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"""Causal convolusion layer modules.""" |
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import torch |
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class CausalConv1d(torch.nn.Module): |
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"""CausalConv1d module with customized initialization.""" |
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def __init__(self, in_channels, out_channels, kernel_size, |
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dilation=1, bias=True, pad="ConstantPad1d", pad_params={"value": 0.0}): |
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"""Initialize CausalConv1d module.""" |
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super(CausalConv1d, self).__init__() |
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self.pad = getattr(torch.nn, pad)((kernel_size - 1) * dilation, **pad_params) |
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self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size, |
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dilation=dilation, bias=bias) |
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def forward(self, x): |
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"""Calculate forward propagation. |
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Args: |
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x (Tensor): Input tensor (B, in_channels, T). |
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Returns: |
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Tensor: Output tensor (B, out_channels, T). |
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""" |
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return self.conv(self.pad(x))[:, :, :x.size(2)] |
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class CausalConvTranspose1d(torch.nn.Module): |
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"""CausalConvTranspose1d module with customized initialization.""" |
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def __init__(self, in_channels, out_channels, kernel_size, stride, bias=True): |
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"""Initialize CausalConvTranspose1d module.""" |
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super(CausalConvTranspose1d, self).__init__() |
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self.deconv = torch.nn.ConvTranspose1d( |
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in_channels, out_channels, kernel_size, stride, bias=bias) |
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self.stride = stride |
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def forward(self, x): |
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"""Calculate forward propagation. |
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Args: |
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x (Tensor): Input tensor (B, in_channels, T_in). |
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Returns: |
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Tensor: Output tensor (B, out_channels, T_out). |
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""" |
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return self.deconv(x)[:, :, :-self.stride] |
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