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41 lines
1.7 KiB
Python
41 lines
1.7 KiB
Python
from typing import Any
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from immich_ml.models.base import InferenceModel
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from immich_ml.models.clip.textual import MClipTextualEncoder, OpenClipTextualEncoder
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from immich_ml.models.clip.visual import OpenClipVisualEncoder
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from immich_ml.schemas import ModelSource, ModelTask, ModelType
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from .constants import get_model_source
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from .facial_recognition.detection import FaceDetector
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from .facial_recognition.recognition import FaceRecognizer
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def get_model_class(model_name: str, model_type: ModelType, model_task: ModelTask) -> type[InferenceModel]:
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source = get_model_source(model_name)
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match source, model_type, model_task:
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case ModelSource.OPENCLIP | ModelSource.MCLIP, ModelType.VISUAL, ModelTask.SEARCH:
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return OpenClipVisualEncoder
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case ModelSource.OPENCLIP, ModelType.TEXTUAL, ModelTask.SEARCH:
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return OpenClipTextualEncoder
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case ModelSource.MCLIP, ModelType.TEXTUAL, ModelTask.SEARCH:
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return MClipTextualEncoder
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case ModelSource.INSIGHTFACE, ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION:
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return FaceDetector
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case ModelSource.INSIGHTFACE, ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION:
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return FaceRecognizer
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case _:
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raise ValueError(f"Unknown model combination: {source}, {model_type}, {model_task}")
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def from_model_type(model_name: str, model_type: ModelType, model_task: ModelTask, **kwargs: Any) -> InferenceModel:
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return get_model_class(model_name, model_type, model_task)(model_name, **kwargs)
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def get_model_deps(model_name: str, model_type: ModelType, model_task: ModelTask) -> list[tuple[ModelType, ModelTask]]:
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return get_model_class(model_name, model_type, model_task).depends
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