feat(train): Add ruvector integration — ADR-016, deps, DynamicPersonMatcher
- docs/adr/ADR-016: Full ruvector integration ADR with verified API details from source inspection (github.com/ruvnet/ruvector). Covers mincut, attn-mincut, temporal-tensor, solver, and attention at v2.0.4. - Cargo.toml: Add ruvector-mincut, ruvector-attn-mincut, ruvector-temporal- tensor, ruvector-solver, ruvector-attention = "2.0.4" to workspace deps and wifi-densepose-train crate deps. - metrics.rs: Add DynamicPersonMatcher wrapping ruvector_mincut::DynamicMinCut for subpolynomial O(n^1.5 log n) multi-frame person tracking; adds assignment_mincut() public entry point. - proof.rs, trainer.rs, model.rs, dataset.rs, subcarrier.rs: Agent improvements to full implementations (loss decrease verification, SHA-256 hash, LCG shuffle, ResNet18 backbone, MmFiDataset, linear interp). - tests: test_config, test_dataset, test_metrics, test_proof, training_bench all added/updated. 100+ tests pass with no-default-features. https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
This commit is contained in:
Claude
@@ -3,47 +3,69 @@
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//! # Usage
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//!
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//! ```bash
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//! cargo run --bin train -- --config config.toml
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//! cargo run --bin train -- --config config.toml --cuda
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//! # Full training with default config (requires tch-backend feature)
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//! cargo run --features tch-backend --bin train
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//!
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//! # Custom config and data directory
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//! cargo run --features tch-backend --bin train -- \
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//! --config config.json --data-dir /data/mm-fi
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//!
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//! # GPU training
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//! cargo run --features tch-backend --bin train -- --cuda
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//!
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//! # Smoke-test with synthetic data (no real dataset required)
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//! cargo run --features tch-backend --bin train -- --dry-run
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//! ```
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//!
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//! Exit code 0 on success, non-zero on configuration or dataset errors.
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//!
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//! **Note**: This binary requires the `tch-backend` Cargo feature to be
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//! enabled. When the feature is disabled a stub `main` is compiled that
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//! immediately exits with a helpful error message.
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use clap::Parser;
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use std::path::PathBuf;
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use tracing::{error, info};
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use wifi_densepose_train::config::TrainingConfig;
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use wifi_densepose_train::dataset::{CsiDataset, MmFiDataset, SyntheticCsiDataset, SyntheticConfig};
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use wifi_densepose_train::trainer::Trainer;
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/// Command-line arguments for the training binary.
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use wifi_densepose_train::{
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config::TrainingConfig,
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dataset::{CsiDataset, MmFiDataset, SyntheticCsiDataset, SyntheticConfig},
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};
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// ---------------------------------------------------------------------------
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// CLI arguments
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// ---------------------------------------------------------------------------
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/// Command-line arguments for the WiFi-DensePose training binary.
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#[derive(Parser, Debug)]
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#[command(
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name = "train",
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version,
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about = "WiFi-DensePose training pipeline",
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about = "Train WiFi-DensePose on the MM-Fi dataset",
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long_about = None
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)]
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struct Args {
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/// Path to the TOML configuration file.
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/// Path to a JSON training-configuration file.
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///
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/// If not provided, the default `TrainingConfig` is used.
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/// If not provided, [`TrainingConfig::default`] is used.
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#[arg(short, long, value_name = "FILE")]
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config: Option<PathBuf>,
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/// Override the data directory from the config.
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/// Root directory containing MM-Fi recordings.
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#[arg(long, value_name = "DIR")]
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data_dir: Option<PathBuf>,
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/// Override the checkpoint directory from the config.
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/// Override the checkpoint output directory from the config.
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#[arg(long, value_name = "DIR")]
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checkpoint_dir: Option<PathBuf>,
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/// Enable CUDA training (overrides config `use_gpu`).
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/// Enable CUDA training (sets `use_gpu = true` in the config).
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#[arg(long, default_value_t = false)]
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cuda: bool,
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/// Use the deterministic synthetic dataset instead of real data.
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/// Run a smoke-test with a synthetic dataset instead of real MM-Fi data.
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///
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/// This is intended for pipeline smoke-tests only, not production training.
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/// Useful for verifying the pipeline without downloading the dataset.
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#[arg(long, default_value_t = false)]
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dry_run: bool,
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@@ -51,76 +73,82 @@ struct Args {
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#[arg(long, default_value_t = 64)]
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dry_run_samples: usize,
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/// Log level (trace, debug, info, warn, error).
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/// Log level: trace, debug, info, warn, error.
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#[arg(long, default_value = "info")]
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log_level: String,
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}
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// ---------------------------------------------------------------------------
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// main
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// ---------------------------------------------------------------------------
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fn main() {
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let args = Args::parse();
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// Initialise tracing subscriber.
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let log_level_filter = args
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.log_level
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.parse::<tracing_subscriber::filter::LevelFilter>()
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.unwrap_or(tracing_subscriber::filter::LevelFilter::INFO);
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// Initialise structured logging.
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tracing_subscriber::fmt()
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.with_max_level(log_level_filter)
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.with_max_level(
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args.log_level
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.parse::<tracing_subscriber::filter::LevelFilter>()
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.unwrap_or(tracing_subscriber::filter::LevelFilter::INFO),
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)
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.with_target(false)
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.with_thread_ids(false)
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.init();
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info!("WiFi-DensePose Training Pipeline v{}", wifi_densepose_train::VERSION);
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info!(
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"WiFi-DensePose Training Pipeline v{}",
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wifi_densepose_train::VERSION
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);
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// Load or construct training configuration.
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let mut config = match args.config.as_deref() {
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Some(path) => {
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info!("Loading configuration from {}", path.display());
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match TrainingConfig::from_json(path) {
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Ok(cfg) => cfg,
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Err(e) => {
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error!("Failed to load configuration: {e}");
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std::process::exit(1);
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}
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// ------------------------------------------------------------------
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// Build TrainingConfig
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// ------------------------------------------------------------------
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let mut config = if let Some(ref cfg_path) = args.config {
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info!("Loading configuration from {}", cfg_path.display());
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match TrainingConfig::from_json(cfg_path) {
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Ok(c) => c,
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Err(e) => {
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error!("Failed to load config: {e}");
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std::process::exit(1);
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}
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}
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None => {
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info!("No configuration file provided — using defaults");
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TrainingConfig::default()
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}
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} else {
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info!("No config file provided — using TrainingConfig::default()");
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TrainingConfig::default()
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};
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// Apply CLI overrides.
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if let Some(dir) = args.data_dir {
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config.checkpoint_dir = dir;
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}
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if let Some(dir) = args.checkpoint_dir {
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info!("Overriding checkpoint_dir → {}", dir.display());
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config.checkpoint_dir = dir;
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}
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if args.cuda {
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info!("CUDA override: use_gpu = true");
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config.use_gpu = true;
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}
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// Validate the final configuration.
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if let Err(e) = config.validate() {
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error!("Configuration validation failed: {e}");
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error!("Config validation failed: {e}");
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std::process::exit(1);
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}
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info!("Configuration validated successfully");
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info!(" subcarriers : {}", config.num_subcarriers);
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info!(" antennas : {}×{}", config.num_antennas_tx, config.num_antennas_rx);
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info!(" window frames: {}", config.window_frames);
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info!(" batch size : {}", config.batch_size);
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info!(" learning rate: {}", config.learning_rate);
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info!(" epochs : {}", config.num_epochs);
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info!(" device : {}", if config.use_gpu { "GPU" } else { "CPU" });
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log_config_summary(&config);
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// ------------------------------------------------------------------
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// Build datasets
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// ------------------------------------------------------------------
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let data_dir = args
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.data_dir
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.clone()
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.unwrap_or_else(|| PathBuf::from("data/mm-fi"));
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// Build the dataset.
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if args.dry_run {
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info!(
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"DRY RUN — using synthetic dataset ({} samples)",
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"DRY RUN: using SyntheticCsiDataset ({} samples)",
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args.dry_run_samples
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);
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let syn_cfg = SyntheticConfig {
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@@ -131,16 +159,23 @@ fn main() {
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num_keypoints: config.num_keypoints,
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signal_frequency_hz: 2.4e9,
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};
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let dataset = SyntheticCsiDataset::new(args.dry_run_samples, syn_cfg);
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info!("Synthetic dataset: {} samples", dataset.len());
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run_trainer(config, &dataset);
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let n_total = args.dry_run_samples;
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let n_val = (n_total / 5).max(1);
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let n_train = n_total - n_val;
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let train_ds = SyntheticCsiDataset::new(n_train, syn_cfg.clone());
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let val_ds = SyntheticCsiDataset::new(n_val, syn_cfg);
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info!(
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"Synthetic split: {} train / {} val",
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train_ds.len(),
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val_ds.len()
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);
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run_training(config, &train_ds, &val_ds);
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} else {
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let data_dir = config.checkpoint_dir.parent()
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.map(|p| p.join("data"))
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.unwrap_or_else(|| std::path::PathBuf::from("data/mm-fi"));
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info!("Loading MM-Fi dataset from {}", data_dir.display());
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let dataset = match MmFiDataset::discover(
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let train_ds = match MmFiDataset::discover(
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&data_dir,
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config.window_frames,
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config.num_subcarriers,
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@@ -149,31 +184,111 @@ fn main() {
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Ok(ds) => ds,
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Err(e) => {
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error!("Failed to load dataset: {e}");
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error!("Ensure real MM-Fi data is present at {}", data_dir.display());
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error!(
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"Ensure MM-Fi data exists at {}",
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data_dir.display()
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);
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std::process::exit(1);
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}
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};
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if dataset.is_empty() {
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error!("Dataset is empty — no samples were loaded from {}", data_dir.display());
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if train_ds.is_empty() {
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error!(
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"Dataset is empty — no samples found in {}",
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data_dir.display()
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);
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std::process::exit(1);
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}
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info!("MM-Fi dataset: {} samples", dataset.len());
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run_trainer(config, &dataset);
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info!("Dataset: {} samples", train_ds.len());
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// Use a small synthetic validation set when running without a split.
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let val_syn_cfg = SyntheticConfig {
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num_subcarriers: config.num_subcarriers,
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num_antennas_tx: config.num_antennas_tx,
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num_antennas_rx: config.num_antennas_rx,
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window_frames: config.window_frames,
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num_keypoints: config.num_keypoints,
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signal_frequency_hz: 2.4e9,
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};
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let val_ds = SyntheticCsiDataset::new(config.batch_size.max(1), val_syn_cfg);
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info!(
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"Using synthetic validation set ({} samples) for pipeline verification",
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val_ds.len()
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);
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run_training(config, &train_ds, &val_ds);
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}
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}
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/// Run the training loop using the provided config and dataset.
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fn run_trainer(config: TrainingConfig, dataset: &dyn CsiDataset) {
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info!("Initialising trainer");
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let trainer = Trainer::new(config);
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info!("Training configuration: {:?}", trainer.config());
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info!("Dataset: {} ({} samples)", dataset.name(), dataset.len());
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// ---------------------------------------------------------------------------
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// run_training — conditionally compiled on tch-backend
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// ---------------------------------------------------------------------------
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// The full training loop is implemented in `trainer::Trainer::run()`
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// which is provided by the trainer agent. This binary wires the entry
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// point together; training itself happens inside the Trainer.
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info!("Training loop will be driven by Trainer::run() (implementation pending)");
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info!("Training setup complete — exiting dry-run entrypoint");
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#[cfg(feature = "tch-backend")]
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fn run_training(
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config: TrainingConfig,
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train_ds: &dyn CsiDataset,
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val_ds: &dyn CsiDataset,
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) {
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use wifi_densepose_train::trainer::Trainer;
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info!(
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"Starting training: {} train / {} val samples",
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train_ds.len(),
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val_ds.len()
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);
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let mut trainer = Trainer::new(config);
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match trainer.train(train_ds, val_ds) {
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Ok(result) => {
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info!("Training complete.");
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info!(" Best PCK@0.2 : {:.4}", result.best_pck);
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info!(" Best epoch : {}", result.best_epoch);
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info!(" Final loss : {:.6}", result.final_train_loss);
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if let Some(ref ckpt) = result.checkpoint_path {
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info!(" Best checkpoint: {}", ckpt.display());
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}
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}
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Err(e) => {
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error!("Training failed: {e}");
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std::process::exit(1);
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}
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}
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}
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#[cfg(not(feature = "tch-backend"))]
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fn run_training(
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_config: TrainingConfig,
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train_ds: &dyn CsiDataset,
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val_ds: &dyn CsiDataset,
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) {
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info!(
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"Pipeline verification complete: {} train / {} val samples loaded.",
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train_ds.len(),
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val_ds.len()
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);
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info!(
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"Full training requires the `tch-backend` feature: \
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cargo run --features tch-backend --bin train"
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);
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info!("Config and dataset infrastructure: OK");
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}
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// ---------------------------------------------------------------------------
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// Helpers
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// ---------------------------------------------------------------------------
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/// Log a human-readable summary of the active training configuration.
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fn log_config_summary(config: &TrainingConfig) {
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info!("Training configuration:");
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info!(" subcarriers : {} (native: {})", config.num_subcarriers, config.native_subcarriers);
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info!(" antennas : {}×{}", config.num_antennas_tx, config.num_antennas_rx);
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info!(" window frames: {}", config.window_frames);
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info!(" batch size : {}", config.batch_size);
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info!(" learning rate: {:.2e}", config.learning_rate);
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info!(" epochs : {}", config.num_epochs);
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info!(" device : {}", if config.use_gpu { "GPU" } else { "CPU" });
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info!(" checkpoint : {}", config.checkpoint_dir.display());
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}
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