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Right here is a dinky PyTorch-essentially based equipment which enables for atmosphere pleasurable batched operations, e.g. for computing distances with out having to slowly loop over all instance pairs of a batch of records.

After having encountered mulitple conditions of torch modules/methods promising to handling batches whereas most attention-grabbing returning a vector of pairwise results (discover about instance beneath) as an alternate of the paunchy matrix, this equipment serves as a tool to wrap such methods in shriek to return paunchy matrices (e.g. distance matrices) the utilize of lickety-split, batched operations (with out loops).

## Instance

First, let’s account for a customized distance aim that most attention-grabbing computes pair-wise distances for batches, so two batches of every 10 samples are
remodeled to a distance vector of shape (10,).

```>>> def dummy_distance(x,y):
"""
Right here is a dummy distance d which enables for a batch dimension
(bid with n conditions in a batch), however doesn't return the paunchy
n x n distance matrix however most attention-grabbing 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 paunchy n x n matrix with batched operations (no loops).

>>> import batchdist as bd
>>> batched = bd.BatchDistance(dummy_distance)

# generate records (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 dinky print, consult the incorporated examples.

## Set up

`\$ pip install batchdist`