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import torch
from torch_geometric.data import Data
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform
from torch_geometric.utils import degree, one_hot
@functional_transform('one_hot_degree')
class OneHotDegree(BaseTransform):
r"""Adds the node degree as one hot encodings to the node features
(functional name: :obj:`one_hot_degree`).
Args:
max_degree (int): Maximum degree.
in_degree (bool, optional): If set to :obj:`True`, will compute the
in-degree of nodes instead of the out-degree.
(default: :obj:`False`)
cat (bool, optional): Concat node degrees to node features instead
of replacing them. (default: :obj:`True`)
"""
def __init__(
self,
max_degree: int,
in_degree: bool = False,
cat: bool = True,
) -> None:
self.max_degree = max_degree
self.in_degree = in_degree
self.cat = cat
def forward(self, data: Data) -> Data:
assert data.edge_index is not None
idx, x = data.edge_index[1 if self.in_degree else 0], data.x
deg = degree(idx, data.num_nodes, dtype=torch.long)
deg = one_hot(deg, num_classes=self.max_degree + 1)
if x is not None and self.cat:
x = x.view(-1, 1) if x.dim() == 1 else x
data.x = torch.cat([x, deg.to(x.dtype)], dim=-1)
else:
data.x = deg
return data
def __repr__(self) -> str:
return f'{self.__class__.__name__}({self.max_degree})'
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