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>
26 lines
1001 B
Python
26 lines
1001 B
Python
"""
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ROCm backend — GPU-accelerated inference for Radeon 760M-class hardware.
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Stub: raises NotImplementedError on all factory methods. To enable,
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set `vision.backend: rocm` in mulita.yml once ROCm support is implemented.
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"""
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from app.config import VisionSettings
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from app.services.vision.base import Embedder, OCREngine, ObjectDetector, FaceProcessor
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class ROCmBackend:
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def __init__(self, vision_settings: VisionSettings):
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self._settings = vision_settings
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def create_embedder(self) -> Embedder:
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raise NotImplementedError("ROCm backend not yet implemented — use 'onnx'")
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def create_ocr(self) -> OCREngine:
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raise NotImplementedError("ROCm backend not yet implemented — use 'onnx'")
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def create_detector(self) -> ObjectDetector:
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raise NotImplementedError("ROCm backend not yet implemented — use 'onnx'")
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def create_face_processor(self) -> FaceProcessor:
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raise NotImplementedError("ROCm backend not yet implemented — use 'onnx'")
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