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https://github.com/ruvnet/RuView
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rUv
f49c722764
The Rust port lived two directories deep (rust-port/wifi-densepose-rs/) without any sibling under rust-port/ that warranted the extra level. Move the whole workspace up to v2/ to match v1/ (Python) at the same depth and shorten every cd / build command across the repo. git mv preserves history for all tracked files. 60 files updated for path references (CI workflows, ADRs, docs, scripts, READMEs, internal .claude-flow state). Two manual fixes for relative-cd paths in CLAUDE.md and ADR-043 that became wrong after the depth change (cd ../.. → cd ..). Validated: - cargo check --workspace --no-default-features → clean (after target/ nuke; the gitignored target/ was carried by the OS rename and had hard-coded old paths in build scripts) - cargo test --workspace --no-default-features → 1,539 passed, 0 failed, 8 ignored (same totals as pre-rename) - ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm) After-merge follow-up: contributors should `rm -rf v2/target` once and let cargo regenerate from the new path.
58 lines
1.5 KiB
Markdown
58 lines
1.5 KiB
Markdown
# ADR-003: Neural Network Inference Strategy
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## Status
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Accepted
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## Context
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The WiFi-DensePose system requires neural network inference for:
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1. Modality translation (CSI → visual features)
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2. DensePose estimation (body part segmentation + UV mapping)
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We need to select an inference strategy that supports pre-trained models and multiple backends.
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## Decision
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We will implement a multi-backend inference engine:
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### Primary Backend: ONNX Runtime (`ort` crate)
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- Load pre-trained PyTorch models exported to ONNX
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- GPU acceleration via CUDA/TensorRT
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- Cross-platform support
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### Alternative Backends (Feature-gated)
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- `tch-rs`: PyTorch C++ bindings
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- `candle`: Pure Rust ML framework
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### Architecture
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```rust
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pub trait Backend: Send + Sync {
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fn load_model(&mut self, path: &Path) -> NnResult<()>;
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fn run(&self, inputs: HashMap<String, Tensor>) -> NnResult<HashMap<String, Tensor>>;
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fn input_specs(&self) -> Vec<TensorSpec>;
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fn output_specs(&self) -> Vec<TensorSpec>;
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}
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```
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### Feature Flags
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```toml
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[features]
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default = ["onnx"]
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onnx = ["ort"]
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tch-backend = ["tch"]
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candle-backend = ["candle-core", "candle-nn"]
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cuda = ["ort/cuda"]
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tensorrt = ["ort/tensorrt"]
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```
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## Consequences
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### Positive
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- Use existing trained models (no retraining)
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- Multiple backend options for different deployments
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- GPU acceleration when available
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- Feature flags minimize binary size
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### Negative
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- ONNX model conversion required
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- ort crate pulls in C++ dependencies
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- tch requires libtorch installation
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