一维数据输入到transformer网络的代码

以下是一维数据输入到Transformer网络的代码示例，假设输入数据维度为`(batch_size, seq_length, input_size)`：

import torch
import torch.nn as nn

class TransformerModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
        super(TransformerModel, self).__init__()
        self.embedding = nn.Linear(input_size, hidden_size)
        self.pos_encoder = PositionalEncoding(hidden_size, dropout)
        encoder_layer = nn.TransformerEncoderLayer(hidden_size, num_heads, hidden_size, dropout)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        # x shape: (batch_size, seq_length, input_size)
        x = self.embedding(x)
        x = self.pos_encoder(x)
        # x shape: (seq_length, batch_size, hidden_size)
        x = x.permute(1, 0, 2)
        # transformer_encoder requires input shape (seq_length, batch_size, hidden_size)
        x = self.transformer_encoder(x)
        # x shape: (seq_length, batch_size, hidden_size)
        x = x.permute(1, 0, 2)
        # x shape: (batch_size, seq_length, hidden_size)
        x = self.fc(x)
        # x shape: (batch_size, seq_length, output_size)
        return x

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

其中`PositionalEncoding`是用来添加位置编码的模块，`TransformerModel`是实现了Transformer网络的模型。在`forward`函数中，首先对输入数据做线性变换和位置编码，然后将维度`batch_size`和`seq_length`交换，输入到TransformerEncoder中进行处理，最后将维度交换回来并做一次线性变换得到输出。