Source code for diffsptk.modules.idst

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# Copyright 2022 SPTK Working Group                                        #
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import torch

from ..typing import Precomputed
from ..utils.private import check_size, filter_values
from .base import BaseFunctionalModule
from .dst import DiscreteSineTransform as DST




class InverseDiscreteSineTransform(BaseFunctionalModule):
    """This is the opposite module to :func:`~diffsptk.DiscreteSineTransform`.

    Parameters
    ----------
    dst_length : int >= 1
        The DST length, :math:`L`.

    dst_type : int in [1, 4]
        The DST type.

    device : torch.device or None
        The device of this module.

    dtype : torch.dtype or None
        The data type of this module.

    """

    def __init__(
        self,
        dst_length: int,
        dst_type: int = 2,
        device: torch.device | None = None,
        dtype: torch.dtype | None = None,
    ) -> None:
        super().__init__()

        self.in_dim = dst_length

        _, _, tensors = self._precompute(**filter_values(locals()))
        self.register_buffer("W", tensors[0])



    def forward(self, y: torch.Tensor) -> torch.Tensor:
        """Apply inverse DST to the input.

        Parameters
        ----------
        y : Tensor [shape=(..., L)]
            The input.

        Returns
        -------
        out : Tensor [shape=(..., L)]
            The inverse DST output.

        Examples
        --------
        >>> import diffsptk
        >>> dst = diffsptk.DST(4)
        >>> idst = diffsptk.IDST(4)
        >>> x = diffsptk.ramp(1, 4)
        >>> x2 = idst(dst(x))
        >>> x2
        tensor([1.0000, 2.0000, 3.0000, 4.0000])

        """
        check_size(y.size(-1), self.in_dim, "dimension of input")
        return self._forward(y, **self._buffers)


    @staticmethod
    def _func(y: torch.Tensor, *args, **kwargs) -> torch.Tensor:
        _, _, tensors = InverseDiscreteSineTransform._precompute(
            y.size(-1), *args, **kwargs, device=y.device, dtype=y.dtype
        )
        return InverseDiscreteSineTransform._forward(y, *tensors)

    @staticmethod
    def _takes_input_size() -> bool:
        return True

    @staticmethod
    def _check(*args, **kwargs) -> None:
        raise NotImplementedError

    @staticmethod
    def _precompute(
        dst_length: int,
        dst_type: int,
        device: torch.device | None,
        dtype: torch.dtype | None,
    ) -> Precomputed:
        type2type = {1: 1, 2: 3, 3: 2, 4: 4}
        return DST._precompute(
            dst_length, type2type[dst_type], device=device, dtype=dtype
        )

    @staticmethod
    def _forward(y: torch.Tensor, W: torch.Tensor) -> torch.Tensor:
        return torch.matmul(y, W)