models.py 文件源码

def forward(self, inp, hidden):
        outp = self.bilstm.forward(inp, hidden)[0]
        size = outp.size()  # [bsz, len, nhid]
        compressed_embeddings = outp.view(-1, size[2])  # [bsz*len, nhid*2]
        transformed_inp = torch.transpose(inp, 0, 1).contiguous()  # [bsz, len]
        transformed_inp = transformed_inp.view(size[0], 1, size[1])  # [bsz, 1, len]
        concatenated_inp = [transformed_inp for i in range(self.attention_hops)]
        concatenated_inp = torch.cat(concatenated_inp, 1)  # [bsz, hop, len]

        hbar = self.tanh(self.ws1(self.drop(compressed_embeddings)))  # [bsz*len, attention-unit]
        alphas = self.ws2(hbar).view(size[0], size[1], -1)  # [bsz, len, hop]
        alphas = torch.transpose(alphas, 1, 2).contiguous()  # [bsz, hop, len]
        penalized_alphas = alphas + (
            -10000 * (concatenated_inp == self.dictionary.word2idx['<pad>']).float())
            # [bsz, hop, len] + [bsz, hop, len]
        alphas = self.softmax(penalized_alphas.view(-1, size[1]))  # [bsz*hop, len]
        alphas = alphas.view(size[0], self.attention_hops, size[1])  # [bsz, hop, len]
        return torch.bmm(alphas, outp), alphas