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allow 1D input to global_*_pool functions #6504

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merged 6 commits into from
Jan 24, 2023
Merged

allow 1D input to global_*_pool functions #6504

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076d667
allow 1D input to global_*_pool functions
kgajdamo Jan 24, 2023
3471784
always keep dim
kgajdamo Jan 24, 2023
62b6ff5
update CHANGELOG.md
kgajdamo Jan 24, 2023
2b8be67
Update CHANGELOG.md
rusty1s Jan 24, 2023
e4d4739
Merge branch 'master' into global_pool_1d
rusty1s Jan 24, 2023
dac9afe
update
rusty1s Jan 24, 2023
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18 changes: 12 additions & 6 deletions torch_geometric/nn/pool/glob.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,10 +26,12 @@ def global_add_pool(x: Tensor, batch: Optional[Tensor],
size (int, optional): The number of examples :math:`B`.
Automatically calculated if not given. (default: :obj:`None`)
"""
dim = -1 if x.dim() == 1 else -2

if batch is None:
return x.sum(dim=-2, keepdim=x.dim() == 2)
return x.sum(dim=dim, keepdim=x.dim() == 2 or x.dim() == 1)
size = int(batch.max().item() + 1) if size is None else size
return scatter(x, batch, dim=-2, dim_size=size, reduce='sum')
return scatter(x, batch, dim=dim, dim_size=size, reduce='sum')


def global_mean_pool(x: Tensor, batch: Optional[Tensor],
Expand All @@ -53,10 +55,12 @@ def global_mean_pool(x: Tensor, batch: Optional[Tensor],
size (int, optional): The number of examples :math:`B`.
Automatically calculated if not given. (default: :obj:`None`)
"""
dim = -1 if x.dim() == 1 else -2

if batch is None:
return x.mean(dim=-2, keepdim=x.dim() == 2)
return x.mean(dim=dim, keepdim=x.dim() == 2 or x.dim() == 1)
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size = int(batch.max().item() + 1) if size is None else size
return scatter(x, batch, dim=-2, dim_size=size, reduce='mean')
return scatter(x, batch, dim=dim, dim_size=size, reduce='mean')


def global_max_pool(x: Tensor, batch: Optional[Tensor],
Expand All @@ -80,7 +84,9 @@ def global_max_pool(x: Tensor, batch: Optional[Tensor],
size (int, optional): The number of examples :math:`B`.
Automatically calculated if not given. (default: :obj:`None`)
"""
dim = -1 if x.dim() == 1 else -2

if batch is None:
return x.max(dim=-2, keepdim=x.dim() == 2)[0]
return x.max(dim=dim, keepdim=x.dim() == 2 or x.dim() == 1)[0]
size = int(batch.max().item() + 1) if size is None else size
return scatter(x, batch, dim=-2, dim_size=size, reduce='max')
return scatter(x, batch, dim=dim, dim_size=size, reduce='max')