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fix: Review fixes for end-to-end training pipeline

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- Snapshot best-epoch weights during training and restore before
  checkpoint/RVF export (prevents exporting overfit final-epoch params)
- Add CsiToPoseTransformer::zeros() for fast zero-init when weights
  will be overwritten, avoiding wasteful Xavier init during gradient
  estimation (~2*param_count transformer constructions per batch)
- Deduplicate synthetic data generation in main.rs training mode

Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
2026-02-28 23:58:20 -05:00
parent 4cabffa726
commit 45f0304d52
3 changed files with 57 additions and 52 deletions
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75
rust-port/wifi-densepose-rs/crates/wifi-densepose-sensing-server/src/main.rs
View File

@@ -1551,61 +1551,42 @@ async fn main() {
..Default::default()
});
// Load samples
// Generate synthetic training data (50 samples with deterministic CSI + keypoints)
let generate_synthetic = || -> Vec<dataset::TrainingSample> {
(0..50).map(|i| {
let csi: Vec<Vec<f32>> = (0..4).map(|a| {
(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
}).collect();
let mut kps = [(0.0f32, 0.0f32, 1.0f32); 17];
for (k, kp) in kps.iter_mut().enumerate() {
kp.0 = (k as f32 * 0.1 + i as f32 * 0.02).sin() * 100.0 + 320.0;
kp.1 = (k as f32 * 0.15 + i as f32 * 0.03).cos() * 80.0 + 240.0;
}
dataset::TrainingSample {
csi_window: csi,
pose_label: dataset::PoseLabel {
keypoints: kps,
body_parts: Vec::new(),
confidence: 1.0,
},
source: "synthetic",
}
}).collect()
};
// Load samples (fall back to synthetic if dataset missing/empty)
let samples = match pipeline.load() {
Ok(s) if !s.is_empty() => {
eprintln!("Loaded {} samples from {}", s.len(), ds_path.display());
s
}
Ok(_) => {
eprintln!("No samples found at {}. Generating synthetic training data...", ds_path.display());
// Generate synthetic samples for testing the pipeline
let mut synth = Vec::new();
for i in 0..50 {
let csi: Vec<Vec<f32>> = (0..4).map(|a| {
(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
}).collect();
let mut kps = [(0.0f32, 0.0f32, 1.0f32); 17];
for (k, kp) in kps.iter_mut().enumerate() {
kp.0 = (k as f32 * 0.1 + i as f32 * 0.02).sin() * 100.0 + 320.0;
kp.1 = (k as f32 * 0.15 + i as f32 * 0.03).cos() * 80.0 + 240.0;
}
synth.push(dataset::TrainingSample {
csi_window: csi,
pose_label: dataset::PoseLabel {
keypoints: kps,
body_parts: Vec::new(),
confidence: 1.0,
},
source: "synthetic",
});
}
synth
eprintln!("No samples found at {}. Using synthetic data.", ds_path.display());
generate_synthetic()
}
Err(e) => {
eprintln!("Failed to load dataset: {e}");
eprintln!("Generating synthetic training data...");
let mut synth = Vec::new();
for i in 0..50 {
let csi: Vec<Vec<f32>> = (0..4).map(|a| {
(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
}).collect();
let mut kps = [(0.0f32, 0.0f32, 1.0f32); 17];
for (k, kp) in kps.iter_mut().enumerate() {
kp.0 = (k as f32 * 0.1 + i as f32 * 0.02).sin() * 100.0 + 320.0;
kp.1 = (k as f32 * 0.15 + i as f32 * 0.03).cos() * 80.0 + 240.0;
}
synth.push(dataset::TrainingSample {
csi_window: csi,
pose_label: dataset::PoseLabel {
keypoints: kps,
body_parts: Vec::new(),
confidence: 1.0,
},
source: "synthetic",
});
}
synth
eprintln!("Failed to load dataset: {e}. Using synthetic data.");
generate_synthetic()
}
};