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/Cargo.toml

@@ -0,0 +1,80 @@
+[package]
+name = "wifi-densepose-train"
+version = "0.1.0"
+edition = "2021"
+authors = ["WiFi-DensePose Contributors"]
+license = "MIT OR Apache-2.0"
+description = "Training pipeline for WiFi-DensePose pose estimation"
+keywords = ["wifi", "training", "pose-estimation", "deep-learning"]
+
+[[bin]]
+name = "train"
+path = "src/bin/train.rs"
+
+[[bin]]
+name = "verify-training"
+path = "src/bin/verify_training.rs"
+
+[features]
+default = ["tch-backend"]
+tch-backend = ["tch"]
+cuda = ["tch-backend"]
+
+[dependencies]
+# Internal crates
+wifi-densepose-signal = { path = "../wifi-densepose-signal" }
+wifi-densepose-nn = { path = "../wifi-densepose-nn", default-features = false }
+
+# Core
+thiserror = "1.0"
+anyhow = "1.0"
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+
+# Tensor / math
+ndarray = { version = "0.15", features = ["serde"] }
+ndarray-linalg = { version = "0.16", features = ["openblas-static"] }
+num-complex = "0.4"
+num-traits = "0.2"
+
+# PyTorch bindings (training)
+tch = { version = "0.14", optional = true }
+
+# Graph algorithms (min-cut for optimal keypoint assignment)
+petgraph = "0.6"
+
+# Data loading
+ndarray-npy = "0.8"
+memmap2 = "0.9"
+walkdir = "2.4"
+
+# Serialization
+csv = "1.3"
+toml = "0.8"
+
+# Logging / progress
+tracing = "0.1"
+tracing-subscriber = { version = "0.3", features = ["env-filter"] }
+indicatif = "0.17"
+
+# Async
+tokio = { version = "1.35", features = ["rt", "rt-multi-thread", "macros", "fs"] }
+
+# Crypto (for proof hash)
+sha2 = "0.10"
+
+# CLI
+clap = { version = "4.4", features = ["derive"] }
+
+# Time
+chrono = { version = "0.4", features = ["serde"] }
+
+[dev-dependencies]
+criterion = { version = "0.5", features = ["html_reports"] }
+proptest = "1.4"
+tempfile = "3.10"
+approx = "0.5"
+
+[[bench]]
+name = "training_bench"
+harness = false