This package implements interpolation routines in PyTorch,
making them GPU-capable and differentiable.
The only interpolation routine supported so far is RegularGridInterpolator, from scipy.
Install the preview of PyTorch here: https://pytorch.org/. Ensure that you have torch version >= 1.7.0. Then navigate to the main directory of this repository and run:
$ python setup.py install
First, you construct a torch_interpolations.RegularGridInterpolator object by supplying
it points (a list of torch Tensors) and values (a torch Tensor):
rgi = torch_interpolations.RegularGridInterpolator(points, values)
Then, to interpolate a set of points, you run:
rgi(points_to_interp)
where points_to_interp is a list of torch Tensors, each with the same shape.
First, install pytest:
pip install pytest
Then, from the main directory, run:
pytest .
To run the examples, first install the dependencies:
pip install scipy matplotlib
Then navigate to the examples folder.
The examples are the basic example:
python basic_example.py
and the two-dimensional example:
python two_dimensional.py
We include a performance script in the perf/ folder. To run it, navigate to the perf/ folder, and run:
python perf.py
It compares the torch CPU and GPU implementations, and the scipy CPU implementation.
The test case involves interpolating 4.5 million points on a 300 x 300 grid.
The timing results on my machine (Intel i9-9980XE and Nvidia GeForce 1080 TI) are:
Interpolating 4500000 points on 300 by 300 grid
PyTorch took 146.812 +\- 7.167 ms
PyTorch Cuda took 14.799 +\- 6.816 ms
Scipy took 545.935 +\- 1.089 ms
So the torch GPU implementation is 20 times faster than the torch CPU implementation, which itself is twice as fast as the scipy implementation.
torch_interpolations carries an Apache 2.0 license.