New `wifi-densepose-train::csi_mae` module (ADR-027 §2.0):
- MaeConfig (+ validate), MaskStrategy {Random, InfoGuided}
- TokenLayout — flattens a [T,tx,rx,sub] CSI window to [N=T*tx*rx, sub] tokens
(the same layout model.rs::ModalityTranslator consumes)
- mask_csi_window — deterministic visible/masked token partition + amplitude &
phase reconstruction targets; reproducible via a tiny inline SplitMix64 PRNG
(no extra dependency); clamps so both partitions are non-empty
- reassemble_tokens — round-trips encoder-visible + decoder-predicted tokens
back to a full [N, sub] grid (for reconstruction eval/viz)
- model submodule (gated behind `tch-backend`): v0 skeleton — the
encoder/decoder networks, reconstruction loss, and pretrain_step land in
iteration 2 (transformer blocks, per-sample masking, info-guided masking,
a `pretrain-mae` bin)
8 new unit tests; builds and tests green under
`cargo test -p wifi-densepose-train --no-default-features` (118 lib tests pass).
The tch-gated `model` submodule is not exercised by the default workspace test
job — compile-checking it needs a LibTorch toolchain.
Co-Authored-By: claude-flow <ruv@ruv.net>
603ad585b6