feat(rust): Add wifi-densepose-train crate with full training pipeline
Implements the training infrastructure described in ADR-015:
- config.rs: TrainingConfig with all hyperparams (batch size, LR,
loss weights, subcarrier interp method, validation split)
- dataset.rs: MmFiDataset (real MM-Fi .npy loader) + SyntheticDataset
(deterministic LCG, seed=42, proof/testing only — never production)
- subcarrier.rs: Linear/cubic interpolation 114→56 subcarriers
- error.rs: Typed errors (DataNotFound, InvalidFormat, IoError)
- losses.rs: Keypoint heatmap (MSE), DensePose (CE + Smooth L1),
teacher-student transfer (MSE), Gaussian heatmap generation
- metrics.rs: PCK@0.2, OKS with Hungarian min-cut bipartite assignment
via petgraph (optimal multi-person keypoint matching)
- model.rs: WiFiDensePoseModel end-to-end with tch-rs (PyTorch bindings)
- trainer.rs: Full training loop, LR scheduling, gradient clipping,
early stopping, CSV logging, best-checkpoint saving
- proof.rs: Deterministic training proof (SHA-256 trust kill switch)
No random data in production paths. SyntheticDataset uses deterministic
LCG (a=1664525, c=1013904223) — same seed always produces same output.
https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
ec98e40fff