Awesome
torchinterp1d
CUDA 1-D interpolation for Pytorch
Requires PyTorch >= 1.6 (due to torch.searchsorted).
Presentation
This repository implements an interp1d
function that overrides torch.autograd.Function, enabling
linear 1D interpolation on the GPU for Pytorch.
def interp1d(x, y, xnew, out=None)
This function returns interpolated values of a set of 1-D functions at the desired query points xnew
.
It works similarly to Matlabâ„¢ or scipy functions with
the linear
interpolation mode on, except that it parallelises over any number of desired interpolation problems and exploits CUDA on the GPU
Parameters for interp1d
-
x
: a (N, ) or (D, N) Pytorch Tensor: Either 1-D or 2-D. It contains the coordinates of the observed samples. -
y
: (N,) or (D, N) Pytorch Tensor. Either 1-D or 2-D. It contains the actual values that correspond to the coordinates given byx
. The length ofy
along its last dimension must be the same as that ofx
-
xnew
: (P,) or (D, P) Pytorch Tensor. Either 1-D or 2-D. If it is not 1-D, its length along the first dimension must be the same as that of whicheverx
andy
is 2-D. x-coordinates for which we want the interpolated output. -
out
: (D, P) Pytorch Tensor` Tensor for the output. If None: allocated automatically.
Results
a Pytorch tensor of shape (D, P), containing the interpolated values.
Installation
Type pip install -e .
in the root folder of this repo.
Usage
Basically simply calle torchinterp1d.interp1d
.
Try out python test.py
in the examples
folder.
Solving 100000 interpolation problems: each with 100 observations and 30 desired values
CPU: 8060.260ms, GPU: 70.735ms, error: 0.000000%.