def forward(self, x, adj, edge_type):
"""Forward-passing
Parameters
----------
x : torch.Tensor
The node features
adj : torch_sparse.SparseMatrix
Graph's adjacency matrix
edge_type : torch.LongTensor
The types of the edges
Returns
-------
torch.Tensor
The new node features
"""
# adj normalization. Does not use edge classes!
out = x @ self.root + self.bias
for i in range(self.num_relations):
mask = edge_type == i
if mask.sum() > 0:
tmp = masked_select_nnz(adj, mask, layout="coo")
h = self.propagate(tmp, x=x, size=(x.size(-2), x.size(-2)))
out = out + h @ self.weight[i]
return out
def forward(self, x: Union[Tensor, PairTensor], edge_index: Adj,
edge_type: OptTensor = None) -> Tensor:
""""""
if isinstance(x, Tensor):
x: PairTensor = (x, x)
beta, gamma = self.film_skip(x[1]).split(self.out_channels, dim=-1)
out = gamma * self.lin_skip(x[1]) + beta
if self.act is not None:
out = self.act(out)
# propagate_type: (x: Tensor, beta: Tensor, gamma: Tensor)
if self.num_relations <= 1:
beta, gamma = self.films[0](x[1]).split(self.out_channels, dim=-1)
out = out + self.propagate(edge_index, x=self.lins[0](x[0]),
beta=beta, gamma=gamma, size=None)
else:
for i, (lin, film) in enumerate(zip(self.lins, self.films)):
beta, gamma = film(x[1]).split(self.out_channels, dim=-1)
if isinstance(edge_index, SparseTensor):
edge_type = edge_index.storage.value()
assert edge_type is not None
mask = edge_type == i
out = out + self.propagate(
masked_select_nnz(edge_index, mask, layout='coo'),
x=lin(x[0]), beta=beta, gamma=gamma, size=None)
else:
assert edge_type is not None
mask = edge_type == i
out = out + self.propagate(edge_index[:, mask], x=lin(
x[0]), beta=beta, gamma=gamma, size=None)
return out
def masked_edge_index(edge_index, edge_mask):
if isinstance(edge_index, Tensor):
return edge_index[:, edge_mask]
else:
return masked_select_nnz(edge_index, edge_mask, layout='coo')
