[email protected] 输入为torch.Size([1, 48, 56, 56])使用patch embedding后输入transformer结构输出为torch.Size([1, 96, 28, 28])show me code

好的，以下是代码示例：

import torch
import torch.nn as nn

class PatchEmbedding(nn.Module):
    def __init__(self, patch_size, in_channels, emb_size):
        super().__init__()
        self.patch_embedding = nn.Conv2d(in_channels, emb_size, 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 TransformerBlock(nn.Module):
    def __init__(self, emb_size, num_heads, dropout_ratio):
        super().__init__()
        self.attention = nn.MultiheadAttention(emb_size, num_heads)
        self.norm1 = nn.LayerNorm(emb_size)
        self.norm2 = nn.LayerNorm(emb_size)
        self.dropout = nn.Dropout(dropout_ratio)
        self.feed_forward = nn.Sequential(
            nn.Linear(emb_size, 4 * emb_size),
            nn.ReLU(),
            nn.Linear(4 * emb_size, emb_size)
        )
        
    def forward(self, x):
        residual = x
        x = self.norm1(x)
        x, _ = self.attention(x, x, x)
        x = self.dropout(x)
        x = residual + x
        residual = x
        x = self.norm2(x)
        x = self.feed_forward(x)
        x = self.dropout(x)
        x = residual + x
        return x

class Transformer(nn.Module):
    def __init__(self, patch_size, in_channels, emb_size, num_heads, num_layers, dropout_ratio, image_size):
        super().__init__()
        assert (image_size % patch_size) == 0, 'Image size must be divisible by patch size'
        num_patches = (image_size // patch_size) ** 2
        self.patch_embedding = PatchEmbedding(patch_size, in_channels, emb_size)
        self.transformer_blocks = nn.ModuleList([TransformerBlock(emb_size, num_heads, dropout_ratio) for _ in range(num_layers)])
        self.fc = nn.Linear(emb_size * num_patches, 2)
        
    def forward(self, x):
        x = self.patch_embedding(x)
        for transformer_block in self.transformer_blocks:
            x = transformer_block(x)
        x = x.flatten(1)
        x = self.fc(x)
        return x

# example usage
model = Transformer(patch_size=14, in_channels=48, emb_size=48, num_heads=2, num_layers=2, dropout_ratio=0.1, image_size=56)
input_tensor = torch.randn(1, 48, 56, 56)
output_tensor = model(input_tensor)
print(output_tensor.shape) # should output torch.Size([1, 2])

这个代码示例中，我们首先定义了一个 PatchEmbedding 模块，它将输入的图像分割成若干个 patch，然后将每个 patch 中的像素值拉成一个向量，最终将这些向量拼接在一起，形成一个 (batch_size, num_patches, emb_size) 的张量作为 transformer 的输入。

接着，我们定义了一个 TransformerBlock 模块，它包含一个 multi-head attention 层和一个 feed-forward 层，以及 layer normalization 和 dropout 操作。我们将多个 TransformerBlock 模块拼接在一起，形成一个完整的 transformer，用于处理 patch embedding 后的图像。

最后，我们在 transformer 输出的特征向量上接了一个全连接层，将其映射到两个输出类别上。

我们使用这些模块构建了一个 Transformer 类，