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import torch.nn as nn
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from torch.nn import functional as F
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from .filter import LowPassFilter1d
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from .filter import kaiser_sinc_filter1d
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class UpSample1d(nn.Module):
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def __init__(self, ratio=2, kernel_size=None):
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super().__init__()
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self.ratio = ratio
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self.kernel_size = (
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int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
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)
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self.stride = ratio
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self.pad = self.kernel_size // ratio - 1
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self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
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self.pad_right = (
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self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
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)
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filter = kaiser_sinc_filter1d(
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cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size
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)
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self.register_buffer("filter", filter)
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def forward(self, x):
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_, C, _ = x.shape
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x = F.pad(x, (self.pad, self.pad), mode="replicate")
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x = self.ratio * F.conv_transpose1d(
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x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C
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)
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x = x[..., self.pad_left : -self.pad_right]
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return x
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class DownSample1d(nn.Module):
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def __init__(self, ratio=2, kernel_size=None):
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super().__init__()
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self.ratio = ratio
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self.kernel_size = (
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int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
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)
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self.lowpass = LowPassFilter1d(
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cutoff=0.5 / ratio,
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half_width=0.6 / ratio,
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stride=ratio,
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kernel_size=self.kernel_size,
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)
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def forward(self, x):
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xx = self.lowpass(x)
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return xx
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