vit transformer代码

以下是一个简单的示例代码，用于实现Vision Transformer (ViT)的Transformer模型部分：

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


class PatchEmbedding(nn.Module):
    def __init__(self, image_size, patch_size, in_channels, embed_dim):
        super().__init__()
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_patches = (image_size // patch_size) ** 2
        self.patch_embedding = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        x = self.patch_embedding(x)
        x = x.flatten(2).transpose(1, 2)
        return x


class Transformer(nn.Module):
    def __init__(self, embed_dim, num_heads, num_layers, hidden_dim, dropout):
        super().__init__()
        self.encoder_layers = nn.ModuleList([
            nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads, dim_feedforward=hidden_dim, dropout=dropout)
            for _ in range(num_layers)
        ])

    def forward(self, x):
        for layer in self.encoder_layers:
            x = layer(x)
        return x


class ViT(nn.Module):
    def __init__(self, image_size, patch_size, in_channels, embed_dim, num_heads, num_layers, hidden_dim, num_classes):
        super().__init__()
        self.patch_embedding = PatchEmbedding(image_size, patch_size, in_channels, embed_dim)
        self.transformer = Transformer(embed_dim, num_heads, num_layers, hidden_dim, dropout=0.1)
        self.classifier = nn.Linear(embed_dim, num_classes)

    def forward(self, x):
        x = self.patch_embedding(x)
        x = self.transformer(x)
        x = x.mean(1)
        x = self.classifier(x)
        return x

这段代码定义了一个简单的Vision Transformer模型，包括PatchEmbedding模块、Transformer模块和ViT模型。你可以根据需要进行修改和扩展。请注意，此代码只包括Transformer的模型部分，有关数据加载和训练的部分需要根据具体任务进行实现。