PyTorch Unique

PyTorch 0.4.1 now supports unique both for CPU and GPU. Therefore, this package is no longer needed and will not be updated. In contrast to this package, PyTorch's version does not return an index array. However, you can easily generate it by using the following code:

import torch

unique, inverse = torch.unique(x, sorted=True, return_inverse=True)
perm = torch.arange(inverse.size(0), dtype=inverse.dtype, device=inverse.device)
inverse, perm = inverse.flip([0]), perm.flip([0])
perm = inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)

This package consists of a small extension library of a highly optimized unique operation for the use in PyTorch, which is missing in the main package. The operation works on varying data types and is implemented both for CPU and GPU.

Installation

Ensure that PyTorch 0.4.0 is installed and verify that cuda/bin and cuda/include are in your $PATH and $CPATH respectively, e.g.:

$ python -c "import torch; print(torch.__version__)"
>>> 0.4.0

$ echo $PATH
>>> /usr/local/cuda/bin:...

$ echo $CPATH
>>> /usr/local/cuda/include:...

Then run:

pip install torch-unique

If you are running into any installation problems, please create an issue. Be sure to import torch first before using this package to resolve symbols the dynamic linker must see.

Usage

torch_unique.unique(src) -> (Tensor, LongTensor)

Returns the sorted unique scalar elements of the input tensor as an one-dimensional tensor.

A tuple of (unique_tensor, unique_indices) is returned, where the unique_indices are the indices of the elements in the original input tensor. Note that unique_indices is not guaranteed to be stable on GPU.

Parameters

• src (Tensor) - The input tensor.

Returns

• out (Tensor) - The unique elements from src as an one-dimensional tensor.
• perm (LongTensor) - The unique indices from src as an one-dimensional tensor.

Example

import torch
from torch_unique import unique

src = torch.tensor([100, 10, 100, 1, 1000, 1, 1000, 1])
out, perm = unique(src)

print(out)
tensor([    1,    10,   100,  1000])
print(perm)
tensor([ 3,  1,  0,  4])

Running tests

python setup.py test