History

ruv 7d26b15eef feat(train): MERIDIAN-MAE — csi_mae::model + pretrain loop + pretrain-mae bin (iter 2b, #68 ) Real CSI masked-autoencoder behind feature `tch-backend` (ADR-027 §2.0): - CsiMae: dual-stream per-token amp+phase embed → fuse → residual-MLP encoder over the visible tokens → flatten-to-latent bottleneck → learned per-position query + broadcast latent → residual-MLP decoder → dec_amp_head / dec_ph_head → index_select the masked positions. (MLP-based v0; self-attention transformer blocks are iter 3.) - CsiMae::reconstruction_loss(pred_amp, pred_phase, tgt_amp, tgt_phase, phase_w) = MSE(amp) + phase_w * MSE(phase). - MaeBatch::from_windows — partition computed once from window 0 and reused across the batch (the bottleneck fixes n_tokens), ndarray → tch conversion. - pretrain_step(model, opt, batch) -> f64 — one Adam step, returns the loss. - src/bin/pretrain_mae.rs — synthetic-data pre-train driver (required-features = ["tch-backend"]); clap args for epochs/batch/samples/lr/mask-ratio/save. - #[cfg(feature="tch-backend")] smoke test: loss halves when overfitting one batch over 60 steps; also asserts model.n_visible/n_masked match mask_csi_window's clamping. v0 limits (documented in the module): fixed n_tokens; batch-shared masking; MSE on unwrapped phase (vs a circular loss). The dev box has no LibTorch, so the tch path is CI-verified (`--features tch-backend`), not locally. The default `cargo test -p wifi-densepose-train --no-default-features` stays green (121 lib tests) — the model module and the bin are both feature-gated. Co-Authored-By: claude-flow <ruv@ruv.net>		2026-05-11 13:05:27 -04:00
..
benches	chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427 )	2026-04-25 21:28:13 -04:00
src	feat(train): MERIDIAN-MAE — csi_mae::model + pretrain loop + pretrain-mae bin (iter 2b, #68 )	2026-05-11 13:05:27 -04:00
tests	chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427 )	2026-04-25 21:28:13 -04:00
Cargo.toml	feat(train): MERIDIAN-MAE — csi_mae::model + pretrain loop + pretrain-mae bin (iter 2b, #68 )	2026-05-11 13:05:27 -04:00
README.md	chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427 )	2026-04-25 21:28:13 -04:00

README.md

wifi-densepose-train

Complete training pipeline for WiFi-DensePose, integrated with all five ruvector crates.

Overview

wifi-densepose-train provides everything needed to train the WiFi-to-DensePose model: dataset loading, subcarrier interpolation, loss functions, evaluation metrics, and the training loop orchestrator. It supports both the MM-Fi dataset (NeurIPS 2023) and deterministic synthetic data for reproducible experiments.

Without the tch-backend feature the crate still provides the dataset, configuration, and subcarrier interpolation APIs needed for data preprocessing and proof verification.

Features

MM-Fi dataset loader -- Reads the MM-Fi multimodal dataset (NeurIPS 2023) from disk with memory-mapped .npy files.
Synthetic dataset -- Deterministic, fixed-seed CSI generation for unit tests and proofs.
Subcarrier interpolation -- 114 -> 56 subcarrier compression via ruvector-solver sparse interpolation with variance-based selection.
Loss functions (tch-backend) -- Pose estimation losses including MSE, OKS, and combined multi-task loss.
Metrics (tch-backend) -- PCKh, OKS-AP, and per-keypoint evaluation with ruvector-mincut-based person matching.
Training orchestrator (tch-backend) -- Full training loop with learning rate scheduling, gradient clipping, checkpointing, and reproducible proofs.
All 5 ruvector crates -- ruvector-mincut, ruvector-attn-mincut, ruvector-temporal-tensor, ruvector-solver, and ruvector-attention integrated across dataset loading, metrics, and model attention.

Feature flags

Flag	Default	Description
`tch-backend`	no	Enable PyTorch training via `tch-rs`
`cuda`	no	CUDA GPU acceleration (implies `tch`)

Binaries

Binary	Description
`train`	Main training entry point
`verify-training`	Proof verification (requires `tch-backend`)

Quick Start

use wifi_densepose_train::config::TrainingConfig;
use wifi_densepose_train::dataset::{SyntheticCsiDataset, SyntheticConfig, CsiDataset};

// Build and validate 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());

Architecture

wifi-densepose-train/src/
  lib.rs            -- Re-exports, VERSION
  config.rs         -- TrainingConfig, hyperparameters, validation
  dataset.rs        -- CsiDataset trait, MmFiDataset, SyntheticCsiDataset, DataLoader
  error.rs          -- TrainError, ConfigError, DatasetError, SubcarrierError
  subcarrier.rs     -- interpolate_subcarriers (114->56), variance-based selection
  losses.rs         -- (tch) MSE, OKS, multi-task loss        [feature-gated]
  metrics.rs        -- (tch) PCKh, OKS-AP, person matching     [feature-gated]
  model.rs          -- (tch) Model definition with attention    [feature-gated]
  proof.rs          -- (tch) Deterministic training proofs      [feature-gated]
  trainer.rs        -- (tch) Training loop orchestrator         [feature-gated]

Crate	Role
`wifi-densepose-signal`	Signal preprocessing consumed by dataset loaders
`wifi-densepose-nn`	Inference engine that loads trained models
`ruvector-mincut`	Person matching in metrics
`ruvector-attn-mincut`	Attention-weighted graph cuts
`ruvector-temporal-tensor`	Compressed CSI buffering in datasets
`ruvector-solver`	Sparse subcarrier interpolation
`ruvector-attention`	Spatial attention in model

License

MIT OR Apache-2.0