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nn.dense_mincut_pool Add temperature parameter to mincut pool operator #5908

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merged 9 commits into from
Nov 7, 2022
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nn.dense_mincut_pool Add temperature parameter to mincut pool operator #5908

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3f783e7
Add temperature to dense mincut pool operator
shhs29 Nov 5, 2022
2b3dda6
Update CHANGELOG
shhs29 Nov 5, 2022
754f635
Add documentation for temp argument
shhs29 Nov 5, 2022
de3c3cb
Add documentation for temp argument
shhs29 Nov 5, 2022
3dab200
Update temp parameter type in mincut pool
shhs29 Nov 6, 2022
edf053f
Update CHANGELOG.md
rusty1s Nov 7, 2022
859e2c8
Update torch_geometric/nn/dense/mincut_pool.py
rusty1s Nov 7, 2022
a384664
Update torch_geometric/nn/dense/mincut_pool.py
rusty1s Nov 7, 2022
22f1d04
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Nov 7, 2022
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Expand Up @@ -52,6 +52,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
- Added `BaseStorage.get()` functionality ([#5240](https://github.com/pyg-team/pytorch_geometric/pull/5240))
- Added a test to confirm that `to_hetero` works with `SparseTensor` ([#5222](https://github.com/pyg-team/pytorch_geometric/pull/5222))
### Changed
- Support temperature with `float` or `int` in `dense_mincut_pool` ([#5908](https://github.com/pyg-team/pytorch_geometric/pull/5908))
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- Fixed a bug in which `VirtualNode` mistakenly treated node features as edge features ([#5819](https://github.com/pyg-team/pytorch_geometric/pull/5819))
- Fixed `setter` and `getter` handling in `BaseStorage` ([#5815](https://github.com/pyg-team/pytorch_geometric/pull/5815))
- Fixed `path` in `hetero_conv_dblp.py` example ([#5686](https://github.com/pyg-team/pytorch_geometric/pull/5686))
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11 changes: 5 additions & 6 deletions torch_geometric/nn/dense/mincut_pool.py
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Expand Up @@ -5,11 +5,8 @@


def dense_mincut_pool(
x: Tensor,
adj: Tensor,
s: Tensor,
mask: Optional[Tensor] = None,
) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
x: Tensor, adj: Tensor, s: Tensor, mask: Optional[Tensor] = None,
temp: float = 1.0) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
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r"""The MinCut pooling operator from the `"Spectral Clustering in Graph
Neural Networks for Graph Pooling" <https://arxiv.org/abs/1907.00481>`_
paper
Expand Down Expand Up @@ -54,6 +51,8 @@ def dense_mincut_pool(
mask (BoolTensor, optional): Mask matrix
:math:`\mathbf{M} \in {\{ 0, 1 \}}^{B \times N}` indicating
the valid nodes for each graph. (default: :obj:`None`)
temp (float): Temperature parameter for softmax
function. (default: :obj:`1.0`)

:rtype: (:class:`Tensor`, :class:`Tensor`, :class:`Tensor`,
:class:`Tensor`)
Expand All @@ -65,7 +64,7 @@ def dense_mincut_pool(

(batch_size, num_nodes, _), k = x.size(), s.size(-1)

s = torch.softmax(s, dim=-1)
s = torch.softmax(s / temp, dim=-1)
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if mask is not None:
mask = mask.view(batch_size, num_nodes, 1).to(x.dtype)
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