帮我写一个基于Vision Transformer将一个.png图片集进行分类的代码

好的，我可以回答这个问题。以下是一个基于Vision Transformer的图片分类代码示例：

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image

# 定义Vision Transformer模型
class VisionTransformer(nn.Module):
    def __init__(self, num_classes=10, dim=512, depth=6, heads=8, mlp_dim=2048):
        super().__init__()
        self.patch_size = 16
        self.num_classes = num_classes
        self.pos_embedding = nn.Parameter(torch.zeros(1, (224 // self.patch_size) ** 2 + 1, dim))
        self.patch_embedding = nn.Conv2d(3, dim, kernel_size=self.patch_size, stride=self.patch_size)
        self.transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=dim, nhead=heads, dim_feedforward=mlp_dim), num_layers=depth)
        self.fc = nn.Linear(dim, num_classes)

    def forward(self, x):
        x = self.patch_embedding(x).flatten(2).transpose(1, 2)
        x = torch.cat([self.pos_embedding[:, :1, :].expand(-1, x.size(1), -1), x], dim=1)
        x = self.transformer(x)
        x = x.mean(dim=1)
        x = self.fc(x)
        return x

# 加载模型和预训练权重
model = VisionTransformer()
model.load_state_dict(torch.load('vision_transformer.pth'))

# 定义图片预处理
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[.485, .456, .406], std=[.229, .224, .225])
])

# 加载图片并进行预测
img = Image.open('test.png')
img = transform(img)
img = img.unsqueeze()
output = model(img)
pred = torch.argmax(output, dim=1).item()
print('预测结果:', pred)

这个代码可以将一个.png图片集进行分类，其中使用了Vision Transformer模型和预训练权重。你可以根据自己的需要修改代码中的参数和预处理方式。