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chore(ml): installable package (#17153)

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* app -> immich_ml

* fix test ci

* omit file name

* add new line

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Mert 2025-03-27 15:49:09 -04:00 • committed by GitHub
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commit 84c35e35d6
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machine-learning/app/models/clip/visual.py
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import json
from abc import abstractmethod
from functools import cached_property
from pathlib import Path
from typing import Any
import numpy as np
from numpy.typing import NDArray
from PIL import Image
from app.config import log
from app.models.base import InferenceModel
from app.models.transforms import (
crop_pil,
decode_pil,
get_pil_resampling,
normalize,
resize_pil,
serialize_np_array,
to_numpy,
)
from app.schemas import ModelSession, ModelTask, ModelType
class BaseCLIPVisualEncoder(InferenceModel):
depends = []
identity = (ModelType.VISUAL, ModelTask.SEARCH)
def _predict(self, inputs: Image.Image | bytes, **kwargs: Any) -> str:
image = decode_pil(inputs)
res: NDArray[np.float32] = self.session.run(None, self.transform(image))[0][0]
return serialize_np_array(res)
@abstractmethod
def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]:
pass
@property
def model_cfg_path(self) -> Path:
return self.cache_dir / "config.json"
@property
def preprocess_cfg_path(self) -> Path:
return self.model_dir / "preprocess_cfg.json"
@cached_property
def model_cfg(self) -> dict[str, Any]:
log.debug(f"Loading model config for CLIP model '{self.model_name}'")
model_cfg: dict[str, Any] = json.load(self.model_cfg_path.open())
log.debug(f"Loaded model config for CLIP model '{self.model_name}'")
return model_cfg
@cached_property
def preprocess_cfg(self) -> dict[str, Any]:
log.debug(f"Loading visual preprocessing config for CLIP model '{self.model_name}'")
preprocess_cfg: dict[str, Any] = json.load(self.preprocess_cfg_path.open())
log.debug(f"Loaded visual preprocessing config for CLIP model '{self.model_name}'")
return preprocess_cfg
class OpenClipVisualEncoder(BaseCLIPVisualEncoder):
def _load(self) -> ModelSession:
size: list[int] | int = self.preprocess_cfg["size"]
self.size = size[0] if isinstance(size, list) else size
self.resampling = get_pil_resampling(self.preprocess_cfg["interpolation"])
self.mean = np.array(self.preprocess_cfg["mean"], dtype=np.float32)
self.std = np.array(self.preprocess_cfg["std"], dtype=np.float32)
return super()._load()
def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]:
image = resize_pil(image, self.size)
image = crop_pil(image, self.size)
image_np = to_numpy(image)
image_np = normalize(image_np, self.mean, self.std)
return {"image": np.expand_dims(image_np.transpose(2, 0, 1), 0)}