帮我写一个基于Vision Transformer将一个图片集分类并进行以图搜图的代码

好的，我可以回答这个问题。以下是基于Vision Transformer的图片分类和以图搜图的代码示例：

import torch
import torchvision
from PIL import Image
import requests
from io import BytesIO

# 加载预训练的Vision Transformer模型
model = torch.hub.load('facebookresearch/deit:main', 'deit_base_patch16_224', pretrained=True)

# 加载ImageNet标签
labels_url = 'https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt'
response = requests.get(labels_url)
labels = response.text.split('\n')

# 定义分类函数
def classify_image(image_path):
    # 加载图片并进行预处理
    image = Image.open(image_path)
    preprocess = torchvision.transforms.Compose([
        torchvision.transforms.Resize(256),
        torchvision.transforms.CenterCrop(224),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(
            mean=[.485, .456, .406],
            std=[.229, .224, .225]
        )
    ])
    image_tensor = preprocess(image)
    image_batch = image_tensor.unsqueeze() # 添加批次维度

    # 使用模型进行推理
    with torch.no_grad():
        output = model(image_batch)

    # 获取预测结果
    probabilities = torch.nn.functional.softmax(output[], dim=)
    top_prob, top_label = torch.topk(probabilities, 1)

    # 返回预测结果
    return labels[top_label[]], top_prob[]

# 测试分类函数
image_url = 'https://images.pexels.com/photos/414612/pexels-photo-414612.jpeg'
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
image.save('test.jpg')
label, prob = classify_image('test.jpg')
print(f'预测结果: {label}, 置信度: {prob:.2f}')

# 定义以图搜图函数
def search_similar_images(image_path, dataset_path, num_results=5):
    # 加载图片并进行预处理
    image = Image.open(image_path)
    preprocess = torchvision.transforms.Compose([
        torchvision.transforms.Resize(256),
        torchvision.transforms.CenterCrop(224),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(
            mean=[.485, .456, .406],
            std=[.229, .224, .225]
        )
    ])
    image_tensor = preprocess(image)
    image_batch = image_tensor.unsqueeze() # 添加批次维度

    # 使用模型进行特征提取
    with torch.no_grad():
        features = model.backbone(image_batch).squeeze()

    # 加载数据集并进行特征提取
    dataset = torchvision.datasets.ImageFolder(dataset_path, transform=preprocess)
    dataset_features = []
    for i in range(len(dataset)):
        with torch.no_grad():
            feature = model.backbone(dataset[i][].unsqueeze()).squeeze()
        dataset_features.append(feature)

    # 计算相似度并返回结果
    similarities = torch.nn.functional.cosine_similarity(features, torch.stack(dataset_features))
    top_similarities, top_indices = torch.topk(similarities, num_results)
    results = []
    for i in range(num_results):
        image_path = dataset.samples[top_indices[i]][]
        results.append((image_path, top_similarities[i]))
    return results

# 测试以图搜图函数
results = search_similar_images('test.jpg', 'dataset')
for result in results:
    print(f'相似图片: {result[]}, 相似度: {result[1]:.2f}')

请注意，这只是一个简单的示例代码，实际应用中可能需要进行更多的优化和调整。