cnn_encoder.py 文件源码

def forward(self, tokens: torch.Tensor, mask: torch.Tensor):  # pylint: disable=arguments-differ
        if mask is not None:
            tokens = tokens * mask.unsqueeze(-1).float()

        # Our input is expected to have shape `(batch_size, num_tokens, embedding_dim)`.  The
        # convolution layers expect input of shape `(batch_size, in_channels, sequence_length)`,
        # where the conv layer `in_channels` is our `embedding_dim`.  We thus need to transpose the
        # tensor first.
        tokens = torch.transpose(tokens, 1, 2)
        # Each convolution layer returns output of size `(batch_size, num_filters, pool_length)`,
        # where `pool_length = num_tokens - ngram_size + 1`.  We then do an activation function,
        # then do max pooling over each filter for the whole input sequence.  Because our max
        # pooling is simple, we just use `torch.max`.  The resultant tensor of has shape
        # `(batch_size, num_conv_layers * num_filters)`, which then gets projected using the
        # projection layer, if requested.

        filter_outputs = [self._activation(convolution_layer(tokens)).max(dim=2)[0]
                          for convolution_layer in self._convolution_layers]

        # Now we have a list of `num_conv_layers` tensors of shape `(batch_size, num_filters)`.
        # Concatenating them gives us a tensor of shape `(batch_size, num_filters * num_conv_layers)`.
        maxpool_output = torch.cat(filter_outputs, dim=1) if len(filter_outputs) > 1 else filter_outputs[0]

        if self.projection_layer:
            result = self.projection_layer(maxpool_output)
        else:
            result = maxpool_output
        return result