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

rust-port/wifi-densepose-rs/crates/wifi-densepose-train/src/lib.rs

@@ -0,0 +1,61 @@
+//! # WiFi-DensePose Training Infrastructure
+//!
+//! This crate provides the complete training pipeline for the WiFi-DensePose pose
+//! estimation model. It includes configuration management, dataset loading with
+//! subcarrier interpolation, loss functions, evaluation metrics, and the training
+//! loop orchestrator.
+//!
+//! ## Architecture
+//!
+//! ```text
+//! TrainingConfig ──► Trainer ──► Model
+//!       │               │
+//!       │           DataLoader
+//!       │               │
+//!       │         CsiDataset (MmFiDataset | SyntheticCsiDataset)
+//!       │               │
+//!       │         subcarrier::interpolate_subcarriers
+//!       │
+//!       └──► losses / metrics
+//! ```
+//!
+//! ## Quick Start
+//!
+//! ```rust,no_run
+//! use wifi_densepose_train::config::TrainingConfig;
+//! use wifi_densepose_train::dataset::{SyntheticCsiDataset, SyntheticConfig, CsiDataset};
+//!
+//! // Build 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());
+//! ```
+
+#![forbid(unsafe_code)]
+#![warn(missing_docs)]
+
+pub mod config;
+pub mod dataset;
+pub mod error;
+pub mod losses;
+pub mod metrics;
+pub mod model;
+pub mod proof;
+pub mod subcarrier;
+pub mod trainer;
+
+// Convenient re-exports at the crate root.
+pub use config::TrainingConfig;
+pub use dataset::{CsiDataset, CsiSample, DataLoader, MmFiDataset, SyntheticCsiDataset, SyntheticConfig};
+pub use error::{ConfigError, DatasetError, SubcarrierError, TrainError, TrainResult};
+pub use subcarrier::{compute_interp_weights, interpolate_subcarriers, select_subcarriers_by_variance};
+
+/// Crate version string.
+pub const VERSION: &str = env!("CARGO_PKG_VERSION");