espnet2.enh.layers.dc_crn.GluConvTranspose2d

source

class espnet2.enh.layers.dc_crn.GluConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding=0, output_padding=(0, 0))

Bases: Module

ConvTranspose2d with Gated Linear Units (GLU).

This layer applies a transposed convolution operation followed by a gated linear unit activation. The input and output shapes are the same as regular ConvTranspose2d layers.

Reference: Section III-B in [1]

Args: : in_channels (int): Number of input channels. out_channels (int): Number of output channels. kernel_size (int/tuple): Kernel size in ConvTranspose2d. stride (int/tuple): Stride size in ConvTranspose2d. padding (int/tuple): Padding size in ConvTranspose2d. output_padding (int/tuple): Additional size added to one side of each <br/>

dimension in the output shape.

ConvTranspose2d with Gated Linear Units (GLU).

Input and output shapes are the same as regular ConvTranspose2d layers.

Reference: Section III-B in [1]

• Parameters:
  • in_channels (int) – number of input channels
  • out_channels (int) – number of output channels
  • kernel_size (int/tuple) – kernel size in ConvTranspose2d
  • stride (int/tuple) – stride size in ConvTranspose2d
  • padding (int/tuple) – padding size in ConvTranspose2d
  • output_padding (int/tuple) – Additional size added to one side of each dimension in the output shape

forward(x)

ConvTranspose2d with Gated Linear Units (GLU).

Input and output shapes are the same as regular ConvTranspose2d layers.

Reference: Section III-B in [1]

• Parameters:
  • in_channels (int) – number of input channels.
  • out_channels (int) – number of output channels.
  • kernel_size (int/tuple) – kernel size in ConvTranspose2d.
  • stride (int/tuple) – stride size in ConvTranspose2d.
  • padding (int/tuple) – padding size in ConvTranspose2d.
  • output_padding (int/tuple) – Additional size added to one side of each dimension in the output shape.