| """Generate embeddings for a dataset's images with the *active* embedder, with terminal progress. |
| |
| Use this instead of the UI "Embed" button for large datasets / the real (CPU) HF model, where the |
| synchronous HTTP endpoint would time out. Idempotent: skips pictures that already have an embedding |
| for the active model, so it's safe to re-run / resume. |
| |
| Usage: |
| python -m scripts.embed_dataset --dataset-id 3 # one dataset |
| python -m scripts.embed_dataset --all # every dataset |
| python -m scripts.embed_dataset --all --limit 5 # quick smoke (first 5 pictures each) |
| """ |
| from __future__ import annotations |
|
|
| import argparse |
| import time |
|
|
| from sqlalchemy import select |
|
|
| from app.db import SessionLocal, engine |
| from app.ml import get_embedder |
| from app.models import Base, Dataset, Picture |
| from app.services.datasets import _known_ids, _picture_ids, _unknown_ids |
| from app.services.images import embed_picture |
|
|
| BATCH = 100 |
|
|
|
|
| def run(dataset_id: int | None, do_all: bool, limit: int | None) -> None: |
| Base.metadata.create_all(bind=engine) |
| db = SessionLocal() |
| try: |
| embedder = get_embedder() |
| print(f"Active embedder: {embedder.name}/{embedder.version} (dim {embedder.dim})") |
|
|
| if do_all: |
| datasets = db.execute(select(Dataset).order_by(Dataset.id)).scalars().all() |
| elif dataset_id is not None: |
| d = db.get(Dataset, dataset_id) |
| datasets = [d] if d else [] |
| if not d: |
| print(f"Dataset #{dataset_id} not found.") |
| else: |
| print("Pass --dataset-id N or --all.") |
| return |
|
|
| for ds in datasets: |
| pic_ids = _picture_ids(db, _known_ids(db, ds.id), _unknown_ids(db, ds.id)) |
| if limit: |
| pic_ids = pic_ids[:limit] |
| print(f"\nDataset #{ds.id} {ds.name!r}: {len(pic_ids)} picture(s) to consider") |
| embedded = skipped = errors = 0 |
| start = time.time() |
| for i, pid in enumerate(pic_ids, 1): |
| pic = db.get(Picture, pid) |
| if pic is None: |
| continue |
| try: |
| if embed_picture(db, pic, skip_if_exists=True): |
| embedded += 1 |
| else: |
| skipped += 1 |
| except Exception as exc: |
| errors += 1 |
| print(f" ! picture {pid}: {exc}") |
| if i % BATCH == 0: |
| db.commit() |
| rate = i / max(time.time() - start, 1e-6) |
| print(f" {i}/{len(pic_ids)} embedded={embedded} skipped={skipped} " |
| f"errors={errors} ({rate:.1f} img/s)") |
| db.commit() |
| print(f" done: embedded={embedded} skipped={skipped} errors={errors} " |
| f"in {time.time() - start:.0f}s") |
| finally: |
| db.close() |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description="Embed a dataset's images with the active embedder.") |
| parser.add_argument("--dataset-id", type=int, default=None) |
| parser.add_argument("--all", action="store_true", help="Embed every dataset") |
| parser.add_argument("--limit", type=int, default=None, help="Only the first N pictures (smoke test)") |
| args = parser.parse_args() |
| run(args.dataset_id, args.all, args.limit) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
