dtoro
2a6661f779
- Add export_models.py for OpenCLIP ViT-B/32 and YOLOv8n ONNX export - Fix ArgMax(13) ORT ARM64 incompatibility by passing eot_indices as a separate ONNX input (computed outside the graph in embed.py) - Use legacy TorchScript exporter (dynamo=False) for IR version 9 compat - Upgrade onnxruntime to 1.18.1 - Rewrite bootstrap_models.py with clear separation of auto-downloadable models (YuNet, SFace) vs manually-exported ones (OpenCLIP, YOLOv8n) - Wire bootstrap into worker CMD (runs before Celery) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
97 lines
3.1 KiB
Python
97 lines
3.1 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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DOWNLOADS = [
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# YuNet — face detection (Apache 2.0, opencv_zoo, ~233 KB)
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(
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"face/yunet.onnx",
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"https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx",
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"YuNet face detector",
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),
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# SFace — face recognition (Apache 2.0, opencv_zoo, ~37 MB)
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(
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"face/sface.onnx",
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"https://github.com/opencv/opencv_zoo/raw/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx",
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"SFace face recognizer",
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),
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]
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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(s) that require manual export via export_models.py:",
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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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logger.warning(
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"Run: python -m app.services.vision.export_models --models-dir %s",
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base,
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)
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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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