models.py 文件源码

def forward(self, inp, hidden):
        emb = self.drop(self.encoder(inp))
        outp = self.bilstm(emb, hidden)[0]
        if self.pooling == 'mean':
            outp = torch.mean(outp, 0).squeeze()
        elif self.pooling == 'max':
            outp = torch.max(outp, 0)[0].squeeze()
        elif self.pooling == 'all' or self.pooling == 'all-word':
            outp = torch.transpose(outp, 0, 1).contiguous()
        return outp, emb