Class TorchCausalConv1d

numpy_ml/tests/nn_torch_models.py:541–575

https://github.com/pytorch/pytorch/issues/1333 NB: this is only ensures that the convolution out length is the same as the input length IFF stride = 1. Otherwise, in/out lengths will differ.

class TorchCausalConv1d(torch.nn.Conv1d):
    """https://github.com/pytorch/pytorch/issues/1333

    NB: this is only ensures that the convolution out length is the same as
    the input length IFF stride = 1. Otherwise, in/out lengths will differ.
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        dilation=1,
        groups=1,
        bias=True,
    ):
        self.__padding = (kernel_size - 1) * dilation

        super(TorchCausalConv1d, self).__init__(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=self.__padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
        )

    def forward(self, input):
        result = super(TorchCausalConv1d, self).forward(input)
        if self.__padding != 0:
            return result[:, :, : -self.__padding]
        return result


class TorchWavenetModule(nn.Module):