Claude
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81ad09d05b
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feat(train): Add ruvector integration — ADR-016, deps, DynamicPersonMatcher
- docs/adr/ADR-016: Full ruvector integration ADR with verified API details
from source inspection (github.com/ruvnet/ruvector). Covers mincut,
attn-mincut, temporal-tensor, solver, and attention at v2.0.4.
- Cargo.toml: Add ruvector-mincut, ruvector-attn-mincut, ruvector-temporal-
tensor, ruvector-solver, ruvector-attention = "2.0.4" to workspace deps
and wifi-densepose-train crate deps.
- metrics.rs: Add DynamicPersonMatcher wrapping ruvector_mincut::DynamicMinCut
for subpolynomial O(n^1.5 log n) multi-frame person tracking; adds
assignment_mincut() public entry point.
- proof.rs, trainer.rs, model.rs, dataset.rs, subcarrier.rs: Agent
improvements to full implementations (loss decrease verification, SHA-256
hash, LCG shuffle, ResNet18 backbone, MmFiDataset, linear interp).
- tests: test_config, test_dataset, test_metrics, test_proof, training_bench
all added/updated. 100+ tests pass with no-default-features.
https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
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2026-02-28 15:42:10 +00:00 |
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Claude
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ec98e40fff
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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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2026-02-28 15:15:31 +00:00 |
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