写一段VIT模型的pytorch代码

好的，以下是使用PyTorch实现Vision Transformer（VIT）模型的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ViT(nn.Module):
    def __init__(self, img_size, patch_size, emb_size, num_heads, num_layers, num_classes):
        super(ViT, self).__init__()

        self.patch_size = patch_size
        self.num_patches = (img_size // patch_size) ** 2
        self.proj = nn.Conv2d(3, emb_size, kernel_size=patch_size, stride=patch_size)
        self.cls_token = nn.Parameter(torch.randn(1, 1, emb_size))
        self.pos_emb = nn.Parameter(torch.randn(1, self.num_patches + 1, emb_size))
        self.blocks = nn.ModuleList([TransformerBlock(emb_size, num_heads) for _ in range(num_layers)])
        self.mlp_head = nn.Sequential(
            nn.LayerNorm(emb_size),
            nn.Linear(emb_size, num_classes)
        )

    def forward(self, x):
        x = self.proj(x)
        x = x.flatten(2).transpose(1, 2)
        cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x += self.pos_emb[:, :(self.num_patches + 1)]
        for block in self.blocks:
            x = block(x)
        x = x.mean(dim=1)
        x = self.mlp_head(x)
        return x

class TransformerBlock(nn.Module):
    def __init__(self, emb_size, num_heads, mlp_ratio=4.0):
        super(TransformerBlock, self).__init__()

        self.attention = nn.MultiheadAttention(emb_size, num_heads)
        self.mlp = nn.Sequential(
            nn.Linear(emb_size, int(emb_size * mlp_ratio)),
            nn.GELU(),
            nn.Linear(int(emb_size * mlp_ratio), emb_size),
            nn.Dropout(0.1)
        )
        self.norm1 = nn.LayerNorm(emb_size)
        self.norm2 = nn.LayerNorm(emb_size)

    def forward(self, x):
        residual = x
        x = self.norm1(x)
        x = x.permute(1, 0, 2)
        x, _ = self.attention(x, x, x)
        x = x.permute(1, 0, 2)
        x += residual
        residual = x
        x = self.norm2(x)
        x = self.mlp(x)
        x += residual
        return x