parsing.py 文件源码

python
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项目:NeuroNLP2 作者: XuezheMax 项目源码 文件源码
def _get_encoder_output(self, input_word, input_char, input_pos, mask_e=None, length_e=None, hx=None):
        # [batch, length, word_dim]
        word = self.word_embedd(input_word)
        # [batch, length, pos_dim]
        pos = self.pos_embedd(input_pos)

        # [batch, length, char_length, char_dim]
        char = self.char_embedd(input_char)
        char_size = char.size()
        # first transform to [batch *length, char_length, char_dim]
        # then transpose to [batch * length, char_dim, char_length]
        char = char.view(char_size[0] * char_size[1], char_size[2], char_size[3]).transpose(1, 2)
        # put into cnn [batch*length, char_filters, char_length]
        # then put into maxpooling [batch * length, char_filters]
        char, _ = self.conv1d(char).max(dim=2)
        # reshape to [batch, length, char_filters]
        char = torch.tanh(char).view(char_size[0], char_size[1], -1)

        # apply dropout on input
        word = self.dropout_in(word)
        pos = self.dropout_in(pos)
        char = self.dropout_in(char)

        # concatenate word and char [batch, length, word_dim+char_filter]
        src_encoding = torch.cat([word, char, pos], dim=2)
        # output from rnn [batch, length, hidden_size]
        output, hn = self.encoder(src_encoding, mask_e, hx=hx)

        # apply dropout
        # [batch, length, hidden_size] --> [batch, hidden_size, length] --> [batch, length, hidden_size]
        output = self.dropout_out(output.transpose(1, 2)).transpose(1, 2)

        return src_encoding, output, hn, mask_e, length_e
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