ret >>> 0; + EmbedderConfigFinalization.register(this, this.__wbg_ptr, this); + return this; + } + /** + * Output embedding dimension + * @returns {number} + */ + get embedding_dim() { + const ret = wasm.__wbg_get_embedderconfig_embedding_dim(this.__wbg_ptr); + return ret >>> 0; + } + /** + * Input image size (square) + * @returns {number} + */ + get input_size() { + const ret = wasm.__wbg_get_embedderconfig_input_size(this.__wbg_ptr); + return ret >>> 0; + } + /** + * Whether to L2 normalize embeddings + * @returns {boolean} + */ + get normalize() { + const ret = wasm.__wbg_get_embedderconfig_normalize(this.__wbg_ptr); + return ret !== 0; + } + /** + * Output embedding dimension + * @param {number} arg0 + */ + set embedding_dim(arg0) { + wasm.__wbg_set_embedderconfig_embedding_dim(this.__wbg_ptr, arg0); + } + /** + * Input image size (square) + * @param {number} arg0 + */ + set input_size(arg0) { + wasm.__wbg_set_embedderconfig_input_size(this.__wbg_ptr, arg0); + } + /** + * Whether to L2 normalize embeddings + * @param {boolean} arg0 + */ + set normalize(arg0) { + wasm.__wbg_set_embedderconfig_normalize(this.__wbg_ptr, arg0); + } +} +if (Symbol.dispose) EmbedderConfig.prototype[Symbol.dispose] = EmbedderConfig.prototype.free; + +/** + * Layer operations for building custom networks + */ +export class LayerOps { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + LayerOpsFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_layerops_free(ptr, 0); + } + /** + * Apply batch normalization (returns new array) + * @param {Float32Array} input + * @param {Float32Array} gamma + * @param {Float32Array} beta + * @param {Float32Array} mean + * @param {Float32Array} _var + * @param {number} epsilon + * @returns {Float32Array} + */ + static batch_norm(input, gamma, beta, mean, _var, epsilon) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(input, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(gamma, wasm.__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + const ptr2 = passArrayF32ToWasm0(beta, wasm.__wbindgen_export2); + const len2 = WASM_VECTOR_LEN; + const ptr3 = passArrayF32ToWasm0(mean, wasm.__wbindgen_export2); + const len3 = WASM_VECTOR_LEN; + const ptr4 = passArrayF32ToWasm0(_var, wasm.__wbindgen_export2); + const len4 = WASM_VECTOR_LEN; + wasm.layerops_batch_norm(retptr, ptr0, len0, ptr1, len1, ptr2, len2, ptr3, len3, ptr4, len4, epsilon); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var v6 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export(r0, r1 * 4, 4); + return v6; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Apply global average pooling + * Returns one value per channel + * @param {Float32Array} input + * @param {number} height + * @param {number} width + * @param {number} channels + * @returns {Float32Array} + */ + static global_avg_pool(input, height, width, channels) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(input, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + wasm.layerops_global_avg_pool(retptr, ptr0, len0, height, width, channels); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } +} +if (Symbol.dispose) LayerOps.prototype[Symbol.dispose] = LayerOps.prototype.free; + +/** + * SIMD-optimized operations + */ +export class SimdOps { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + SimdOpsFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_simdops_free(ptr, 0); + } + /** + * Dot product of two vectors + * @param {Float32Array} a + * @param {Float32Array} b + * @returns {number} + */ + static dot_product(a, b) { + const ptr0 = passArrayF32ToWasm0(a, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(b, wasm.__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + const ret = wasm.simdops_dot_product(ptr0, len0, ptr1, len1); + return ret; + } + /** + * L2 normalize a vector (returns new array) + * @param {Float32Array} data + * @returns {Float32Array} + */ + static l2_normalize(data) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(data, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + wasm.simdops_l2_normalize(retptr, ptr0, len0); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * ReLU activation (returns new array) + * @param {Float32Array} data + * @returns {Float32Array} + */ + static relu(data) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(data, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + wasm.simdops_relu(retptr, ptr0, len0); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * ReLU6 activation (returns new array) + * @param {Float32Array} data + * @returns {Float32Array} + */ + static relu6(data) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(data, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + wasm.simdops_relu6(retptr, ptr0, len0); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } +} +if (Symbol.dispose) SimdOps.prototype[Symbol.dispose] = SimdOps.prototype.free; + +/** + * WASM CNN Embedder for image feature extraction + */ +export class WasmCnnEmbedder { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmCnnEmbedderFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmcnnembedder_free(ptr, 0); + } + /** + * Compute cosine similarity between two embeddings + * @param {Float32Array} a + * @param {Float32Array} b + * @returns {number} + */ + cosine_similarity(a, b) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(a, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(b, wasm.__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + wasm.wasmcnnembedder_cosine_similarity(retptr, this.__wbg_ptr, ptr0, len0, ptr1, len1); + var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + if (r2) { + throw takeObject(r1); + } + return r0; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Get the embedding dimension + * @returns {number} + */ + get embedding_dim() { + const ret = wasm.wasmcnnembedder_embedding_dim(this.__wbg_ptr); + return ret >>> 0; + } + /** + * Extract embedding from image data (RGB format, row-major) + * @param {Uint8Array} image_data + * @param {number} width + * @param {number} height + * @returns {Float32Array} + */ + extract(image_data, width, height) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArray8ToWasm0(image_data, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + wasm.wasmcnnembedder_extract(retptr, this.__wbg_ptr, ptr0, len0, width, height); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true); + if (r3) { + throw takeObject(r2); + } + var v2 = getArrayF32FromWasm0(r0, r1).slice(); + wasm.__wbindgen_export(r0, r1 * 4, 4); + return v2; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Create a new CNN embedder + * @param {EmbedderConfig | null} [config] + */ + constructor(config) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + let ptr0 = 0; + if (!isLikeNone(config)) { + _assertClass(config, EmbedderConfig); + ptr0 = config.__destroy_into_raw(); + } + wasm.wasmcnnembedder_new(retptr, ptr0); + var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + if (r2) { + throw takeObject(r1); + } + this.__wbg_ptr = r0 >>> 0; + WasmCnnEmbedderFinalization.register(this, this.__wbg_ptr, this); + return this; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } +} +if (Symbol.dispose) WasmCnnEmbedder.prototype[Symbol.dispose] = WasmCnnEmbedder.prototype.free; + +/** + * InfoNCE loss for contrastive learning (SimCLR style) + */ +export class WasmInfoNCELoss { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmInfoNCELossFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasminfonceloss_free(ptr, 0); + } + /** + * Compute loss for a batch of embedding pairs + * embeddings: [2N, D] flattened where (i, i+N) are positive pairs + * @param {Float32Array} embeddings + * @param {number} batch_size + * @param {number} dim + * @returns {number} + */ + forward(embeddings, batch_size, dim) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(embeddings, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + wasm.wasminfonceloss_forward(retptr, this.__wbg_ptr, ptr0, len0, batch_size, dim); + var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + if (r2) { + throw takeObject(r1); + } + return r0; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Create new InfoNCE loss with temperature parameter + * @param {number} temperature + */ + constructor(temperature) { + const ret = wasm.wasminfonceloss_new(temperature); + this.__wbg_ptr = ret >>> 0; + WasmInfoNCELossFinalization.register(this, this.__wbg_ptr, this); + return this; + } + /** + * Get the temperature parameter + * @returns {number} + */ + get temperature() { + const ret = wasm.wasminfonceloss_temperature(this.__wbg_ptr); + return ret; + } +} +if (Symbol.dispose) WasmInfoNCELoss.prototype[Symbol.dispose] = WasmInfoNCELoss.prototype.free; + +/** + * Triplet loss for metric learning + */ +export class WasmTripletLoss { + __destroy_into_raw() { + const ptr = this.__wbg_ptr; + this.__wbg_ptr = 0; + WasmTripletLossFinalization.unregister(this); + return ptr; + } + free() { + const ptr = this.__destroy_into_raw(); + wasm.__wbg_wasmtripletloss_free(ptr, 0); + } + /** + * Compute loss for a batch of triplets + * @param {Float32Array} anchors + * @param {Float32Array} positives + * @param {Float32Array} negatives + * @param {number} dim + * @returns {number} + */ + forward(anchors, positives, negatives, dim) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(anchors, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(positives, wasm.__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + const ptr2 = passArrayF32ToWasm0(negatives, wasm.__wbindgen_export2); + const len2 = WASM_VECTOR_LEN; + wasm.wasmtripletloss_forward(retptr, this.__wbg_ptr, ptr0, len0, ptr1, len1, ptr2, len2, dim); + var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + if (r2) { + throw takeObject(r1); + } + return r0; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Compute loss for a single triplet + * @param {Float32Array} anchor + * @param {Float32Array} positive + * @param {Float32Array} negative + * @returns {number} + */ + forward_single(anchor, positive, negative) { + try { + const retptr = wasm.__wbindgen_add_to_stack_pointer(-16); + const ptr0 = passArrayF32ToWasm0(anchor, wasm.__wbindgen_export2); + const len0 = WASM_VECTOR_LEN; + const ptr1 = passArrayF32ToWasm0(positive, wasm.__wbindgen_export2); + const len1 = WASM_VECTOR_LEN; + const ptr2 = passArrayF32ToWasm0(negative, wasm.__wbindgen_export2); + const len2 = WASM_VECTOR_LEN; + wasm.wasmtripletloss_forward_single(retptr, this.__wbg_ptr, ptr0, len0, ptr1, len1, ptr2, len2); + var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true); + var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true); + var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true); + if (r2) { + throw takeObject(r1); + } + return r0; + } finally { + wasm.__wbindgen_add_to_stack_pointer(16); + } + } + /** + * Get the margin parameter + * @returns {number} + */ + get margin() { + const ret = wasm.wasmtripletloss_margin(this.__wbg_ptr); + return ret; + } + /** + * Create new triplet loss with margin + * @param {number} margin + */ + constructor(margin) { + const ret = wasm.wasmtripletloss_new(margin); + this.__wbg_ptr = ret >>> 0; + WasmTripletLossFinalization.register(this, this.__wbg_ptr, this); + return this; + } +} +if (Symbol.dispose) WasmTripletLoss.prototype[Symbol.dispose] = WasmTripletLoss.prototype.free; + +/** + * Initialize panic hook for better error messages + */ +export function init() { + wasm.init(); +} + +function __wbg_get_imports() { + const import0 = { + __proto__: null, + __wbg___wbindgen_throw_39bc967c0e5a9b58: function(arg0, arg1) { + throw new Error(getStringFromWasm0(arg0, arg1)); + }, + __wbg_error_a6fa202b58aa1cd3: function(arg0, arg1) { + let deferred0_0; + let deferred0_1; + try { + deferred0_0 = arg0; + deferred0_1 = arg1; + console.error(getStringFromWasm0(arg0, arg1)); + } finally { + wasm.__wbindgen_export(deferred0_0, deferred0_1, 1); + } + }, + __wbg_new_227d7c05414eb861: function() { + const ret = new Error(); + return addHeapObject(ret); + }, + __wbg_stack_3b0d974bbf31e44f: function(arg0, arg1) { + const ret = getObject(arg1).stack; + const ptr1 = passStringToWasm0(ret, wasm.__wbindgen_export2, wasm.__wbindgen_export3); + const len1 = WASM_VECTOR_LEN; + getDataViewMemory0().setInt32(arg0 + 4 * 1, len1, true); + getDataViewMemory0().setInt32(arg0 + 4 * 0, ptr1, true); + }, + __wbindgen_cast_0000000000000001: function(arg0, arg1) { + // Cast intrinsic for `Ref(String) -> Externref`. + const ret = getStringFromWasm0(arg0, arg1); + return addHeapObject(ret); + }, + __wbindgen_object_drop_ref: function(arg0) { + takeObject(arg0); + }, + }; + return { + __proto__: null, + "./ruvector_cnn_wasm_bg.js": import0, + }; +} + +const EmbedderConfigFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_embedderconfig_free(ptr >>> 0, 1)); +const LayerOpsFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_layerops_free(ptr >>> 0, 1)); +const SimdOpsFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_simdops_free(ptr >>> 0, 1)); +const WasmCnnEmbedderFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmcnnembedder_free(ptr >>> 0, 1)); +const WasmInfoNCELossFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasminfonceloss_free(ptr >>> 0, 1)); +const WasmTripletLossFinalization = (typeof FinalizationRegistry === 'undefined') + ? { register: () => {}, unregister: () => {} } + : new FinalizationRegistry(ptr => wasm.__wbg_wasmtripletloss_free(ptr >>> 0, 1)); + +function addHeapObject(obj) { + if (heap_next === heap.length) heap.push(heap.length + 1); + const idx = heap_next; + heap_next = heap[idx]; + + heap[idx] = obj; + return idx; +} + +function _assertClass(instance, klass) { + if (!(instance instanceof klass)) { + throw new Error(`expected instance of ${klass.name}`); + } +} + +function dropObject(idx) { + if (idx < 1028) return; + heap[idx] = heap_next; + heap_next = idx; +} + +function getArrayF32FromWasm0(ptr, len) { + ptr = ptr >>> 0; + return getFloat32ArrayMemory0().subarray(ptr / 4, ptr / 4 + len); +} + +let cachedDataViewMemory0 = null; +function getDataViewMemory0() { + if (cachedDataViewMemory0 === null || cachedDataViewMemory0.buffer.detached === true || (cachedDataViewMemory0.buffer.detached === undefined && cachedDataViewMemory0.buffer !== wasm.memory.buffer)) { + cachedDataViewMemory0 = new DataView(wasm.memory.buffer); + } + return cachedDataViewMemory0; +} + +let cachedFloat32ArrayMemory0 = null; +function getFloat32ArrayMemory0() { + if (cachedFloat32ArrayMemory0 === null || cachedFloat32ArrayMemory0.byteLength === 0) { + cachedFloat32ArrayMemory0 = new Float32Array(wasm.memory.buffer); + } + return cachedFloat32ArrayMemory0; +} + +function getStringFromWasm0(ptr, len) { + ptr = ptr >>> 0; + return decodeText(ptr, len); +} + +let cachedUint8ArrayMemory0 = null; +function getUint8ArrayMemory0() { + if (cachedUint8ArrayMemory0 === null || cachedUint8ArrayMemory0.byteLength === 0) { + cachedUint8ArrayMemory0 = new Uint8Array(wasm.memory.buffer); + } + return cachedUint8ArrayMemory0; +} + +function getObject(idx) { return heap[idx]; } + +let heap = new Array(1024).fill(undefined); +heap.push(undefined, null, true, false); + +let heap_next = heap.length; + +function isLikeNone(x) { + return x === undefined || x === null; +} + +function passArray8ToWasm0(arg, malloc) { + const ptr = malloc(arg.length * 1, 1) >>> 0; + getUint8ArrayMemory0().set(arg, ptr / 1); + WASM_VECTOR_LEN = arg.length; + return ptr; +} + +function passArrayF32ToWasm0(arg, malloc) { + const ptr = malloc(arg.length * 4, 4) >>> 0; + getFloat32ArrayMemory0().set(arg, ptr / 4); + WASM_VECTOR_LEN = arg.length; + return ptr; +} + +function passStringToWasm0(arg, malloc, realloc) { + if (realloc === undefined) { + const buf = cachedTextEncoder.encode(arg); + const ptr = malloc(buf.length, 1) >>> 0; + getUint8ArrayMemory0().subarray(ptr, ptr + buf.length).set(buf); + WASM_VECTOR_LEN = buf.length; + return ptr; + } + + let len = arg.length; + let ptr = malloc(len, 1) >>> 0; + + const mem = getUint8ArrayMemory0(); + + let offset = 0; + + for (; offset < len; offset++) { + const code = arg.charCodeAt(offset); + if (code > 0x7F) break; + mem[ptr + offset] = code; + } + if (offset !== len) { + if (offset !== 0) { + arg = arg.slice(offset); + } + ptr = realloc(ptr, len, len = offset + arg.length * 3, 1) >>> 0; + const view = getUint8ArrayMemory0().subarray(ptr + offset, ptr + len); + const ret = cachedTextEncoder.encodeInto(arg, view); + + offset += ret.written; + ptr = realloc(ptr, len, offset, 1) >>> 0; + } + + WASM_VECTOR_LEN = offset; + return ptr; +} + +function takeObject(idx) { + const ret = getObject(idx); + dropObject(idx); + return ret; +} + +let cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true }); +cachedTextDecoder.decode(); +const MAX_SAFARI_DECODE_BYTES = 2146435072; +let numBytesDecoded = 0; +function decodeText(ptr, len) { + numBytesDecoded += len; + if (numBytesDecoded >= MAX_SAFARI_DECODE_BYTES) { + cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true }); + cachedTextDecoder.decode(); + numBytesDecoded = len; + } + return cachedTextDecoder.decode(getUint8ArrayMemory0().subarray(ptr, ptr + len)); +} + +const cachedTextEncoder = new TextEncoder(); + +if (!('encodeInto' in cachedTextEncoder)) { + cachedTextEncoder.encodeInto = function (arg, view) { + const buf = cachedTextEncoder.encode(arg); + view.set(buf); + return { + read: arg.length, + written: buf.length + }; + }; +} + +let WASM_VECTOR_LEN = 0; + +let wasmModule, wasm; +function __wbg_finalize_init(instance, module) { + wasm = instance.exports; + wasmModule = module; + cachedDataViewMemory0 = null; + cachedFloat32ArrayMemory0 = null; + cachedUint8ArrayMemory0 = null; + wasm.__wbindgen_start(); + return wasm; +} + +async function __wbg_load(module, imports) { + if (typeof Response === 'function' && module instanceof Response) { + if (typeof WebAssembly.instantiateStreaming === 'function') { + try { + return await WebAssembly.instantiateStreaming(module, imports); + } catch (e) { + const validResponse = module.ok && expectedResponseType(module.type); + + if (validResponse && module.headers.get('Content-Type') !== 'application/wasm') { + console.warn("`WebAssembly.instantiateStreaming` failed because your server does not serve Wasm with `application/wasm` MIME type. Falling back to `WebAssembly.instantiate` which is slower. Original error:\n", e); + + } else { throw e; } + } + } + + const bytes = await module.arrayBuffer(); + return await WebAssembly.instantiate(bytes, imports); + } else { + const instance = await WebAssembly.instantiate(module, imports); + + if (instance instanceof WebAssembly.Instance) { + return { instance, module }; + } else { + return instance; + } + } + + function expectedResponseType(type) { + switch (type) { + case 'basic': case 'cors': case 'default': return true; + } + return false; + } +} + +function initSync(module) { + if (wasm !== undefined) return wasm; + + + if (module !== undefined) { + if (Object.getPrototypeOf(module) === Object.prototype) { + ({module} = module) + } else { + console.warn('using deprecated parameters for `initSync()`; pass a single object instead') + } + } + + const imports = __wbg_get_imports(); + if (!(module instanceof WebAssembly.Module)) { + module = new WebAssembly.Module(module); + } + const instance = new WebAssembly.Instance(module, imports); + return __wbg_finalize_init(instance, module); +} + +async function __wbg_init(module_or_path) { + if (wasm !== undefined) return wasm; + + + if (module_or_path !== undefined) { + if (Object.getPrototypeOf(module_or_path) === Object.prototype) { + ({module_or_path} = module_or_path) + } else { + console.warn('using deprecated parameters for the initialization function; pass a single object instead') + } + } + + if (module_or_path === undefined) { + module_or_path = new URL('ruvector_cnn_wasm_bg.wasm', import.meta.url); + } + const imports = __wbg_get_imports(); + + if (typeof module_or_path === 'string' || (typeof Request === 'function' && module_or_path instanceof Request) || (typeof URL === 'function' && module_or_path instanceof URL)) { + module_or_path = fetch(module_or_path); + } + + const { instance, module } = await __wbg_load(await module_or_path, imports); + + return __wbg_finalize_init(instance, module); +} + +export { initSync, __wbg_init as default }; diff --git a/ui/pose-fusion/pkg/ruvector_cnn_wasm/ruvector_cnn_wasm_bg.wasm b/ui/pose-fusion/pkg/ruvector_cnn_wasm/ruvector_cnn_wasm_bg.wasm new file mode 100644 index 00000000..a1a54ee2 Binary files /dev/null and b/ui/pose-fusion/pkg/ruvector_cnn_wasm/ruvector_cnn_wasm_bg.wasm differ