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from typing import Tuple
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 scatter
@functional_transform('local_cartesian')
class LocalCartesian(BaseTransform):
r"""Saves the relative Cartesian coordinates of linked nodes in its edge
attributes (functional name: :obj:`local_cartesian`). Each coordinate gets
*neighborhood-normalized* to a specified interval
(:math:`[0, 1]` by default).
Args:
norm (bool, optional): If set to :obj:`False`, the output will not be
normalized. (default: :obj:`True`)
cat (bool, optional): If set to :obj:`False`, all existing edge
attributes will be replaced. (default: :obj:`True`)
interval ((float, float), optional): A tuple specifying the lower and
upper bound for normalization. (default: :obj:`(0.0, 1.0)`)
"""
def __init__(
self,
norm: bool = True,
cat: bool = True,
interval: Tuple[float, float] = (0.0, 1.0),
):
self.norm = norm
self.cat = cat
self.interval = interval
def forward(self, data: Data) -> Data:
assert data.pos is not None
assert data.edge_index is not None
(row, col), pos, pseudo = data.edge_index, data.pos, data.edge_attr
cart = pos[row] - pos[col]
cart = cart.view(-1, 1) if cart.dim() == 1 else cart
if self.norm:
max_value = scatter(cart.abs(), col, 0, pos.size(0), reduce='max')
max_value = max_value.max(dim=-1, keepdim=True)[0]
length = self.interval[1] - self.interval[0]
center = (self.interval[0] + self.interval[1]) / 2
cart = length * cart / (2 * max_value[col]) + center
if pseudo is not None and self.cat:
pseudo = pseudo.view(-1, 1) if pseudo.dim() == 1 else pseudo
data.edge_attr = torch.cat([pseudo, cart.type_as(pseudo)], dim=-1)
else:
data.edge_attr = cart
return data
|
