funasr_detach/models/mossformer/mossformer_decoder.py

import torch
import torch.nn as nn


class MossFormerDecoder(nn.ConvTranspose1d):
    """A decoder layer that consists of ConvTranspose1d.

    Arguments
    ---------
    kernel_size : int
        Length of filters.
    in_channels : int
        Number of  input channels.
    out_channels : int
        Number of output channels.


    Example
    ---------
    >>> x = torch.randn(2, 100, 1000)
    >>> decoder = Decoder(kernel_size=4, in_channels=100, out_channels=1)
    >>> h = decoder(x)
    >>> h.shape
    torch.Size([2, 1003])
    """

    def __init__(self, *args, **kwargs):
        super(MossFormerDecoder, self).__init__(*args, **kwargs)

    def forward(self, x):
        """Return the decoded output.

        Arguments
        ---------
        x : torch.Tensor
            Input tensor with dimensionality [B, N, L].
                where, B = Batchsize,
                       N = number of filters
                       L = time points
        """

        if x.dim() not in [2, 3]:
            raise RuntimeError("{} accept 3/4D tensor as input".format(self.__name__))
        x = super().forward(x if x.dim() == 3 else torch.unsqueeze(x, 1))

        if torch.squeeze(x).dim() == 1:
            x = torch.squeeze(x, dim=1)
        else:
            x = torch.squeeze(x)
        return x