https://github.com/francois-rozet/torchist

NumPy-style histograms in PyTorch
https://github.com/francois-rozet/torchist

histogram numpy pytorch

Last synced: about 2 months ago
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NumPy-style histograms in PyTorch

• Host: GitHub
• URL: https://github.com/francois-rozet/torchist
• Owner: francois-rozet
• License: mit
• Created: 2021-02-18T15:52:15.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2024-04-03T14:48:11.000Z (9 months ago)
• Last Synced: 2024-10-14T06:41:04.405Z (2 months ago)
• Topics: histogram, numpy, pytorch
• Language: Python
• Homepage: https://pypi.org/project/torchist
• Size: 29.3 KB
• Stars: 51
• Watchers: 5
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# NumPy-style histograms in PyTorch

The `torchist` package implements NumPy's [`histogram`](https://numpy.org/doc/stable/reference/generated/numpy.histogram.html) and [`histogramdd`](https://numpy.org/doc/stable/reference/generated/numpy.histogramdd.html) functions in PyTorch with CUDA support. The package also features implementations of [`ravel_multi_index`](https://numpy.org/doc/stable/reference/generated/numpy.ravel_multi_index.html), [`unravel_index`](https://numpy.org/doc/stable/reference/generated/numpy.unravel_index.html) and some useful functionals like `entropy` or `kl_divergence`.

## Installation

The `torchist` package is available on [PyPI](https://pypi.org/project/torchist), 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

```

## Getting Started

```python

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)

```

## Benchmark

The implementations of `torchist` are on par or faster than those of `numpy` on CPU and benefit greately from CUDA capabilities.

```console

$ 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

```