90a88ada9a
* feat(train): metric-locked PCK/MPJPE accuracy harness — resolve PCK-definition ambiguity
The SOTA brief (docs/research/sota-nn-train-benchmark-brief.md §1/§3.1/§4)
identifies metric ambiguity as the single biggest threat to any beyond-SOTA
claim: three PCK@20 numbers (96.09% WiFlow-STD image-normalized, 81.63%
AetherArena torso-PCK, 61.1% GraphPose-Fi standard PCK) cannot be lined up
because each silently uses a different normalization. The project was retracted
twice over this (a withdrawn 92.9% used absolute pixels, not torso).
New src/accuracy.rs makes the normalizer explicit, selectable, and carried with
every reported number:
- PckNormalization enum: TorsoDiameter (standard MM-Fi/GraphPose-Fi hip↔hip),
BoundingBoxDiagonal (looser WiFlow-STD image-normalized), AbsolutePixels(t)
(retracted convention, reproducible + clearly non-comparable).
- pck_at(pred, gt, vis, k, normalization) — one canonical PCK reusing the
metrics_core geometric primitives (no duplicate kernel).
- mpjpe(pred, gt, vis) — 2D/3D, mm.
- PoseAccuracy { pck_at: BTreeMap<u8,f32>, mpjpe, normalization, n_keypoints,
n_frames } via accuracy_report(frames, ks, normalization) — an unlabeled PCK
number is structurally impossible.
17 hand-computed deterministic tests (no GPU, no datasets) prove the harness
arithmetic, including the key proof that identical predictions score
0.50 / 1.00 / 0.75 under the three normalizations, plus graceful degenerate
handling (zero torso, empty frames, NaN coords — no panic, never false-perfect).
This is measurement infrastructure, NOT an accuracy claim. Public API worth an
ADR — needs ADR slot 173 (parent to write).
wifi-densepose-train lib 191→206, test_metrics 12→14, 0 failed; full workspace
green (exit 0); Python deterministic proof unchanged
(f8e76f21a0f9852b70b6d9dd5318239f6b20cbcb4cdd995863263cecdc446f7a).
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs(adr): ADR-173 — metric-locked PCK/MPJPE accuracy harness
Documents the accuracy harness (committed
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||
|---|---|---|
| .. | ||
| benches | ||
| src | ||
| tests | ||
| Cargo.toml | ||
| README.md | ||
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
.npyfiles. - Synthetic dataset -- Deterministic, fixed-seed CSI generation for unit tests and proofs.
- Subcarrier interpolation -- 114 -> 56 subcarrier compression via
ruvector-solversparse 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 withruvector-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, andruvector-attentionintegrated 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]
Related Crates
| 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