光谱transformer代码

光谱Transformer是一种自然语言处理模型，它使用了一种称为光谱注意力的注意力机制。光谱注意力是在Transformer模型中引入的一种注意力机制，它可以帮助模型更好地处理长距离依赖关系。

以下是使用PyTorch实现光谱Transformer的示例代码：

import torch
import torch.nn as nn

class SpectralTransformer(nn.Module):
    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu"):
        super(SpectralTransformer, self).__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        # Position-wise feedforward network
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

        self.activation = _get_activation_fn(activation)

    def forward(self, src, src_mask=None, src_key_padding_mask=None):
        src2 = self.self_attn(src, src, src, attn_mask=src_mask,
                              key_padding_mask=src_key_padding_mask)[0]
        src = src + self.dropout1(src2)
        src = self.norm1(src)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
        src = src + self.dropout2(src2)
        src = self.norm2(src)
        return src

def _get_activation_fn(activation):
    if activation == "relu":
        return F.relu
    elif activation == "gelu":
        return F.gelu
    elif activation == "swish":
        return F.swish
    else:
        raise ValueError("Invalid activation function: {}".format(activation))

在这段代码中，SpectralTransformer类继承了PyTorch的nn.Module类，并实现了forward方法。forward方法定