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# Copyright 2022 SPTK Working Group #
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# http://www.apache.org/licenses/LICENSE-2.0 #
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import torch
import torch.nn.functional as F
from ..typing import Precomputed
from ..utils.private import filter_values
from .base import BaseFunctionalModule
[docs]
class LinearInterpolation(BaseFunctionalModule):
"""Perform linear interpolation.
Note that this is not related to `linear_intpl` in the C/C++ version of SPTK, but
is instead intended for interpolation of filter coefficients in digital filtering.
Parameters
----------
upsampling_factor : int >= 1
The upsampling factor, :math:`P`.
"""
def __init__(self, upsampling_factor: int) -> None:
super().__init__()
self.values = self._precompute(**filter_values(locals()))
[docs]
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Interpolate filter coefficients.
Parameters
----------
x : Tensor [shape=(B, N, D) or (N, D) or (N,)]
The filter coefficients.
Returns
-------
out : Tensor [shape=(B, NxP, D) or (NxP, D) or (NxP,)]
The upsampled filter coefficients.
Examples
--------
>>> import diffsptk
>>> linear_intpl = diffsptk.LinearInterpolation(2)
>>> x = diffsptk.ramp(2)
>>> x
tensor([0., 1., 2.])
>>> y = linear_intpl(x)
>>> y
tensor([0.0000, 0.5000, 1.0000, 1.5000, 2.0000, 2.0000])
"""
return self._forward(x, *self.values)
@staticmethod
def _func(x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
values = LinearInterpolation._precompute(*args, **kwargs)
return LinearInterpolation._forward(x, *values)
@staticmethod
def _takes_input_size() -> bool:
return False
@staticmethod
def _check(upsampling_factor: int) -> None:
if upsampling_factor <= 0:
raise ValueError("The upsampling factor must be positive.")
@staticmethod
def _precompute(upsampling_factor: int) -> Precomputed:
LinearInterpolation._check(upsampling_factor)
return (upsampling_factor,)
@staticmethod
def _forward(x: torch.Tensor, upsampling_factor: int) -> torch.Tensor:
if upsampling_factor == 1:
return x
d = x.dim()
if d == 1:
x = x.view(1, -1, 1)
elif d == 2:
x = x.unsqueeze(0)
if x.dim() != 3:
raise ValueError("Input must be 1D, 2D, or 3D tensor.")
B, N, D = x.shape
x = x.transpose(-2, -1) # (B, D, N)
x = F.pad(x, (0, 1), mode="replicate")
x = F.interpolate(
x,
size=N * upsampling_factor + 1,
mode="linear",
align_corners=True,
)[..., :-1] # Remove the padded value.
y = x.transpose(-2, -1)
if d == 1:
y = y.view(-1)
elif d == 2:
y = y.squeeze(0)
return y
