mirror of
https://github.com/SikongJueluo/Mini-Nav.git
synced 2026-03-12 12:25:32 +08:00
133 lines
3.9 KiB
Python
133 lines
3.9 KiB
Python
from typing import Any, Dict, List, Optional, cast
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import torch
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from database import db_manager
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from datasets import Dataset, load_dataset
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from tqdm.auto import tqdm
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from transformers import AutoImageProcessor, AutoModel
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class FeatureRetrieval:
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"""Singleton feature retrieval manager for image feature extraction."""
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_instance: Optional["FeatureRetrieval"] = None
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_initialized: bool = False
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processor: Any
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model: Any
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def __new__(cls, *args, **kwargs) -> "FeatureRetrieval":
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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return cls._instance
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def __init__(
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self, processor: Optional[Any] = None, model: Optional[Any] = None
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) -> None:
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"""Initialize the singleton with processor and model.
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Args:
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processor: Image processor for preprocessing images.
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model: Model for feature extraction.
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"""
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# 如果已经初始化过，直接返回
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if self._initialized:
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return
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# 首次初始化时必须提供 processor 和 model
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if processor is None or model is None:
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raise ValueError(
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"Processor and model must be provided on first initialization."
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)
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self.processor = processor
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self.model = model
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self._initialized = True
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@torch.no_grad()
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def establish_database(
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self,
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images: List[Any],
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labels: List[int] | List[str],
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batch_size: int = 64,
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label_map: Optional[Dict[int, str] | List[str]] = None,
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) -> None:
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"""Extract features from images and store them in the database.
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Args:
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images: List of images to process.
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labels: List of labels corresponding to images.
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batch_size: Number of images to process in a batch.
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label_map: Optional mapping from label indices to string names.
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"""
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device = self.model.device
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self.model.eval()
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for i in tqdm(range(0, len(images), batch_size)):
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batch_imgs = images[i : i + batch_size]
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inputs = self.processor(images=batch_imgs, return_tensors="pt")
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# 迁移数据到GPU
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inputs.to(device, non_blocking=True)
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outputs = self.model(**inputs)
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# 后处理
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feats = outputs.last_hidden_state # [B, N, D]
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cls_tokens = feats[:, 0] # Get CLS token (first token) for all batch items
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cls_tokens = cast(torch.Tensor, cls_tokens)
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# 迁移输出到CPU
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cls_tokens = cls_tokens.cpu()
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batch_labels = (
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labels[i : i + batch_size]
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if label_map is None
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else list(
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map(lambda x: label_map[cast(int, x)], labels[i : i + batch_size])
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)
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)
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actual_batch_size = len(batch_labels)
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# 存库
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db_manager.table.add(
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[
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{
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"id": i + j,
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"label": batch_labels[j],
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"vector": cls_tokens[j].numpy(),
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}
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for j in range(actual_batch_size)
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]
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)
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if __name__ == "__main__":
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train_dataset = load_dataset("uoft-cs/cifar10", split="train")
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train_dataset = cast(Dataset, train_dataset)
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label_map = [
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"airplane",
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"automobile",
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"bird",
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"cat",
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"deer",
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"dog",
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"frog",
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"horse",
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"ship",
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"truck",
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]
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processor = AutoImageProcessor.from_pretrained(
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"facebook/dinov2-large", device_map="cuda"
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)
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model = AutoModel.from_pretrained("facebook/dinov2-large", device_map="cuda")
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feature_retrieval = FeatureRetrieval(processor, model)
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feature_retrieval.establish_database(
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train_dataset["img"],
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train_dataset["label"],
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label_map=label_map,
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)
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