transformer 做图像增强的代码

Transformer 在图像处理中的应用主要是通过自注意力机制来获取图像中各个位置之间的关联信息，进而进行图像增强、图像修复等任务。以下是一份基于 PyTorch 实现的图像增强代码示例：

import torch
import torch.nn as nn

class TransformerEncoder(nn.Module):
    def __init__(self, in_channels, num_heads, hidden_dim):
        super(TransformerEncoder, self).__init__()
        self.norm1 = nn.LayerNorm(in_channels)
        self.attn = nn.MultiheadAttention(in_channels, num_heads)
        self.norm2 = nn.LayerNorm(in_channels)
        self.mlp = nn.Sequential(
            nn.Linear(in_channels, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, in_channels),
        )

    def forward(self, x):
        # Input shape: (batch_size, channels, height, width)
        residual = x
        x = self.norm1(x)
        x, _ = self.attn(x, x, x)
        x += residual
        x = self.norm2(x)
        x = self.mlp(x)
        x += residual
        return x

class ImageTransformer(nn.Module):
    def __init__(self, in_channels, num_heads, hidden_dim, num_layers):
        super(ImageTransformer, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, hidden_dim, kernel_size=3, padding=1),
            nn.ReLU(),
        )
        self.transformer_layers = nn.ModuleList()
        for _ in range(num_layers):
            self.transformer_layers.append(
                TransformerEncoder(hidden_dim, num_heads, hidden_dim)
            )
        self.norm = nn.LayerNorm(hidden_dim)
        self.out_conv = nn.Conv2d(hidden_dim, in_channels, kernel_size=3, padding=1)

    def forward(self, x):
        x = self.conv(x)
        for layer in self.transformer_layers:
            x = layer(x)
        x = self.norm(x)
        x = self.out_conv(x)
        return x

在使用时，可以将输入的图像作为模型的输入，然后输出增强后的图像。例如：

# Input shape: (batch_size, channels, height, width)
x = torch.randn(1, 3, 256, 256)
model = ImageTransformer(in_channels=3, num_heads=8, hidden_dim=64, num_layers=6)
output = model(x)

这里的 `ImageTransformer` 模型包含了若干个 `TransformerEncoder` 层，每个层都可以通过自注意力机制获取图像中各个位置之间的关联信息，从而进行图像增强。最后通过一个卷积层将增强后的图像输出，输出的尺寸和输入的尺寸相同。