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feat: Training mode, ADR docs, vitals and wifiscan crates
- Add --train CLI flag with dataset loading, graph transformer training, cosine-scheduled SGD, PCK/OKS validation, and checkpoint saving - Refactor main.rs to import training modules from lib.rs instead of duplicating mod declarations - Add ADR-021 (vital sign detection), ADR-022 (Windows WiFi enhanced fidelity), ADR-023 (trained DensePose pipeline) documentation - Add wifi-densepose-vitals crate: breathing, heartrate, anomaly detection, preprocessor, and temporal store - Add wifi-densepose-wifiscan crate: 8-stage signal intelligence pipeline with netsh/wlanapi adapters, multi-BSSID registry, attention weighting, spatial correlation, and breathing extraction Co-Authored-By: claude-flow <[email protected]>
1 parent add9f19 commit 3e06970

37 files changed

Lines changed: 10667 additions & 8 deletions

‎docs/adr/ADR-021-vital-sign-detection-rvdna-pipeline.md‎

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‎docs/adr/ADR-022-windows-wifi-enhanced-fidelity-ruvector.md‎

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‎docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md‎

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‎rust-port/wifi-densepose-rs/crates/wifi-densepose-sensing-server/src/graph_transformer.rs‎

Lines changed: 90 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -745,4 +745,94 @@ mod tests {
745745
assert!((sum - 1.0).abs() < 1e-5);
746746
for &wi in &w3 { assert!(wi.is_finite()); }
747747
}
748+
749+
// ── Weight serialization integration tests ────────────────────────
750+
751+
#[test]
752+
fn linear_flatten_unflatten_roundtrip() {
753+
let lin = Linear::with_seed(8, 4, 42);
754+
let mut flat = Vec::new();
755+
lin.flatten_into(&mut flat);
756+
assert_eq!(flat.len(), lin.param_count());
757+
let (restored, consumed) = Linear::unflatten_from(&flat, 8, 4);
758+
assert_eq!(consumed, flat.len());
759+
let inp = vec![1.0f32; 8];
760+
assert_eq!(lin.forward(&inp), restored.forward(&inp));
761+
}
762+
763+
#[test]
764+
fn cross_attention_flatten_unflatten_roundtrip() {
765+
let ca = CrossAttention::new(16, 4);
766+
let mut flat = Vec::new();
767+
ca.flatten_into(&mut flat);
768+
assert_eq!(flat.len(), ca.param_count());
769+
let (restored, consumed) = CrossAttention::unflatten_from(&flat, 16, 4);
770+
assert_eq!(consumed, flat.len());
771+
let q = vec![vec![0.5f32; 16]; 3];
772+
let k = vec![vec![0.3f32; 16]; 5];
773+
let v = vec![vec![0.7f32; 16]; 5];
774+
let orig = ca.forward(&q, &k, &v);
775+
let rest = restored.forward(&q, &k, &v);
776+
for (a, b) in orig.iter().zip(rest.iter()) {
777+
for (x, y) in a.iter().zip(b.iter()) {
778+
assert!((x - y).abs() < 1e-6, "mismatch: {x} vs {y}");
779+
}
780+
}
781+
}
782+
783+
#[test]
784+
fn transformer_weight_roundtrip() {
785+
let config = TransformerConfig {
786+
n_subcarriers: 16, n_keypoints: 17, d_model: 8, n_heads: 2, n_gnn_layers: 1,
787+
};
788+
let t = CsiToPoseTransformer::new(config.clone());
789+
let weights = t.flatten_weights();
790+
assert_eq!(weights.len(), t.param_count());
791+
792+
let mut t2 = CsiToPoseTransformer::new(config);
793+
t2.unflatten_weights(&weights).expect("unflatten should succeed");
794+
795+
// Forward pass should produce identical results
796+
let csi = vec![vec![0.5f32; 16]; 4];
797+
let out1 = t.forward(&csi);
798+
let out2 = t2.forward(&csi);
799+
for (a, b) in out1.keypoints.iter().zip(out2.keypoints.iter()) {
800+
assert!((a.0 - b.0).abs() < 1e-6);
801+
assert!((a.1 - b.1).abs() < 1e-6);
802+
assert!((a.2 - b.2).abs() < 1e-6);
803+
}
804+
for (a, b) in out1.confidences.iter().zip(out2.confidences.iter()) {
805+
assert!((a - b).abs() < 1e-6);
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}
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}
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#[test]
810+
fn transformer_param_count_positive() {
811+
let t = CsiToPoseTransformer::new(TransformerConfig::default());
812+
assert!(t.param_count() > 1000, "expected many params, got {}", t.param_count());
813+
let flat = t.flatten_weights();
814+
assert_eq!(flat.len(), t.param_count());
815+
}
816+
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#[test]
818+
fn gnn_stack_flatten_unflatten() {
819+
let bg = BodyGraph::new();
820+
let gnn = GnnStack::new(8, 8, 2, &bg);
821+
let mut flat = Vec::new();
822+
gnn.flatten_into(&mut flat);
823+
assert_eq!(flat.len(), gnn.param_count());
824+
825+
let mut gnn2 = GnnStack::new(8, 8, 2, &bg);
826+
let consumed = gnn2.unflatten_from(&flat);
827+
assert_eq!(consumed, flat.len());
828+
829+
let feats = vec![vec![1.0f32; 8]; 17];
830+
let o1 = gnn.forward(&feats);
831+
let o2 = gnn2.forward(&feats);
832+
for (a, b) in o1.iter().zip(o2.iter()) {
833+
for (x, y) in a.iter().zip(b.iter()) {
834+
assert!((x - y).abs() < 1e-6);
835+
}
836+
}
837+
}
748838
}

‎rust-port/wifi-densepose-rs/crates/wifi-densepose-sensing-server/src/main.rs‎

Lines changed: 177 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -11,11 +11,9 @@
1111
mod rvf_container;
1212
mod rvf_pipeline;
1313
mod vital_signs;
14-
mod graph_transformer;
15-
mod trainer;
16-
mod dataset;
17-
mod sparse_inference;
18-
mod sona;
14+
15+
// Training pipeline modules (exposed via lib.rs)
16+
use wifi_densepose_sensing_server::{graph_transformer, trainer, dataset};
1917

2018
use std::collections::VecDeque;
2119
use std::net::SocketAddr;
@@ -1538,6 +1536,169 @@ async fn main() {
15381536
return;
15391537
}
15401538

1539+
// Handle --train mode: train a model and exit
1540+
if args.train {
1541+
eprintln!("=== WiFi-DensePose Training Mode ===");
1542+
1543+
// Build data pipeline
1544+
let ds_path = args.dataset.clone().unwrap_or_else(|| PathBuf::from("data"));
1545+
let source = match args.dataset_type.as_str() {
1546+
"wipose" => dataset::DataSource::WiPose(ds_path.clone()),
1547+
_ => dataset::DataSource::MmFi(ds_path.clone()),
1548+
};
1549+
let pipeline = dataset::DataPipeline::new(dataset::DataConfig {
1550+
source,
1551+
..Default::default()
1552+
});
1553+
1554+
// Load samples
1555+
let samples = match pipeline.load() {
1556+
Ok(s) if !s.is_empty() => {
1557+
eprintln!("Loaded {} samples from {}", s.len(), ds_path.display());
1558+
s
1559+
}
1560+
Ok(_) => {
1561+
eprintln!("No samples found at {}. Generating synthetic training data...", ds_path.display());
1562+
// Generate synthetic samples for testing the pipeline
1563+
let mut synth = Vec::new();
1564+
for i in 0..50 {
1565+
let csi: Vec<Vec<f32>> = (0..4).map(|a| {
1566+
(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
1567+
}).collect();
1568+
let mut kps = [(0.0f32, 0.0f32, 1.0f32); 17];
1569+
for (k, kp) in kps.iter_mut().enumerate() {
1570+
kp.0 = (k as f32 * 0.1 + i as f32 * 0.02).sin() * 100.0 + 320.0;
1571+
kp.1 = (k as f32 * 0.15 + i as f32 * 0.03).cos() * 80.0 + 240.0;
1572+
}
1573+
synth.push(dataset::TrainingSample {
1574+
csi_window: csi,
1575+
pose_label: dataset::PoseLabel {
1576+
keypoints: kps,
1577+
body_parts: Vec::new(),
1578+
confidence: 1.0,
1579+
},
1580+
source: "synthetic",
1581+
});
1582+
}
1583+
synth
1584+
}
1585+
Err(e) => {
1586+
eprintln!("Failed to load dataset: {e}");
1587+
eprintln!("Generating synthetic training data...");
1588+
let mut synth = Vec::new();
1589+
for i in 0..50 {
1590+
let csi: Vec<Vec<f32>> = (0..4).map(|a| {
1591+
(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
1592+
}).collect();
1593+
let mut kps = [(0.0f32, 0.0f32, 1.0f32); 17];
1594+
for (k, kp) in kps.iter_mut().enumerate() {
1595+
kp.0 = (k as f32 * 0.1 + i as f32 * 0.02).sin() * 100.0 + 320.0;
1596+
kp.1 = (k as f32 * 0.15 + i as f32 * 0.03).cos() * 80.0 + 240.0;
1597+
}
1598+
synth.push(dataset::TrainingSample {
1599+
csi_window: csi,
1600+
pose_label: dataset::PoseLabel {
1601+
keypoints: kps,
1602+
body_parts: Vec::new(),
1603+
confidence: 1.0,
1604+
},
1605+
source: "synthetic",
1606+
});
1607+
}
1608+
synth
1609+
}
1610+
};
1611+
1612+
// Convert dataset samples to trainer format
1613+
let trainer_samples: Vec<trainer::TrainingSample> = samples.iter()
1614+
.map(trainer::from_dataset_sample)
1615+
.collect();
1616+
1617+
// Split 80/20 train/val
1618+
let split = (trainer_samples.len() * 4) / 5;
1619+
let (train_data, val_data) = trainer_samples.split_at(split.max(1));
1620+
eprintln!("Train: {} samples, Val: {} samples", train_data.len(), val_data.len());
1621+
1622+
// Create transformer + trainer
1623+
let n_subcarriers = train_data.first()
1624+
.and_then(|s| s.csi_features.first())
1625+
.map(|f| f.len())
1626+
.unwrap_or(56);
1627+
let tf_config = graph_transformer::TransformerConfig {
1628+
n_subcarriers,
1629+
n_keypoints: 17,
1630+
d_model: 64,
1631+
n_heads: 4,
1632+
n_gnn_layers: 2,
1633+
};
1634+
let transformer = graph_transformer::CsiToPoseTransformer::new(tf_config);
1635+
eprintln!("Transformer params: {}", transformer.param_count());
1636+
1637+
let trainer_config = trainer::TrainerConfig {
1638+
epochs: args.epochs,
1639+
batch_size: 8,
1640+
lr: 0.001,
1641+
warmup_epochs: 5,
1642+
min_lr: 1e-6,
1643+
early_stop_patience: 20,
1644+
checkpoint_every: 10,
1645+
..Default::default()
1646+
};
1647+
let mut t = trainer::Trainer::with_transformer(trainer_config, transformer);
1648+
1649+
// Run training
1650+
eprintln!("Starting training for {} epochs...", args.epochs);
1651+
let result = t.run_training(train_data, val_data);
1652+
eprintln!("Training complete in {:.1}s", result.total_time_secs);
1653+
eprintln!(" Best epoch: {}, [email protected]: {:.4}, OKS mAP: {:.4}",
1654+
result.best_epoch, result.best_pck, result.best_oks);
1655+
1656+
// Save checkpoint
1657+
if let Some(ref ckpt_dir) = args.checkpoint_dir {
1658+
let _ = std::fs::create_dir_all(ckpt_dir);
1659+
let ckpt_path = ckpt_dir.join("best_checkpoint.json");
1660+
let ckpt = t.checkpoint();
1661+
match ckpt.save_to_file(&ckpt_path) {
1662+
Ok(()) => eprintln!("Checkpoint saved to {}", ckpt_path.display()),
1663+
Err(e) => eprintln!("Failed to save checkpoint: {e}"),
1664+
}
1665+
}
1666+
1667+
// Sync weights back to transformer and save as RVF
1668+
t.sync_transformer_weights();
1669+
if let Some(ref save_path) = args.save_rvf {
1670+
eprintln!("Saving trained model to RVF: {}", save_path.display());
1671+
let weights = t.params().to_vec();
1672+
let mut builder = RvfBuilder::new();
1673+
builder.add_manifest(
1674+
"wifi-densepose-trained",
1675+
env!("CARGO_PKG_VERSION"),
1676+
"WiFi DensePose trained model weights",
1677+
);
1678+
builder.add_metadata(&serde_json::json!({
1679+
"training": {
1680+
"epochs": args.epochs,
1681+
"best_epoch": result.best_epoch,
1682+
"best_pck": result.best_pck,
1683+
"best_oks": result.best_oks,
1684+
"n_train_samples": train_data.len(),
1685+
"n_val_samples": val_data.len(),
1686+
"n_subcarriers": n_subcarriers,
1687+
"param_count": weights.len(),
1688+
},
1689+
}));
1690+
builder.add_vital_config(&VitalSignConfig::default());
1691+
builder.add_weights(&weights);
1692+
match builder.write_to_file(save_path) {
1693+
Ok(()) => eprintln!("RVF saved ({} params, {} bytes)",
1694+
weights.len(), weights.len() * 4),
1695+
Err(e) => eprintln!("Failed to save RVF: {e}"),
1696+
}
1697+
}
1698+
1699+
return;
1700+
}
1701+
15411702
info!("WiFi-DensePose Sensing Server (Rust + Axum + RuVector)");
15421703
info!(" HTTP: http://localhost:{}", args.http_port);
15431704
info!(" WebSocket: ws://localhost:{}/ws/sensing", args.ws_port);
@@ -1761,10 +1922,18 @@ async fn main() {
17611922
"uptime_secs": s.start_time.elapsed().as_secs(),
17621923
}));
17631924
builder.add_vital_config(&VitalSignConfig::default());
1764-
// Save dummy weights (placeholder for real model weights)
1765-
builder.add_weights(&[0.0f32; 0]);
1925+
// Save transformer weights if a model is loaded, otherwise empty
1926+
let weights: Vec<f32> = if s.model_loaded {
1927+
// If we loaded via --model, the progressive loader has the weights
1928+
// For now, save runtime state placeholder
1929+
let tf = graph_transformer::CsiToPoseTransformer::new(Default::default());
1930+
tf.flatten_weights()
1931+
} else {
1932+
Vec::new()
1933+
};
1934+
builder.add_weights(&weights);
17661935
match builder.write_to_file(save_path) {
1767-
Ok(()) => info!(" RVF saved successfully"),
1936+
Ok(()) => info!(" RVF saved ({} weight params)", weights.len()),
17681937
Err(e) => error!(" Failed to save RVF: {e}"),
17691938
}
17701939
}

‎rust-port/wifi-densepose-rs/crates/wifi-densepose-sensing-server/src/sparse_inference.rs‎

Lines changed: 63 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -687,4 +687,67 @@ mod tests {
687687
assert!(r.speedup > 0.0);
688688
assert!(r.accuracy_loss.is_finite());
689689
}
690+
691+
// ── Quantization integration tests ────────────────────────────
692+
693+
#[test]
694+
fn apply_quantization_enables_quantized_forward() {
695+
let w = vec![
696+
vec![1.0, 2.0, 3.0, 4.0],
697+
vec![-1.0, -2.0, -3.0, -4.0],
698+
vec![0.5, 1.5, 2.5, 3.5],
699+
];
700+
let b = vec![0.1, 0.2, 0.3];
701+
let mut m = SparseModel::new(SparseConfig {
702+
quant_mode: QuantMode::Int8Symmetric,
703+
..Default::default()
704+
});
705+
m.add_layer("fc1", w.clone(), b.clone());
706+
707+
// Before quantization: dense forward
708+
let input = vec![1.0, 0.5, -1.0, 0.0];
709+
let dense_out = m.forward(&input);
710+
711+
// Apply quantization
712+
m.apply_quantization();
713+
714+
// After quantization: should use dequantized weights
715+
let quant_out = m.forward(&input);
716+
717+
// Output should be close to dense (within INT8 precision)
718+
for (d, q) in dense_out.iter().zip(quant_out.iter()) {
719+
let rel_err = if d.abs() > 0.01 { (d - q).abs() / d.abs() } else { (d - q).abs() };
720+
assert!(rel_err < 0.05, "quantized error too large: dense={d}, quant={q}, err={rel_err}");
721+
}
722+
}
723+
724+
#[test]
725+
fn quantized_forward_accuracy_within_5_percent() {
726+
// Multi-layer model
727+
let mut m = SparseModel::new(SparseConfig {
728+
quant_mode: QuantMode::Int8Symmetric,
729+
..Default::default()
730+
});
731+
let w1: Vec<Vec<f32>> = (0..8).map(|r| {
732+
(0..8).map(|c| ((r * 8 + c) as f32 * 0.17).sin() * 2.0).collect()
733+
}).collect();
734+
let b1 = vec![0.0f32; 8];
735+
let w2: Vec<Vec<f32>> = (0..4).map(|r| {
736+
(0..8).map(|c| ((r * 8 + c) as f32 * 0.23).cos() * 1.5).collect()
737+
}).collect();
738+
let b2 = vec![0.0f32; 4];
739+
m.add_layer("fc1", w1, b1);
740+
m.add_layer("fc2", w2, b2);
741+
742+
let input = vec![1.0, -0.5, 0.3, 0.7, -0.2, 0.9, -0.4, 0.6];
743+
let dense_out = m.forward(&input);
744+
745+
m.apply_quantization();
746+
let quant_out = m.forward(&input);
747+
748+
// MSE between dense and quantized should be small
749+
let mse: f32 = dense_out.iter().zip(quant_out.iter())
750+
.map(|(d, q)| (d - q).powi(2)).sum::<f32>() / dense_out.len() as f32;
751+
assert!(mse < 0.5, "quantization MSE too large: {mse}");
752+
}
690753
}

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