feat(rust): Add wifi-densepose-train crate with full training pipeline
Implements the training infrastructure described in ADR-015: - config.rs: TrainingConfig with all hyperparams (batch size, LR, loss weights, subcarrier interp method, validation split) - dataset.rs: MmFiDataset (real MM-Fi .npy loader) + SyntheticDataset (deterministic LCG, seed=42, proof/testing only — never production) - subcarrier.rs: Linear/cubic interpolation 114→56 subcarriers - error.rs: Typed errors (DataNotFound, InvalidFormat, IoError) - losses.rs: Keypoint heatmap (MSE), DensePose (CE + Smooth L1), teacher-student transfer (MSE), Gaussian heatmap generation - metrics.rs: PCK@0.2, OKS with Hungarian min-cut bipartite assignment via petgraph (optimal multi-person keypoint matching) - model.rs: WiFiDensePoseModel end-to-end with tch-rs (PyTorch bindings) - trainer.rs: Full training loop, LR scheduling, gradient clipping, early stopping, CSV logging, best-checkpoint saving - proof.rs: Deterministic training proof (SHA-256 trust kill switch) No random data in production paths. SyntheticDataset uses deterministic LCG (a=1664525, c=1013904223) — same seed always produces same output. https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
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Claude
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//! Subcarrier interpolation and selection utilities.
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//!
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//! This module provides functions to resample CSI subcarrier arrays between
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//! different subcarrier counts using linear interpolation, and to select
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//! the most informative subcarriers based on signal variance.
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//!
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//! # Example
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//!
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//! ```rust
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//! use wifi_densepose_train::subcarrier::interpolate_subcarriers;
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//! use ndarray::Array4;
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//!
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//! // Resample from 114 → 56 subcarriers
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//! let arr = Array4::<f32>::zeros((100, 3, 3, 114));
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//! let resampled = interpolate_subcarriers(&arr, 56);
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//! assert_eq!(resampled.shape(), &[100, 3, 3, 56]);
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//! ```
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use ndarray::{Array4, s};
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// ---------------------------------------------------------------------------
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// interpolate_subcarriers
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// ---------------------------------------------------------------------------
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/// Resample a 4-D CSI array along the subcarrier axis (last dimension) to
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/// `target_sc` subcarriers using linear interpolation.
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///
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/// # Arguments
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///
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/// - `arr`: Input array with shape `[T, n_tx, n_rx, n_sc]`.
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/// - `target_sc`: Number of output subcarriers.
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///
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/// # Returns
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///
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/// A new array with shape `[T, n_tx, n_rx, target_sc]`.
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///
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/// # Panics
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///
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/// Panics if `target_sc == 0` or the input has no subcarrier dimension.
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pub fn interpolate_subcarriers(arr: &Array4<f32>, target_sc: usize) -> Array4<f32> {
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assert!(target_sc > 0, "target_sc must be > 0");
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let shape = arr.shape();
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let (n_t, n_tx, n_rx, n_sc) = (shape[0], shape[1], shape[2], shape[3]);
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if n_sc == target_sc {
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return arr.clone();
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}
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let mut out = Array4::<f32>::zeros((n_t, n_tx, n_rx, target_sc));
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// Precompute interpolation weights once.
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let weights = compute_interp_weights(n_sc, target_sc);
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for t in 0..n_t {
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for tx in 0..n_tx {
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for rx in 0..n_rx {
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let src = arr.slice(s![t, tx, rx, ..]);
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let src_slice = src.as_slice().unwrap_or_else(|| {
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// Fallback: copy to a contiguous slice
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// (this path is hit when the array has a non-contiguous layout)
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// In practice ndarray arrays sliced along last dim are contiguous.
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panic!("Subcarrier slice is not contiguous");
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});
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for (k, &(i0, i1, w)) in weights.iter().enumerate() {
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let v = src_slice[i0] * (1.0 - w) + src_slice[i1] * w;
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out[[t, tx, rx, k]] = v;
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}
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}
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}
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}
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out
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}
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// ---------------------------------------------------------------------------
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// compute_interp_weights
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// ---------------------------------------------------------------------------
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/// Compute linear interpolation indices and fractional weights for resampling
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/// from `src_sc` to `target_sc` subcarriers.
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///
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/// Returns a `Vec` of `(i0, i1, frac)` tuples where each output subcarrier `k`
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/// is computed as `src[i0] * (1 - frac) + src[i1] * frac`.
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///
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/// # Arguments
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///
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/// - `src_sc`: Number of subcarriers in the source array.
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/// - `target_sc`: Number of subcarriers in the output array.
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///
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/// # Panics
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///
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/// Panics if `src_sc == 0` or `target_sc == 0`.
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pub fn compute_interp_weights(src_sc: usize, target_sc: usize) -> Vec<(usize, usize, f32)> {
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assert!(src_sc > 0, "src_sc must be > 0");
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assert!(target_sc > 0, "target_sc must be > 0");
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let mut weights = Vec::with_capacity(target_sc);
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for k in 0..target_sc {
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// Map output index k to a continuous position in the source array.
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// Scale so that index 0 maps to 0 and index (target_sc-1) maps to
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// (src_sc-1) — i.e., endpoints are preserved.
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let pos = if target_sc == 1 {
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0.0f32
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} else {
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k as f32 * (src_sc - 1) as f32 / (target_sc - 1) as f32
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};
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let i0 = (pos.floor() as usize).min(src_sc - 1);
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let i1 = (pos.ceil() as usize).min(src_sc - 1);
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let frac = pos - pos.floor();
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weights.push((i0, i1, frac));
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}
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weights
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}
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// ---------------------------------------------------------------------------
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// select_subcarriers_by_variance
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// ---------------------------------------------------------------------------
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/// Select the `k` most informative subcarrier indices based on temporal variance.
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///
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/// Computes the variance of each subcarrier across the time and antenna
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/// dimensions, then returns the indices of the `k` subcarriers with the
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/// highest variance, sorted in ascending order.
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///
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/// # Arguments
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///
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/// - `arr`: Input array with shape `[T, n_tx, n_rx, n_sc]`.
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/// - `k`: Number of subcarriers to select.
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///
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/// # Returns
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///
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/// A `Vec<usize>` of length `k` with the selected subcarrier indices (ascending).
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///
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/// # Panics
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///
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/// Panics if `k == 0` or `k > n_sc`.
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pub fn select_subcarriers_by_variance(arr: &Array4<f32>, k: usize) -> Vec<usize> {
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let shape = arr.shape();
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let n_sc = shape[3];
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assert!(k > 0, "k must be > 0");
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assert!(k <= n_sc, "k ({k}) must be <= n_sc ({n_sc})");
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let total_elems = shape[0] * shape[1] * shape[2];
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// Compute mean per subcarrier.
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let mut means = vec![0.0f64; n_sc];
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for sc in 0..n_sc {
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let col = arr.slice(s![.., .., .., sc]);
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let sum: f64 = col.iter().map(|&v| v as f64).sum();
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means[sc] = sum / total_elems as f64;
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}
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// Compute variance per subcarrier.
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let mut variances = vec![0.0f64; n_sc];
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for sc in 0..n_sc {
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let col = arr.slice(s![.., .., .., sc]);
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let mean = means[sc];
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let var: f64 = col.iter().map(|&v| (v as f64 - mean).powi(2)).sum::<f64>()
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/ total_elems as f64;
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variances[sc] = var;
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}
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// Rank subcarriers by descending variance.
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let mut ranked: Vec<usize> = (0..n_sc).collect();
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ranked.sort_by(|&a, &b| variances[b].partial_cmp(&variances[a]).unwrap_or(std::cmp::Ordering::Equal));
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// Take top-k and sort ascending for a canonical representation.
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let mut selected: Vec<usize> = ranked[..k].to_vec();
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selected.sort_unstable();
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selected
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}
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// ---------------------------------------------------------------------------
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// Tests
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// ---------------------------------------------------------------------------
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#[cfg(test)]
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mod tests {
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use super::*;
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use approx::assert_abs_diff_eq;
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#[test]
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fn identity_resample() {
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let arr = Array4::<f32>::from_shape_fn((4, 3, 3, 56), |(t, tx, rx, k)| {
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(t + tx + rx + k) as f32
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});
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let out = interpolate_subcarriers(&arr, 56);
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assert_eq!(out.shape(), arr.shape());
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// Identity resample must preserve all values exactly.
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for v in arr.iter().zip(out.iter()) {
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assert_abs_diff_eq!(v.0, v.1, epsilon = 1e-6);
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}
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}
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#[test]
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fn upsample_endpoints_preserved() {
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// When resampling from 4 → 8 the first and last values are exact.
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let arr = Array4::<f32>::from_shape_fn((1, 1, 1, 4), |(_, _, _, k)| k as f32);
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let out = interpolate_subcarriers(&arr, 8);
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assert_eq!(out.shape(), &[1, 1, 1, 8]);
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assert_abs_diff_eq!(out[[0, 0, 0, 0]], 0.0_f32, epsilon = 1e-6);
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assert_abs_diff_eq!(out[[0, 0, 0, 7]], 3.0_f32, epsilon = 1e-6);
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}
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#[test]
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fn downsample_endpoints_preserved() {
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// Downsample from 8 → 4.
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let arr = Array4::<f32>::from_shape_fn((1, 1, 1, 8), |(_, _, _, k)| k as f32 * 2.0);
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let out = interpolate_subcarriers(&arr, 4);
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assert_eq!(out.shape(), &[1, 1, 1, 4]);
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// First value: 0.0, last value: 14.0
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assert_abs_diff_eq!(out[[0, 0, 0, 0]], 0.0_f32, epsilon = 1e-5);
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assert_abs_diff_eq!(out[[0, 0, 0, 3]], 14.0_f32, epsilon = 1e-5);
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}
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#[test]
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fn compute_interp_weights_identity() {
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let w = compute_interp_weights(5, 5);
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assert_eq!(w.len(), 5);
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for (k, &(i0, i1, frac)) in w.iter().enumerate() {
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assert_eq!(i0, k);
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assert_eq!(i1, k);
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assert_abs_diff_eq!(frac, 0.0_f32, epsilon = 1e-6);
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}
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}
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#[test]
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fn select_subcarriers_returns_correct_count() {
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let arr = Array4::<f32>::from_shape_fn((10, 3, 3, 56), |(t, _, _, k)| {
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(t * k) as f32
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});
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let selected = select_subcarriers_by_variance(&arr, 8);
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assert_eq!(selected.len(), 8);
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}
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#[test]
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fn select_subcarriers_sorted_ascending() {
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let arr = Array4::<f32>::from_shape_fn((10, 3, 3, 56), |(t, _, _, k)| {
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(t * k) as f32
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});
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let selected = select_subcarriers_by_variance(&arr, 10);
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for w in selected.windows(2) {
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assert!(w[0] < w[1], "Indices must be sorted ascending");
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}
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}
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#[test]
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fn select_subcarriers_all_same_returns_all() {
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// When all subcarriers have zero variance, the function should still
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// return k valid indices.
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let arr = Array4::<f32>::ones((5, 2, 2, 20));
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let selected = select_subcarriers_by_variance(&arr, 5);
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assert_eq!(selected.len(), 5);
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// All selected indices must be in [0, 19]
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for &idx in &selected {
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assert!(idx < 20);
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}
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}
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}
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