mirror of
https://github.com/ruvnet/RuView
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ruv
0f336b7d36
- train/ablation.rs: FeatureSet matrix (CSI/CIR/CSI+CIR/+Doppler/+BFLD/+UWB); AblationMetrics (presence acc, loc err, FP/FN, latency p50/p95, privacy leakage, cross-room degradation) derived deterministically from VariantRun - membership_inference_leakage(): MIA proxy = |AUC-0.5|*2 (0 indistinguishable, 1 perfectly separable); latency_percentiles_ms (nearest-rank); confusion_rates - AblationReport.to_markdown() (deterministic), csi_cir_beats_csi_only() acceptance check - 5 tests; workspace 0 errors Co-Authored-By: claude-flow <ruv@ruv.net>
98 lines
3.4 KiB
Rust
98 lines
3.4 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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/// ADR-145 — ablation evaluation harness (feature matrix + privacy/latency metrics).
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pub mod ablation;
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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::{
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CsiDataset, CsiSample, DataLoader, MmFiDataset, SyntheticConfig, SyntheticCsiDataset,
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};
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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::{
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compute_interp_weights, interpolate_subcarriers, select_subcarriers_by_variance,
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};
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// MERIDIAN (ADR-027) re-exports.
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pub use domain::{AdversarialSchedule, DomainClassifier, DomainFactorizer, GradientReversalLayer};
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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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