| |
|
| |
|
| | import torch.nn as nn
|
| | from .resample import UpSample1d, DownSample1d
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| |
|
| |
|
| | class Activation1d(nn.Module):
|
| | def __init__(
|
| | self,
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| | activation,
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| | up_ratio: int = 2,
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| | down_ratio: int = 2,
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| | up_kernel_size: int = 12,
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| | down_kernel_size: int = 12,
|
| | ):
|
| | super().__init__()
|
| | self.up_ratio = up_ratio
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| | self.down_ratio = down_ratio
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| | self.act = activation
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| | self.upsample = UpSample1d(up_ratio, up_kernel_size)
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| | self.downsample = DownSample1d(down_ratio, down_kernel_size)
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| |
|
| |
|
| | def forward(self, x):
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| | x = self.upsample(x)
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| | x = self.act(x)
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| | x = self.downsample(x)
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| |
|
| | return x
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| |
|