README.md

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# PyTorch BinCount

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**PyTorch 0.4.1 now supports [`bincount`](https://pytorch.org/docs/stable/torch.html#torch.bincount) both for CPU and GPU.
Therefore, this package is no longer needed and will not be updated.**

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This package consists of a small extension library of a highly optimized `bincount` operation for the use in [PyTorch](http://pytorch.org/), 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 at least PyTorch 0.4.1 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__)"
>>> 0.4.1

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

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

Then run:

```
pip install torch-bincount
```

If you are running into any installation problems, please create an [issue](https://github.com/rusty1s/pytorch_bincount/issues).
Be sure to import `torch` first before using this package to resolve symbols the dynamic linker must see.

## Usage

```
torch_bincount.bincount(src, size=None) -> LongTensor
```

Counts the number of occurrences of each value in a non-negative tensor.

### Parameters

* **src** *(Tensor)* - The input tensor.
* **size** *(int, optional)* - The maximum number of bins for the output array. (default: `None`)

### Returns

* **out** *(LongTensor)* - The result of binning the input tensor.

### Example

```py
import torch
from torch_bincount import bincount

src = torch.tensor([2, 1, 1, 2, 4, 4, 2])
out = bincount(src)
```

```
print(out)
tensor([ 0,  2,  3,  0,  2])
```

## Running tests

```
python setup.py test
```