dtoro
9282a5c734
Introduce the app/services/vision/ module with ABC interfaces, ONNX Runtime backend, model registry, and per-task implementations: - OpenCLIP ViT-B/32 embedder (image + text, 512-d) - RapidOCR engine (PP-OCRv4 via ONNX, no PaddlePaddle) - YOLOv8n object detector (raw ONNX, no ultralytics runtime) - YuNet + SFace face processor (Apache 2.0, opencv_zoo, 128-d) - DBSCAN face clustering helper Add VisionSettings to config (mulita.yml + Pydantic), bootstrap_models.py for first-boot weight downloads, models_data Docker volume, and ROCm backend stub for future GPU acceleration. No Celery tasks wired yet — models load but nothing invokes them. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
"""
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ModelRegistry — singleton that lazy-loads vision models per worker process.
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Usage from Celery tasks:
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from app.services.vision.registry import registry
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embedder = registry.get_embedder()
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vec = embedder.embed_image(img)
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Models are created on first access and cached for the worker's lifetime.
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The registry reads settings.vision to decide which backend to use and
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where model weights live.
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"""
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import logging
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from functools import lru_cache
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from app.config import settings
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from app.services.vision.base import Embedder, OCREngine, ObjectDetector, FaceProcessor
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logger = logging.getLogger(__name__)
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class ModelRegistry:
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"""Central access point for all vision models."""
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def __init__(self):
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self._vision = settings.vision
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@lru_cache(maxsize=1)
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def get_embedder(self) -> Embedder:
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logger.info("Loading embedder: %s (backend=%s)", self._vision.embedder.name, self._vision.backend)
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return self._load_backend().create_embedder()
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@lru_cache(maxsize=1)
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def get_ocr(self) -> OCREngine:
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logger.info("Loading OCR engine (backend=%s)", self._vision.backend)
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return self._load_backend().create_ocr()
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@lru_cache(maxsize=1)
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def get_detector(self) -> ObjectDetector:
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logger.info("Loading object detector (backend=%s)", self._vision.backend)
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return self._load_backend().create_detector()
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@lru_cache(maxsize=1)
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def get_face_processor(self) -> FaceProcessor:
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logger.info("Loading face processor (backend=%s)", self._vision.backend)
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return self._load_backend().create_face_processor()
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@lru_cache(maxsize=1)
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def _load_backend(self):
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"""Import and instantiate the configured backend."""
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backend_name = self._vision.backend
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if backend_name == "onnx":
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from app.services.vision.onnx_backend import ONNXBackend
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return ONNXBackend(self._vision)
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elif backend_name == "rocm":
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from app.services.vision.rocm_backend import ROCmBackend
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return ROCmBackend(self._vision)
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else:
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raise ValueError(f"Unknown vision backend: {backend_name}")
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def warmup(self):
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"""Pre-load all enabled models. Called from Celery worker_process_init
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on the vision queue to avoid cold-start latency on the first task."""
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logger.info("Warming up vision models...")
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self.get_embedder()
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if self._vision.ocr.enabled:
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self.get_ocr()
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if self._vision.detector.enabled:
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self.get_detector()
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if self._vision.faces.enabled:
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self.get_face_processor()
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logger.info("Vision model warmup complete")
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# Module-level singleton. Import this from tasks.
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registry = ModelRegistry()
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