pytorch调用transformer

以下是使用PyTorch调用Transformer的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import TransformerEncoder, TransformerEncoderLayer

# 定义TransformerEncoder模型
class TransformerModel(nn.Module):
    def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
        super(TransformerModel, self).__init__()
        self.model_type = 'Transformer'
        self.src_mask = None
        self.pos_encoder = PositionalEncoding(ninp, dropout)
        encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)
        self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
        self.encoder = nn.Embedding(ntoken, ninp)
        self.ninp = ninp
        self.decoder = nn.Linear(ninp, ntoken)

        self.init_weights()

    def _generate_square_subsequent_mask(self, sz):
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        return mask

    def init_weights(self):
        initrange = 0.1
        self.encoder.weight.data.uniform_(-initrange, initrange)
        self.decoder.bias.data.zero_()
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, src):
        if self.src_mask is None or self.src_mask.size(0) != len(src):
            device = src.device
            mask = self._generate_square_subsequent_mask(len(src)).to(device)
            self.src_mask = mask

        src = self.encoder(src) * math.sqrt(self.ninp)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src, self.src_mask)
        output = self.decoder(output)
        return output

在这个示例中，我们定义了一个名为TransformerModel的类，它继承自nn.Module。在__init__函数中，我们定义了Transformer Encoder的各个参数，包括输入和输出的维度，注意力头数，隐藏层维度，编码器层数等。在forward函数中，我们首先对输入进行编码，然后使用Transformer Encoder进行多头注意力机制和前向传播，最后输出结果。