espnet.nets.pytorch_backend.transformer.dynamic_conv2d.DynamicConvolution2D

source

class espnet.nets.pytorch_backend.transformer.dynamic_conv2d.DynamicConvolution2D(wshare, n_feat, dropout_rate, kernel_size, use_kernel_mask=False, use_bias=False)

Bases: Module

Dynamic 2-Dimensional Convolution layer.

This implementation is based on https://github.com/pytorch/fairseq/tree/master/fairseq

• Parameters:
  • wshare (int) – the number of kernel of convolution
  • n_feat (int) – the number of features
  • dropout_rate (float) – dropout_rate
  • kernel_size (int) – kernel size (length)
  • use_kernel_mask (bool) – Use causal mask or not for convolution kernel
  • use_bias (bool) – Use bias term or not.

Construct Dynamic 2-Dimensional Convolution layer.

forward(query, key, value, mask)

Forward of ‘Dynamic 2-Dimensional Convolution’.

This function takes query, key and value but uses only query. This is just for compatibility with self-attention layer (attention.py)

• Parameters:
  • query (torch.Tensor) – (batch, time1, d_model) input tensor
  • key (torch.Tensor) – (batch, time2, d_model) NOT USED
  • value (torch.Tensor) – (batch, time2, d_model) NOT USED
  • mask (torch.Tensor) – (batch, time1, time2) mask
• Returns: (batch, time1, d_model) output
• Return type: x (torch.Tensor)