mirror of
https://github.com/immich-app/immich.git
synced 2025-01-06 03:46:47 +01:00
54b2779b79
* fixed `minScore` not being set correctly * apply to init * don't send `enabled` * fix eslint warning * added logger * added logging * refinements * enable access log for info level * formatting * merged strings --------- Co-authored-by: Alex <alex.tran1502@gmail.com>
75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
from io import BytesIO
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from pathlib import Path
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from typing import Any
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from huggingface_hub import snapshot_download
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from optimum.onnxruntime import ORTModelForImageClassification
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from optimum.pipelines import pipeline
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from PIL import Image
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from transformers import AutoImageProcessor
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from ..config import log
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from ..schemas import ModelType
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from .base import InferenceModel
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class ImageClassifier(InferenceModel):
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_model_type = ModelType.IMAGE_CLASSIFICATION
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def __init__(
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self,
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model_name: str,
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min_score: float = 0.9,
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cache_dir: Path | str | None = None,
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**model_kwargs: Any,
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) -> None:
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self.min_score = model_kwargs.pop("minScore", min_score)
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super().__init__(model_name, cache_dir, **model_kwargs)
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def _download(self, **model_kwargs: Any) -> None:
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snapshot_download(
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cache_dir=self.cache_dir,
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repo_id=self.model_name,
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allow_patterns=["*.bin", "*.json", "*.txt"],
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local_dir=self.cache_dir,
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local_dir_use_symlinks=True,
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)
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def _load(self, **model_kwargs: Any) -> None:
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processor = AutoImageProcessor.from_pretrained(self.cache_dir, cache_dir=self.cache_dir)
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model_path = self.cache_dir / "model.onnx"
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model_kwargs |= {
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"cache_dir": self.cache_dir,
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"provider": self.providers[0],
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"provider_options": self.provider_options[0],
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"session_options": self.sess_options,
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}
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if model_path.exists():
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model = ORTModelForImageClassification.from_pretrained(self.cache_dir, **model_kwargs)
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self.model = pipeline(self.model_type.value, model, feature_extractor=processor)
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else:
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log.info(
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(
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f"ONNX model not found in cache directory for '{self.model_name}'."
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"Exporting optimized model for future use."
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),
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)
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self.sess_options.optimized_model_filepath = model_path.as_posix()
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self.model = pipeline(
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self.model_type.value,
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self.model_name,
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model_kwargs=model_kwargs,
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feature_extractor=processor,
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)
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def _predict(self, image: Image.Image | bytes) -> list[str]:
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if isinstance(image, bytes):
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image = Image.open(BytesIO(image))
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predictions: list[dict[str, Any]] = self.model(image) # type: ignore
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tags = [tag for pred in predictions for tag in pred["label"].split(", ") if pred["score"] >= self.min_score]
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return tags
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def configure(self, **model_kwargs: Any) -> None:
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self.min_score = model_kwargs.pop("minScore", self.min_score)
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