81ad09d05b
- 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
77 lines
2.8 KiB
Rust
77 lines
2.8 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 error;
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pub mod subcarrier;
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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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/// Crate version string.
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pub const VERSION: &str = env!("CARGO_PKG_VERSION");
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