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ruv
99fea9df9b
Root cause (investigated, same rigor as the MAT fix): `wifi-densepose-train` declared `wifi-densepose-nn` as a dependency but NEVER imported it anywhere in src/ or bin/ — a dead dependency. (The tch-backend model path uses the `tch` crate directly, not wifi-densepose-nn; `grep -r wifi_densepose_nn src/` returns nothing.) That dead dep pulled `ort` (ONNX Runtime) + reqwest/hyper into every downstream consumer, including the ADR-185 `[meridian]` wheel, which binds only train's pure-compute geometry / rapid_adapt / eval modules + signal::hardware_norm. `cargo tree -i wifi-densepose-nn` confirmed train was the SOLE path pulling nn into the wheel; after removal, the package is absent from the graph. Fix: remove the dead dep line from train's Cargo.toml (no code change, no feature-gating needed — nothing referenced it). Not a hoist: the coupling turned out to be nonexistent, not deep. Measured (maturin build --release --features meridian --strip): BEFORE: 1.8 MB AFTER: 1.7 MB (both already within the ADR-117 §5.4 <=5 MB budget). Honest note: unlike MAT — which instantiates OnnxSession and thus bundles the ort native runtime (8.4 MB) — train never references ort, so the native lib was dead-stripped and the wheel was already under budget. The real win is removing ort/reqwest/hyper from train's dependency graph and compile for ALL consumers, not a dramatic wheel shrink. Verified: cargo build -p wifi-densepose-train (default=[]) OK cargo tree -i wifi-densepose-nn -> no longer in the graph cargo test --features meridian --test meridian_parity 2/2 pass maturin develop --features meridian + pytest test_meridian 13/13 pass
wifi-densepose-train
Complete training pipeline for WiFi-DensePose, integrated with all five ruvector crates.
Overview
wifi-densepose-train provides everything needed to train the WiFi-to-DensePose model: dataset
loading, subcarrier interpolation, loss functions, evaluation metrics, and the training loop
orchestrator. It supports both the MM-Fi dataset (NeurIPS 2023) and deterministic synthetic data
for reproducible experiments.
Without the tch-backend feature the crate still provides the dataset, configuration, and
subcarrier interpolation APIs needed for data preprocessing and proof verification.
Features
- MM-Fi dataset loader -- Reads the MM-Fi multimodal dataset (NeurIPS 2023) from disk with
memory-mapped
.npyfiles. - Synthetic dataset -- Deterministic, fixed-seed CSI generation for unit tests and proofs.
- Subcarrier interpolation -- 114 -> 56 subcarrier compression via
ruvector-solversparse interpolation with variance-based selection. - Loss functions (
tch-backend) -- Pose estimation losses including MSE, OKS, and combined multi-task loss. - Metrics (
tch-backend) -- PCKh, OKS-AP, and per-keypoint evaluation withruvector-mincut-based person matching. - Training orchestrator (
tch-backend) -- Full training loop with learning rate scheduling, gradient clipping, checkpointing, and reproducible proofs. - All 5 ruvector crates --
ruvector-mincut,ruvector-attn-mincut,ruvector-temporal-tensor,ruvector-solver, andruvector-attentionintegrated across dataset loading, metrics, and model attention.
Feature flags
| Flag | Default | Description |
|---|---|---|
tch-backend |
no | Enable PyTorch training via tch-rs |
cuda |
no | CUDA GPU acceleration (implies tch) |
Binaries
| Binary | Description |
|---|---|
train |
Main training entry point |
verify-training |
Proof verification (requires tch-backend) |
Quick Start
use wifi_densepose_train::config::TrainingConfig;
use wifi_densepose_train::dataset::{SyntheticCsiDataset, SyntheticConfig, CsiDataset};
// Build and validate config
let config = TrainingConfig::default();
config.validate().expect("config is valid");
// Create a synthetic dataset (deterministic, fixed-seed)
let syn_cfg = SyntheticConfig::default();
let dataset = SyntheticCsiDataset::new(200, syn_cfg);
// Load one sample
let sample = dataset.get(0).unwrap();
println!("amplitude shape: {:?}", sample.amplitude.shape());
Architecture
wifi-densepose-train/src/
lib.rs -- Re-exports, VERSION
config.rs -- TrainingConfig, hyperparameters, validation
dataset.rs -- CsiDataset trait, MmFiDataset, SyntheticCsiDataset, DataLoader
error.rs -- TrainError, ConfigError, DatasetError, SubcarrierError
subcarrier.rs -- interpolate_subcarriers (114->56), variance-based selection
losses.rs -- (tch) MSE, OKS, multi-task loss [feature-gated]
metrics.rs -- (tch) PCKh, OKS-AP, person matching [feature-gated]
model.rs -- (tch) Model definition with attention [feature-gated]
proof.rs -- (tch) Deterministic training proofs [feature-gated]
trainer.rs -- (tch) Training loop orchestrator [feature-gated]
Related Crates
| Crate | Role |
|---|---|
wifi-densepose-signal |
Signal preprocessing consumed by dataset loaders |
wifi-densepose-nn |
Inference engine that loads trained models |
ruvector-mincut |
Person matching in metrics |
ruvector-attn-mincut |
Attention-weighted graph cuts |
ruvector-temporal-tensor |
Compressed CSI buffering in datasets |
ruvector-solver |
Sparse subcarrier interpolation |
ruvector-attention |
Spatial attention in model |
License
MIT OR Apache-2.0