import torch
import torch.nn.functional as F
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import remove_self_loops
from ..inits import reset
[docs]class GINConv(MessagePassing):
r"""The graph isomorphism operator from the `"How Powerful are
Graph Neural Networks?" <https://arxiv.org/abs/1810.00826>`_ paper
.. math::
\mathbf{x}^{\prime}_i = h_{\mathbf{\Theta}} \left( (1 + \epsilon) \cdot
\mathbf{x}_i + \sum_{j \in \mathcal{N}(i)} \mathbf{x}_j \right)
or
.. math::
\mathbf{X}^{\prime} = h_{\mathbf{\Theta}} \left( \left( \mathbf{A} +
(1 + \epsilon) \cdot \mathbf{I} \right) \cdot \mathbf{X} \right),
here :math:`h_{\mathbf{\Theta}}` denotes a neural network, *.i.e.* an MLP.
Args:
nn (torch.nn.Module): A neural network :math:`h_{\mathbf{\Theta}}` that
maps node features :obj:`x` of shape :obj:`[-1, in_channels]` to
shape :obj:`[-1, out_channels]`, *e.g.*, defined by
:class:`torch.nn.Sequential`.
eps (float, optional): (Initial) :math:`\epsilon` value.
(default: :obj:`0`)
train_eps (bool, optional): If set to :obj:`True`, :math:`\epsilon`
will be a trainable parameter. (default: :obj:`False`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.nn.conv.MessagePassing`.
"""
def __init__(self, nn, eps=0, train_eps=False, **kwargs):
super(GINConv, self).__init__(aggr='add', **kwargs)
self.nn = nn
self.initial_eps = eps
if train_eps:
self.eps = torch.nn.Parameter(torch.Tensor([eps]))
else:
self.register_buffer('eps', torch.Tensor([eps]))
self.reset_parameters()
[docs] def reset_parameters(self):
reset(self.nn)
self.eps.data.fill_(self.initial_eps)
[docs] def forward(self, x, edge_index):
""""""
x = x.unsqueeze(-1) if x.dim() == 1 else x
edge_index, _ = remove_self_loops(edge_index)
out = self.nn((1 + self.eps) * x + self.propagate(edge_index, x=x))
return out
def message(self, x_j):
return x_j
def __repr__(self):
return '{}(nn={})'.format(self.__class__.__name__, self.nn)
[docs]class GINEConv(MessagePassing):
r"""The modified :class:`GINConv` operator from the `"Strategies for
Pre-training Graph Neural Networks" <https://arxiv.org/abs/1905.12265>`_
paper
.. math::
\mathbf{x}^{\prime}_i = h_{\mathbf{\Theta}} \left( (1 + \epsilon) \cdot
\mathbf{x}_i + \sum_{j \in \mathcal{N}(i)} \mathrm{ReLU}
( \mathbf{x}_j + \mathbf{e}_{j,i} ) \right)
that is able to incorporate edge features :math:`\mathbf{e}_{j,i}` into
the aggregation procedure.
Args:
nn (torch.nn.Module): A neural network :math:`h_{\mathbf{\Theta}}` that
maps node features :obj:`x` of shape :obj:`[-1, in_channels]` to
shape :obj:`[-1, out_channels]`, *e.g.*, defined by
:class:`torch.nn.Sequential`.
eps (float, optional): (Initial) :math:`\epsilon` value.
(default: :obj:`0`)
train_eps (bool, optional): If set to :obj:`True`, :math:`\epsilon`
will be a trainable parameter. (default: :obj:`False`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.nn.conv.MessagePassing`.
"""
def __init__(self, nn, eps=0, train_eps=False, **kwargs):
super(GINEConv, self).__init__(aggr='add', **kwargs)
self.nn = nn
self.initial_eps = eps
if train_eps:
self.eps = torch.nn.Parameter(torch.Tensor([eps]))
else:
self.register_buffer('eps', torch.Tensor([eps]))
self.reset_parameters()
[docs] def reset_parameters(self):
reset(self.nn)
self.eps.data.fill_(self.initial_eps)
[docs] def forward(self, x, edge_index, edge_attr):
""""""
x = x.unsqueeze(-1) if x.dim() == 1 else x
if edge_attr.dim() == 1:
edge_attr = edge_attr.unsqueeze(-1)
assert x.size(-1) == edge_attr.size(-1)
edge_index, _ = remove_self_loops(edge_index)
out = self.nn((1 + self.eps) * x +
self.propagate(edge_index, x=x, edge_attr=edge_attr))
return out
def message(self, x_j, edge_attr):
return F.relu(x_j + edge_attr)
def __repr__(self):
return '{}(nn={})'.format(self.__class__.__name__, self.nn)