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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.
1.5 KiB
1.5 KiB
ADR-003: Neural Network Inference Strategy
Status
Accepted
Context
The WiFi-DensePose system requires neural network inference for:
- Modality translation (CSI → visual features)
- DensePose estimation (body part segmentation + UV mapping)
We need to select an inference strategy that supports pre-trained models and multiple backends.
Decision
We will implement a multi-backend inference engine:
Primary Backend: ONNX Runtime (ort crate)
- Load pre-trained PyTorch models exported to ONNX
- GPU acceleration via CUDA/TensorRT
- Cross-platform support
Alternative Backends (Feature-gated)
tch-rs: PyTorch C++ bindingscandle: Pure Rust ML framework
Architecture
pub trait Backend: Send + Sync {
fn load_model(&mut self, path: &Path) -> NnResult<()>;
fn run(&self, inputs: HashMap<String, Tensor>) -> NnResult<HashMap<String, Tensor>>;
fn input_specs(&self) -> Vec<TensorSpec>;
fn output_specs(&self) -> Vec<TensorSpec>;
}
Feature Flags
[features]
default = ["onnx"]
onnx = ["ort"]
tch-backend = ["tch"]
candle-backend = ["candle-core", "candle-nn"]
cuda = ["ort/cuda"]
tensorrt = ["ort/tensorrt"]
Consequences
Positive
- Use existing trained models (no retraining)
- Multiple backend options for different deployments
- GPU acceleration when available
- Feature flags minimize binary size
Negative
- ONNX model conversion required
- ort crate pulls in C++ dependencies
- tch requires libtorch installation