dialog_model.py 文件源码

def score_sent(self, sent, lang_h, ctx_h, temperature):
        """Computes likelihood of a given sentence."""
        score = 0
        # remove batch dimension from the language and context hidden states
        lang_h = lang_h.squeeze(1)
        ctx_h = ctx_h.squeeze(1)
        inpt = Variable(torch.LongTensor(1))
        inpt.data.fill_(self.word_dict.get_idx('YOU:'))
        inpt = self.to_device(inpt)
        lang_hs = []

        for word in sent:
            # add the context to the word embedding
            inpt_emb = torch.cat([self.word_encoder(inpt), ctx_h], 1)
            # update RNN state with last word
            lang_h = self.writer(inpt_emb, lang_h)
            lang_hs.append(lang_h)

            # decode words using the inverse of the word embedding matrix
            out = self.decoder(lang_h)
            scores = F.linear(out, self.word_encoder.weight).div(temperature)
            # subtract constant to avoid overflows in exponentiation
            scores = scores.add(-scores.max().data[0]).squeeze(0)

            mask = Variable(self.special_token_mask)
            scores = scores.add(mask)

            logprob = F.log_softmax(scores)
            score += logprob[word[0]].data[0]
            inpt = Variable(word)

        # update the hidden state with the <eos> token
        inpt_emb = torch.cat([self.word_encoder(inpt), ctx_h], 1)
        lang_h = self.writer(inpt_emb, lang_h)
        lang_hs.append(lang_h)

        # add batch dimension back
        lang_h = lang_h.unsqueeze(1)

        return score, lang_h, torch.cat(lang_hs)