from typing import Optional, Tuple
import torch
from .utils import broadcast
def scatter_sum(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
out: Optional[torch.Tensor] = None,
dim_size: Optional[int] = None) -> torch.Tensor:
index = broadcast(index, src, dim)
if out is None:
size = list(src.size())
if dim_size is not None:
size[dim] = dim_size
elif index.numel() == 0:
size[dim] = 0
else:
size[dim] = int(index.max()) + 1
out = torch.zeros(size, dtype=src.dtype, device=src.device)
return out.scatter_add_(dim, index, src)
else:
return out.scatter_add_(dim, index, src)
def scatter_add(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
out: Optional[torch.Tensor] = None,
dim_size: Optional[int] = None) -> torch.Tensor:
return scatter_sum(src, index, dim, out, dim_size)
def scatter_mul(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
out: Optional[torch.Tensor] = None,
dim_size: Optional[int] = None) -> torch.Tensor:
return torch.ops.torch_scatter.scatter_mul(src, index, dim, out, dim_size)
def scatter_mean(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
out: Optional[torch.Tensor] = None,
dim_size: Optional[int] = None) -> torch.Tensor:
out = scatter_sum(src, index, dim, out, dim_size)
dim_size = out.size(dim)
index_dim = dim
if index_dim < 0:
index_dim = index_dim + src.dim()
if index.dim() <= index_dim:
index_dim = index.dim() - 1
ones = torch.ones(index.size(), dtype=src.dtype, device=src.device)
count = scatter_sum(ones, index, index_dim, None, dim_size)
count[count < 1] = 1
count = broadcast(count, out, dim)
if out.is_floating_point():
out.true_divide_(count)
else:
out.div_(count, rounding_mode='floor')
return out
def scatter_min(
src: torch.Tensor, index: torch.Tensor, dim: int = -1,
out: Optional[torch.Tensor] = None,
dim_size: Optional[int] = None) -> Tuple[torch.Tensor, torch.Tensor]:
return torch.ops.torch_scatter.scatter_min(src, index, dim, out, dim_size)
def scatter_max(
src: torch.Tensor, index: torch.Tensor, dim: int = -1,
out: Optional[torch.Tensor] = None,
dim_size: Optional[int] = None) -> Tuple[torch.Tensor, torch.Tensor]:
return torch.ops.torch_scatter.scatter_max(src, index, dim, out, dim_size)
[docs]def scatter(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
out: Optional[torch.Tensor] = None, dim_size: Optional[int] = None,
reduce: str = "sum") -> torch.Tensor:
r"""
|
.. image:: https://raw.githubusercontent.com/rusty1s/pytorch_scatter/
master/docs/source/_figures/add.svg?sanitize=true
:align: center
:width: 400px
|
Reduces 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:`src` for dimensions outside of :attr:`dim` and by the
corresponding value in :attr:`index` for dimension :attr:`dim`.
The applied reduction is defined via the :attr:`reduce` argument.
Formally, if :attr:`src` and :attr:`index` are :math:`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 :math:`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 :math:`0` and
:math:`y - 1`, although no specific ordering of indices is required.
The :attr:`index` tensor supports broadcasting in case its dimensions do
not match with :attr:`src`.
For one-dimensional tensors with :obj:`reduce="sum"`, 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`.
.. note::
This operation is implemented via atomic operations on the GPU and is
therefore **non-deterministic** since the order of parallel operations
to the same value is undetermined.
For floating-point variables, this results in a source of variance in
the result.
:param src: The source tensor.
:param index: The indices of elements to scatter.
:param dim: The axis along which to index. (default: :obj:`-1`)
:param out: The destination tensor.
:param dim_size: 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
according to :obj:`index.max() + 1` is returned.
:param reduce: The reduce operation (:obj:`"sum"`, :obj:`"mul"`,
:obj:`"mean"`, :obj:`"min"` or :obj:`"max"`). (default: :obj:`"sum"`)
:rtype: :class:`Tensor`
.. code-block:: python
from torch_scatter import scatter
src = torch.randn(10, 6, 64)
index = torch.tensor([0, 1, 0, 1, 2, 1])
# Broadcasting in the first and last dim.
out = scatter(src, index, dim=1, reduce="sum")
print(out.size())
.. code-block::
torch.Size([10, 3, 64])
"""
if reduce == 'sum' or reduce == 'add':
return scatter_sum(src, index, dim, out, dim_size)
if reduce == 'mul':
return scatter_mul(src, index, dim, out, dim_size)
elif reduce == 'mean':
return scatter_mean(src, index, dim, out, dim_size)
elif reduce == 'min':
return scatter_min(src, index, dim, out, dim_size)[0]
elif reduce == 'max':
return scatter_max(src, index, dim, out, dim_size)[0]
else:
raise ValueError