mirror of
https://github.com/immich-app/immich.git
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109 lines
4.0 KiB
Python
109 lines
4.0 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 tokenizers import Encoding, Tokenizer
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from app.config import log
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from app.models.base import InferenceModel
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from app.models.transforms import clean_text
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from app.schemas import ModelSession, ModelTask, ModelType
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class BaseCLIPTextualEncoder(InferenceModel):
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depends = []
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identity = (ModelType.TEXTUAL, ModelTask.SEARCH)
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def _predict(self, inputs: str, **kwargs: Any) -> NDArray[np.float32]:
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res: NDArray[np.float32] = self.session.run(None, self.tokenize(inputs))[0][0]
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return res
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def _load(self) -> ModelSession:
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session = super()._load()
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log.debug(f"Loading tokenizer for CLIP model '{self.model_name}'")
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self.tokenizer = self._load_tokenizer()
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tokenizer_kwargs: dict[str, Any] | None = self.text_cfg.get("tokenizer_kwargs")
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self.canonicalize = tokenizer_kwargs is not None and tokenizer_kwargs.get("clean") == "canonicalize"
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log.debug(f"Loaded tokenizer for CLIP model '{self.model_name}'")
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return session
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@abstractmethod
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def _load_tokenizer(self) -> Tokenizer:
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pass
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@abstractmethod
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def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
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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 tokenizer_file_path(self) -> Path:
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return self.model_dir / "tokenizer.json"
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@property
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def tokenizer_cfg_path(self) -> Path:
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return self.model_dir / "tokenizer_config.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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@property
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def text_cfg(self) -> dict[str, Any]:
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text_cfg: dict[str, Any] = self.model_cfg["text_cfg"]
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return text_cfg
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@cached_property
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def tokenizer_file(self) -> dict[str, Any]:
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log.debug(f"Loading tokenizer file for CLIP model '{self.model_name}'")
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tokenizer_file: dict[str, Any] = json.load(self.tokenizer_file_path.open())
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log.debug(f"Loaded tokenizer file for CLIP model '{self.model_name}'")
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return tokenizer_file
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@cached_property
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def tokenizer_cfg(self) -> dict[str, Any]:
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log.debug(f"Loading tokenizer config for CLIP model '{self.model_name}'")
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tokenizer_cfg: dict[str, Any] = json.load(self.tokenizer_cfg_path.open())
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log.debug(f"Loaded tokenizer config for CLIP model '{self.model_name}'")
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return tokenizer_cfg
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class OpenClipTextualEncoder(BaseCLIPTextualEncoder):
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def _load_tokenizer(self) -> Tokenizer:
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context_length: int = self.text_cfg.get("context_length", 77)
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pad_token: str = self.tokenizer_cfg["pad_token"]
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tokenizer: Tokenizer = Tokenizer.from_file(self.tokenizer_file_path.as_posix())
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pad_id: int = tokenizer.token_to_id(pad_token)
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tokenizer.enable_padding(length=context_length, pad_token=pad_token, pad_id=pad_id)
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tokenizer.enable_truncation(max_length=context_length)
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return tokenizer
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def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
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text = clean_text(text, canonicalize=self.canonicalize)
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tokens: Encoding = self.tokenizer.encode(text)
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return {"text": np.array([tokens.ids], dtype=np.int32)}
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class MClipTextualEncoder(OpenClipTextualEncoder):
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def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
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text = clean_text(text, canonicalize=self.canonicalize)
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tokens: Encoding = self.tokenizer.encode(text)
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return {
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"input_ids": np.array([tokens.ids], dtype=np.int32),
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"attention_mask": np.array([tokens.attention_mask], dtype=np.int32),
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}
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