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/src/losses.rs

@@ -26,9 +26,12 @@ use tch::{Kind, Reduction, Tensor};
 // Public types
 // ─────────────────────────────────────────────────────────────────────────────
 
-/// Scalar components produced by a single forward pass through the combined loss.
+/// Scalar components produced by a single forward pass through [`WiFiDensePoseLoss::forward`].
+///
+/// Contains `f32` scalar values extracted from the computation graph for
+/// logging and checkpointing (they are not used for back-propagation).
 #[derive(Debug, Clone)]
-pub struct LossOutput {
+pub struct WiFiLossComponents {
     /// Total weighted loss value (scalar, in ℝ≥0).
     pub total: f32,
     /// Keypoint heatmap MSE loss component.
@@ -159,7 +162,7 @@ impl WiFiDensePoseLoss {
 
         // ── 2. UV regression: Smooth-L1 masked by foreground pixels ────────
         // Foreground mask: pixels where target part ≠ 0, shape [B, H, W].
-        let fg_mask = target_int.not_equal(0);
+        let fg_mask = target_int.not_equal(0_i64);
         // Expand to [B, 1, H, W] then broadcast to [B, 48, H, W].
         let fg_mask_f = fg_mask
             .unsqueeze(1)
@@ -218,7 +221,7 @@ impl WiFiDensePoseLoss {
         target_uv: Option<&Tensor>,
         student_features: Option<&Tensor>,
         teacher_features: Option<&Tensor>,
-    ) -> (Tensor, LossOutput) {
+    ) -> (Tensor, WiFiLossComponents) {
         let mut details = HashMap::new();
 
         // ── Keypoint loss (always computed) ───────────────────────────────
@@ -243,7 +246,7 @@ impl WiFiDensePoseLoss {
                 let part_val = part_loss.double_value(&[]) as f32;
 
                 // UV loss (foreground masked)
-                let fg_mask = target_int.not_equal(0);
+                let fg_mask = target_int.not_equal(0_i64);
                 let fg_mask_f = fg_mask
                     .unsqueeze(1)
                     .expand_as(pu)
@@ -280,7 +283,7 @@ impl WiFiDensePoseLoss {
 
         let total_val = total.double_value(&[]) as f32;
 
-        let output = LossOutput {
+        let output = WiFiLossComponents {
             total: total_val,
             keypoint: kp_val as f32,
             densepose: dp_val,