ConvTransposed

ConvTransposed

Bases: Module

ConvTransposed applies a 1D convolution operation, with the main difference that it transposes the last two dimensions of the input tensor before and after applying the BSConv1d convolution operation. This can be useful in certain architectures where the tensor dimensions are processed in a different order.

The ConvTransposed class performs a BSConv operation after transposing the input tensor dimensions. Specifically, it swaps the channels and width dimensions of a tensor, applies the convolution, and then swaps the dimensions back to their original order. The intuition behind swapping dimensions can depend on the specific use case in the larger architecture; typically, it's used when the operation or sequence of operations expected a different arrangement of dimensions.

Parameters:

Name	Type	Description	Default
in_channels	int	Number of channels in the input	required
out_channels	int	Number of channels produced by the convolution	required
kernel_size	int	Size of the kernel used in convolution	1
padding	int	Zero-padding added around the input tensor along the width direction	0

Attributes:

Name	Type	Description
conv	BSConv1d	BSConv1d module to apply convolution.

Source code in models/tts/delightful_tts/conv_blocks/conv_transposed.py

class ConvTransposed(Module):
    r"""`ConvTransposed` applies a 1D convolution operation, with the main difference that it transposes the
    last two dimensions of the input tensor before and after applying the `BSConv1d` convolution operation.
    This can be useful in certain architectures where the tensor dimensions are processed in a different order.

    The `ConvTransposed` class performs a `BSConv` operation after transposing the input tensor dimensions. Specifically, it swaps the channels and width dimensions of a tensor, applies the convolution, and then swaps the dimensions back to their original order. The intuition behind swapping dimensions can depend on the specific use case in the larger architecture; typically, it's used when the operation or sequence of operations expected a different arrangement of dimensions.

    Args:
        in_channels (int): Number of channels in the input
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int): Size of the kernel used in convolution
        padding (int): Zero-padding added around the input tensor along the width direction

    Attributes:
        conv (BSConv1d): `BSConv1d` module to apply convolution.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int = 1,
        padding: int = 0,
    ):
        super().__init__()

        # Define BSConv1d convolutional layer
        self.conv = BSConv1d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            padding=padding,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward propagation method for the ConvTransposed layer.

        Args:
            x (torch.Tensor): input tensor

        Returns:
            x (torch.Tensor): output tensor after application of ConvTransposed
        """
        # Transpose the last two dimensions (dimension 1 and 2 here). Now the tensor has shape (N, W, C)
        x = x.contiguous().transpose(1, 2)

        # Apply BSConv1d convolution.
        x = self.conv(x)

        # Transpose the last two dimensions back to their original order. Now the tensor has shape (N, C, W)
        # Return final output tensor
        return x.contiguous().transpose(1, 2)

forward(x)

Forward propagation method for the ConvTransposed layer.

Parameters:

Name	Type	Description	Default
x	Tensor	input tensor	required

Returns:

Name	Type	Description
x	Tensor	output tensor after application of ConvTransposed

Source code in models/tts/delightful_tts/conv_blocks/conv_transposed.py

def forward(self, x: torch.Tensor) -> torch.Tensor:
    """Forward propagation method for the ConvTransposed layer.

    Args:
        x (torch.Tensor): input tensor

    Returns:
        x (torch.Tensor): output tensor after application of ConvTransposed
    """
    # Transpose the last two dimensions (dimension 1 and 2 here). Now the tensor has shape (N, W, C)
    x = x.contiguous().transpose(1, 2)

    # Apply BSConv1d convolution.
    x = self.conv(x)

    # Transpose the last two dimensions back to their original order. Now the tensor has shape (N, C, W)
    # Return final output tensor
    return x.contiguous().transpose(1, 2)