from torch_scatter.utils.gen import gen
[docs]def scatter_add(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
r"""
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.. image:: https://raw.githubusercontent.com/rusty1s/pytorch_scatter/
master/docs/source/_figures/add.svg?sanitize=true
:align: center
:width: 400px
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Sums all values from the :attr:`src` tensor into :attr:`out` at the indices
specified in the :attr:`index` tensor along a given axis :attr:`dim`. For
each value in :attr:`src`, its output index is specified by its index in
:attr:`input` for dimensions outside of :attr:`dim` and by the
corresponding value in :attr:`index` for dimension :attr:`dim`. If
multiple indices reference the same location, their **contributions add**.
Formally, if :attr:`src` and :attr:`index` are n-dimensional tensors with
size :math:`(x_0, ..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})` and
:attr:`dim` = `i`, then :attr:`out` must be an n-dimensional tensor with
size :math:`(x_0, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})`. Moreover, the
values of :attr:`index` must be between `0` and `out.size(dim) - 1`.
Both :attr:`src` and :attr:`index` are broadcasted in case their dimensions
do not match.
For one-dimensional tensors, the operation computes
.. math::
\mathrm{out}_i = \mathrm{out}_i + \sum_j \mathrm{src}_j
where :math:`\sum_j` is over :math:`j` such that
:math:`\mathrm{index}_j = i`.
Args:
src (Tensor): The source tensor.
index (LongTensor): The indices of elements to scatter.
dim (int, optional): The axis along which to index.
(default: :obj:`-1`)
out (Tensor, optional): The destination tensor. (default: :obj:`None`)
dim_size (int, optional): If :attr:`out` is not given, automatically
create output with size :attr:`dim_size` at dimension :attr:`dim`.
If :attr:`dim_size` is not given, a minimal sized output tensor is
returned. (default: :obj:`None`)
fill_value (int, optional): If :attr:`out` is not given, automatically
fill output tensor with :attr:`fill_value`. (default: :obj:`0`)
:rtype: :class:`Tensor`
.. testsetup::
import torch
.. testcode::
from torch_scatter import scatter_add
src = torch.Tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]])
index = torch.tensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]])
out = src.new_zeros((2, 6))
out = scatter_add(src, index, out=out)
print(out)
.. testoutput::
tensor([[0., 0., 4., 3., 3., 0.],
[2., 4., 4., 0., 0., 0.]])
"""
src, out, index, dim = gen(src, index, dim, out, dim_size, fill_value)
return out.scatter_add_(dim, index, src)