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 <ruv@ruv.net>
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ruv
@@ -11,11 +11,9 @@
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mod rvf_container;
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mod rvf_pipeline;
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mod vital_signs;
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mod graph_transformer;
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mod trainer;
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mod dataset;
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mod sparse_inference;
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mod sona;
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// Training pipeline modules (exposed via lib.rs)
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use wifi_densepose_sensing_server::{graph_transformer, trainer, dataset};
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use std::collections::VecDeque;
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use std::net::SocketAddr;
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@@ -1538,6 +1536,169 @@ async fn main() {
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return;
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}
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// Handle --train mode: train a model and exit
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if args.train {
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eprintln!("=== WiFi-DensePose Training Mode ===");
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// Build data pipeline
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let ds_path = args.dataset.clone().unwrap_or_else(|| PathBuf::from("data"));
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let source = match args.dataset_type.as_str() {
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"wipose" => dataset::DataSource::WiPose(ds_path.clone()),
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_ => dataset::DataSource::MmFi(ds_path.clone()),
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};
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let pipeline = dataset::DataPipeline::new(dataset::DataConfig {
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source,
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..Default::default()
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});
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// Load samples
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let samples = match pipeline.load() {
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Ok(s) if !s.is_empty() => {
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eprintln!("Loaded {} samples from {}", s.len(), ds_path.display());
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s
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}
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Ok(_) => {
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eprintln!("No samples found at {}. Generating synthetic training data...", ds_path.display());
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// Generate synthetic samples for testing the pipeline
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let mut synth = Vec::new();
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for i in 0..50 {
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let csi: Vec<Vec<f32>> = (0..4).map(|a| {
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(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
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}).collect();
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let mut kps = [(0.0f32, 0.0f32, 1.0f32); 17];
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for (k, kp) in kps.iter_mut().enumerate() {
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kp.0 = (k as f32 * 0.1 + i as f32 * 0.02).sin() * 100.0 + 320.0;
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kp.1 = (k as f32 * 0.15 + i as f32 * 0.03).cos() * 80.0 + 240.0;
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}
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synth.push(dataset::TrainingSample {
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csi_window: csi,
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pose_label: dataset::PoseLabel {
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keypoints: kps,
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body_parts: Vec::new(),
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confidence: 1.0,
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},
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source: "synthetic",
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});
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}
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synth
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}
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Err(e) => {
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eprintln!("Failed to load dataset: {e}");
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eprintln!("Generating synthetic training data...");
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let mut synth = Vec::new();
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for i in 0..50 {
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let csi: Vec<Vec<f32>> = (0..4).map(|a| {
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(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
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}).collect();
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let mut kps = [(0.0f32, 0.0f32, 1.0f32); 17];
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for (k, kp) in kps.iter_mut().enumerate() {
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kp.0 = (k as f32 * 0.1 + i as f32 * 0.02).sin() * 100.0 + 320.0;
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kp.1 = (k as f32 * 0.15 + i as f32 * 0.03).cos() * 80.0 + 240.0;
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}
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synth.push(dataset::TrainingSample {
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csi_window: csi,
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pose_label: dataset::PoseLabel {
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keypoints: kps,
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body_parts: Vec::new(),
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confidence: 1.0,
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},
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source: "synthetic",
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});
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}
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synth
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}
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};
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// Convert dataset samples to trainer format
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let trainer_samples: Vec<trainer::TrainingSample> = samples.iter()
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.map(trainer::from_dataset_sample)
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.collect();
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// Split 80/20 train/val
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let split = (trainer_samples.len() * 4) / 5;
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let (train_data, val_data) = trainer_samples.split_at(split.max(1));
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eprintln!("Train: {} samples, Val: {} samples", train_data.len(), val_data.len());
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// Create transformer + trainer
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let n_subcarriers = train_data.first()
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.and_then(|s| s.csi_features.first())
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.map(|f| f.len())
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.unwrap_or(56);
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let tf_config = graph_transformer::TransformerConfig {
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n_subcarriers,
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n_keypoints: 17,
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d_model: 64,
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n_heads: 4,
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n_gnn_layers: 2,
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};
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let transformer = graph_transformer::CsiToPoseTransformer::new(tf_config);
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eprintln!("Transformer params: {}", transformer.param_count());
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let trainer_config = trainer::TrainerConfig {
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epochs: args.epochs,
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batch_size: 8,
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lr: 0.001,
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warmup_epochs: 5,
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min_lr: 1e-6,
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early_stop_patience: 20,
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checkpoint_every: 10,
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..Default::default()
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};
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let mut t = trainer::Trainer::with_transformer(trainer_config, transformer);
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// Run training
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eprintln!("Starting training for {} epochs...", args.epochs);
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let result = t.run_training(train_data, val_data);
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eprintln!("Training complete in {:.1}s", result.total_time_secs);
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eprintln!(" Best epoch: {}, PCK@0.2: {:.4}, OKS mAP: {:.4}",
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result.best_epoch, result.best_pck, result.best_oks);
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// Save checkpoint
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if let Some(ref ckpt_dir) = args.checkpoint_dir {
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let _ = std::fs::create_dir_all(ckpt_dir);
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let ckpt_path = ckpt_dir.join("best_checkpoint.json");
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let ckpt = t.checkpoint();
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match ckpt.save_to_file(&ckpt_path) {
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Ok(()) => eprintln!("Checkpoint saved to {}", ckpt_path.display()),
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Err(e) => eprintln!("Failed to save checkpoint: {e}"),
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}
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}
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// Sync weights back to transformer and save as RVF
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t.sync_transformer_weights();
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if let Some(ref save_path) = args.save_rvf {
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eprintln!("Saving trained model to RVF: {}", save_path.display());
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let weights = t.params().to_vec();
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let mut builder = RvfBuilder::new();
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builder.add_manifest(
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"wifi-densepose-trained",
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env!("CARGO_PKG_VERSION"),
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"WiFi DensePose trained model weights",
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);
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builder.add_metadata(&serde_json::json!({
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"training": {
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"epochs": args.epochs,
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"best_epoch": result.best_epoch,
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"best_pck": result.best_pck,
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"best_oks": result.best_oks,
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"n_train_samples": train_data.len(),
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"n_val_samples": val_data.len(),
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"n_subcarriers": n_subcarriers,
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"param_count": weights.len(),
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},
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}));
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builder.add_vital_config(&VitalSignConfig::default());
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builder.add_weights(&weights);
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match builder.write_to_file(save_path) {
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Ok(()) => eprintln!("RVF saved ({} params, {} bytes)",
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weights.len(), weights.len() * 4),
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Err(e) => eprintln!("Failed to save RVF: {e}"),
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}
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}
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return;
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}
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info!("WiFi-DensePose Sensing Server (Rust + Axum + RuVector)");
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info!(" HTTP: http://localhost:{}", args.http_port);
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info!(" WebSocket: ws://localhost:{}/ws/sensing", args.ws_port);
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@@ -1761,10 +1922,18 @@ async fn main() {
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"uptime_secs": s.start_time.elapsed().as_secs(),
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}));
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builder.add_vital_config(&VitalSignConfig::default());
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// Save dummy weights (placeholder for real model weights)
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builder.add_weights(&[0.0f32; 0]);
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// Save transformer weights if a model is loaded, otherwise empty
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let weights: Vec<f32> = if s.model_loaded {
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// If we loaded via --model, the progressive loader has the weights
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// For now, save runtime state placeholder
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let tf = graph_transformer::CsiToPoseTransformer::new(Default::default());
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tf.flatten_weights()
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} else {
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Vec::new()
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};
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builder.add_weights(&weights);
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match builder.write_to_file(save_path) {
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Ok(()) => info!(" RVF saved successfully"),
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Ok(()) => info!(" RVF saved ({} weight params)", weights.len()),
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Err(e) => error!(" Failed to save RVF: {e}"),
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}
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}
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