mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-03-04 00:17:22 +01:00
d0e9bcbfdc
* add wheels * move extra index url to bottom * config model option * add postprocess * fix config * jina v2 embedding class * use jina v2 in embeddings * fix ov inference * frontend * update reference config * revert device * fix truncation * return np tensors * use correct embeddings from inference * manual preprocess * clean up * docs * lower batch size for v2 only * docs clarity * wording
232 lines
8.5 KiB
Python
232 lines
8.5 KiB
Python
"""JinaV2 Embeddings."""
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import io
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import logging
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import os
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import numpy as np
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from PIL import Image
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from transformers import AutoTokenizer
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from transformers.utils.logging import disable_progress_bar, set_verbosity_error
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from frigate.comms.inter_process import InterProcessRequestor
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from frigate.const import MODEL_CACHE_DIR, UPDATE_MODEL_STATE
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from frigate.types import ModelStatusTypesEnum
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from frigate.util.downloader import ModelDownloader
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from .base_embedding import BaseEmbedding
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from .runner import ONNXModelRunner
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# disables the progress bar and download logging for downloading tokenizers and image processors
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disable_progress_bar()
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set_verbosity_error()
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logger = logging.getLogger(__name__)
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class JinaV2Embedding(BaseEmbedding):
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def __init__(
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self,
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model_size: str,
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requestor: InterProcessRequestor,
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device: str = "AUTO",
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embedding_type: str = None,
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):
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model_file = (
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"model_fp16.onnx" if model_size == "large" else "model_quantized.onnx"
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)
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super().__init__(
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model_name="jinaai/jina-clip-v2",
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model_file=model_file,
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download_urls={
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model_file: f"https://huggingface.co/jinaai/jina-clip-v2/resolve/main/onnx/{model_file}",
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"preprocessor_config.json": "https://huggingface.co/jinaai/jina-clip-v2/resolve/main/preprocessor_config.json",
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},
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)
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self.tokenizer_file = "tokenizer"
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self.embedding_type = embedding_type
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self.requestor = requestor
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self.model_size = model_size
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self.device = device
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self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
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self.tokenizer = None
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self.image_processor = None
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self.runner = None
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files_names = list(self.download_urls.keys()) + [self.tokenizer_file]
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if not all(
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os.path.exists(os.path.join(self.download_path, n)) for n in files_names
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):
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logger.debug(f"starting model download for {self.model_name}")
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self.downloader = ModelDownloader(
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model_name=self.model_name,
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download_path=self.download_path,
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file_names=files_names,
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download_func=self._download_model,
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)
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self.downloader.ensure_model_files()
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else:
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self.downloader = None
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ModelDownloader.mark_files_state(
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self.requestor,
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self.model_name,
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files_names,
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ModelStatusTypesEnum.downloaded,
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)
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self._load_model_and_utils()
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logger.debug(f"models are already downloaded for {self.model_name}")
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def _download_model(self, path: str):
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try:
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file_name = os.path.basename(path)
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if file_name in self.download_urls:
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ModelDownloader.download_from_url(self.download_urls[file_name], path)
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elif file_name == self.tokenizer_file:
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if not os.path.exists(os.path.join(path, self.model_name)):
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logger.info(f"Downloading {self.model_name} tokenizer")
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tokenizer = AutoTokenizer.from_pretrained(
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self.model_name,
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trust_remote_code=True,
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cache_dir=os.path.join(
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MODEL_CACHE_DIR, self.model_name, "tokenizer"
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),
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clean_up_tokenization_spaces=True,
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)
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tokenizer.save_pretrained(path)
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self.requestor.send_data(
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UPDATE_MODEL_STATE,
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{
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"model": f"{self.model_name}-{file_name}",
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"state": ModelStatusTypesEnum.downloaded,
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},
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)
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except Exception:
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self.requestor.send_data(
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UPDATE_MODEL_STATE,
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{
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"model": f"{self.model_name}-{file_name}",
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"state": ModelStatusTypesEnum.error,
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},
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)
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def _load_model_and_utils(self):
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if self.runner is None:
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if self.downloader:
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self.downloader.wait_for_download()
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tokenizer_path = os.path.join(
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f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer"
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)
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self.tokenizer = AutoTokenizer.from_pretrained(
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self.model_name,
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cache_dir=tokenizer_path,
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trust_remote_code=True,
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clean_up_tokenization_spaces=True,
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)
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self.runner = ONNXModelRunner(
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os.path.join(self.download_path, self.model_file),
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self.device,
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self.model_size,
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)
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def _preprocess_image(self, image_data: bytes | Image.Image) -> np.ndarray:
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"""
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Manually preprocess a single image from bytes or PIL.Image to (3, 512, 512).
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"""
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if isinstance(image_data, bytes):
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image = Image.open(io.BytesIO(image_data))
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else:
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image = image_data
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if image.mode != "RGB":
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image = image.convert("RGB")
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image = image.resize((512, 512), Image.Resampling.LANCZOS)
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# Convert to numpy array, normalize to [0, 1], and transpose to (channels, height, width)
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image_array = np.array(image, dtype=np.float32) / 255.0
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image_array = np.transpose(image_array, (2, 0, 1)) # (H, W, C) -> (C, H, W)
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return image_array
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def _preprocess_inputs(self, raw_inputs):
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"""
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Preprocess inputs into a list of real input tensors (no dummies).
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- For text: Returns list of input_ids.
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- For vision: Returns list of pixel_values.
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"""
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if not isinstance(raw_inputs, list):
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raw_inputs = [raw_inputs]
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processed = []
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if self.embedding_type == "text":
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for text in raw_inputs:
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input_ids = self.tokenizer([text], return_tensors="np")["input_ids"]
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processed.append(input_ids)
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elif self.embedding_type == "vision":
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for img in raw_inputs:
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pixel_values = self._preprocess_image(img)
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processed.append(
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pixel_values[np.newaxis, ...]
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) # Add batch dim: (1, 3, 512, 512)
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else:
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raise ValueError(
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f"Invalid embedding_type: {self.embedding_type}. Must be 'text' or 'vision'."
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)
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return processed
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def _postprocess_outputs(self, outputs):
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"""
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Process ONNX model outputs, truncating each embedding in the array to truncate_dim.
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- outputs: NumPy array of embeddings.
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- Returns: List of truncated embeddings.
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"""
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# size of vector in database
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truncate_dim = 768
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# jina v2 defaults to 1024 and uses Matryoshka representation, so
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# truncating only causes an extremely minor decrease in retrieval accuracy
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if outputs.shape[-1] > truncate_dim:
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outputs = outputs[..., :truncate_dim]
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return outputs
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def __call__(
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self, inputs: list[str] | list[Image.Image] | list[str], embedding_type=None
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) -> list[np.ndarray]:
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self.embedding_type = embedding_type
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if not self.embedding_type:
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raise ValueError(
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"embedding_type must be specified either in __init__ or __call__"
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)
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self._load_model_and_utils()
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processed = self._preprocess_inputs(inputs)
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batch_size = len(processed)
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# Prepare ONNX inputs with matching batch sizes
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onnx_inputs = {}
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if self.embedding_type == "text":
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onnx_inputs["input_ids"] = np.stack([x[0] for x in processed])
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onnx_inputs["pixel_values"] = np.zeros(
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(batch_size, 3, 512, 512), dtype=np.float32
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)
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elif self.embedding_type == "vision":
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onnx_inputs["input_ids"] = np.zeros((batch_size, 16), dtype=np.int64)
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onnx_inputs["pixel_values"] = np.stack([x[0] for x in processed])
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else:
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raise ValueError("Invalid embedding type")
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# Run inference
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outputs = self.runner.run(onnx_inputs)
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if self.embedding_type == "text":
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embeddings = outputs[2] # text embeddings
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elif self.embedding_type == "vision":
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embeddings = outputs[3] # image embeddings
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else:
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raise ValueError("Invalid embedding type")
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embeddings = self._postprocess_outputs(embeddings)
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return [embedding for embedding in embeddings]
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