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import torch.nn as nn |
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from .resample import * |
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class Activation1d(nn.Module): |
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def __init__( |
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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, |
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): |
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super().__init__() |
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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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