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from typing import List, Optional, Union
import torch
from torch_geometric.data import Data, HeteroData
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform
@functional_transform('constant')
class Constant(BaseTransform):
r"""Appends a constant value to each node feature :obj:`x`
(functional name: :obj:`constant`).
Args:
value (float, optional): The value to add. (default: :obj:`1.0`)
cat (bool, optional): If set to :obj:`False`, existing node features
will be replaced. (default: :obj:`True`)
node_types (str or List[str], optional): The specified node type(s) to
append constant values for if used on heterogeneous graphs.
If set to :obj:`None`, constants will be added to each node feature
:obj:`x` for all existing node types. (default: :obj:`None`)
"""
def __init__(
self,
value: float = 1.0,
cat: bool = True,
node_types: Optional[Union[str, List[str]]] = None,
):
if isinstance(node_types, str):
node_types = [node_types]
self.value = value
self.cat = cat
self.node_types = node_types
def forward(
self,
data: Union[Data, HeteroData],
) -> Union[Data, HeteroData]:
for store in data.node_stores:
if self.node_types is None or store._key in self.node_types:
num_nodes = store.num_nodes
assert num_nodes is not None
c = torch.full((num_nodes, 1), self.value, dtype=torch.float)
if hasattr(store, 'x') and self.cat:
x = store.x.view(-1, 1) if store.x.dim() == 1 else store.x
store.x = torch.cat([x, c.to(x.device, x.dtype)], dim=-1)
else:
store.x = c
return data
def __repr__(self) -> str:
return f'{self.__class__.__name__}(value={self.value})'
|
