mirror of
https://github.com/ruvnet/RuView
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rUv
eaedfded6f
Addresses three findings from the 2026-05-11 training-pipeline audit: #1/#2 — `wifi-densepose-signal` was a phantom dependency of `wifi-densepose-train` (listed in Cargo.toml, never imported), and vitals/CSI signal features were absent from the pipeline. New module `wifi_densepose_train::signal_features`: `extract_signal_features(&Array4<f32>, &Array4<f32>) -> Array1<f32>` (and the convenience method `CsiSample::signal_features()`) runs a windowed observation's centre frame through `wifi_densepose_signal::features::FeatureExtractor`, producing a fixed-length (FEATURE_LEN=12) amplitude / phase-coherence / PSD feature vector — the hook for a future vitals / multi-task supervision head (breathing- and heart-rate-band power are read off the PSD summary). The vector is produced on demand and is not yet fed back into the loss; wiring it as a training target is the documented follow-up. `wifi-densepose-signal` is now an actually-used dependency. 5 new tests (2 unit in signal_features.rs, 3 integration in tests/test_dataset.rs); existing wifi-densepose-train tests unchanged and green. #3 — `docs/huggingface/MODEL_CARD.md` presented PIR/BME280 environmental-sensor weak-label fine-tuning as a current capability; there is no env-sensor ingestion in the training pipeline. Marked that path as planned/not-implemented in the training-steps list and the data-provenance section. (#5 — README's "92.9% PCK@20" overclaim — fixed separately in PR #535.) CHANGELOG updated.
93 lines
3.3 KiB
Rust
93 lines
3.3 KiB
Rust
//! # WiFi-DensePose Training Infrastructure
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//!
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//! This crate provides the complete training pipeline for the WiFi-DensePose pose
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//! estimation model. It includes configuration management, dataset loading with
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//! subcarrier interpolation, loss functions, evaluation metrics, and the training
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//! loop orchestrator.
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//!
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//! ## Architecture
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//!
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//! ```text
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//! TrainingConfig ──► Trainer ──► Model
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//! │ │
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//! │ DataLoader
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//! │ │
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//! │ CsiDataset (MmFiDataset | SyntheticCsiDataset)
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//! │ │
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//! │ subcarrier::interpolate_subcarriers
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//! │
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//! └──► losses / metrics
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//! ```
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//!
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//! ## Quick Start
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//!
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//! ```rust,no_run
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//! use wifi_densepose_train::config::TrainingConfig;
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//! use wifi_densepose_train::dataset::{SyntheticCsiDataset, SyntheticConfig, CsiDataset};
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//!
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//! // Build config
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//! let config = TrainingConfig::default();
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//! config.validate().expect("config is valid");
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//!
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//! // Create a synthetic dataset (deterministic, fixed-seed)
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//! let syn_cfg = SyntheticConfig::default();
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//! let dataset = SyntheticCsiDataset::new(200, syn_cfg);
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//!
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//! // Load one sample
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//! let sample = dataset.get(0).unwrap();
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//! println!("amplitude shape: {:?}", sample.amplitude.shape());
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//! ```
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// Note: #![forbid(unsafe_code)] is intentionally absent because the `tch`
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// dependency (PyTorch Rust bindings) internally requires unsafe code via FFI.
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// All *this* crate's code is written without unsafe blocks.
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#![warn(missing_docs)]
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pub mod config;
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pub mod dataset;
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pub mod domain;
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pub mod error;
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pub mod eval;
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pub mod geometry;
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pub mod rapid_adapt;
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pub mod ruview_metrics;
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pub mod signal_features;
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pub mod subcarrier;
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pub mod virtual_aug;
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// The following modules use `tch` (PyTorch Rust bindings) for GPU-accelerated
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// training and are only compiled when the `tch-backend` feature is enabled.
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// Without the feature the crate still provides the dataset / config / subcarrier
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// APIs needed for data preprocessing and proof verification.
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#[cfg(feature = "tch-backend")]
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pub mod losses;
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#[cfg(feature = "tch-backend")]
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pub mod metrics;
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#[cfg(feature = "tch-backend")]
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pub mod model;
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#[cfg(feature = "tch-backend")]
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pub mod proof;
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#[cfg(feature = "tch-backend")]
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pub mod trainer;
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// Convenient re-exports at the crate root.
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pub use config::TrainingConfig;
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pub use dataset::{CsiDataset, CsiSample, DataLoader, MmFiDataset, SyntheticCsiDataset, SyntheticConfig};
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pub use error::{ConfigError, DatasetError, SubcarrierError, TrainError};
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// TrainResult<T> is the generic Result alias from error.rs; the concrete
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// TrainResult struct from trainer.rs is accessed via trainer::TrainResult.
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pub use error::TrainResult as TrainResultAlias;
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pub use subcarrier::{compute_interp_weights, interpolate_subcarriers, select_subcarriers_by_variance};
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// MERIDIAN (ADR-027) re-exports.
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pub use domain::{
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AdversarialSchedule, DomainClassifier, DomainFactorizer, GradientReversalLayer,
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};
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pub use eval::CrossDomainEvaluator;
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pub use geometry::{FilmLayer, FourierPositionalEncoding, GeometryEncoder, MeridianGeometryConfig};
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pub use rapid_adapt::{AdaptError, AdaptationLoss, AdaptationResult, RapidAdaptation};
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pub use virtual_aug::VirtualDomainAugmentor;
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/// Crate version string.
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pub const VERSION: &str = env!("CARGO_PKG_VERSION");
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