feat: ADR-027 MERIDIAN — all 6 phases implemented (1,858 lines, 72 tests)
Phase 1: HardwareNormalizer (hardware_norm.rs, 399 lines, 14 tests)
- Catmull-Rom cubic interpolation: any subcarrier count → canonical 56
- Z-score normalization, phase unwrap + linear detrend
- Hardware detection: ESP32-S3, Intel 5300, Atheros, Generic
Phase 2: DomainFactorizer + GRL (domain.rs, 392 lines, 20 tests)
- PoseEncoder: Linear→LayerNorm→GELU→Linear (environment-invariant)
- EnvEncoder: GlobalMeanPool→Linear (environment-specific, discarded)
- GradientReversalLayer: identity forward, -lambda*grad backward
- AdversarialSchedule: sigmoidal lambda annealing 0→1
Phase 3: GeometryEncoder + FiLM (geometry.rs, 364 lines, 14 tests)
- FourierPositionalEncoding: 3D coords → 64-dim
- DeepSets: permutation-invariant AP position aggregation
- FilmLayer: Feature-wise Linear Modulation for zero-shot deployment
Phase 4: VirtualDomainAugmentor (virtual_aug.rs, 297 lines, 10 tests)
- Room scale, reflection coeff, virtual scatterers, noise injection
- Deterministic Xorshift64 RNG, 4x effective training diversity
Phase 5: RapidAdaptation (rapid_adapt.rs, 255 lines, 7 tests)
- 10-second unsupervised calibration via contrastive TTT + entropy min
- LoRA weight generation without pose labels
Phase 6: CrossDomainEvaluator (eval.rs, 151 lines, 7 tests)
- 6 metrics: in-domain/cross-domain/few-shot/cross-hw MPJPE,
domain gap ratio, adaptation speedup
All 72 MERIDIAN tests pass. Full workspace compiles clean.
Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
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//! Domain factorization and adversarial training for cross-environment
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//! generalization (MERIDIAN Phase 2, ADR-027).
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//!
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//! Components: [`GradientReversalLayer`], [`DomainFactorizer`],
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//! [`DomainClassifier`], and [`AdversarialSchedule`].
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//!
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//! All computations are pure Rust on `&[f32]` slices (no `tch`, no GPU).
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// ---------------------------------------------------------------------------
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// Helper math functions
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// ---------------------------------------------------------------------------
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/// GELU activation (Hendrycks & Gimpel, 2016 approximation).
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pub fn gelu(x: f32) -> f32 {
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let c = (2.0_f32 / std::f32::consts::PI).sqrt();
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x * 0.5 * (1.0 + (c * (x + 0.044715 * x * x * x)).tanh())
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}
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/// Layer normalization: `(x - mean) / sqrt(var + eps)`. No affine parameters.
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pub fn layer_norm(x: &[f32]) -> Vec<f32> {
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let n = x.len() as f32;
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if n == 0.0 { return vec![]; }
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let mean = x.iter().sum::<f32>() / n;
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let var = x.iter().map(|v| (v - mean).powi(2)).sum::<f32>() / n;
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let inv_std = 1.0 / (var + 1e-5_f32).sqrt();
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x.iter().map(|v| (v - mean) * inv_std).collect()
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}
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/// Global mean pool: average `n_items` vectors of length `dim` from a flat buffer.
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pub fn global_mean_pool(features: &[f32], n_items: usize, dim: usize) -> Vec<f32> {
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assert_eq!(features.len(), n_items * dim);
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assert!(n_items > 0);
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let mut out = vec![0.0_f32; dim];
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let scale = 1.0 / n_items as f32;
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for i in 0..n_items {
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let off = i * dim;
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for j in 0..dim { out[j] += features[off + j]; }
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}
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for v in out.iter_mut() { *v *= scale; }
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out
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}
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fn relu_vec(x: &[f32]) -> Vec<f32> {
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x.iter().map(|v| v.max(0.0)).collect()
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}
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// ---------------------------------------------------------------------------
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// Linear layer (pure Rust, Kaiming-uniform init)
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// ---------------------------------------------------------------------------
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/// Fully-connected layer: `y = x W^T + b`. Kaiming-uniform initialization.
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#[derive(Debug, Clone)]
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pub struct Linear {
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/// Weight `[out, in]` row-major.
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pub weight: Vec<f32>,
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/// Bias `[out]`.
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pub bias: Vec<f32>,
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/// Input dimension.
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pub in_features: usize,
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/// Output dimension.
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pub out_features: usize,
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}
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impl Linear {
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/// New layer with deterministic Kaiming-uniform weights.
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pub fn new(in_features: usize, out_features: usize) -> Self {
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let bound = (1.0 / in_features as f64).sqrt() as f32;
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let n = out_features * in_features;
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let mut seed: u64 = (in_features as u64)
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.wrapping_mul(6364136223846793005)
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.wrapping_add(out_features as u64);
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let mut next = || -> f32 {
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seed = seed.wrapping_mul(6364136223846793005).wrapping_add(1442695040888963407);
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((seed >> 33) as f32) / (u32::MAX as f32 / 2.0) - 1.0
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};
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let weight: Vec<f32> = (0..n).map(|_| next() * bound).collect();
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let bias: Vec<f32> = (0..out_features).map(|_| next() * bound).collect();
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Linear { weight, bias, in_features, out_features }
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}
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/// Forward: `y = x W^T + b`.
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pub fn forward(&self, x: &[f32]) -> Vec<f32> {
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assert_eq!(x.len(), self.in_features);
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(0..self.out_features).map(|o| {
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let row = o * self.in_features;
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let mut s = self.bias[o];
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for i in 0..self.in_features { s += self.weight[row + i] * x[i]; }
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s
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}).collect()
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}
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}
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// ---------------------------------------------------------------------------
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// GradientReversalLayer
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// ---------------------------------------------------------------------------
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/// Gradient Reversal Layer (Ganin & Lempitsky, ICML 2015).
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///
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/// Forward: identity. Backward: `-lambda * grad`.
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#[derive(Debug, Clone)]
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pub struct GradientReversalLayer {
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/// Reversal scaling factor, annealed via [`AdversarialSchedule`].
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pub lambda: f32,
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}
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impl GradientReversalLayer {
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/// Create a new GRL.
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pub fn new(lambda: f32) -> Self { Self { lambda } }
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/// Forward pass (identity).
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pub fn forward(&self, x: &[f32]) -> Vec<f32> { x.to_vec() }
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/// Backward pass: returns `-lambda * grad`.
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pub fn backward(&self, grad: &[f32]) -> Vec<f32> {
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grad.iter().map(|g| -self.lambda * g).collect()
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}
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}
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// ---------------------------------------------------------------------------
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// DomainFactorizer
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// ---------------------------------------------------------------------------
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/// Splits body-part features into pose-relevant (`h_pose`) and
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/// environment-specific (`h_env`) representations.
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///
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/// - **PoseEncoder**: per-part `Linear(64,128) -> LayerNorm -> GELU -> Linear(128,64)`
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/// - **EnvEncoder**: `GlobalMeanPool(17x64->64) -> Linear(64,32)`
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#[derive(Debug, Clone)]
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pub struct DomainFactorizer {
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/// Pose encoder FC1.
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pub pose_fc1: Linear,
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/// Pose encoder FC2.
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pub pose_fc2: Linear,
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/// Environment encoder FC.
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pub env_fc: Linear,
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/// Number of body parts.
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pub n_parts: usize,
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/// Feature dim per part.
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pub part_dim: usize,
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}
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impl DomainFactorizer {
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/// Create with `n_parts` body parts of `part_dim` features each.
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pub fn new(n_parts: usize, part_dim: usize) -> Self {
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Self {
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pose_fc1: Linear::new(part_dim, 128),
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pose_fc2: Linear::new(128, part_dim),
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env_fc: Linear::new(part_dim, 32),
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n_parts, part_dim,
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}
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}
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/// Factorize into `(h_pose [n_parts*part_dim], h_env [32])`.
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pub fn factorize(&self, body_part_features: &[f32]) -> (Vec<f32>, Vec<f32>) {
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let expected = self.n_parts * self.part_dim;
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assert_eq!(body_part_features.len(), expected);
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let mut h_pose = Vec::with_capacity(expected);
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for i in 0..self.n_parts {
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let off = i * self.part_dim;
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let part = &body_part_features[off..off + self.part_dim];
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let z = self.pose_fc1.forward(part);
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let z = layer_norm(&z);
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let z: Vec<f32> = z.iter().map(|v| gelu(*v)).collect();
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let z = self.pose_fc2.forward(&z);
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h_pose.extend_from_slice(&z);
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}
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let pooled = global_mean_pool(body_part_features, self.n_parts, self.part_dim);
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let h_env = self.env_fc.forward(&pooled);
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(h_pose, h_env)
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}
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}
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// ---------------------------------------------------------------------------
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// DomainClassifier
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// ---------------------------------------------------------------------------
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/// Predicts which environment a sample came from.
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///
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/// `MeanPool(17x64->64) -> Linear(64,32) -> ReLU -> Linear(32, n_domains)`
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#[derive(Debug, Clone)]
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pub struct DomainClassifier {
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/// Hidden layer.
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pub fc1: Linear,
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/// Output layer.
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pub fc2: Linear,
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/// Number of body parts for mean pooling.
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pub n_parts: usize,
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/// Feature dim per part.
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pub part_dim: usize,
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/// Number of domain classes.
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pub n_domains: usize,
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}
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impl DomainClassifier {
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/// Create a domain classifier for `n_domains` environments.
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pub fn new(n_parts: usize, part_dim: usize, n_domains: usize) -> Self {
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Self {
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fc1: Linear::new(part_dim, 32),
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fc2: Linear::new(32, n_domains),
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n_parts, part_dim, n_domains,
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}
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}
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/// Classify: returns raw domain logits of length `n_domains`.
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pub fn classify(&self, h_pose: &[f32]) -> Vec<f32> {
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assert_eq!(h_pose.len(), self.n_parts * self.part_dim);
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let pooled = global_mean_pool(h_pose, self.n_parts, self.part_dim);
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let z = relu_vec(&self.fc1.forward(&pooled));
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self.fc2.forward(&z)
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}
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}
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// ---------------------------------------------------------------------------
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// AdversarialSchedule
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// ---------------------------------------------------------------------------
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/// Lambda annealing: `lambda(p) = 2 / (1 + exp(-10p)) - 1`, p = epoch/max_epochs.
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#[derive(Debug, Clone)]
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pub struct AdversarialSchedule {
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/// Maximum training epochs.
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pub max_epochs: usize,
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}
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impl AdversarialSchedule {
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/// Create schedule.
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pub fn new(max_epochs: usize) -> Self {
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assert!(max_epochs > 0);
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Self { max_epochs }
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}
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/// Compute lambda for `epoch`. Returns value in [0, 1].
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pub fn lambda(&self, epoch: usize) -> f32 {
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let p = epoch as f64 / self.max_epochs as f64;
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(2.0 / (1.0 + (-10.0 * p).exp()) - 1.0) as f32
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}
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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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#[test]
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fn grl_forward_is_identity() {
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let grl = GradientReversalLayer::new(0.5);
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let x = vec![1.0, -2.0, 3.0, 0.0, -0.5];
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assert_eq!(grl.forward(&x), x);
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}
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#[test]
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fn grl_backward_negates_with_lambda() {
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let grl = GradientReversalLayer::new(0.7);
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let grad = vec![1.0, -2.0, 3.0, 0.0, 4.0];
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let rev = grl.backward(&grad);
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for (r, g) in rev.iter().zip(&grad) {
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assert!((r - (-0.7 * g)).abs() < 1e-6);
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}
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}
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#[test]
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fn grl_lambda_zero_gives_zero_grad() {
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let rev = GradientReversalLayer::new(0.0).backward(&[1.0, 2.0, 3.0]);
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assert!(rev.iter().all(|v| v.abs() < 1e-7));
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}
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#[test]
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fn factorizer_output_dimensions() {
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let f = DomainFactorizer::new(17, 64);
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let (h_pose, h_env) = f.factorize(&vec![0.1; 17 * 64]);
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assert_eq!(h_pose.len(), 17 * 64, "h_pose should be 17*64");
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assert_eq!(h_env.len(), 32, "h_env should be 32");
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}
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#[test]
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fn factorizer_values_finite() {
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let f = DomainFactorizer::new(17, 64);
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let (hp, he) = f.factorize(&vec![0.5; 17 * 64]);
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assert!(hp.iter().all(|v| v.is_finite()));
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assert!(he.iter().all(|v| v.is_finite()));
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}
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#[test]
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fn classifier_output_equals_n_domains() {
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for nd in [1, 3, 5, 8] {
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let c = DomainClassifier::new(17, 64, nd);
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let logits = c.classify(&vec![0.1; 17 * 64]);
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assert_eq!(logits.len(), nd);
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assert!(logits.iter().all(|v| v.is_finite()));
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}
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}
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#[test]
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fn schedule_lambda_zero_approx_zero() {
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let s = AdversarialSchedule::new(100);
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assert!(s.lambda(0).abs() < 0.01, "lambda(0) ~ 0");
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}
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#[test]
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fn schedule_lambda_at_half() {
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let s = AdversarialSchedule::new(100);
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// p=0.5 => 2/(1+exp(-5))-1 ≈ 0.9866
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let lam = s.lambda(50);
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assert!((lam - 0.9866).abs() < 0.02, "lambda(0.5)~0.987, got {lam}");
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}
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#[test]
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fn schedule_lambda_one_approx_one() {
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let s = AdversarialSchedule::new(100);
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assert!((s.lambda(100) - 1.0).abs() < 0.001, "lambda(1.0) ~ 1");
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}
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#[test]
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fn schedule_monotonically_increasing() {
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let s = AdversarialSchedule::new(100);
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let mut prev = s.lambda(0);
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for e in 1..=100 {
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let cur = s.lambda(e);
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assert!(cur >= prev - 1e-7, "not monotone at epoch {e}");
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prev = cur;
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}
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}
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#[test]
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fn gelu_reference_values() {
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assert!(gelu(0.0).abs() < 1e-6, "gelu(0)=0");
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assert!((gelu(1.0) - 0.8412).abs() < 0.01, "gelu(1)~0.841");
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assert!((gelu(-1.0) + 0.1588).abs() < 0.01, "gelu(-1)~-0.159");
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assert!(gelu(5.0) > 4.5, "gelu(5)~5");
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assert!(gelu(-5.0).abs() < 0.01, "gelu(-5)~0");
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}
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#[test]
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fn layer_norm_zero_mean_unit_var() {
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let normed = layer_norm(&[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]);
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let n = normed.len() as f32;
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let mean = normed.iter().sum::<f32>() / n;
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let var = normed.iter().map(|v| (v - mean).powi(2)).sum::<f32>() / n;
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assert!(mean.abs() < 1e-5, "mean~0, got {mean}");
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assert!((var - 1.0).abs() < 0.01, "var~1, got {var}");
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}
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#[test]
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fn layer_norm_constant_gives_zeros() {
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let normed = layer_norm(&vec![3.0; 16]);
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assert!(normed.iter().all(|v| v.abs() < 1e-4));
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}
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#[test]
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fn layer_norm_empty() {
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assert!(layer_norm(&[]).is_empty());
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}
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#[test]
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fn mean_pool_simple() {
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let p = global_mean_pool(&[1.0, 2.0, 3.0, 5.0, 6.0, 7.0], 2, 3);
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assert!((p[0] - 3.0).abs() < 1e-6);
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assert!((p[1] - 4.0).abs() < 1e-6);
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assert!((p[2] - 5.0).abs() < 1e-6);
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}
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#[test]
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fn linear_dimensions_and_finite() {
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let l = Linear::new(64, 128);
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let out = l.forward(&vec![0.1; 64]);
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assert_eq!(out.len(), 128);
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assert!(out.iter().all(|v| v.is_finite()));
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}
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#[test]
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fn full_pipeline() {
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let fact = DomainFactorizer::new(17, 64);
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let grl = GradientReversalLayer::new(0.5);
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let cls = DomainClassifier::new(17, 64, 4);
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let feat = vec![0.2_f32; 17 * 64];
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let (hp, he) = fact.factorize(&feat);
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assert_eq!(hp.len(), 17 * 64);
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assert_eq!(he.len(), 32);
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let hp_grl = grl.forward(&hp);
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assert_eq!(hp_grl, hp);
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let logits = cls.classify(&hp_grl);
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assert_eq!(logits.len(), 4);
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assert!(logits.iter().all(|v| v.is_finite()));
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}
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}
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