# batchdist

This is a small PyTorch-based package which allows for efficient batched operations, e.g. for computing distances without having to slowly loop over all instance pairs of a batch of data.

After having encountered mulitple instances of torch modules/methods promising to handling batches while only returning a vector of pairwise results (see example below) instead of the full matrix, this package serves as a tool to wrap such methods in order to return full matrices (e.g. distance matrices) using fast, batched operations (without loops).

## Example

First, let's define a custom distance function that only computes pair-wise distances for batches, so two batches of each 10 samples are converted to a distance vector of shape (10,).

```
>>> def dummy_distance(x,y):
"""
This is a dummy distance d which allows for a batch dimension
(say with n instances in a batch), but does not return the full
n x n distance matrix but only a n-dimensional vector of the
pair-wise distances d(x_i,y_i) for all i in (1,...,n).
"""
x_ = x.sum(axis=[1,2])
y_ = y.sum(axis=[1,2])
return x_ + y_
# batchdist wraps a torch module around this callable to compute
# the full n x n matrix with batched operations (no loops).
>>> import batchdist as bd
>>> batched = bd.BatchDistance(dummy_distance)
# generate data (two batches of 256 samples of dimension [4,3])
>>> x1 = torch.rand(256,4,3)
>>> x2 = torch.rand(256,4,3)
>>> out1 = batched(x1, x2) # distance matrix of shape [256,256]
```

For more details, consult the included examples.

## Installation

With poetry:

`$ poetry add batchdist`

With pip:

`$ pip install batchdist`