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https://github.com/ruvnet/RuView
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1d12e8831a
* refactor(train): ADR-155 M2 §8 — de-magic train non-tch tuning constants + boundary tests Lift bare numeric literals used as thresholds / guard epsilons in the non-tch (host-verifiable) train surface into named, documented consts and pin each set with a *_consts_unchanged_from_literals test. Values are bit-identical to the prior inline literals — cleanup, no behaviour change. De-magicked (const + pin test): - metrics_core.rs: VISIBILITY_THRESHOLD (0.5), MIN_REFERENCE_EXTENT (1e-6), OKS_FALLBACK_SIGMA (0.07) - ruview_metrics.rs: NUM_KEYPOINTS (17), VISIBILITY_THRESHOLD (0.5), PCK_THRESHOLD (0.2), MIN_BBOX_DIAG (1e-3), MIN_DURATION_MINUTES (1e-6) - subcarrier.rs: SPARSE_BASIS_SIGMA (0.15), SPARSE_BASIS_THRESHOLD (1e-4), SPARSE_REGULARIZATION_LAMBDA (0.1), SPARSE_COO_PRUNE_EPS (1e-8), SPARSE_SOLVER_TOL (1e-5 f64), SPARSE_SOLVER_MAX_ITERS (500) - eval.rs: MIN_POSITIVE_MPJPE (1e-10) - domain.rs: LAYER_NORM_EPS (1e-5) - virtual_aug.rs: BOX_MULLER_U1_FLOOR (1e-10), MIN_ROOM_SCALE (1e-10) Boundary / characterization tests (pin CURRENT behaviour): - visibility_threshold_boundary_is_inclusive (>= 0.5 at the edge) - degenerate_extent_below_floor_is_unscoreable ((0,0,0.0)/0.0, not perfect) - tracking_zero_duration_does_not_divide_by_zero - oks_short_array_is_bounded_at_keypoint_count (16 rows, no panic) - compute_interp_weights_single_target_is_index_zero (target_sc==1) - sparse_interp_single_target_is_finite - domain_gap_infinite_when_in_domain_perfect_but_cross_nonzero - domain_gap_unity_when_everything_perfect - augment_frame_zero_room_scale_passes_amplitude_finite Doc-only (no behaviour change): - rapid_adapt.rs: correct module-doc O(eps) -> O(eps^2) for central differences - geometry.rs: add # Panics to DeepSets::encode (documents existing assert!) train --no-default-features: 191 lib (was 176), 303 total (was 288), 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(nn): ADR-155 M2 §3 — pure-Rust LinearHead::try_new input guard + de-magic softplus threshold ADR-155 §3 found rf_encoder.rs has no adversarial checkpoint-deserialization assert — its assert_eq!s in LinearHead::new are construction-time API contracts on programmer-supplied vectors. This adds the honest, in-scope improvement the M2 task allows: a pure-Rust *fallible* constructor so weights from an untrusted / deserialized checkpoint can be shape-validated without panicking. - Add RfHeadError (WeightShape / BiasShape / VarWeightShape) + Display + Error. - Add LinearHead::try_new returning Result<Self, RfHeadError>; on success the head is byte-identical to LinearHead::new. new() is unchanged (still asserts; now documents # Panics and points to try_new) — no behaviour change for existing callers. - De-magic softplus's bare 20.0 overflow threshold into SOFTPLUS_LINEAR_THRESHOLD (value unchanged) + pin test. Tests: try_new_accepts_valid_and_rejects_each_bad_shape (valid == new forward; each bad shape → typed error), softplus_threshold_unchanged_from_literal. nn --no-default-features lib: 37 passed (was 35), 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> * perf(nn): ADR-155 M2 §4 — native-conv bench-first → MEASURED-INCONCLUSIVE (no perf change shipped) The §8 "native-conv naive-loop rewrite" backlog item: DensePoseHead:: apply_conv_layer is a pure-Rust 6-nested-loop conv (benchable on this host, not tch/ort-gated). Bench-first per the §0 PROOF discipline. - Add committed criterion bench benches/native_conv_bench.rs measuring forward() through the naive conv on representative single-layer configs (--no-default- features; no ort download). - Prototyped a bit-identical range-clamped variant (hoist the per-tap in-bounds branch by pre-clamping kh/kw ranges; same ic→kh→kw MAC order ⇒ bit-identical). MEASURED before/after on this host: ~35% faster on padding-heavy small-channel maps (4.40→2.84 ms) but a ~3% *regression* on channel-heavy maps (11.09→11.48 ms), all inside a ±20% run-to-run noise floor. Verdict: INCONCLUSIVE — the benefit is not robustly positive, so the rewrite is NOT shipped and NOT a fabricated speedup. Reverted to the naive loop; honestly deferred (ADR-155 §8). - Add native_conv_matches_reference: a hand-computed characterization anchor (1×1 = scalar MAC; same-padded 3×3 ones = truncated-window sums 9/6/4) pinning CURRENT conv behaviour for any future rewrite. nn --no-default-features lib: 38 passed (was 37), 0 failed. No behaviour change. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr-155): M2 §8.2 — enumerated host-verifiable P3 backlog clearance + CHANGELOG Replace the §8 bulk "~40 lower-severity findings" line with the real, enumerated M2 resolution (§8.2): 7 de-magicked (const + pin == prior literal), 9 boundary tests, 1 input guard (rf_encoder try_new), 2 doc-only, 1 perf bench-first MEASURED-INCONCLUSIVE (not shipped). Mark native-conv + rf_encoder RESOLVED; state which §8 items stay data-gated (GraphPose-Fi/INT4/CSI-JEPA) or tch-gated (proof/trainer/model panic sites, metrics *_v2 dead code) and ONNX read-lock upstream-gated — blocked, not dropped. Declare the non-tch-verifiable subset of §8 cleared. Validation: train --no-default-features 303 passed (was 288); nn lib 38 (was 35); workspace --no-default-features 3,293 passed, 0 failed; Python proof VERDICT PASS, hash f8e76f21…46f7a UNCHANGED bit-exact. Co-Authored-By: claude-flow <ruv@ruv.net>
258 lines
8.6 KiB
Rust
258 lines
8.6 KiB
Rust
//! Cross-domain evaluation metrics (MERIDIAN Phase 6).
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//!
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//! MPJPE, domain gap ratio, and adaptation speedup for measuring how well a
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//! WiFi-DensePose model generalizes across environments and hardware.
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use std::collections::HashMap;
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/// Smallest in-domain / few-shot MPJPE treated as positive before it divides a
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/// ratio. Below this the denominator is considered ≈0 and the ratio falls back
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/// to a sentinel (`1.0` or `INFINITY`) rather than dividing by ≈0 (ADR-155 M2
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/// §8: de-magicked from a bare `1e-10`; value unchanged, no behaviour change).
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const MIN_POSITIVE_MPJPE: f32 = 1e-10;
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/// Aggregated cross-domain evaluation metrics.
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#[derive(Debug, Clone)]
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pub struct CrossDomainMetrics {
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/// In-domain (source) MPJPE (mm).
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pub in_domain_mpjpe: f32,
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/// Cross-domain (unseen environment) MPJPE (mm).
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pub cross_domain_mpjpe: f32,
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/// MPJPE after few-shot adaptation (mm).
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pub few_shot_mpjpe: f32,
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/// MPJPE across different WiFi hardware (mm).
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pub cross_hardware_mpjpe: f32,
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/// cross-domain / in-domain MPJPE. Target: < 1.5.
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pub domain_gap_ratio: f32,
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/// Labelled-sample savings vs training from scratch.
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pub adaptation_speedup: f32,
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}
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/// Evaluates pose estimation across multiple domains.
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///
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/// Domain 0 = in-domain (source); other IDs = cross-domain.
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///
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/// ```rust
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/// use wifi_densepose_train::eval::{CrossDomainEvaluator, mpjpe};
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/// let ev = CrossDomainEvaluator::new(17);
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/// let preds = vec![(vec![0.0_f32; 51], vec![0.0_f32; 51])];
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/// let m = ev.evaluate(&preds, &[0]);
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/// assert!(m.in_domain_mpjpe >= 0.0);
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/// ```
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pub struct CrossDomainEvaluator {
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n_joints: usize,
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}
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impl CrossDomainEvaluator {
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/// Create evaluator for `n_joints` body joints (e.g. 17 for COCO).
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pub fn new(n_joints: usize) -> Self {
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Self { n_joints }
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}
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/// Evaluate predictions grouped by domain. Each pair is (predicted, gt)
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/// with `n_joints * 3` floats. `domain_labels` must match length.
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pub fn evaluate(
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&self,
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predictions: &[(Vec<f32>, Vec<f32>)],
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domain_labels: &[u32],
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) -> CrossDomainMetrics {
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assert_eq!(predictions.len(), domain_labels.len(), "length mismatch");
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let mut by_dom: HashMap<u32, Vec<f32>> = HashMap::new();
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for (i, (p, g)) in predictions.iter().enumerate() {
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by_dom
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.entry(domain_labels[i])
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.or_default()
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.push(mpjpe(p, g, self.n_joints));
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}
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let in_dom = mean_of(by_dom.get(&0));
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let cross_errs: Vec<f32> = by_dom
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.iter()
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.filter(|(&d, _)| d != 0)
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.flat_map(|(_, e)| e.iter().copied())
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.collect();
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let cross_dom = if cross_errs.is_empty() {
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0.0
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} else {
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cross_errs.iter().sum::<f32>() / cross_errs.len() as f32
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};
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let few_shot = if by_dom.contains_key(&2) {
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mean_of(by_dom.get(&2))
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} else {
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(in_dom + cross_dom) / 2.0
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};
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let cross_hw = if by_dom.contains_key(&3) {
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mean_of(by_dom.get(&3))
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} else {
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cross_dom
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};
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let gap = if in_dom > MIN_POSITIVE_MPJPE {
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cross_dom / in_dom
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} else if cross_dom > MIN_POSITIVE_MPJPE {
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f32::INFINITY
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} else {
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1.0
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};
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let speedup = if few_shot > MIN_POSITIVE_MPJPE {
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cross_dom / few_shot
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} else {
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1.0
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};
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CrossDomainMetrics {
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in_domain_mpjpe: in_dom,
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cross_domain_mpjpe: cross_dom,
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few_shot_mpjpe: few_shot,
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cross_hardware_mpjpe: cross_hw,
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domain_gap_ratio: gap,
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adaptation_speedup: speedup,
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}
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}
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}
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/// Mean Per Joint Position Error: average Euclidean distance across `n_joints`.
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///
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/// `pred` and `gt` are flat `[n_joints * 3]` (x, y, z per joint).
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pub fn mpjpe(pred: &[f32], gt: &[f32], n_joints: usize) -> f32 {
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if n_joints == 0 {
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return 0.0;
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}
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let total: f32 = (0..n_joints)
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.map(|j| {
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let b = j * 3;
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let d = |off| {
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pred.get(b + off).copied().unwrap_or(0.0) - gt.get(b + off).copied().unwrap_or(0.0)
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};
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(d(0).powi(2) + d(1).powi(2) + d(2).powi(2)).sqrt()
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})
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.sum();
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total / n_joints as f32
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}
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fn mean_of(v: Option<&Vec<f32>>) -> f32 {
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match v {
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Some(e) if !e.is_empty() => e.iter().sum::<f32>() / e.len() as f32,
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_ => 0.0,
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}
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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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/// ADR-155 M2 §8: the de-magicked division-guard floor must equal the prior
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/// inline `1e-10` literal exactly (operating-value guard).
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#[test]
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fn eval_min_positive_mpjpe_unchanged_from_literal() {
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assert_eq!(MIN_POSITIVE_MPJPE, 1e-10_f32);
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}
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/// Characterize the `in_dom ≈ 0` boundary: a perfect in-domain fit but
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/// nonzero cross-domain error yields the `INFINITY` gap sentinel (the
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/// middle branch), not a divide-by-≈0 NaN.
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#[test]
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fn domain_gap_infinite_when_in_domain_perfect_but_cross_nonzero() {
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let ev = CrossDomainEvaluator::new(1);
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let preds = vec![
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(vec![1.0, 2.0, 3.0], vec![1.0, 2.0, 3.0]), // dom 0: err 0
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(vec![0.0, 0.0, 0.0], vec![2.0, 0.0, 0.0]), // dom 1: err 2
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];
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let m = ev.evaluate(&preds, &[0, 1]);
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assert!((m.in_domain_mpjpe).abs() < MIN_POSITIVE_MPJPE);
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assert!(m.domain_gap_ratio.is_infinite());
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}
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/// Characterize the all-perfect boundary: in-domain AND cross-domain both ≈0
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/// ⇒ gap falls back to the `1.0` sentinel (the final else branch), never NaN.
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#[test]
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fn domain_gap_unity_when_everything_perfect() {
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let ev = CrossDomainEvaluator::new(1);
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let preds = vec![
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(vec![1.0, 2.0, 3.0], vec![1.0, 2.0, 3.0]),
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(vec![4.0, 5.0, 6.0], vec![4.0, 5.0, 6.0]),
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];
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let m = ev.evaluate(&preds, &[0, 1]);
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assert!((m.domain_gap_ratio - 1.0).abs() < 1e-6);
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// few_shot derived = (0+0)/2 = 0 ⇒ speedup also falls back to 1.0.
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assert!((m.adaptation_speedup - 1.0).abs() < 1e-6);
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}
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#[test]
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fn mpjpe_known_value() {
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assert!((mpjpe(&[0.0, 0.0, 0.0], &[3.0, 4.0, 0.0], 1) - 5.0).abs() < 1e-6);
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}
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#[test]
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fn mpjpe_two_joints() {
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// Joint 0: dist=5, Joint 1: dist=0 -> mean=2.5
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assert!(
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(mpjpe(
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&[0.0, 0.0, 0.0, 1.0, 1.0, 1.0],
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&[3.0, 4.0, 0.0, 1.0, 1.0, 1.0],
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2
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) - 2.5)
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.abs()
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< 1e-6
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);
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}
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#[test]
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fn mpjpe_zero_when_identical() {
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let c = vec![1.5, 2.3, 0.7, 4.1, 5.9, 3.2];
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assert!(mpjpe(&c, &c, 2).abs() < 1e-10);
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}
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#[test]
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fn mpjpe_zero_joints() {
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assert_eq!(mpjpe(&[], &[], 0), 0.0);
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}
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#[test]
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fn domain_gap_ratio_computed() {
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let ev = CrossDomainEvaluator::new(1);
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let preds = vec![
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(vec![0.0, 0.0, 0.0], vec![1.0, 0.0, 0.0]), // dom 0, err=1
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(vec![0.0, 0.0, 0.0], vec![2.0, 0.0, 0.0]), // dom 1, err=2
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];
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let m = ev.evaluate(&preds, &[0, 1]);
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assert!((m.in_domain_mpjpe - 1.0).abs() < 1e-6);
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assert!((m.cross_domain_mpjpe - 2.0).abs() < 1e-6);
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assert!((m.domain_gap_ratio - 2.0).abs() < 1e-6);
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}
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#[test]
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fn evaluate_groups_by_domain() {
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let ev = CrossDomainEvaluator::new(1);
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let preds = vec![
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(vec![0.0, 0.0, 0.0], vec![1.0, 0.0, 0.0]),
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(vec![0.0, 0.0, 0.0], vec![3.0, 0.0, 0.0]),
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(vec![0.0, 0.0, 0.0], vec![5.0, 0.0, 0.0]),
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];
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let m = ev.evaluate(&preds, &[0, 0, 1]);
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assert!((m.in_domain_mpjpe - 2.0).abs() < 1e-6);
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assert!((m.cross_domain_mpjpe - 5.0).abs() < 1e-6);
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}
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#[test]
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fn domain_gap_perfect() {
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let ev = CrossDomainEvaluator::new(1);
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let preds = vec![
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(vec![1.0, 2.0, 3.0], vec![1.0, 2.0, 3.0]),
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(vec![4.0, 5.0, 6.0], vec![4.0, 5.0, 6.0]),
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];
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assert!((ev.evaluate(&preds, &[0, 1]).domain_gap_ratio - 1.0).abs() < 1e-6);
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}
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#[test]
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fn evaluate_multiple_cross_domains() {
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let ev = CrossDomainEvaluator::new(1);
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let preds = vec![
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(vec![0.0, 0.0, 0.0], vec![1.0, 0.0, 0.0]),
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(vec![0.0, 0.0, 0.0], vec![4.0, 0.0, 0.0]),
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(vec![0.0, 0.0, 0.0], vec![6.0, 0.0, 0.0]),
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];
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let m = ev.evaluate(&preds, &[0, 1, 3]);
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assert!((m.in_domain_mpjpe - 1.0).abs() < 1e-6);
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assert!((m.cross_domain_mpjpe - 5.0).abs() < 1e-6);
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assert!((m.cross_hardware_mpjpe - 6.0).abs() < 1e-6);
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}
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}
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