model.py 文件源码

def forward(self, x, x_lens, ys, ys_lens, xys_idx):
        x = self.embeddings(x)
        h = self._encode_embed(x, x_lens)

        if self.batch_first:
            ys = ys.transpose(1, 0)
            ys_lens = ys_lens.transpose(1, 0)
            xys_idx = xys_idx.transpose(1, 0)

        logits_list = []

        for dec_idx, (y, y_lens, xy_idx) in enumerate(
                zip(ys, ys_lens, xys_idx)):
            h_dec = torch.index_select(h, 0, xy_idx)
            logits = self._decode(dec_idx, h_dec, y, y_lens)

            nil_batches = len(h_dec) - len(logits)
            if nil_batches:
                logits = pad_batch(logits, nil_batches, True)

            logits_list.append(logits.unsqueeze(0))

        logits = torch.cat(logits_list)

        if self.batch_first:
            logits = logits.transpose(1, 0)

        return logits, h