用python写一个informer模型的架构

以下是一个简单的Informer模型的Python代码实现：

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

class EncoderLayer(nn.Module):
    def __init__(self, input_size, hidden_size, num_heads):
        super(EncoderLayer, self).__init__()
        self.self_attn = nn.MultiheadAttention(hidden_size, num_heads)
        self.feed_forward = nn.Sequential(
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, hidden_size)
        )
        self.norm1 = nn.LayerNorm(hidden_size)
        self.norm2 = nn.LayerNorm(hidden_size)
        self.dropout1 = nn.Dropout(0.1)
        self.dropout2 = nn.Dropout(0.1)

    def forward(self, x):
        x2 = self.norm1(x)
        x = x + self.dropout1(self.self_attn(x2, x2, x2)[0])
        x2 = self.norm2(x)
        x = x + self.dropout2(self.feed_forward(x2))
        return x

class DecoderLayer(nn.Module):
    def __init__(self, input_size, hidden_size, num_heads):
        super(DecoderLayer, self).__init__()
        self.self_attn = nn.MultiheadAttention(hidden_size, num_heads)
        self.src_attn = nn.MultiheadAttention(hidden_size, num_heads)
        self.feed_forward = nn.Sequential(
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, hidden_size)
        )
        self.norm1 = nn.LayerNorm(hidden_size)
        self.norm2 = nn.LayerNorm(hidden_size)
        self.norm3 = nn.LayerNorm(hidden_size)
        self.dropout1 = nn.Dropout(0.1)
        self.dropout2 = nn.Dropout(0.1)
        self.dropout3 = nn.Dropout(0.1)

    def forward(self, x, enc_output):
        x2 = self.norm1(x)
        x = x + self.dropout1(self.self_attn(x2, x2, x2)[0])
        x2 = self.norm2(x)
        x = x + self.dropout2(self.src_attn(x2, enc_output, enc_output)[0])
        x2 = self.norm3(x)
        x = x + self.dropout3(self.feed_forward(x2))
        return x

class Informer(nn.Module):
    def __init__(self, input_size, output_size, enc_seq_len, dec_seq_len, factor=5, d_model=512, n_heads=8, n_layers=2):
        super(Informer, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.enc_seq_len = enc_seq_len
        self.dec_seq_len = dec_seq_len
        self.factor = factor
        self.d_model = d_model
        self.n_heads = n_heads
        self.n_layers = n_layers

        self.encoder_layers = nn.ModuleList([EncoderLayer(self.input_size, self.d_model, self.n_heads) for _ in range(self.n_layers)])
        self.decoder_layers = nn.ModuleList([DecoderLayer(self.output_size, self.d_model, self.n_heads) for _ in range(self.n_layers)])

        self.pos_encoder = nn.Linear(self.input_size, self.d_model)
        self.pos_decoder = nn.Linear(self.output_size, self.d_model)
        self.fc = nn.Linear(self.d_model, self.output_size)

    def forward(self, inputs):
        enc_inputs, dec_inputs = inputs[:, :self.enc_seq_len, :], inputs[:, self.enc_seq_len:, :]

        # Encoding phase
        enc_outputs = self.pos_encoder(enc_inputs)
        for layer in self.encoder_layers:
            enc_outputs = layer(enc_outputs)

        # Decoding phase
        dec_outputs = self.pos_decoder(dec_inputs)
        for layer in self.decoder_layers:
            dec_outputs = layer(dec_outputs, enc_outputs)

        # Final output
        outputs = self.fc(dec_outputs)
        return outputs

该模型包含了Informer模型的关键组件，包括编码器层、解码器层、位置编码、全连接层等。在编码器和解码器中，我们使用了多头自注意力和多头注意力机制，以捕捉序列中的时间和空间关系。我们还使用了残差连接和层归一化来缓解梯度消失问题。在训练过程中，我们使用了dropout来进一步缓解过拟合问题。

在模型的前向传播过程中，我们首先将输入序列分为编码器输入和解码器输入，然后对编码器输入进行位置编码和编码器层计算。接下来，在解码器中，我们对解码器输入进行位置编码和解码器层计算，并利用编码器输出和解码器输出之间的多头注意力来捕捉输入和输出之间的关系。最后，我们使用全连接层将输出映射到所需的输出形状。