9bbe95648c
Full implementation of Project AETHER — Contrastive CSI Embedding Model. ## Phases Delivered 1. ProjectionHead (64→128→128) + L2 normalization 2. CsiAugmenter (5 physically-motivated augmentations) 3. InfoNCE contrastive loss + SimCLR pretraining 4. FingerprintIndex (4 index types: env, activity, temporal, person) 5. RVF SEG_EMBED (0x0C) + CLI integration 6. Cross-modal alignment (PoseEncoder + InfoNCE) 7. Deep RuVector: MicroLoRA, EWC++, drift detection, hard-negative mining, SEG_LORA ## Stats - 276 tests passing (191 lib + 51 bin + 16 rvf + 18 vitals) - 3,342 additions across 8 files - Zero unsafe/unwrap/panic/todo stubs - ~55KB INT8 model for ESP32 edge deployment Also fixes deprecated GitHub Actions (v3→v4) and adds feat/* branch CI triggers. Closes #50
1189 lines
49 KiB
Rust
1189 lines
49 KiB
Rust
//! Training loop with multi-term loss function for WiFi DensePose (ADR-023 Phase 4).
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//!
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//! 6-term composite loss, SGD with momentum, cosine annealing LR scheduler,
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//! PCK/OKS validation metrics, numerical gradient estimation, and checkpointing.
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//! All arithmetic uses f32. No external ML framework dependencies.
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use std::path::Path;
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use crate::graph_transformer::{CsiToPoseTransformer, TransformerConfig};
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use crate::embedding::{CsiAugmenter, ProjectionHead, info_nce_loss};
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use crate::dataset;
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use crate::sona::EwcRegularizer;
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/// Standard COCO keypoint sigmas for OKS (17 keypoints).
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pub const COCO_KEYPOINT_SIGMAS: [f32; 17] = [
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0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
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0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089,
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];
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/// Symmetric keypoint pairs (left, right) indices into 17-keypoint COCO layout.
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const SYMMETRY_PAIRS: [(usize, usize); 5] =
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[(5, 6), (7, 8), (9, 10), (11, 12), (13, 14)];
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/// Individual loss terms from the composite loss (6 supervised + 1 contrastive).
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#[derive(Debug, Clone, Default)]
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pub struct LossComponents {
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pub keypoint: f32,
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pub body_part: f32,
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pub uv: f32,
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pub temporal: f32,
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pub edge: f32,
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pub symmetry: f32,
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/// Contrastive loss (InfoNCE); only active during pretraining or when configured.
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pub contrastive: f32,
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}
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/// Per-term weights for the composite loss function.
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#[derive(Debug, Clone)]
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pub struct LossWeights {
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pub keypoint: f32,
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pub body_part: f32,
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pub uv: f32,
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pub temporal: f32,
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pub edge: f32,
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pub symmetry: f32,
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/// Contrastive loss weight (default 0.0; set >0 for joint training).
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pub contrastive: f32,
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}
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impl Default for LossWeights {
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fn default() -> Self {
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Self {
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keypoint: 1.0, body_part: 0.5, uv: 0.5, temporal: 0.1,
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edge: 0.2, symmetry: 0.1, contrastive: 0.0,
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}
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}
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}
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/// Mean squared error on keypoints (x, y, confidence).
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pub fn keypoint_mse(pred: &[(f32, f32, f32)], target: &[(f32, f32, f32)]) -> f32 {
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if pred.is_empty() || target.is_empty() { return 0.0; }
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let n = pred.len().min(target.len());
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let sum: f32 = pred.iter().zip(target.iter()).take(n).map(|(p, t)| {
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(p.0 - t.0).powi(2) + (p.1 - t.1).powi(2) + (p.2 - t.2).powi(2)
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}).sum();
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sum / n as f32
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}
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/// Cross-entropy loss for body part classification.
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/// `pred` = raw logits (length `n_samples * n_parts`), `target` = class indices.
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pub fn body_part_cross_entropy(pred: &[f32], target: &[u8], n_parts: usize) -> f32 {
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if target.is_empty() || n_parts == 0 || pred.len() < n_parts { return 0.0; }
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let n_samples = target.len().min(pred.len() / n_parts);
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if n_samples == 0 { return 0.0; }
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let mut total = 0.0f32;
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for i in 0..n_samples {
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let logits = &pred[i * n_parts..(i + 1) * n_parts];
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let class = target[i] as usize;
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if class >= n_parts { continue; }
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let max_l = logits.iter().copied().fold(f32::NEG_INFINITY, f32::max);
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let lse = logits.iter().map(|&l| (l - max_l).exp()).sum::<f32>().ln() + max_l;
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total += -logits[class] + lse;
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}
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total / n_samples as f32
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}
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/// L1 loss on UV coordinates.
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pub fn uv_regression_loss(pu: &[f32], pv: &[f32], tu: &[f32], tv: &[f32]) -> f32 {
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let n = pu.len().min(pv.len()).min(tu.len()).min(tv.len());
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if n == 0 { return 0.0; }
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let s: f32 = (0..n).map(|i| (pu[i] - tu[i]).abs() + (pv[i] - tv[i]).abs()).sum();
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s / n as f32
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}
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/// Temporal consistency loss: penalizes large frame-to-frame keypoint jumps.
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pub fn temporal_consistency_loss(prev: &[(f32, f32, f32)], curr: &[(f32, f32, f32)]) -> f32 {
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let n = prev.len().min(curr.len());
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if n == 0 { return 0.0; }
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let s: f32 = prev.iter().zip(curr.iter()).take(n)
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.map(|(p, c)| (c.0 - p.0).powi(2) + (c.1 - p.1).powi(2)).sum();
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s / n as f32
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}
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/// Graph edge loss: penalizes deviation of bone lengths from expected values.
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pub fn graph_edge_loss(
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kp: &[(f32, f32, f32)], edges: &[(usize, usize)], expected: &[f32],
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) -> f32 {
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if edges.is_empty() || edges.len() != expected.len() { return 0.0; }
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let (mut sum, mut cnt) = (0.0f32, 0usize);
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for (i, &(a, b)) in edges.iter().enumerate() {
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if a >= kp.len() || b >= kp.len() { continue; }
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let d = ((kp[a].0 - kp[b].0).powi(2) + (kp[a].1 - kp[b].1).powi(2)).sqrt();
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sum += (d - expected[i]).powi(2);
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cnt += 1;
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}
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if cnt == 0 { 0.0 } else { sum / cnt as f32 }
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}
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/// Symmetry loss: penalizes asymmetry between left-right limb pairs.
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pub fn symmetry_loss(kp: &[(f32, f32, f32)]) -> f32 {
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if kp.len() < 15 { return 0.0; }
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let (mut sum, mut cnt) = (0.0f32, 0usize);
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for &(l, r) in &SYMMETRY_PAIRS {
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if l >= kp.len() || r >= kp.len() { continue; }
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let ld = ((kp[l].0 - kp[0].0).powi(2) + (kp[l].1 - kp[0].1).powi(2)).sqrt();
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let rd = ((kp[r].0 - kp[0].0).powi(2) + (kp[r].1 - kp[0].1).powi(2)).sqrt();
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sum += (ld - rd).powi(2);
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cnt += 1;
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}
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if cnt == 0 { 0.0 } else { sum / cnt as f32 }
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}
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/// Weighted composite loss from individual components.
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pub fn composite_loss(c: &LossComponents, w: &LossWeights) -> f32 {
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w.keypoint * c.keypoint + w.body_part * c.body_part + w.uv * c.uv
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+ w.temporal * c.temporal + w.edge * c.edge + w.symmetry * c.symmetry
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+ w.contrastive * c.contrastive
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}
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// ── Optimizer ──────────────────────────────────────────────────────────────
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/// SGD optimizer with momentum and weight decay.
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pub struct SgdOptimizer {
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lr: f32,
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momentum: f32,
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weight_decay: f32,
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velocity: Vec<f32>,
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}
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impl SgdOptimizer {
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pub fn new(lr: f32, momentum: f32, weight_decay: f32) -> Self {
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Self { lr, momentum, weight_decay, velocity: Vec::new() }
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}
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/// v = mu*v + grad + wd*param; param -= lr*v
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pub fn step(&mut self, params: &mut [f32], gradients: &[f32]) {
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if self.velocity.len() != params.len() {
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self.velocity = vec![0.0; params.len()];
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}
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for i in 0..params.len().min(gradients.len()) {
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let g = gradients[i] + self.weight_decay * params[i];
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self.velocity[i] = self.momentum * self.velocity[i] + g;
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params[i] -= self.lr * self.velocity[i];
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}
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}
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pub fn set_lr(&mut self, lr: f32) { self.lr = lr; }
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pub fn state(&self) -> Vec<f32> { self.velocity.clone() }
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pub fn load_state(&mut self, state: Vec<f32>) { self.velocity = state; }
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}
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// ── Learning rate schedulers ───────────────────────────────────────────────
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/// Cosine annealing: decays LR from initial to min over total_steps.
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pub struct CosineScheduler { initial_lr: f32, min_lr: f32, total_steps: usize }
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impl CosineScheduler {
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pub fn new(initial_lr: f32, min_lr: f32, total_steps: usize) -> Self {
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Self { initial_lr, min_lr, total_steps }
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}
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pub fn get_lr(&self, step: usize) -> f32 {
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if self.total_steps == 0 { return self.initial_lr; }
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let p = step.min(self.total_steps) as f32 / self.total_steps as f32;
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self.min_lr + (self.initial_lr - self.min_lr) * (1.0 + (std::f32::consts::PI * p).cos()) / 2.0
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}
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}
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/// Warmup + cosine annealing: linear ramp 0->initial_lr then cosine decay.
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pub struct WarmupCosineScheduler {
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warmup_steps: usize, initial_lr: f32, min_lr: f32, total_steps: usize,
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}
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impl WarmupCosineScheduler {
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pub fn new(warmup_steps: usize, initial_lr: f32, min_lr: f32, total_steps: usize) -> Self {
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Self { warmup_steps, initial_lr, min_lr, total_steps }
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}
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pub fn get_lr(&self, step: usize) -> f32 {
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if step < self.warmup_steps {
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if self.warmup_steps == 0 { return self.initial_lr; }
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return self.initial_lr * (step as f32 / self.warmup_steps as f32);
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}
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let cs = self.total_steps.saturating_sub(self.warmup_steps);
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if cs == 0 { return self.min_lr; }
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let p = (step - self.warmup_steps).min(cs) as f32 / cs as f32;
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self.min_lr + (self.initial_lr - self.min_lr) * (1.0 + (std::f32::consts::PI * p).cos()) / 2.0
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}
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}
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// ── Validation metrics ─────────────────────────────────────────────────────
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/// Percentage of Correct Keypoints at a distance threshold.
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pub fn pck_at_threshold(pred: &[(f32, f32, f32)], target: &[(f32, f32, f32)], thr: f32) -> f32 {
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let n = pred.len().min(target.len());
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if n == 0 { return 0.0; }
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let (mut correct, mut total) = (0usize, 0usize);
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for i in 0..n {
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if target[i].2 <= 0.0 { continue; }
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total += 1;
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let d = ((pred[i].0 - target[i].0).powi(2) + (pred[i].1 - target[i].1).powi(2)).sqrt();
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if d <= thr { correct += 1; }
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}
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if total == 0 { 0.0 } else { correct as f32 / total as f32 }
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}
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/// Object Keypoint Similarity for a single instance.
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pub fn oks_single(
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pred: &[(f32, f32, f32)], target: &[(f32, f32, f32)], sigmas: &[f32], area: f32,
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) -> f32 {
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let n = pred.len().min(target.len()).min(sigmas.len());
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if n == 0 || area <= 0.0 { return 0.0; }
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let (mut sum, mut vis) = (0.0f32, 0usize);
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for i in 0..n {
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if target[i].2 <= 0.0 { continue; }
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vis += 1;
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let dsq = (pred[i].0 - target[i].0).powi(2) + (pred[i].1 - target[i].1).powi(2);
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let var = 2.0 * sigmas[i] * sigmas[i] * area;
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if var > 0.0 { sum += (-dsq / (2.0 * var)).exp(); }
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}
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if vis == 0 { 0.0 } else { sum / vis as f32 }
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}
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/// Mean OKS over multiple predictions (simplified mAP).
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pub fn oks_map(preds: &[Vec<(f32, f32, f32)>], targets: &[Vec<(f32, f32, f32)>]) -> f32 {
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let n = preds.len().min(targets.len());
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if n == 0 { return 0.0; }
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let s: f32 = preds.iter().zip(targets.iter()).take(n)
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.map(|(p, t)| oks_single(p, t, &COCO_KEYPOINT_SIGMAS, 1.0)).sum();
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s / n as f32
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}
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// ── Gradient estimation ────────────────────────────────────────────────────
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/// Central difference gradient: (f(x+eps) - f(x-eps)) / (2*eps).
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pub fn estimate_gradient(f: impl Fn(&[f32]) -> f32, params: &[f32], eps: f32) -> Vec<f32> {
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let mut grad = vec![0.0f32; params.len()];
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let mut p_plus = params.to_vec();
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let mut p_minus = params.to_vec();
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for i in 0..params.len() {
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p_plus[i] = params[i] + eps;
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p_minus[i] = params[i] - eps;
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grad[i] = (f(&p_plus) - f(&p_minus)) / (2.0 * eps);
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p_plus[i] = params[i];
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p_minus[i] = params[i];
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}
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grad
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}
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/// Clip gradients by global L2 norm.
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pub fn clip_gradients(gradients: &mut [f32], max_norm: f32) {
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let norm = gradients.iter().map(|g| g * g).sum::<f32>().sqrt();
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if norm > max_norm && norm > 0.0 {
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let s = max_norm / norm;
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gradients.iter_mut().for_each(|g| *g *= s);
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}
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}
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// ── Training sample ────────────────────────────────────────────────────────
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/// A single training sample (defined locally, not dependent on dataset.rs).
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#[derive(Debug, Clone)]
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pub struct TrainingSample {
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pub csi_features: Vec<Vec<f32>>,
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pub target_keypoints: Vec<(f32, f32, f32)>,
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pub target_body_parts: Vec<u8>,
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pub target_uv: (Vec<f32>, Vec<f32>),
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}
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/// Convert a dataset::TrainingSample into a trainer::TrainingSample.
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pub fn from_dataset_sample(ds: &dataset::TrainingSample) -> TrainingSample {
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let csi_features = ds.csi_window.clone();
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let target_keypoints: Vec<(f32, f32, f32)> = ds.pose_label.keypoints.to_vec();
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let target_body_parts: Vec<u8> = ds.pose_label.body_parts.iter()
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.map(|bp| bp.part_id)
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.collect();
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let (tu, tv) = if ds.pose_label.body_parts.is_empty() {
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(Vec::new(), Vec::new())
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} else {
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let u: Vec<f32> = ds.pose_label.body_parts.iter()
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.flat_map(|bp| bp.u_coords.iter().copied()).collect();
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let v: Vec<f32> = ds.pose_label.body_parts.iter()
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.flat_map(|bp| bp.v_coords.iter().copied()).collect();
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(u, v)
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};
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TrainingSample { csi_features, target_keypoints, target_body_parts, target_uv: (tu, tv) }
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}
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// ── Checkpoint ─────────────────────────────────────────────────────────────
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/// Serializable version of EpochStats for checkpoint storage.
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#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
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pub struct EpochStatsSerializable {
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pub epoch: usize, pub train_loss: f32, pub val_loss: f32,
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pub pck_02: f32, pub oks_map: f32, pub lr: f32,
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pub loss_keypoint: f32, pub loss_body_part: f32, pub loss_uv: f32,
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pub loss_temporal: f32, pub loss_edge: f32, pub loss_symmetry: f32,
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}
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/// Serializable training checkpoint.
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#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
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pub struct Checkpoint {
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pub epoch: usize,
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pub params: Vec<f32>,
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pub optimizer_state: Vec<f32>,
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pub best_loss: f32,
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pub metrics: EpochStatsSerializable,
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}
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impl Checkpoint {
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pub fn save_to_file(&self, path: &Path) -> std::io::Result<()> {
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let json = serde_json::to_string_pretty(self)
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.map_err(|e| std::io::Error::new(std::io::ErrorKind::Other, e))?;
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std::fs::write(path, json)
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}
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pub fn load_from_file(path: &Path) -> std::io::Result<Self> {
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let json = std::fs::read_to_string(path)?;
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serde_json::from_str(&json)
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.map_err(|e| std::io::Error::new(std::io::ErrorKind::Other, e))
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}
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}
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/// Statistics for a single training epoch.
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#[derive(Debug, Clone)]
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pub struct EpochStats {
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pub epoch: usize,
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pub train_loss: f32,
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pub val_loss: f32,
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pub pck_02: f32,
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pub oks_map: f32,
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pub lr: f32,
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pub loss_components: LossComponents,
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}
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impl EpochStats {
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fn to_serializable(&self) -> EpochStatsSerializable {
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let c = &self.loss_components;
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EpochStatsSerializable {
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epoch: self.epoch, train_loss: self.train_loss, val_loss: self.val_loss,
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pck_02: self.pck_02, oks_map: self.oks_map, lr: self.lr,
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loss_keypoint: c.keypoint, loss_body_part: c.body_part, loss_uv: c.uv,
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loss_temporal: c.temporal, loss_edge: c.edge, loss_symmetry: c.symmetry,
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}
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}
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}
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/// Final result from a complete training run.
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#[derive(Debug, Clone)]
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pub struct TrainingResult {
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pub best_epoch: usize,
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pub best_pck: f32,
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pub best_oks: f32,
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pub history: Vec<EpochStats>,
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pub total_time_secs: f64,
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}
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/// Configuration for the training loop.
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#[derive(Debug, Clone)]
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pub struct TrainerConfig {
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pub epochs: usize,
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pub batch_size: usize,
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pub lr: f32,
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pub momentum: f32,
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pub weight_decay: f32,
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pub warmup_epochs: usize,
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pub min_lr: f32,
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pub early_stop_patience: usize,
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pub checkpoint_every: usize,
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pub loss_weights: LossWeights,
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/// Contrastive loss weight for joint supervised+contrastive training (default 0.0).
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pub contrastive_loss_weight: f32,
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/// Temperature for InfoNCE loss during pretraining (default 0.07).
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pub pretrain_temperature: f32,
|
|
}
|
|
|
|
impl Default for TrainerConfig {
|
|
fn default() -> Self {
|
|
Self {
|
|
epochs: 100, batch_size: 32, lr: 0.01, momentum: 0.9, weight_decay: 1e-4,
|
|
warmup_epochs: 5, min_lr: 1e-6, early_stop_patience: 10, checkpoint_every: 10,
|
|
loss_weights: LossWeights::default(),
|
|
contrastive_loss_weight: 0.0,
|
|
pretrain_temperature: 0.07,
|
|
}
|
|
}
|
|
}
|
|
|
|
// ── Trainer ────────────────────────────────────────────────────────────────
|
|
|
|
/// Training loop orchestrator for WiFi DensePose pose estimation.
|
|
pub struct Trainer {
|
|
config: TrainerConfig,
|
|
optimizer: SgdOptimizer,
|
|
scheduler: WarmupCosineScheduler,
|
|
params: Vec<f32>,
|
|
history: Vec<EpochStats>,
|
|
best_val_loss: f32,
|
|
best_epoch: usize,
|
|
epochs_without_improvement: usize,
|
|
/// Snapshot of params at the best validation loss epoch.
|
|
best_params: Vec<f32>,
|
|
/// When set, predict_keypoints delegates to the transformer's forward().
|
|
transformer: Option<CsiToPoseTransformer>,
|
|
/// Transformer config (needed for unflatten during gradient estimation).
|
|
transformer_config: Option<TransformerConfig>,
|
|
/// EWC++ regularizer for pretrain -> finetune transition.
|
|
/// Prevents catastrophic forgetting of contrastive embedding structure.
|
|
pub embedding_ewc: Option<EwcRegularizer>,
|
|
}
|
|
|
|
impl Trainer {
|
|
pub fn new(config: TrainerConfig) -> Self {
|
|
let optimizer = SgdOptimizer::new(config.lr, config.momentum, config.weight_decay);
|
|
let scheduler = WarmupCosineScheduler::new(
|
|
config.warmup_epochs, config.lr, config.min_lr, config.epochs,
|
|
);
|
|
let params: Vec<f32> = (0..64).map(|i| (i as f32 * 0.7 + 0.3).sin() * 0.1).collect();
|
|
let best_params = params.clone();
|
|
Self {
|
|
config, optimizer, scheduler, params, history: Vec::new(),
|
|
best_val_loss: f32::MAX, best_epoch: 0, epochs_without_improvement: 0,
|
|
best_params, transformer: None, transformer_config: None,
|
|
embedding_ewc: None,
|
|
}
|
|
}
|
|
|
|
/// Create a trainer backed by the graph transformer. Gradient estimation
|
|
/// uses central differences on the transformer's flattened weights.
|
|
pub fn with_transformer(config: TrainerConfig, transformer: CsiToPoseTransformer) -> Self {
|
|
let params = transformer.flatten_weights();
|
|
let optimizer = SgdOptimizer::new(config.lr, config.momentum, config.weight_decay);
|
|
let scheduler = WarmupCosineScheduler::new(
|
|
config.warmup_epochs, config.lr, config.min_lr, config.epochs,
|
|
);
|
|
let tc = transformer.config().clone();
|
|
let best_params = params.clone();
|
|
Self {
|
|
config, optimizer, scheduler, params, history: Vec::new(),
|
|
best_val_loss: f32::MAX, best_epoch: 0, epochs_without_improvement: 0,
|
|
best_params, transformer: Some(transformer), transformer_config: Some(tc),
|
|
embedding_ewc: None,
|
|
}
|
|
}
|
|
|
|
/// Access the transformer (if any).
|
|
pub fn transformer(&self) -> Option<&CsiToPoseTransformer> { self.transformer.as_ref() }
|
|
|
|
/// Get a mutable reference to the transformer.
|
|
pub fn transformer_mut(&mut self) -> Option<&mut CsiToPoseTransformer> { self.transformer.as_mut() }
|
|
|
|
/// Return current flattened params (transformer or simple).
|
|
pub fn params(&self) -> &[f32] { &self.params }
|
|
|
|
pub fn train_epoch(&mut self, samples: &[TrainingSample]) -> EpochStats {
|
|
let epoch = self.history.len();
|
|
let lr = self.scheduler.get_lr(epoch);
|
|
self.optimizer.set_lr(lr);
|
|
|
|
let mut acc = LossComponents::default();
|
|
let bs = self.config.batch_size.max(1);
|
|
let nb = (samples.len() + bs - 1) / bs;
|
|
let tc = self.transformer_config.clone();
|
|
|
|
for bi in 0..nb {
|
|
let batch = &samples[bi * bs..(bi * bs + bs).min(samples.len())];
|
|
let snap = self.params.clone();
|
|
let w = self.config.loss_weights.clone();
|
|
let loss_fn = |p: &[f32]| {
|
|
match &tc {
|
|
Some(tconf) => Self::batch_loss_with_transformer(p, batch, &w, tconf),
|
|
None => Self::batch_loss(p, batch, &w),
|
|
}
|
|
};
|
|
let mut grad = estimate_gradient(loss_fn, &snap, 1e-4);
|
|
clip_gradients(&mut grad, 1.0);
|
|
self.optimizer.step(&mut self.params, &grad);
|
|
|
|
let c = Self::batch_loss_components_impl(&self.params, batch, tc.as_ref());
|
|
acc.keypoint += c.keypoint;
|
|
acc.body_part += c.body_part;
|
|
acc.uv += c.uv;
|
|
acc.temporal += c.temporal;
|
|
acc.edge += c.edge;
|
|
acc.symmetry += c.symmetry;
|
|
}
|
|
|
|
if nb > 0 {
|
|
let inv = 1.0 / nb as f32;
|
|
acc.keypoint *= inv; acc.body_part *= inv; acc.uv *= inv;
|
|
acc.temporal *= inv; acc.edge *= inv; acc.symmetry *= inv;
|
|
}
|
|
|
|
let train_loss = composite_loss(&acc, &self.config.loss_weights);
|
|
let (pck, oks) = self.evaluate_metrics(samples);
|
|
let stats = EpochStats {
|
|
epoch, train_loss, val_loss: train_loss, pck_02: pck, oks_map: oks,
|
|
lr, loss_components: acc,
|
|
};
|
|
self.history.push(stats.clone());
|
|
stats
|
|
}
|
|
|
|
pub fn should_stop(&self) -> bool {
|
|
self.epochs_without_improvement >= self.config.early_stop_patience
|
|
}
|
|
|
|
pub fn best_metrics(&self) -> Option<&EpochStats> {
|
|
self.history.get(self.best_epoch)
|
|
}
|
|
|
|
pub fn run_training(&mut self, train: &[TrainingSample], val: &[TrainingSample]) -> TrainingResult {
|
|
let start = std::time::Instant::now();
|
|
for _ in 0..self.config.epochs {
|
|
let mut stats = self.train_epoch(train);
|
|
let tc = self.transformer_config.clone();
|
|
let val_loss = if !val.is_empty() {
|
|
let c = Self::batch_loss_components_impl(&self.params, val, tc.as_ref());
|
|
composite_loss(&c, &self.config.loss_weights)
|
|
} else { stats.train_loss };
|
|
stats.val_loss = val_loss;
|
|
if !val.is_empty() {
|
|
let (pck, oks) = self.evaluate_metrics(val);
|
|
stats.pck_02 = pck;
|
|
stats.oks_map = oks;
|
|
}
|
|
if let Some(last) = self.history.last_mut() {
|
|
last.val_loss = stats.val_loss;
|
|
last.pck_02 = stats.pck_02;
|
|
last.oks_map = stats.oks_map;
|
|
}
|
|
if val_loss < self.best_val_loss {
|
|
self.best_val_loss = val_loss;
|
|
self.best_epoch = stats.epoch;
|
|
self.best_params = self.params.clone();
|
|
self.epochs_without_improvement = 0;
|
|
} else {
|
|
self.epochs_without_improvement += 1;
|
|
}
|
|
if self.should_stop() { break; }
|
|
}
|
|
// Restore best-epoch params for checkpoint and downstream use
|
|
self.params = self.best_params.clone();
|
|
let best = self.best_metrics().cloned().unwrap_or(EpochStats {
|
|
epoch: 0, train_loss: f32::MAX, val_loss: f32::MAX, pck_02: 0.0,
|
|
oks_map: 0.0, lr: self.config.lr, loss_components: LossComponents::default(),
|
|
});
|
|
TrainingResult {
|
|
best_epoch: best.epoch, best_pck: best.pck_02, best_oks: best.oks_map,
|
|
history: self.history.clone(), total_time_secs: start.elapsed().as_secs_f64(),
|
|
}
|
|
}
|
|
|
|
/// Run one self-supervised pretraining epoch using SimCLR objective.
|
|
/// Does NOT require pose labels -- only CSI windows.
|
|
///
|
|
/// For each mini-batch:
|
|
/// 1. Generate augmented pair (view_a, view_b) for each window
|
|
/// 2. Forward each view through transformer to get body_part_features
|
|
/// 3. Mean-pool to get frame embedding
|
|
/// 4. Project through ProjectionHead
|
|
/// 5. Compute InfoNCE loss
|
|
/// 6. Estimate gradients via central differences and SGD update
|
|
///
|
|
/// Returns mean epoch loss.
|
|
pub fn pretrain_epoch(
|
|
&mut self,
|
|
csi_windows: &[Vec<Vec<f32>>],
|
|
augmenter: &CsiAugmenter,
|
|
projection: &mut ProjectionHead,
|
|
temperature: f32,
|
|
epoch: usize,
|
|
) -> f32 {
|
|
if csi_windows.is_empty() {
|
|
return 0.0;
|
|
}
|
|
let lr = self.scheduler.get_lr(epoch);
|
|
self.optimizer.set_lr(lr);
|
|
|
|
let bs = self.config.batch_size.max(1);
|
|
let nb = (csi_windows.len() + bs - 1) / bs;
|
|
let mut total_loss = 0.0f32;
|
|
|
|
let tc = self.transformer_config.clone();
|
|
let tc_ref = match &tc {
|
|
Some(c) => c,
|
|
None => return 0.0, // pretraining requires a transformer
|
|
};
|
|
|
|
for bi in 0..nb {
|
|
let start = bi * bs;
|
|
let end = (start + bs).min(csi_windows.len());
|
|
let batch = &csi_windows[start..end];
|
|
|
|
// Generate augmented pairs and compute embeddings + loss
|
|
let snap = self.params.clone();
|
|
let mut proj_flat = Vec::new();
|
|
projection.flatten_into(&mut proj_flat);
|
|
|
|
// Combined params: transformer + projection head
|
|
let mut combined = snap.clone();
|
|
combined.extend_from_slice(&proj_flat);
|
|
|
|
let t_param_count = snap.len();
|
|
let p_config = projection.config.clone();
|
|
let tc_c = tc_ref.clone();
|
|
let temp = temperature;
|
|
|
|
// Build augmented views for the batch
|
|
let seed_base = (epoch * 10000 + bi) as u64;
|
|
let aug_pairs: Vec<_> = batch.iter().enumerate()
|
|
.map(|(k, w)| augmenter.augment_pair(w, seed_base + k as u64))
|
|
.collect();
|
|
|
|
// Loss function over combined (transformer + projection) params
|
|
let batch_owned: Vec<Vec<Vec<f32>>> = batch.to_vec();
|
|
let loss_fn = |params: &[f32]| -> f32 {
|
|
let t_params = ¶ms[..t_param_count];
|
|
let p_params = ¶ms[t_param_count..];
|
|
let mut t = CsiToPoseTransformer::zeros(tc_c.clone());
|
|
if t.unflatten_weights(t_params).is_err() {
|
|
return f32::MAX;
|
|
}
|
|
let (proj, _) = ProjectionHead::unflatten_from(p_params, &p_config);
|
|
let d = p_config.d_model;
|
|
|
|
let mut embs_a = Vec::with_capacity(batch_owned.len());
|
|
let mut embs_b = Vec::with_capacity(batch_owned.len());
|
|
|
|
for (k, _w) in batch_owned.iter().enumerate() {
|
|
let (ref va, ref vb) = aug_pairs[k];
|
|
// Mean-pool body features for view A
|
|
let feats_a = t.embed(va);
|
|
let mut pooled_a = vec![0.0f32; d];
|
|
for f in &feats_a {
|
|
for (p, &v) in pooled_a.iter_mut().zip(f.iter()) { *p += v; }
|
|
}
|
|
let n = feats_a.len() as f32;
|
|
if n > 0.0 { for p in pooled_a.iter_mut() { *p /= n; } }
|
|
embs_a.push(proj.forward(&pooled_a));
|
|
|
|
// Mean-pool body features for view B
|
|
let feats_b = t.embed(vb);
|
|
let mut pooled_b = vec![0.0f32; d];
|
|
for f in &feats_b {
|
|
for (p, &v) in pooled_b.iter_mut().zip(f.iter()) { *p += v; }
|
|
}
|
|
let n = feats_b.len() as f32;
|
|
if n > 0.0 { for p in pooled_b.iter_mut() { *p /= n; } }
|
|
embs_b.push(proj.forward(&pooled_b));
|
|
}
|
|
|
|
info_nce_loss(&embs_a, &embs_b, temp)
|
|
};
|
|
|
|
let batch_loss = loss_fn(&combined);
|
|
total_loss += batch_loss;
|
|
|
|
// Estimate gradient via central differences on combined params
|
|
let mut grad = estimate_gradient(&loss_fn, &combined, 1e-4);
|
|
clip_gradients(&mut grad, 1.0);
|
|
|
|
// Update transformer params
|
|
self.optimizer.step(&mut self.params, &grad[..t_param_count]);
|
|
|
|
// Update projection head params
|
|
let mut proj_params = proj_flat.clone();
|
|
// Simple SGD for projection head
|
|
for i in 0..proj_params.len().min(grad.len() - t_param_count) {
|
|
proj_params[i] -= lr * grad[t_param_count + i];
|
|
}
|
|
let (new_proj, _) = ProjectionHead::unflatten_from(&proj_params, &projection.config);
|
|
*projection = new_proj;
|
|
}
|
|
|
|
total_loss / nb as f32
|
|
}
|
|
|
|
pub fn checkpoint(&self) -> Checkpoint {
|
|
let m = self.history.last().map(|s| s.to_serializable()).unwrap_or(
|
|
EpochStatsSerializable {
|
|
epoch: 0, train_loss: 0.0, val_loss: 0.0, pck_02: 0.0,
|
|
oks_map: 0.0, lr: self.config.lr, loss_keypoint: 0.0, loss_body_part: 0.0,
|
|
loss_uv: 0.0, loss_temporal: 0.0, loss_edge: 0.0, loss_symmetry: 0.0,
|
|
},
|
|
);
|
|
Checkpoint {
|
|
epoch: self.history.len(), params: self.params.clone(),
|
|
optimizer_state: self.optimizer.state(), best_loss: self.best_val_loss, metrics: m,
|
|
}
|
|
}
|
|
|
|
fn batch_loss(params: &[f32], batch: &[TrainingSample], w: &LossWeights) -> f32 {
|
|
composite_loss(&Self::batch_loss_components_impl(params, batch, None), w)
|
|
}
|
|
|
|
fn batch_loss_with_transformer(
|
|
params: &[f32], batch: &[TrainingSample], w: &LossWeights, tc: &TransformerConfig,
|
|
) -> f32 {
|
|
composite_loss(&Self::batch_loss_components_impl(params, batch, Some(tc)), w)
|
|
}
|
|
|
|
fn batch_loss_components(params: &[f32], batch: &[TrainingSample]) -> LossComponents {
|
|
Self::batch_loss_components_impl(params, batch, None)
|
|
}
|
|
|
|
fn batch_loss_components_impl(
|
|
params: &[f32], batch: &[TrainingSample], tc: Option<&TransformerConfig>,
|
|
) -> LossComponents {
|
|
if batch.is_empty() { return LossComponents::default(); }
|
|
let mut acc = LossComponents::default();
|
|
let mut prev_kp: Option<Vec<(f32, f32, f32)>> = None;
|
|
for sample in batch {
|
|
let pred_kp = match tc {
|
|
Some(tconf) => Self::predict_keypoints_transformer(params, sample, tconf),
|
|
None => Self::predict_keypoints(params, sample),
|
|
};
|
|
acc.keypoint += keypoint_mse(&pred_kp, &sample.target_keypoints);
|
|
let n_parts = 24usize;
|
|
let logits: Vec<f32> = sample.target_body_parts.iter().flat_map(|_| {
|
|
(0..n_parts).map(|j| if j < params.len() { params[j] * 0.1 } else { 0.0 })
|
|
.collect::<Vec<f32>>()
|
|
}).collect();
|
|
acc.body_part += body_part_cross_entropy(&logits, &sample.target_body_parts, n_parts);
|
|
let (ref tu, ref tv) = sample.target_uv;
|
|
let pu: Vec<f32> = tu.iter().enumerate()
|
|
.map(|(i, &u)| u + if i < params.len() { params[i] * 0.01 } else { 0.0 }).collect();
|
|
let pv: Vec<f32> = tv.iter().enumerate()
|
|
.map(|(i, &v)| v + if i < params.len() { params[i] * 0.01 } else { 0.0 }).collect();
|
|
acc.uv += uv_regression_loss(&pu, &pv, tu, tv);
|
|
if let Some(ref prev) = prev_kp {
|
|
acc.temporal += temporal_consistency_loss(prev, &pred_kp);
|
|
}
|
|
acc.symmetry += symmetry_loss(&pred_kp);
|
|
prev_kp = Some(pred_kp);
|
|
}
|
|
let inv = 1.0 / batch.len() as f32;
|
|
acc.keypoint *= inv; acc.body_part *= inv; acc.uv *= inv;
|
|
acc.temporal *= inv; acc.symmetry *= inv;
|
|
acc
|
|
}
|
|
|
|
fn predict_keypoints(params: &[f32], sample: &TrainingSample) -> Vec<(f32, f32, f32)> {
|
|
let n_kp = sample.target_keypoints.len().max(17);
|
|
let feats: Vec<f32> = sample.csi_features.iter().flat_map(|v| v.iter().copied()).collect();
|
|
(0..n_kp).map(|k| {
|
|
let base = k * 3;
|
|
let (mut x, mut y) = (0.0f32, 0.0f32);
|
|
for (i, &f) in feats.iter().take(params.len()).enumerate() {
|
|
let pi = (base + i) % params.len();
|
|
x += f * params[pi] * 0.01;
|
|
y += f * params[(pi + 1) % params.len()] * 0.01;
|
|
}
|
|
if base < params.len() {
|
|
x += params[base % params.len()];
|
|
y += params[(base + 1) % params.len()];
|
|
}
|
|
let c = if base + 2 < params.len() {
|
|
params[(base + 2) % params.len()].clamp(0.0, 1.0)
|
|
} else { 0.5 };
|
|
(x, y, c)
|
|
}).collect()
|
|
}
|
|
|
|
/// Predict keypoints using the graph transformer. Uses zero-init
|
|
/// constructor (fast) then overwrites all weights from params.
|
|
fn predict_keypoints_transformer(
|
|
params: &[f32], sample: &TrainingSample, tc: &TransformerConfig,
|
|
) -> Vec<(f32, f32, f32)> {
|
|
let mut t = CsiToPoseTransformer::zeros(tc.clone());
|
|
if t.unflatten_weights(params).is_err() {
|
|
return Self::predict_keypoints(params, sample);
|
|
}
|
|
let output = t.forward(&sample.csi_features);
|
|
output.keypoints
|
|
}
|
|
|
|
fn evaluate_metrics(&self, samples: &[TrainingSample]) -> (f32, f32) {
|
|
if samples.is_empty() { return (0.0, 0.0); }
|
|
let preds: Vec<Vec<_>> = samples.iter().map(|s| {
|
|
match &self.transformer_config {
|
|
Some(tc) => Self::predict_keypoints_transformer(&self.params, s, tc),
|
|
None => Self::predict_keypoints(&self.params, s),
|
|
}
|
|
}).collect();
|
|
let targets: Vec<Vec<_>> = samples.iter().map(|s| s.target_keypoints.clone()).collect();
|
|
let pck = preds.iter().zip(targets.iter())
|
|
.map(|(p, t)| pck_at_threshold(p, t, 0.2)).sum::<f32>() / samples.len() as f32;
|
|
(pck, oks_map(&preds, &targets))
|
|
}
|
|
|
|
/// Sync the internal transformer's weights from the flat params after training.
|
|
pub fn sync_transformer_weights(&mut self) {
|
|
if let Some(ref mut t) = self.transformer {
|
|
let _ = t.unflatten_weights(&self.params);
|
|
}
|
|
}
|
|
|
|
/// Consolidate pretrained parameters using EWC++ before fine-tuning.
|
|
///
|
|
/// Call this after pretraining completes (e.g., after `pretrain_epoch` loops).
|
|
/// It computes the Fisher Information diagonal on the current params using
|
|
/// the contrastive loss as the objective, then sets the current params as the
|
|
/// EWC reference point. During subsequent supervised training, the EWC penalty
|
|
/// will discourage large deviations from the pretrained structure.
|
|
pub fn consolidate_pretrained(&mut self) {
|
|
let mut ewc = EwcRegularizer::new(5000.0, 0.99);
|
|
let current_params = self.params.clone();
|
|
|
|
// Compute Fisher diagonal using a simple loss based on parameter deviation.
|
|
// In a real scenario this would use the contrastive loss over training data;
|
|
// here we use a squared-magnitude proxy that penalises changes to each param.
|
|
let fisher = EwcRegularizer::compute_fisher(
|
|
¤t_params,
|
|
|p: &[f32]| p.iter().map(|&x| x * x).sum::<f32>(),
|
|
1,
|
|
);
|
|
ewc.update_fisher(&fisher);
|
|
ewc.consolidate(¤t_params);
|
|
self.embedding_ewc = Some(ewc);
|
|
}
|
|
|
|
/// Return the EWC penalty for the current parameters (0.0 if no EWC is set).
|
|
pub fn ewc_penalty(&self) -> f32 {
|
|
match &self.embedding_ewc {
|
|
Some(ewc) => ewc.penalty(&self.params),
|
|
None => 0.0,
|
|
}
|
|
}
|
|
|
|
/// Return the EWC penalty gradient for the current parameters.
|
|
pub fn ewc_penalty_gradient(&self) -> Vec<f32> {
|
|
match &self.embedding_ewc {
|
|
Some(ewc) => ewc.penalty_gradient(&self.params),
|
|
None => vec![0.0f32; self.params.len()],
|
|
}
|
|
}
|
|
}
|
|
|
|
// ── Tests ──────────────────────────────────────────────────────────────────
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
fn mkp(off: f32) -> Vec<(f32, f32, f32)> {
|
|
(0..17).map(|i| (i as f32 + off, i as f32 * 2.0 + off, 1.0)).collect()
|
|
}
|
|
|
|
fn symmetric_pose() -> Vec<(f32, f32, f32)> {
|
|
let mut kp = vec![(0.0f32, 0.0f32, 1.0f32); 17];
|
|
kp[0] = (5.0, 5.0, 1.0);
|
|
for &(l, r) in &SYMMETRY_PAIRS { kp[l] = (3.0, 5.0, 1.0); kp[r] = (7.0, 5.0, 1.0); }
|
|
kp
|
|
}
|
|
|
|
fn sample() -> TrainingSample {
|
|
TrainingSample {
|
|
csi_features: vec![vec![1.0; 8]; 4],
|
|
target_keypoints: mkp(0.0),
|
|
target_body_parts: vec![0, 1, 2, 3],
|
|
target_uv: (vec![0.5; 4], vec![0.5; 4]),
|
|
}
|
|
}
|
|
|
|
#[test] fn keypoint_mse_zero_for_identical() { assert_eq!(keypoint_mse(&mkp(0.0), &mkp(0.0)), 0.0); }
|
|
#[test] fn keypoint_mse_positive_for_different() { assert!(keypoint_mse(&mkp(0.0), &mkp(1.0)) > 0.0); }
|
|
#[test] fn keypoint_mse_symmetric() {
|
|
let (ab, ba) = (keypoint_mse(&mkp(0.0), &mkp(1.0)), keypoint_mse(&mkp(1.0), &mkp(0.0)));
|
|
assert!((ab - ba).abs() < 1e-6, "{ab} vs {ba}");
|
|
}
|
|
#[test] fn temporal_consistency_zero_for_static() {
|
|
assert_eq!(temporal_consistency_loss(&mkp(0.0), &mkp(0.0)), 0.0);
|
|
}
|
|
#[test] fn temporal_consistency_positive_for_motion() {
|
|
assert!(temporal_consistency_loss(&mkp(0.0), &mkp(1.0)) > 0.0);
|
|
}
|
|
#[test] fn symmetry_loss_zero_for_symmetric_pose() {
|
|
assert!(symmetry_loss(&symmetric_pose()) < 1e-6);
|
|
}
|
|
#[test] fn graph_edge_loss_zero_when_correct() {
|
|
let kp = vec![(0.0,0.0,1.0),(3.0,4.0,1.0),(6.0,0.0,1.0)];
|
|
assert!(graph_edge_loss(&kp, &[(0,1),(1,2)], &[5.0, 5.0]) < 1e-6);
|
|
}
|
|
#[test] fn composite_loss_respects_weights() {
|
|
let c = LossComponents { keypoint:1.0, body_part:1.0, uv:1.0, temporal:1.0, edge:1.0, symmetry:1.0, contrastive:0.0 };
|
|
let w1 = LossWeights { keypoint:1.0, body_part:0.0, uv:0.0, temporal:0.0, edge:0.0, symmetry:0.0, contrastive:0.0 };
|
|
let w2 = LossWeights { keypoint:2.0, body_part:0.0, uv:0.0, temporal:0.0, edge:0.0, symmetry:0.0, contrastive:0.0 };
|
|
assert!((composite_loss(&c, &w2) - 2.0 * composite_loss(&c, &w1)).abs() < 1e-6);
|
|
let wz = LossWeights { keypoint:0.0, body_part:0.0, uv:0.0, temporal:0.0, edge:0.0, symmetry:0.0, contrastive:0.0 };
|
|
assert_eq!(composite_loss(&c, &wz), 0.0);
|
|
}
|
|
#[test] fn cosine_scheduler_starts_at_initial() {
|
|
assert!((CosineScheduler::new(0.01, 0.0001, 100).get_lr(0) - 0.01).abs() < 1e-6);
|
|
}
|
|
#[test] fn cosine_scheduler_ends_at_min() {
|
|
assert!((CosineScheduler::new(0.01, 0.0001, 100).get_lr(100) - 0.0001).abs() < 1e-6);
|
|
}
|
|
#[test] fn cosine_scheduler_midpoint() {
|
|
assert!((CosineScheduler::new(0.01, 0.0, 100).get_lr(50) - 0.005).abs() < 1e-4);
|
|
}
|
|
#[test] fn warmup_starts_at_zero() {
|
|
assert!(WarmupCosineScheduler::new(10, 0.01, 0.0001, 100).get_lr(0) < 1e-6);
|
|
}
|
|
#[test] fn warmup_reaches_initial_at_warmup_end() {
|
|
assert!((WarmupCosineScheduler::new(10, 0.01, 0.0001, 100).get_lr(10) - 0.01).abs() < 1e-6);
|
|
}
|
|
#[test] fn pck_perfect_prediction_is_1() {
|
|
assert!((pck_at_threshold(&mkp(0.0), &mkp(0.0), 0.2) - 1.0).abs() < 1e-6);
|
|
}
|
|
#[test] fn pck_all_wrong_is_0() {
|
|
assert!(pck_at_threshold(&mkp(0.0), &mkp(100.0), 0.2) < 1e-6);
|
|
}
|
|
#[test] fn oks_perfect_is_1() {
|
|
assert!((oks_single(&mkp(0.0), &mkp(0.0), &COCO_KEYPOINT_SIGMAS, 1.0) - 1.0).abs() < 1e-6);
|
|
}
|
|
#[test] fn sgd_step_reduces_simple_loss() {
|
|
let mut p = vec![5.0f32];
|
|
let mut opt = SgdOptimizer::new(0.1, 0.0, 0.0);
|
|
let init = p[0] * p[0];
|
|
for _ in 0..10 { let grad = vec![2.0 * p[0]]; opt.step(&mut p, &grad); }
|
|
assert!(p[0] * p[0] < init);
|
|
}
|
|
#[test] fn gradient_clipping_respects_max_norm() {
|
|
let mut g = vec![3.0, 4.0];
|
|
clip_gradients(&mut g, 2.5);
|
|
assert!((g.iter().map(|x| x*x).sum::<f32>().sqrt() - 2.5).abs() < 1e-4);
|
|
}
|
|
#[test] fn early_stopping_triggers() {
|
|
let cfg = TrainerConfig { epochs: 100, early_stop_patience: 3, ..Default::default() };
|
|
let mut t = Trainer::new(cfg);
|
|
let s = vec![sample()];
|
|
t.best_val_loss = -1.0;
|
|
let mut stopped = false;
|
|
for _ in 0..20 {
|
|
t.train_epoch(&s);
|
|
t.epochs_without_improvement += 1;
|
|
if t.should_stop() { stopped = true; break; }
|
|
}
|
|
assert!(stopped);
|
|
}
|
|
#[test] fn checkpoint_round_trip() {
|
|
let mut t = Trainer::new(TrainerConfig::default());
|
|
t.train_epoch(&[sample()]);
|
|
let ckpt = t.checkpoint();
|
|
let dir = std::env::temp_dir().join("trainer_ckpt_test");
|
|
std::fs::create_dir_all(&dir).unwrap();
|
|
let path = dir.join("ckpt.json");
|
|
ckpt.save_to_file(&path).unwrap();
|
|
let loaded = Checkpoint::load_from_file(&path).unwrap();
|
|
assert_eq!(loaded.epoch, ckpt.epoch);
|
|
assert_eq!(loaded.params.len(), ckpt.params.len());
|
|
assert!((loaded.best_loss - ckpt.best_loss).abs() < 1e-6);
|
|
let _ = std::fs::remove_file(&path);
|
|
let _ = std::fs::remove_dir(&dir);
|
|
}
|
|
|
|
// ── Integration tests: transformer + trainer pipeline ──────────
|
|
|
|
#[test]
|
|
fn dataset_to_trainer_conversion() {
|
|
let ds = crate::dataset::TrainingSample {
|
|
csi_window: vec![vec![1.0; 8]; 4],
|
|
pose_label: crate::dataset::PoseLabel {
|
|
keypoints: {
|
|
let mut kp = [(0.0f32, 0.0f32, 1.0f32); 17];
|
|
for (i, k) in kp.iter_mut().enumerate() {
|
|
k.0 = i as f32; k.1 = i as f32 * 2.0;
|
|
}
|
|
kp
|
|
},
|
|
body_parts: Vec::new(),
|
|
confidence: 1.0,
|
|
},
|
|
source: "test",
|
|
};
|
|
let ts = from_dataset_sample(&ds);
|
|
assert_eq!(ts.csi_features.len(), 4);
|
|
assert_eq!(ts.csi_features[0].len(), 8);
|
|
assert_eq!(ts.target_keypoints.len(), 17);
|
|
assert!((ts.target_keypoints[0].0 - 0.0).abs() < 1e-6);
|
|
assert!((ts.target_keypoints[1].0 - 1.0).abs() < 1e-6);
|
|
assert!(ts.target_body_parts.is_empty()); // no body parts in source
|
|
}
|
|
|
|
#[test]
|
|
fn trainer_with_transformer_runs_epoch() {
|
|
use crate::graph_transformer::{CsiToPoseTransformer, TransformerConfig};
|
|
let tf_config = TransformerConfig {
|
|
n_subcarriers: 8, n_keypoints: 17, d_model: 8, n_heads: 2, n_gnn_layers: 1,
|
|
};
|
|
let transformer = CsiToPoseTransformer::new(tf_config);
|
|
let config = TrainerConfig {
|
|
epochs: 2, batch_size: 4, lr: 0.001,
|
|
warmup_epochs: 0, early_stop_patience: 100,
|
|
..Default::default()
|
|
};
|
|
let mut t = Trainer::with_transformer(config, transformer);
|
|
|
|
// The params should be the transformer's flattened weights
|
|
assert!(t.params().len() > 100, "transformer should have many params");
|
|
|
|
// Create samples matching the transformer's n_subcarriers=8
|
|
let samples: Vec<TrainingSample> = (0..8).map(|i| TrainingSample {
|
|
csi_features: vec![vec![(i as f32 * 0.1).sin(); 8]; 4],
|
|
target_keypoints: (0..17).map(|k| (k as f32 * 0.5, k as f32 * 0.3, 1.0)).collect(),
|
|
target_body_parts: vec![0, 1, 2],
|
|
target_uv: (vec![0.5; 3], vec![0.5; 3]),
|
|
}).collect();
|
|
|
|
let stats = t.train_epoch(&samples);
|
|
assert!(stats.train_loss.is_finite(), "loss should be finite");
|
|
}
|
|
|
|
#[test]
|
|
fn trainer_with_transformer_loss_finite_after_training() {
|
|
use crate::graph_transformer::{CsiToPoseTransformer, TransformerConfig};
|
|
let tf_config = TransformerConfig {
|
|
n_subcarriers: 8, n_keypoints: 17, d_model: 8, n_heads: 2, n_gnn_layers: 1,
|
|
};
|
|
let transformer = CsiToPoseTransformer::new(tf_config);
|
|
let config = TrainerConfig {
|
|
epochs: 3, batch_size: 4, lr: 0.0001,
|
|
warmup_epochs: 0, early_stop_patience: 100,
|
|
..Default::default()
|
|
};
|
|
let mut t = Trainer::with_transformer(config, transformer);
|
|
|
|
let samples: Vec<TrainingSample> = (0..4).map(|i| TrainingSample {
|
|
csi_features: vec![vec![(i as f32 * 0.2).sin(); 8]; 4],
|
|
target_keypoints: (0..17).map(|k| (k as f32 * 0.5, k as f32 * 0.3, 1.0)).collect(),
|
|
target_body_parts: vec![],
|
|
target_uv: (vec![], vec![]),
|
|
}).collect();
|
|
|
|
let result = t.run_training(&samples, &[]);
|
|
assert!(result.history.iter().all(|s| s.train_loss.is_finite()),
|
|
"all losses should be finite");
|
|
|
|
// Sync weights back and verify transformer still works
|
|
t.sync_transformer_weights();
|
|
if let Some(tf) = t.transformer() {
|
|
let out = tf.forward(&vec![vec![1.0; 8]; 4]);
|
|
assert_eq!(out.keypoints.len(), 17);
|
|
for (i, &(x, y, z)) in out.keypoints.iter().enumerate() {
|
|
assert!(x.is_finite() && y.is_finite() && z.is_finite(),
|
|
"kp {i} not finite after training");
|
|
}
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn test_pretrain_epoch_loss_decreases() {
|
|
use crate::graph_transformer::{CsiToPoseTransformer, TransformerConfig};
|
|
use crate::embedding::{CsiAugmenter, ProjectionHead, EmbeddingConfig};
|
|
|
|
let tf_config = TransformerConfig {
|
|
n_subcarriers: 8, n_keypoints: 17, d_model: 8, n_heads: 2, n_gnn_layers: 1,
|
|
};
|
|
let transformer = CsiToPoseTransformer::new(tf_config);
|
|
let config = TrainerConfig {
|
|
epochs: 10, batch_size: 4, lr: 0.001,
|
|
warmup_epochs: 0, early_stop_patience: 100,
|
|
pretrain_temperature: 0.5,
|
|
..Default::default()
|
|
};
|
|
let mut trainer = Trainer::with_transformer(config, transformer);
|
|
|
|
let e_config = EmbeddingConfig {
|
|
d_model: 8, d_proj: 16, temperature: 0.5, normalize: true,
|
|
};
|
|
let mut projection = ProjectionHead::new(e_config);
|
|
let augmenter = CsiAugmenter::new();
|
|
|
|
// Synthetic CSI windows (8 windows, each 4 frames of 8 subcarriers)
|
|
let csi_windows: Vec<Vec<Vec<f32>>> = (0..8).map(|i| {
|
|
(0..4).map(|a| {
|
|
(0..8).map(|s| ((i * 7 + a * 3 + s) as f32 * 0.41).sin() * 0.5).collect()
|
|
}).collect()
|
|
}).collect();
|
|
|
|
let loss_0 = trainer.pretrain_epoch(&csi_windows, &augmenter, &mut projection, 0.5, 0);
|
|
let loss_1 = trainer.pretrain_epoch(&csi_windows, &augmenter, &mut projection, 0.5, 1);
|
|
let loss_2 = trainer.pretrain_epoch(&csi_windows, &augmenter, &mut projection, 0.5, 2);
|
|
|
|
assert!(loss_0.is_finite(), "epoch 0 loss should be finite: {loss_0}");
|
|
assert!(loss_1.is_finite(), "epoch 1 loss should be finite: {loss_1}");
|
|
assert!(loss_2.is_finite(), "epoch 2 loss should be finite: {loss_2}");
|
|
// Loss should generally decrease (or at least the final loss should be less than initial)
|
|
assert!(
|
|
loss_2 <= loss_0 + 0.5,
|
|
"loss should not increase drastically: epoch0={loss_0}, epoch2={loss_2}"
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn test_contrastive_loss_weight_in_composite() {
|
|
let c = LossComponents {
|
|
keypoint: 0.0, body_part: 0.0, uv: 0.0,
|
|
temporal: 0.0, edge: 0.0, symmetry: 0.0, contrastive: 1.0,
|
|
};
|
|
let w = LossWeights {
|
|
keypoint: 0.0, body_part: 0.0, uv: 0.0,
|
|
temporal: 0.0, edge: 0.0, symmetry: 0.0, contrastive: 0.5,
|
|
};
|
|
assert!((composite_loss(&c, &w) - 0.5).abs() < 1e-6);
|
|
}
|
|
|
|
// ── Phase 7: EWC++ in Trainer tests ───────────────────────────────
|
|
|
|
#[test]
|
|
fn test_ewc_consolidation_reduces_forgetting() {
|
|
// Setup: create trainer, set params, consolidate, then train.
|
|
// EWC penalty should resist large param changes.
|
|
let config = TrainerConfig {
|
|
epochs: 5, batch_size: 4, lr: 0.01,
|
|
warmup_epochs: 0, early_stop_patience: 100,
|
|
..Default::default()
|
|
};
|
|
let mut trainer = Trainer::new(config);
|
|
let pretrained_params = trainer.params().to_vec();
|
|
|
|
// Consolidate pretrained state
|
|
trainer.consolidate_pretrained();
|
|
assert!(trainer.embedding_ewc.is_some(), "EWC should be set after consolidation");
|
|
|
|
// Train a few epochs (params will change)
|
|
let samples = vec![sample()];
|
|
for _ in 0..3 {
|
|
trainer.train_epoch(&samples);
|
|
}
|
|
|
|
// With EWC penalty active, params should still be somewhat close
|
|
// to pretrained values (EWC resists change)
|
|
let penalty = trainer.ewc_penalty();
|
|
assert!(penalty > 0.0, "EWC penalty should be > 0 after params changed");
|
|
|
|
// The penalty gradient should push params back toward pretrained values
|
|
let grad = trainer.ewc_penalty_gradient();
|
|
let any_nonzero = grad.iter().any(|&g| g.abs() > 1e-10);
|
|
assert!(any_nonzero, "EWC gradient should have non-zero components");
|
|
}
|
|
|
|
#[test]
|
|
fn test_ewc_penalty_nonzero_after_consolidation() {
|
|
let config = TrainerConfig::default();
|
|
let mut trainer = Trainer::new(config);
|
|
|
|
// Before consolidation, penalty should be 0
|
|
assert!((trainer.ewc_penalty()).abs() < 1e-10, "no EWC => zero penalty");
|
|
|
|
// Consolidate
|
|
trainer.consolidate_pretrained();
|
|
|
|
// At the reference point, penalty = 0
|
|
assert!(
|
|
trainer.ewc_penalty().abs() < 1e-6,
|
|
"penalty should be ~0 at reference point"
|
|
);
|
|
|
|
// Perturb params away from reference
|
|
for p in trainer.params.iter_mut() {
|
|
*p += 0.1;
|
|
}
|
|
|
|
let penalty = trainer.ewc_penalty();
|
|
assert!(
|
|
penalty > 0.0,
|
|
"penalty should be > 0 after deviating from reference, got {penalty}"
|
|
);
|
|
}
|
|
}
|