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"""
edge_conv.py includes edge_attr to edge_conv
"""
from typing import Callable, Optional, Union
import torch
from torch import Tensor
from torch.nn import Linear
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size
class EdgeConvConv(MessagePassing):
def __init__(self, nn: Callable, eps: float = 0., train_eps: bool = False,
edge_dim: Optional[int] = None, **kwargs):
kwargs.setdefault('aggr', 'add')
super().__init__(**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]))
if edge_dim is not None:
if hasattr(self.nn, 'in_features'):
in_channels = self.nn.in_features
else:
in_channels = self.nn.in_channels
self.lin = Linear(edge_dim, in_channels)
else:
self.lin = None
def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj,
edge_attr: OptTensor = None, size: Size = None) -> Tensor:
""""""
if isinstance(x, Tensor):
x: OptPairTensor = (x, x)
# propagate_type: (x: OptPairTensor, edge_attr: OptTensor)
out = self.propagate(edge_index, x=x, edge_attr=edge_attr, size=size)
return out
def message(self, x_i: Tensor, x_j: Tensor, edge_attr: Tensor) -> Tensor:
temp = torch.cat([x_i, x_j, edge_attr], dim=1)
return self.nn(temp)
def __repr__(self) -> str:
return f'{self.__class__.__name__}(nn={self.nn})'
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