feat: ADR-024 Contrastive CSI Embedding Model — all 7 phases (#52)
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
This commit was merged in pull request #52.
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rUv
@@ -13,7 +13,7 @@ mod rvf_pipeline;
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mod vital_signs;
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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 wifi_densepose_sensing_server::{graph_transformer, trainer, dataset, embedding};
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use std::collections::VecDeque;
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use std::net::SocketAddr;
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@@ -122,6 +122,22 @@ struct Args {
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/// Directory for training checkpoints
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#[arg(long, value_name = "DIR")]
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checkpoint_dir: Option<PathBuf>,
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/// Run self-supervised contrastive pretraining (ADR-024)
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#[arg(long)]
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pretrain: bool,
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/// Number of pretraining epochs (default 50)
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#[arg(long, default_value = "50")]
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pretrain_epochs: usize,
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/// Extract embeddings mode: load model and extract CSI embeddings
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#[arg(long)]
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embed: bool,
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/// Build fingerprint index from embeddings (env|activity|temporal|person)
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#[arg(long, value_name = "TYPE")]
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build_index: Option<String>,
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}
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// ── Data types ───────────────────────────────────────────────────────────────
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@@ -1536,6 +1552,221 @@ async fn main() {
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return;
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}
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// Handle --pretrain mode: self-supervised contrastive pretraining (ADR-024)
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if args.pretrain {
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eprintln!("=== WiFi-DensePose Contrastive Pretraining (ADR-024) ===");
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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, ..Default::default()
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});
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// Generate synthetic or load real CSI windows
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let generate_synthetic_windows = || -> Vec<Vec<Vec<f32>>> {
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(0..50).map(|i| {
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(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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}).collect()
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};
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let csi_windows: Vec<Vec<Vec<f32>>> = 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.into_iter().map(|s| s.csi_window).collect()
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}
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_ => {
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eprintln!("Using synthetic data for pretraining.");
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generate_synthetic_windows()
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}
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};
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let n_subcarriers = csi_windows.first()
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.and_then(|w| w.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, n_keypoints: 17, d_model: 64, n_heads: 4, 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.pretrain_epochs,
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batch_size: 8, lr: 0.001, warmup_epochs: 2, min_lr: 1e-6,
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early_stop_patience: args.pretrain_epochs + 1,
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pretrain_temperature: 0.07,
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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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let e_config = embedding::EmbeddingConfig {
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d_model: 64, d_proj: 128, temperature: 0.07, normalize: true,
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};
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let mut projection = embedding::ProjectionHead::new(e_config.clone());
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let augmenter = embedding::CsiAugmenter::new();
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eprintln!("Starting contrastive pretraining for {} epochs...", args.pretrain_epochs);
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let start = std::time::Instant::now();
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for epoch in 0..args.pretrain_epochs {
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let loss = t.pretrain_epoch(&csi_windows, &augmenter, &mut projection, 0.07, epoch);
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if epoch % 10 == 0 || epoch == args.pretrain_epochs - 1 {
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eprintln!(" Epoch {epoch}: contrastive loss = {loss:.4}");
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}
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}
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let elapsed = start.elapsed().as_secs_f64();
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eprintln!("Pretraining complete in {elapsed:.1}s");
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// Save pretrained model as RVF with embedding segment
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if let Some(ref save_path) = args.save_rvf {
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eprintln!("Saving pretrained model to RVF: {}", save_path.display());
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t.sync_transformer_weights();
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let weights = t.params().to_vec();
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let mut proj_weights = Vec::new();
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projection.flatten_into(&mut proj_weights);
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let mut builder = RvfBuilder::new();
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builder.add_manifest(
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"wifi-densepose-pretrained",
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env!("CARGO_PKG_VERSION"),
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"WiFi DensePose contrastive pretrained model (ADR-024)",
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);
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builder.add_weights(&weights);
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builder.add_embedding(
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&serde_json::json!({
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"d_model": e_config.d_model,
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"d_proj": e_config.d_proj,
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"temperature": e_config.temperature,
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"normalize": e_config.normalize,
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"pretrain_epochs": args.pretrain_epochs,
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}),
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&proj_weights,
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);
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match builder.write_to_file(save_path) {
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Ok(()) => eprintln!("RVF saved ({} transformer + {} projection params)",
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weights.len(), proj_weights.len()),
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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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// Handle --embed mode: extract embeddings from CSI data
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if args.embed {
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eprintln!("=== WiFi-DensePose Embedding Extraction (ADR-024) ===");
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let model_path = match &args.model {
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Some(p) => p.clone(),
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None => {
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eprintln!("Error: --embed requires --model <path> to a pretrained .rvf file");
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std::process::exit(1);
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}
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};
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let reader = match RvfReader::from_file(&model_path) {
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Ok(r) => r,
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Err(e) => { eprintln!("Failed to load model: {e}"); std::process::exit(1); }
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};
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let weights = reader.weights().unwrap_or_default();
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let (embed_config_json, proj_weights) = reader.embedding().unwrap_or_else(|| {
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eprintln!("Warning: no embedding segment in RVF, using defaults");
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(serde_json::json!({"d_model":64,"d_proj":128,"temperature":0.07,"normalize":true}), Vec::new())
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});
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let d_model = embed_config_json["d_model"].as_u64().unwrap_or(64) as usize;
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let d_proj = embed_config_json["d_proj"].as_u64().unwrap_or(128) as usize;
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let tf_config = graph_transformer::TransformerConfig {
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n_subcarriers: 56, n_keypoints: 17, d_model, n_heads: 4, n_gnn_layers: 2,
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};
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let e_config = embedding::EmbeddingConfig {
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d_model, d_proj, temperature: 0.07, normalize: true,
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};
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let mut extractor = embedding::EmbeddingExtractor::new(tf_config, e_config.clone());
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// Load transformer weights
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if !weights.is_empty() {
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if let Err(e) = extractor.transformer.unflatten_weights(&weights) {
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eprintln!("Warning: failed to load transformer weights: {e}");
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}
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}
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// Load projection weights
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if !proj_weights.is_empty() {
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let (proj, _) = embedding::ProjectionHead::unflatten_from(&proj_weights, &e_config);
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extractor.projection = proj;
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}
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// Load dataset and extract embeddings
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let _ds_path = args.dataset.clone().unwrap_or_else(|| PathBuf::from("data"));
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let csi_windows: Vec<Vec<Vec<f32>>> = (0..10).map(|i| {
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(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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}).collect();
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eprintln!("Extracting embeddings from {} CSI windows...", csi_windows.len());
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let embeddings = extractor.extract_batch(&csi_windows);
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for (i, emb) in embeddings.iter().enumerate() {
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let norm: f32 = emb.iter().map(|x| x * x).sum::<f32>().sqrt();
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eprintln!(" Window {i}: {d_proj}-dim embedding, ||e|| = {norm:.4}");
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}
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eprintln!("Extracted {} embeddings of dimension {d_proj}", embeddings.len());
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return;
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}
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// Handle --build-index mode: build a fingerprint index from embeddings
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if let Some(ref index_type_str) = args.build_index {
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eprintln!("=== WiFi-DensePose Fingerprint Index Builder (ADR-024) ===");
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let index_type = match index_type_str.as_str() {
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"env" | "environment" => embedding::IndexType::EnvironmentFingerprint,
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"activity" => embedding::IndexType::ActivityPattern,
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"temporal" => embedding::IndexType::TemporalBaseline,
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"person" => embedding::IndexType::PersonTrack,
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_ => {
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eprintln!("Unknown index type '{}'. Use: env, activity, temporal, person", index_type_str);
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std::process::exit(1);
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}
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};
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let tf_config = graph_transformer::TransformerConfig::default();
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let e_config = embedding::EmbeddingConfig::default();
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let mut extractor = embedding::EmbeddingExtractor::new(tf_config, e_config);
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// Generate synthetic CSI windows for demo
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let csi_windows: Vec<Vec<Vec<f32>>> = (0..20).map(|i| {
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(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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}).collect();
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let mut index = embedding::FingerprintIndex::new(index_type);
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for (i, window) in csi_windows.iter().enumerate() {
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let emb = extractor.extract(window);
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index.insert(emb, format!("window_{i}"), i as u64 * 100);
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}
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eprintln!("Built {:?} index with {} entries", index_type, index.len());
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// Test a query
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let query_emb = extractor.extract(&csi_windows[0]);
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let results = index.search(&query_emb, 5);
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eprintln!("Top-5 nearest to window_0:");
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for r in &results {
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eprintln!(" entry={}, distance={:.4}, metadata={}", r.entry, r.distance, r.metadata);
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}
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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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