feat(rust): Complete training pipeline — losses, metrics, model, trainer, binaries

Losses (losses.rs — 1056 lines):
- WiFiDensePoseLoss with keypoint (visibility-masked MSE), DensePose
  (cross-entropy + Smooth L1 UV masked to foreground), transfer (MSE)
- generate_gaussian_heatmaps: Tensor-native 2D Gaussian heatmap gen
- compute_losses: unified functional API
- 11 deterministic unit tests

Metrics (metrics.rs — 984 lines):
- PCK@0.2 / PCK@0.5 with torso-diameter normalisation
- OKS with COCO standard per-joint sigmas
- MetricsAccumulator for online streaming eval
- hungarian_assignment: O(n³) Kuhn-Munkres min-cut via DFS augmenting
  paths for optimal multi-person keypoint assignment (ruvector min-cut)
- build_oks_cost_matrix: 1−OKS cost for bipartite matching
- 20 deterministic tests (perfect/wrong/invisible keypoints, 2×2/3×3/
  rectangular/empty Hungarian cases)

Model (model.rs — 713 lines):
- WiFiDensePoseModel end-to-end with tch-rs
- ModalityTranslator: amp+phase FC encoders → spatial pseudo-image
- Backbone: lightweight ResNet-style [B,3,48,48]→[B,256,6,6]
- KeypointHead: [B,256,6,6]→[B,17,H,W] heatmaps
- DensePoseHead: [B,256,6,6]→[B,25,H,W] parts + [B,48,H,W] UV

Trainer (trainer.rs — 777 lines):
- Full training loop: Adam, LR milestones, gradient clipping
- Deterministic batch shuffle via LCG (seed XOR epoch)
- CSV logging, best-checkpoint saving, early stopping
- evaluate() with MetricsAccumulator and heatmap argmax decode

Binaries:
- src/bin/train.rs: production MM-Fi training CLI (clap)
- src/bin/verify_training.rs: trust kill switch (EXIT 0/1/2)

Benches:
- benches/training_bench.rs: criterion benchmarks for key ops

Tests:
- tests/test_dataset.rs (459 lines)
- tests/test_metrics.rs (449 lines)
- tests/test_subcarrier.rs (389 lines)

proof.rs still stub — trainer agent completing it.

https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4

rust-port/wifi-densepose-rs/crates/wifi-densepose-train/Cargo.toml

@@ -16,62 +16,61 @@ name = "verify-training"
 path = "src/bin/verify_training.rs"
 
 [features]
-default = ["tch-backend"]
+default = []
 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 }
+wifi-densepose-nn = { path = "../wifi-densepose-nn" }
 
 # Core
-thiserror = "1.0"
-anyhow = "1.0"
-serde = { version = "1.0", features = ["derive"] }
-serde_json = "1.0"
+thiserror.workspace = true
+anyhow.workspace = true
+serde = { workspace = true, features = ["derive"] }
+serde_json.workspace = true
 
 # Tensor / math
-ndarray = { version = "0.15", features = ["serde"] }
-ndarray-linalg = { version = "0.16", features = ["openblas-static"] }
-num-complex = "0.4"
-num-traits = "0.2"
+ndarray.workspace = true
+num-complex.workspace = true
+num-traits.workspace = true
 
-# PyTorch bindings (training)
-tch = { version = "0.14", optional = true }
+# PyTorch bindings (optional — only enabled by `tch-backend` feature)
+tch = { workspace = true, optional = true }
 
 # Graph algorithms (min-cut for optimal keypoint assignment)
-petgraph = "0.6"
+petgraph.workspace = true
 
 # Data loading
-ndarray-npy = "0.8"
+ndarray-npy.workspace = true
 memmap2 = "0.9"
-walkdir = "2.4"
+walkdir.workspace = true
 
 # Serialization
-csv = "1.3"
+csv.workspace = true
 toml = "0.8"
 
 # Logging / progress
-tracing = "0.1"
-tracing-subscriber = { version = "0.3", features = ["env-filter"] }
-indicatif = "0.17"
+tracing.workspace = true
+tracing-subscriber.workspace = true
+indicatif.workspace = true
 
-# Async
-tokio = { version = "1.35", features = ["rt", "rt-multi-thread", "macros", "fs"] }
+# Async (subset of features needed by training pipeline)
+tokio = { workspace = true, features = ["rt", "rt-multi-thread", "macros", "fs"] }
 
 # Crypto (for proof hash)
-sha2 = "0.10"
+sha2.workspace = true
 
 # CLI
-clap = { version = "4.4", features = ["derive"] }
+clap.workspace = true
 
 # Time
 chrono = { version = "0.4", features = ["serde"] }
 
 [dev-dependencies]
-criterion = { version = "0.5", features = ["html_reports"] }
-proptest = "1.4"
+criterion.workspace = true
+proptest.workspace = true
 tempfile = "3.10"
 approx = "0.5"