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| 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
|
|
