mirror of
https://github.com/ruvnet/RuView
synced 2026-07-29 18:31:44 +00:00
Claude
d925538773
Makes the presence/person-count "beyond SOTA" claim falsifiable in code instead of aspirational (the unfalsifiability gap from the beyond-SOTA system review). occupancy_bench grades predictions vs ground truth and gates a SOTA claim behind one claim_allowed invariant requiring ALL of: - DataProvenance::Measured — synthetic/mock data is scorable for regression but never claimable (anti-mock-contamination; the CLAUDE.md Kconfig-bug lesson made structural). - A leak-free EvalSplit — validate() refuses any split where a subject OR environment id appears in both train and test (subject leakage / per-environment overfitting). - n_test >= min_test_samples (small-N guard). - Presence F1 whose bootstrap-CI lower bound (deterministic seeded splitmix64) clears the threshold — not the point estimate. - Count MAE within threshold. The claim string is unreadable except through the gate (NO_CLAIM otherwise), same discipline as the ruview-gamma acceptance gate. What remains is data, not method: a frozen, SHA-pinned, subject/environment-disjoint measured replay set turns the claim into a passing/failing test. Lives in wifi-densepose-train (the eval bounded context, alongside ablation/ eval/metrics). 10 tests cover each refusal path; warning-clean under the crate's missing_docs lint. Workspace gate 2,914 passed / 0 failed. Doc 03 updated. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH
101 lines
3.6 KiB
Rust
101 lines
3.6 KiB
Rust
//! # WiFi-DensePose Training Infrastructure
|
|
//!
|
|
//! This crate provides the complete training pipeline for the WiFi-DensePose pose
|
|
//! estimation model. It includes configuration management, dataset loading with
|
|
//! subcarrier interpolation, loss functions, evaluation metrics, and the training
|
|
//! loop orchestrator.
|
|
//!
|
|
//! ## Architecture
|
|
//!
|
|
//! ```text
|
|
//! TrainingConfig ──► Trainer ──► Model
|
|
//! │ │
|
|
//! │ DataLoader
|
|
//! │ │
|
|
//! │ CsiDataset (MmFiDataset | SyntheticCsiDataset)
|
|
//! │ │
|
|
//! │ subcarrier::interpolate_subcarriers
|
|
//! │
|
|
//! └──► losses / metrics
|
|
//! ```
|
|
//!
|
|
//! ## Quick Start
|
|
//!
|
|
//! ```rust,no_run
|
|
//! use wifi_densepose_train::config::TrainingConfig;
|
|
//! use wifi_densepose_train::dataset::{SyntheticCsiDataset, SyntheticConfig, CsiDataset};
|
|
//!
|
|
//! // Build config
|
|
//! let config = TrainingConfig::default();
|
|
//! config.validate().expect("config is valid");
|
|
//!
|
|
//! // Create a synthetic dataset (deterministic, fixed-seed)
|
|
//! let syn_cfg = SyntheticConfig::default();
|
|
//! let dataset = SyntheticCsiDataset::new(200, syn_cfg);
|
|
//!
|
|
//! // Load one sample
|
|
//! let sample = dataset.get(0).unwrap();
|
|
//! println!("amplitude shape: {:?}", sample.amplitude.shape());
|
|
//! ```
|
|
|
|
// Note: #![forbid(unsafe_code)] is intentionally absent because the `tch`
|
|
// dependency (PyTorch Rust bindings) internally requires unsafe code via FFI.
|
|
// All *this* crate's code is written without unsafe blocks.
|
|
#![warn(missing_docs)]
|
|
|
|
pub mod config;
|
|
pub mod dataset;
|
|
pub mod domain;
|
|
pub mod error;
|
|
pub mod eval;
|
|
pub mod geometry;
|
|
pub mod rapid_adapt;
|
|
pub mod ruview_metrics;
|
|
pub mod signal_features;
|
|
pub mod subcarrier;
|
|
pub mod virtual_aug;
|
|
|
|
// The following modules use `tch` (PyTorch Rust bindings) for GPU-accelerated
|
|
// training and are only compiled when the `tch-backend` feature is enabled.
|
|
// Without the feature the crate still provides the dataset / config / subcarrier
|
|
// APIs needed for data preprocessing and proof verification.
|
|
#[cfg(feature = "tch-backend")]
|
|
pub mod losses;
|
|
#[cfg(feature = "tch-backend")]
|
|
pub mod metrics;
|
|
#[cfg(feature = "tch-backend")]
|
|
pub mod model;
|
|
#[cfg(feature = "tch-backend")]
|
|
pub mod proof;
|
|
|
|
/// ADR-145 — ablation evaluation harness (feature matrix + privacy/latency metrics).
|
|
pub mod ablation;
|
|
/// Falsifiable occupancy/presence benchmark (real-CSI gate: provenance,
|
|
/// leak-free split, bootstrap-CI thresholds; refuses claims on synthetic/mock).
|
|
pub mod occupancy_bench;
|
|
#[cfg(feature = "tch-backend")]
|
|
pub mod trainer;
|
|
|
|
// Convenient re-exports at the crate root.
|
|
pub use config::TrainingConfig;
|
|
pub use dataset::{
|
|
CsiDataset, CsiSample, DataLoader, MmFiDataset, SyntheticConfig, SyntheticCsiDataset,
|
|
};
|
|
pub use error::{ConfigError, DatasetError, SubcarrierError, TrainError};
|
|
// TrainResult<T> is the generic Result alias from error.rs; the concrete
|
|
// TrainResult struct from trainer.rs is accessed via trainer::TrainResult.
|
|
pub use error::TrainResult as TrainResultAlias;
|
|
pub use subcarrier::{
|
|
compute_interp_weights, interpolate_subcarriers, select_subcarriers_by_variance,
|
|
};
|
|
|
|
// MERIDIAN (ADR-027) re-exports.
|
|
pub use domain::{AdversarialSchedule, DomainClassifier, DomainFactorizer, GradientReversalLayer};
|
|
pub use eval::CrossDomainEvaluator;
|
|
pub use geometry::{FilmLayer, FourierPositionalEncoding, GeometryEncoder, MeridianGeometryConfig};
|
|
pub use rapid_adapt::{AdaptError, AdaptationLoss, AdaptationResult, RapidAdaptation};
|
|
pub use virtual_aug::VirtualDomainAugmentor;
|
|
|
|
/// Crate version string.
|
|
pub const VERSION: &str = env!("CARGO_PKG_VERSION");
|