convolution.py 文件源码

def forward_batch(self, x, mask):
        """
        :param x: (batch, length, dim)
        :param mask: (batch, length, )
        :return: (batch, length, hidden_dim)
        """
        # conv_after_length = length - kernel + 2 * padding_size + 1
        new_x = x
        if self.padding_size > 0:
            # (padding_size + length + padding_size, dim)
            new_x = temporal_padding_3d(x, (self.padding_size, self.padding_size))
            # (batch, conv_after_length)
            mask = temporal_padding_mask(mask, kernel_size=self.kernel_size, padding_size=self.padding_size)
        elif self.padding_size == 0:
            # (batch, conv_after_length)
            mask = temporal_padding_mask(mask, kernel_size=self.kernel_size, padding_size=0)
        else:
            raise RuntimeError("Dilation Rate >= 0")
        # safe_x = temporal_padding_3d(x, (0, self.kernel_size - x.shape[1]))
        # safe_mask = T.ones((x.shape[0], ), dtype=theano.config.floatX).dimshuffle([0, 'x'])
        # !!! convert safe_mask from col to matrix
        # safe_mask = T.unbroadcast(safe_mask, 1)
        # x, mask = ifelse(T.gt(self.kernel_size - x.shape[1], 0),
        #                  (safe_x, safe_mask),
        #                  (new_x, mask))
        # (batch, conv_after_length, hidden_dim)
        conv_result = self.forward_conv_batch(new_x)
        # new_x = Print(new_x)
        # mask = Print()(mask)
        pooling_result = get_pooling_batch(conv_result, mask, self.pooling)
        dropout_out = dropout_from_layer(pooling_result, self.dropout)
        return self.act.activate(dropout_out + self.b)