The torchist package implements NumPy's histogram and histogramdd functions in PyTorch with CUDA support. The package also features implementations of ravel_multi_index, unravel_index and some useful functionals like entropy or kl_divergence.
The torchist package is available on PyPI, which means it is installable with pip.
pip install torchist
Alternatively, if you need the latest features, you can install it from the repository.
pip install git+https://github.com/francois-rozet/torchist
import torch
import torchist
x = torch.rand(100, 3).cuda()
hist = torchist.histogramdd(x, bins=10, low=0.0, upp=1.0)
print(hist.shape) # (10, 10, 10)The implementations of torchist are on par or faster than those of numpy on CPU and benefit greately from CUDA capabilities.
$ python torchist/__init__.py
CPU
---
np.histogram : 1.2559 s
np.histogramdd : 20.7816 s
np.histogram (non-uniform) : 5.4878 s
np.histogramdd (non-uniform) : 17.3757 s
torchist.histogram : 1.3975 s
torchist.histogramdd : 9.6160 s
torchist.histogram (non-uniform) : 5.0883 s
torchist.histogramdd (non-uniform) : 17.2743 s
CUDA
----
torchist.histogram : 0.1363 s
torchist.histogramdd : 0.3754 s
torchist.histogram (non-uniform) : 0.1355 s
torchist.histogramdd (non-uniform) : 0.5137 s