| """Extract frozen paired clinical/dermoscopic features.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import torch |
| from torch.utils.data import DataLoader |
|
|
| from milk10k_new_collapse_research.compat import ensure_legacy_package_path |
| from milk10k_new_collapse_research.config import RESULTS_ROOT |
| from milk10k_new_collapse_research.features import ( |
| PairImageDataset, |
| build_feature_model, |
| extract_pair_features, |
| save_feature_npz, |
| ) |
|
|
| ensure_legacy_package_path() |
| from milk10k_effb2_metadata.data import lesion_split, load_paired_dataframe |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--data-dir", type=Path, required=True) |
| parser.add_argument("--output-dir", type=Path, default=RESULTS_ROOT / "features") |
| parser.add_argument("--model-name", default="dinov2_vitb14") |
| parser.add_argument("--val-size", type=float, default=0.20) |
| parser.add_argument("--seed", type=int, default=42) |
| parser.add_argument("--batch-size", type=int, default=16) |
| parser.add_argument("--num-workers", type=int, default=4) |
| parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") |
| parser.add_argument("--limit", type=int, default=0, help="Optional per-split row cap for smoke tests.") |
| parser.add_argument("--no-l2-normalize", action="store_true") |
| parser.add_argument("--no-pretrained", action="store_true", help="Only for local smoke tests with timm models.") |
| return parser.parse_args() |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
| device = torch.device(args.device) |
| df = load_paired_dataframe(args.data_dir) |
| train_df, val_df = lesion_split(df, args.val_size, args.seed) |
| if args.limit > 0: |
| train_df = train_df.head(args.limit).copy() |
| val_df = val_df.head(args.limit).copy() |
|
|
| model, transform, image_size = build_feature_model(args.model_name, device, pretrained=not args.no_pretrained) |
| args.output_dir.mkdir(parents=True, exist_ok=True) |
| for split, split_df in [("train", train_df), ("val", val_df)]: |
| dataset = PairImageDataset(split_df, transform) |
| loader = DataLoader( |
| dataset, |
| batch_size=args.batch_size, |
| shuffle=False, |
| num_workers=args.num_workers, |
| pin_memory=torch.cuda.is_available(), |
| ) |
| batch = extract_pair_features(model, loader, device, l2_normalize=not args.no_l2_normalize) |
| save_feature_npz(args.output_dir / f"{split}_features.npz", batch, args.model_name, split) |
|
|
| manifest = { |
| "model_name": args.model_name, |
| "image_size": image_size, |
| "data_dir": str(args.data_dir), |
| "train_rows": int(len(train_df)), |
| "val_rows": int(len(val_df)), |
| "val_size": args.val_size, |
| "seed": args.seed, |
| } |
| (args.output_dir / "feature_manifest.json").write_text(json.dumps(manifest, indent=2), encoding="utf-8") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
