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/benches/training_bench.rs

@@ -0,0 +1,149 @@
+//! Benchmarks for the WiFi-DensePose training pipeline.
+//!
+//! Run with:
+//! ```bash
+//! cargo bench -p wifi-densepose-train
+//! ```
+//!
+//! Criterion HTML reports are written to `target/criterion/`.
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
+use ndarray::Array4;
+use wifi_densepose_train::{
+    config::TrainingConfig,
+    dataset::{CsiDataset, SyntheticCsiDataset, SyntheticConfig},
+    subcarrier::{compute_interp_weights, interpolate_subcarriers},
+};
+
+// ---------------------------------------------------------------------------
+// Dataset benchmarks
+// ---------------------------------------------------------------------------
+
+/// Benchmark synthetic sample generation for a single index.
+fn bench_synthetic_get(c: &mut Criterion) {
+    let syn_cfg = SyntheticConfig::default();
+    let dataset = SyntheticCsiDataset::new(1000, syn_cfg);
+
+    c.bench_function("synthetic_dataset_get", |b| {
+        b.iter(|| {
+            let _ = dataset.get(black_box(42)).expect("sample 42 must exist");
+        });
+    });
+}
+
+/// Benchmark full epoch iteration (no I/O — all in-process).
+fn bench_synthetic_epoch(c: &mut Criterion) {
+    let mut group = c.benchmark_group("synthetic_epoch");
+
+    for n_samples in [64usize, 256, 1024] {
+        let syn_cfg = SyntheticConfig::default();
+        let dataset = SyntheticCsiDataset::new(n_samples, syn_cfg);
+
+        group.bench_with_input(
+            BenchmarkId::new("samples", n_samples),
+            &n_samples,
+            |b, &n| {
+                b.iter(|| {
+                    for i in 0..n {
+                        let _ = dataset.get(black_box(i)).expect("sample exists");
+                    }
+                });
+            },
+        );
+    }
+
+    group.finish();
+}
+
+// ---------------------------------------------------------------------------
+// Subcarrier interpolation benchmarks
+// ---------------------------------------------------------------------------
+
+/// Benchmark `interpolate_subcarriers` for the standard 114 → 56 use-case.
+fn bench_interp_114_to_56(c: &mut Criterion) {
+    // Simulate a single sample worth of raw CSI from MM-Fi.
+    let cfg = TrainingConfig::default();
+    let arr: Array4<f32> = Array4::from_shape_fn(
+        (cfg.window_frames, cfg.num_antennas_tx, cfg.num_antennas_rx, 114),
+        |(t, tx, rx, k)| (t + tx + rx + k) as f32 * 0.001,
+    );
+
+    c.bench_function("interp_114_to_56", |b| {
+        b.iter(|| {
+            let _ = interpolate_subcarriers(black_box(&arr), black_box(56));
+        });
+    });
+}
+
+/// Benchmark `compute_interp_weights` to ensure it is fast enough to
+/// precompute at dataset construction time.
+fn bench_compute_interp_weights(c: &mut Criterion) {
+    c.bench_function("compute_interp_weights_114_56", |b| {
+        b.iter(|| {
+            let _ = compute_interp_weights(black_box(114), black_box(56));
+        });
+    });
+}
+
+/// Benchmark interpolation for varying source subcarrier counts.
+fn bench_interp_scaling(c: &mut Criterion) {
+    let mut group = c.benchmark_group("interp_scaling");
+    let cfg = TrainingConfig::default();
+
+    for src_sc in [56usize, 114, 256, 512] {
+        let arr: Array4<f32> = Array4::zeros((
+            cfg.window_frames,
+            cfg.num_antennas_tx,
+            cfg.num_antennas_rx,
+            src_sc,
+        ));
+
+        group.bench_with_input(
+            BenchmarkId::new("src_sc", src_sc),
+            &src_sc,
+            |b, &sc| {
+                if sc == 56 {
+                    // Identity case — skip; interpolate_subcarriers clones.
+                    b.iter(|| {
+                        let _ = arr.clone();
+                    });
+                } else {
+                    b.iter(|| {
+                        let _ = interpolate_subcarriers(black_box(&arr), black_box(56));
+                    });
+                }
+            },
+        );
+    }
+
+    group.finish();
+}
+
+// ---------------------------------------------------------------------------
+// Config benchmarks
+// ---------------------------------------------------------------------------
+
+/// Benchmark TrainingConfig::validate() to ensure it stays O(1).
+fn bench_config_validate(c: &mut Criterion) {
+    let config = TrainingConfig::default();
+    c.bench_function("config_validate", |b| {
+        b.iter(|| {
+            let _ = black_box(&config).validate();
+        });
+    });
+}
+
+// ---------------------------------------------------------------------------
+// Criterion main
+// ---------------------------------------------------------------------------
+
+criterion_group!(
+    benches,
+    bench_synthetic_get,
+    bench_synthetic_epoch,
+    bench_interp_114_to_56,
+    bench_compute_interp_weights,
+    bench_interp_scaling,
+    bench_config_validate,
+);
+criterion_main!(benches);