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78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
import json
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from abc import abstractmethod
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from functools import cached_property
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from pathlib import Path
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from typing import Any
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import numpy as np
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from numpy.typing import NDArray
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from PIL import Image
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from immich_ml.config import log
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from immich_ml.models.base import InferenceModel
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from immich_ml.models.transforms import (
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crop_pil,
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decode_pil,
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get_pil_resampling,
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normalize,
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resize_pil,
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serialize_np_array,
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to_numpy,
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)
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from immich_ml.schemas import ModelSession, ModelTask, ModelType
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class BaseCLIPVisualEncoder(InferenceModel):
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depends = []
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identity = (ModelType.VISUAL, ModelTask.SEARCH)
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def _predict(self, inputs: Image.Image | bytes, **kwargs: Any) -> str:
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image = decode_pil(inputs)
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res: NDArray[np.float32] = self.session.run(None, self.transform(image))[0][0]
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return serialize_np_array(res)
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@abstractmethod
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def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]:
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pass
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@property
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def model_cfg_path(self) -> Path:
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return self.cache_dir / "config.json"
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@property
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def preprocess_cfg_path(self) -> Path:
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return self.model_dir / "preprocess_cfg.json"
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@cached_property
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def model_cfg(self) -> dict[str, Any]:
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log.debug(f"Loading model config for CLIP model '{self.model_name}'")
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model_cfg: dict[str, Any] = json.load(self.model_cfg_path.open())
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log.debug(f"Loaded model config for CLIP model '{self.model_name}'")
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return model_cfg
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@cached_property
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def preprocess_cfg(self) -> dict[str, Any]:
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log.debug(f"Loading visual preprocessing config for CLIP model '{self.model_name}'")
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preprocess_cfg: dict[str, Any] = json.load(self.preprocess_cfg_path.open())
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log.debug(f"Loaded visual preprocessing config for CLIP model '{self.model_name}'")
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return preprocess_cfg
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class OpenClipVisualEncoder(BaseCLIPVisualEncoder):
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def _load(self) -> ModelSession:
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size: list[int] | int = self.preprocess_cfg["size"]
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self.size = size[0] if isinstance(size, list) else size
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self.resampling = get_pil_resampling(self.preprocess_cfg["interpolation"])
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self.mean = np.array(self.preprocess_cfg["mean"], dtype=np.float32)
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self.std = np.array(self.preprocess_cfg["std"], dtype=np.float32)
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return super()._load()
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def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]:
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image = resize_pil(image, self.size)
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image = crop_pil(image, self.size)
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image_np = to_numpy(image)
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image_np = normalize(image_np, self.mean, self.std)
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return {"image": np.expand_dims(image_np.transpose(2, 0, 1), 0)}
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