8da6767273
- domain.rs: atomic instance counter for unique Linear weight seeds (C3) - rapid_adapt.rs: adapt() returns Result instead of panicking (C5), bounded calibration buffer with max_buffer_frames cap (F1-HIGH), validate lora_rank >= 1 (F10) - geometry.rs: 24-bit PRNG precision matching f32 mantissa (C2) - virtual_aug.rs: guard against room_scale=0 division-by-zero (F6) - signal/lib.rs: re-export AmplitudeStats from hardware_norm (W1) - train/lib.rs: crate-root re-exports for all MERIDIAN types (W2) All 201 tests pass (96 unit + 24 integration + 18 subcarrier + 10 metrics + 7 doctests + 105 signal + 10 validation + 1 signal doctest). Co-Authored-By: claude-flow <ruv@ruv.net>
91 lines
3.3 KiB
Rust
91 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 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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