dtoro
fbeefb24a0
- detect_objects, classify_content, recluster_faces now look up the photo's user_id and set it on created Tag rows — fixes tags being invisible to the owning user due to NULL user_id - Initial admin setup creates source root at the mount root (/photos) instead of a subdirectory, since the admin owns the entire library - Revert to OpenCLIP ViT-B/32 (512-d) as default embedder — SigLIP requires transformers version alignment not yet available in the Docker image. SigLIP2 code remains for future enablement. - Add transformers to requirements for future SigLIP support Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
"""
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Download vision model weights on first worker boot.
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Run as: python -m app.services.vision.bootstrap_models
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Or called from the vision worker entrypoint before Celery starts.
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Downloads are idempotent — existing files are skipped.
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For models that require export (OpenCLIP, YOLOv8n), see export_models.py.
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Those must be exported once on any machine with pip, then placed in
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the models volume before the worker starts.
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"""
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import logging
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import os
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from pathlib import Path
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from urllib.request import urlretrieve
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from app.config import settings
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logger = logging.getLogger(__name__)
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# (relative_path, url, description)
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# Models with url=None must be pre-exported via export_models.py.
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# InsightFace (RetinaFace + ArcFace) auto-downloads via the insightface
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# package on first use — no manual download entries needed.
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DOWNLOADS = []
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# Models that need manual export via export_models.py
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EXPORTS = [
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("embed/visual.onnx", "OpenCLIP ViT-B/32 visual encoder"),
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("embed/textual.onnx", "OpenCLIP ViT-B/32 textual encoder"),
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("detect/yolov8n.onnx", "YOLOv8n object detector"),
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]
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def bootstrap(models_dir: str | None = None):
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"""Ensure all model files are present. Download what we can, warn about
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files that need manual export."""
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base = Path(models_dir or settings.vision.models_dir)
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base.mkdir(parents=True, exist_ok=True)
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# Download auto-downloadable models
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for rel_path, url, desc in DOWNLOADS:
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dest = base / rel_path
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dest.parent.mkdir(parents=True, exist_ok=True)
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if dest.exists():
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logger.debug("Already exists: %s (%s)", dest, desc)
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continue
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logger.info("Downloading %s → %s", desc, dest)
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try:
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urlretrieve(url, str(dest))
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size_kb = dest.stat().st_size / 1024
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logger.info("Downloaded %s (%.0f KB)", desc, size_kb)
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except Exception as e:
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logger.error("Failed to download %s: %s", desc, e)
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if dest.exists():
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dest.unlink()
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# Check for manually-exported models
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missing = []
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for rel_path, desc in EXPORTS:
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dest = base / rel_path
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if not dest.exists():
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missing.append((rel_path, desc))
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if missing:
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logger.warning(
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"Missing %d model file(s); attempting automatic export:",
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len(missing),
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)
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for rel_path, desc in missing:
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logger.warning(" %s — %s", base / rel_path, desc)
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try:
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from app.services.vision import export_models
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export_models.export_openclip(base)
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export_models.export_siglip2(base)
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export_models.export_yolov8n(base)
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except Exception as e:
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logger.error(
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"Automatic export failed: %s. "
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"Run `python -m app.services.vision.export_models "
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"--models-dir %s` manually before starting the worker.",
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e,
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base,
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)
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return
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# Re-check what's still missing after the export pass.
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still_missing = [
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(rel_path, desc)
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for rel_path, desc in EXPORTS
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if not (base / rel_path).exists()
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]
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if still_missing:
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for rel_path, desc in still_missing:
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logger.error(" still missing: %s — %s", base / rel_path, desc)
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else:
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logger.info("All model files present in %s", base)
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else:
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logger.info("All model files present in %s", base)
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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bootstrap()
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