transformer图像分割代码

以下是一个基于transformer的图像分割代码示例：

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

class Transformer(nn.Module):
    def __init__(self, in_channels, out_channels, num_heads, hidden_dim, num_layers):
        super(Transformer, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.num_heads = num_heads
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        
        self.transformer_encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=in_channels, nhead=num_heads, dim_feedforward=hidden_dim), 
            num_layers=num_layers)
        
        self.conv1 = nn.Conv2d(in_channels, hidden_dim, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(hidden_dim, out_channels, kernel_size=1)
        
    def forward(self, x):
        # input shape: [batch_size, in_channels, height, width]
        
        # apply 3x3 convolution
        x = F.relu(self.conv1(x))
        
        # reshape to fit transformer input shape
        x = x.permute(2, 3, 0, 1) # [height, width, batch_size, hidden_dim]
        x = x.reshape(x.shape[0]*x.shape[1], x.shape[2], x.shape[3]) # [height*width, batch_size, hidden_dim]
        
        # apply transformer
        x = self.transformer_encoder(x)
        
        # reshape to fit convolutional output shape
        x = x.reshape(x.shape[0]//self.num_heads, self.num_heads, x.shape[1], x.shape[2]) # [height*width, num_heads, batch_size//num_heads, hidden_dim//num_heads]
        x = x.permute(2, 3, 0, 1) # [batch_size//num_heads, hidden_dim//num_heads, height, width, num_heads]
        x = x.reshape(x.shape[0], x.shape[1], x.shape[2], x.shape[3]*x.shape[4]) # [batch_size//num_heads, hidden_dim//num_heads, height, width*num_heads]
        
        # apply 1x1 convolution
        x = self.conv2(x)
        
        return x

该模型包含一个Transformer编码器和两个卷积层，以将输入图像转换为分割掩码。在前向传递期间，输入图像首先通过一个3x3卷积层，然后转换为适合Transformer输入形状的张量。接下来，将该张量输入Transformer编码器进行变换。最后，将输出张量转换为适合卷积层的形状，并通过1x1卷积层生成分割掩码。

使用此代码示例时，您应该将其与其他训练代码和数据集集成，并根据您的实际需求进行调整。