dearsky/wifi-densepose
1
0
Fork 0
Code Issues 20 Pull Requests 5 Actions Packages Projects Releases 1 Wiki Activity

feat(train): Add ruvector integration — ADR-016, deps, DynamicPersonMatcher

Browse Source 
- 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
This commit is contained in:
Claude
2026-02-28 15:42:10 +00:00
parent fce1271140
commit 81ad09d05b
19 changed files with 4171 additions and 1276 deletions
Download Patch File Download Diff File Expand all files Collapse all files

17
rust-port/wifi-densepose-rs/crates/wifi-densepose-train/src/trainer.rs
View File

@@ -16,7 +16,6 @@
//! exclusively on the [`CsiDataset`] passed at call site. The
//! [`SyntheticCsiDataset`] is only used for the deterministic proof protocol.
use std::collections::VecDeque;
use std::io::Write as IoWrite;
use std::path::{Path, PathBuf};
use std::time::Instant;
@@ -26,7 +25,7 @@ use tch::{nn, nn::OptimizerConfig, Device, Kind, Tensor};
use tracing::{debug, info, warn};
use crate::config::TrainingConfig;
use crate::dataset::{CsiDataset, CsiSample, DataLoader};
use crate::dataset::{CsiDataset, CsiSample};
use crate::error::TrainError;
use crate::losses::{LossWeights, WiFiDensePoseLoss};
use crate::losses::generate_target_heatmaps;
@@ -123,14 +122,14 @@ impl Trainer {
// Prepare output directories.
std::fs::create_dir_all(&self.config.checkpoint_dir)
.map_err(|e| TrainError::Io(e))?;
.map_err(|e| TrainError::training_step(format!("create checkpoint dir: {e}")))?;
std::fs::create_dir_all(&self.config.log_dir)
.map_err(|e| TrainError::Io(e))?;
.map_err(|e| TrainError::training_step(format!("create log dir: {e}")))?;
// Build optimizer (AdamW).
let mut opt = nn::AdamW::default()
.wd(self.config.weight_decay)
.build(self.model.var_store(), self.config.learning_rate)
.build(self.model.var_store_mut(), self.config.learning_rate)
.map_err(|e| TrainError::training_step(e.to_string()))?;
let loss_fn = WiFiDensePoseLoss::new(LossWeights {
@@ -146,9 +145,9 @@ impl Trainer {
.create(true)
.truncate(true)
.open(&csv_path)
.map_err(|e| TrainError::Io(e))?;
.map_err(|e| TrainError::training_step(format!("open csv log: {e}")))?;
writeln!(csv_file, "epoch,train_loss,train_kp_loss,val_pck,val_oks,lr,duration_secs")
.map_err(|e| TrainError::Io(e))?;
.map_err(|e| TrainError::training_step(format!("write csv header: {e}")))?;
let mut training_history: Vec<EpochLog> = Vec::new();
let mut best_pck: f32 = -1.0;
@@ -316,7 +315,7 @@ impl Trainer {
log.lr,
log.duration_secs,
)
.map_err(|e| TrainError::Io(e))?;
.map_err(|e| TrainError::training_step(format!("write csv row: {e}")))?;
training_history.push(log);
@@ -394,7 +393,7 @@ impl Trainer {
_epoch: usize,
_metrics: &MetricsResult,
) -> Result<(), TrainError> {
self.model.save(path).map_err(|e| TrainError::checkpoint(e.to_string(), path))
self.model.save(path)
}
/// Load model weights from a checkpoint.