Source code for diffsptk.modules.idct

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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, get_values
from .base import BaseFunctionalModule
from .dct import DiscreteCosineTransform as DCT




class InverseDiscreteCosineTransform(BaseFunctionalModule):
    """See `this page <https://sp-nitech.github.io/sptk/latest/main/idct.html>`_
    for details.

    Parameters
    ----------
    dct_length : int >= 1
        The DCT length, :math:`L`.

    dct_type : int in [1, 4]
        The DCT type.

    """

    def __init__(self, dct_length: int, dct_type: int = 2) -> None:
        super().__init__()

        self.in_dim = dct_length

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



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

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

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

        Examples
        --------
        >>> x = diffsptk.ramp(3)
        >>> dct = diffsptk.DCT(4)
        >>> idct = diffsptk.IDCT(4)
        >>> x2 = idct(dct(x))
        >>> x2
        tensor([-4.4703e-08,  1.0000e+00,  2.0000e+00,  3.0000e+00])

        """
        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 = InverseDiscreteCosineTransform._precompute(
            y.size(-1), *args, **kwargs, device=y.device, dtype=y.dtype
        )
        return InverseDiscreteCosineTransform._forward(y, *tensors)

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

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

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

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