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import torch
def squeeze(x, nonpadding=None, n_sqz=2):
b, c, t = x.size()
t = (t // n_sqz) * n_sqz
x = x[:, :, :t]
x_sqz = x.view(b, c, t // n_sqz, n_sqz)
x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
if nonpadding is not None:
nonpadding = nonpadding[:, :, n_sqz - 1::n_sqz]
else:
nonpadding = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype)
return x_sqz * nonpadding, nonpadding
def unsqueeze(x, nonpadding=None, n_sqz=2):
b, c, t = x.size()
x_unsqz = x.view(b, n_sqz, c // n_sqz, t)
x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz)
if nonpadding is not None:
nonpadding = nonpadding.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz)
else:
nonpadding = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype)
return x_unsqz * nonpadding, nonpadding
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