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38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
from typing import cast
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import torch
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from tqdm.auto import tqdm
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from datasets import Dataset, load_dataset
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from transformers import AutoImageProcessor, AutoModel
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@torch.no_grad()
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def establish_database(processor, model, images, batch_size=64):
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device = model.device
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model.eval()
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for i in tqdm(range(0, len(images), batch_size)):
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batch = images[i : i + batch_size]
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inputs = processor(images=batch, 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 = model(**inputs)
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feats = outputs.last_hidden_state # [B, N, D]
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# 后处理 / 存库
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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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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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establish_database(processor, model, train_dataset["img"])
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