def forward(self,
x: Tensor,
edge_index: Adj,
edge_attr: OptTensor = None) -> Tensor:
""""""
if isinstance(edge_index, SparseTensor):
edge_attr = edge_index.storage.value()
if edge_attr is not None:
edge_attr = self.mlp(edge_attr).squeeze(-1)
if isinstance(edge_index, SparseTensor):
edge_index = edge_index.set_value(edge_attr, layout='coo')
if self.normalize:
if isinstance(edge_index, Tensor):
edge_index, edge_attr = gcn_norm(edge_index, edge_attr,
x.size(self.node_dim), False,
self.add_self_loops)
elif isinstance(edge_index, SparseTensor):
edge_index = gcn_norm(edge_index, None, x.size(self.node_dim),
False, self.add_self_loops)
x = self.lin(x)
# propagate_type: (x: Tensor, edge_weight: OptTensor)
out = self.propagate(edge_index, x=x, edge_weight=edge_attr, size=None)
if self.bias is not None:
out += self.bias
return out
def forward(self, x: Tensor, edge_index: Adj) -> Tensor:
""""""
edge_weight: OptTensor = None
if isinstance(edge_index, Tensor):
num_nodes = x.size(self.node_dim)
if self.add_self_loops:
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes)
row, col = edge_index[0], edge_index[1]
deg_inv = 1. / degree(col, num_nodes=num_nodes).clamp_(1.)
edge_weight = deg_inv[col]
edge_weight[row == col] += self.diag_lambda * deg_inv
elif isinstance(edge_index, SparseTensor):
if self.add_self_loops:
edge_index = set_diag(edge_index)
col, row, _ = edge_index.coo() # Transposed.
deg_inv = 1. / sparsesum(edge_index, dim=1).clamp_(1.)
edge_weight = deg_inv[col]
edge_weight[row == col] += self.diag_lambda * deg_inv
edge_index = edge_index.set_value(edge_weight, layout='coo')
# propagate_type: (x: Tensor, edge_weight: OptTensor)
out = self.propagate(edge_index,
x=x,
edge_weight=edge_weight,
size=None)
out = self.lin_out(out) + self.lin_root(x)
return out
def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, edge_type: Optional[torch.Tensor] = None,
size: Size = None, return_attention_weights=None):
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
assert x.dim() == 2, 'Static graphs not supported in `GATConv`.'
x = x.view(-1, self.heads, self.out_channels)
# propagate_type: (x: OptPairTensor, alpha: OptPairTensor)
out = self.propagate(edge_index, x=x, edge_type=edge_type, size=size)
alpha = self._alpha
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.bias is not None:
out += self.bias
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self,
x: Union[Tensor, PairTensor],
edge_index: Adj,
edge_attr: OptTensor = None,
return_attention_weights=None):
# type: (Union[Tensor, PairTensor], Tensor, OptTensor, NoneType) -> Tensor # noqa
# type: (Union[Tensor, PairTensor], SparseTensor, OptTensor, NoneType) -> Tensor # noqa
# type: (Union[Tensor, PairTensor], Tensor, OptTensor, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Union[Tensor, PairTensor], SparseTensor, OptTensor, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
H, C = self.heads, self.out_channels
if isinstance(x, Tensor):
x: PairTensor = (x, x)
query = self.lin_query(x[1]).view(-1, H, C)
key = self.lin_key(x[0]).view(-1, H, C)
value = self.lin_value(x[0]).view(-1, H, C)
# propagate_type: (query: Tensor, key:Tensor, value: Tensor, edge_attr: OptTensor) # noqa
out = self.propagate(edge_index,
query=query,
key=key,
value=value,
edge_attr=edge_attr,
size=None)
alpha = self._alpha
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.root_weight:
x_r = self.lin_skip(x[1])
if self.lin_beta is not None:
beta = self.lin_beta(torch.cat([out, x_r, out - x_r], dim=-1))
beta = beta.sigmoid()
out = beta * x_r + (1 - beta) * out
else:
out += x_r
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj,
size: Size = None, return_attention_weights=None):
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
H, C = self.heads, self.out_channels
x_l: OptTensor = None
x_r: OptTensor = None
if isinstance(x, Tensor):
assert x.dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = x_r = self.lin_l(x).view(-1, H, C)
else:
x_l, x_r = x[0], x[1]
assert x[0].dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = self.lin_l(x_l).view(-1, H, C)
if x_r is not None:
x_r = self.lin_r(x_r).view(-1, H, C)
assert x_l is not None
if self.add_self_loops:
if isinstance(edge_index, Tensor):
num_nodes = x_l.size(0)
if x_r is not None:
num_nodes = min(num_nodes, x_r.size(0))
if size is not None:
num_nodes = min(size[0], size[1])
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes)
elif isinstance(edge_index, SparseTensor):
edge_index = set_diag(edge_index)
# propagate_type: (x: OptPairTensor, alpha: OptPairTensor)
out = self.propagate(edge_index, x=(x_l, x_r), size=size)
alpha = self._alpha
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.bias is not None:
out += self.bias
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self,
x: Tensor,
edge_index: Adj,
edge_weight: OptTensor = None) -> Tensor:
""""""
if self.normalize:
if isinstance(edge_index, Tensor):
cache = self._cached_edge_index
if cache is None:
edge_index, edge_weight = gcn_norm( # yapf: disable
edge_index,
edge_weight,
x.size(self.node_dim),
False,
self.add_self_loops,
self.flow,
dtype=x.dtype)
if self.cached:
self._cached_edge_index = (edge_index, edge_weight)
else:
edge_index, edge_weight = cache[0], cache[1]
elif isinstance(edge_index, SparseTensor):
cache = self._cached_adj_t
if cache is None:
edge_index = gcn_norm( # yapf: disable
edge_index,
edge_weight,
x.size(self.node_dim),
False,
self.add_self_loops,
self.flow,
dtype=x.dtype)
if self.cached:
self._cached_adj_t = edge_index
else:
edge_index = cache
h = x
for k in range(self.K):
if self.dropout > 0 and self.training:
if isinstance(edge_index, Tensor):
assert edge_weight is not None
edge_weight = F.dropout(edge_weight, p=self.dropout)
else:
value = edge_index.storage.value()
assert value is not None
value = F.dropout(value, p=self.dropout)
edge_index = edge_index.set_value(value, layout='coo')
# propagate_type: (x: Tensor, edge_weight: OptTensor)
x = self.propagate(edge_index,
x=x,
edge_weight=edge_weight,
size=None)
x = x * (1 - self.alpha)
x += self.alpha * h
return x
def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj,
size: Size = None, return_attention_weights=None):
H, C = self.heads, self.out_channels
x_l: OptTensor = None
x_r: OptTensor = None
alpha_l: OptTensor = None
alpha_r: OptTensor = None
if isinstance(x, Tensor):
assert x.dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = x_r = x.view(-1, H, C)
alpha_l = (x_l*self.att_l).sum(dim=-1)
alpha_r = (x_r*self.att_r).sum(dim=-1)
else:
x_l, x_r = x[0], x[1]
assert x[0].dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = x_l.view(-1, H, C)
alpha_l = (x_l*self.att_l).sum(dim=-1)
if x_r is not None:
x_r = x_r.view(-1, H, C)
alpha_r = (x_r*self.att_r).sum(dim=-1)
assert x_l is not None
assert alpha_l is not None
if self.add_self_loops:
if isinstance(edge_index, Tensor):
num_nodes = x_l.size(0)
if x_r is not None:
num_nodes = min(num_nodes, x_r.size(0))
if size is not None:
num_nodes = min(size[0], size[1])
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes)
elif isinstance(edge_index, SparseTensor):
edge_index = set_diag(edge_index)
# propagate_type: (x: OptPairTensor, alpha: OptPairTensor)
out = self.propagate(edge_index, x=(x_l, x_r),
alpha=(alpha_l, alpha_r), size=size)
alpha = self._alpha
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self,
x: Tensor,
edge_index: Adj,
edge_type: OptTensor = None,
edge_attr: OptTensor = None,
size: Size = None,
return_attention_weights=None):
r"""
Args:
x (Tensor): The input node features. Can be either a
:obj:`[num_nodes, in_channels]` node feature matrix, or an
optional one-dimensional node index tensor (in which case
input features are treated as trainable node embeddings).
edge_index (LongTensor or SparseTensor): The edge indices.
edge_type: The one-dimensional relation type/index for each edge in
:obj:`edge_index`.
Should be only :obj:`None` in case :obj:`edge_index` is of type
:class:`torch_sparse.tensor.SparseTensor`.
(default: :obj:`None`)
edge_attr (Tensor, optional): Edge feature matrix.
(default: :obj:`None`)
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
# propagate_type: (x: Tensor, edge_type: OptTensor, edge_attr: OptTensor) # noqa
out = self.propagate(edge_index=edge_index,
edge_type=edge_type,
x=x,
size=size,
edge_attr=edge_attr)
alpha = self._alpha
assert alpha is not None
self._alpha = None
if isinstance(return_attention_weights, bool):
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self, x: Tensor,
edge_index: Adj) -> Tuple[Tensor, SparseTensor]:
adj_t: Optional[SparseTensor] = None
if isinstance(edge_index, Tensor):
adj_t = SparseTensor(row=edge_index[1],
col=edge_index[0],
sparse_sizes=(x.size(0), x.size(0)))
elif isinstance(edge_index, SparseTensor):
adj_t = edge_index.set_value(None)
assert adj_t is not None
adj_t = self.panentropy(adj_t, dtype=x.dtype)
deg = adj_t.storage.rowcount().to(x.dtype)
deg_inv_sqrt = deg.pow_(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0.
M = deg_inv_sqrt.view(1, -1) * adj_t * deg_inv_sqrt.view(-1, 1)
out = self.propagate(M, x=x, edge_weight=None, size=None)
out = self.lin(out)
return out, M
def forward(self,
x: Union[Tensor, PairTensor],
edge_attr: Tensor,
edge_index: Adj,
size: Size = None,
return_attention_weights: bool = None,
propagate_messages: bool = True):
# type: (Union[Tensor, PairTensor], Tensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, PairTensor], SparseTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, PairTensor], Tensor, Size, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Union[Tensor, PairTensor], SparseTensor, Size, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
H, C, E = self.heads, self.node_out_channels, self.edge_out_channels
x_l: OptTensor = None
x_r: OptTensor = None
if isinstance(x, Tensor):
assert x.dim() == 2
x_l = self.lin_l(x).view(-1, H, C)
if self.share_weights:
x_r = x_l
else:
x_r = self.lin_r(x).view(-1, H, C)
else:
x_l, x_r = x[0], x[1]
assert x[0].dim() == 2
x_l = self.lin_l(x_l).view(-1, H, C)
if x_r is not None:
x_r = self.lin_r(x_r).view(-1, H, C)
assert x_l is not None
assert x_r is not None
edge_attr = self.lin_e(edge_attr).view(-1, H, E)
if propagate_messages:
# propagate_type: (x: PairTensor)
out = self.propagate(edge_index,
x=(x_l, x_r),
edge_ij=edge_attr,
size=size)
else:
out = x_l
alpha = self._alpha
self._alpha = None
if self.concat:
if self.pna and not propagate_messages:
out = out.repeat(1, 1,
(len(self.aggregators) * len(self.scalers)))
out = out.view(-1, self.heads * self.node_pna_channels)
out = self.node_lin_out(out)
edge_attr = edge_attr.view(-1, self.heads * self.edge_out_channels)
edge_attr = self.edge_lin_out(edge_attr)
else:
out = out.mean(dim=1)
edge_attr = edge_attr.mean(dim=1)
if self.node_bias is not None:
out += self.node_bias
if self.edge_bias is not None:
out += self.edge_bias
if self.pna:
out = self.node_lin_out(out)
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, edge_attr, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_attr, edge_index.set_value(alpha,
layout='coo')
else:
return out, edge_attr
def forward(self,
x: Union[Tensor, OptPairTensor],
edge_index: Adj,
edge_attr,
size: Size = None,
return_attention_weights=None):
# type: (Union[Tensor, OptPairTensor], Tensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], Tensor, Size, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
H, C = self.heads, self.out_channels
x_l: OptTensor = None
x_r: OptTensor = None
alpha_l: OptTensor = None
alpha_r: OptTensor = None
if isinstance(x, Tensor):
assert x.dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = x_r = self.lin_l(x).view(-1, H, C)
alpha_l = (x_l * self.att_l).sum(dim=-1)
alpha_r = (x_r * self.att_r).sum(dim=-1)
else:
x_l, x_r = x[0], x[1]
assert x[0].dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = self.lin_l(x_l).view(-1, H, C)
alpha_l = (x_l * self.att_l).sum(dim=-1)
if x_r is not None:
x_r = self.lin_r(x_r).view(-1, H, C)
alpha_r = (x_r * self.att_r).sum(dim=-1)
assert x_l is not None
assert alpha_l is not None
# for edge features:
e = self.lin_e(edge_attr).view(-1, H, C)
alpha_e = (e * self.att_e).sum(dim=-1)
# propagate_type: (x: OptPairTensor, alpha: OptPairTensor)
out = self.propagate(edge_index,
x=(x_l, x_r),
alpha=(alpha_l, alpha_r),
alpha_e=alpha_e,
size=size)
alpha = self._alpha
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.bias is not None:
out += self.bias
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self,
x: Union[Tensor, OptPairTensor],
edge_index: Adj,
edge_attr: OptTensor = None,
size: Size = None,
return_attention_weights=None):
# type: (Union[Tensor, OptPairTensor], Tensor, OptTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, OptTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], Tensor, OptTensor, Size, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, OptTensor, Size, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
# NOTE: attention weights will be returned whenever
# `return_attention_weights` is set to a value, regardless of its
# actual value (might be `True` or `False`). This is a current somewhat
# hacky workaround to allow for TorchScript support via the
# `torch.jit._overload` decorator, as we can only change the output
# arguments conditioned on type (`None` or `bool`), not based on its
# actual value.
H, C = self.heads, self.out_channels
# We first transform the input node features. If a tuple is passed, we
# transform source and target node features via separate weights:
if isinstance(x, Tensor):
assert x.dim() == 2, "Static graphs not supported in 'GATConv'"
x_src = x_dst = self.lin_src(x).view(-1, H, C)
else: # Tuple of source and target node features:
x_src, x_dst = x
assert x_src.dim() == 2, "Static graphs not supported in 'GATConv'"
x_src = self.lin_src(x_src).view(-1, H, C)
if x_dst is not None:
x_dst = self.lin_dst(x_dst).view(-1, H, C)
x = (x_src, x_dst)
# Next, we compute node-level attention coefficients, both for source
# and target nodes (if present):
alpha_src = (x_src * self.att_src).sum(dim=-1)
alpha_dst = None if x_dst is None else (x_dst * self.att_dst).sum(-1)
alpha = (alpha_src, alpha_dst)
if self.add_self_loops:
if isinstance(edge_index, Tensor):
# We only want to add self-loops for nodes that appear both as
# source and target nodes:
num_nodes = x_src.size(0)
if x_dst is not None:
num_nodes = min(num_nodes, x_dst.size(0))
num_nodes = min(size) if size is not None else num_nodes
edge_index, edge_attr = remove_self_loops(
edge_index, edge_attr)
edge_index, edge_attr = add_self_loops(
edge_index,
edge_attr,
fill_value=self.fill_value,
num_nodes=num_nodes)
elif isinstance(edge_index, SparseTensor):
if self.edge_dim is None:
edge_index = set_diag(edge_index)
else:
raise NotImplementedError(
"The usage of 'edge_attr' and 'add_self_loops' "
"simultaneously is currently not yet supported for "
"'edge_index' in a 'SparseTensor' form")
# propagate_type: (x: OptPairTensor, alpha: OptPairTensor, edge_attr: OptTensor) # noqa
out = self.propagate(edge_index,
x=x,
alpha=alpha,
edge_attr=edge_attr,
size=size)
alpha = self._alpha
assert alpha is not None
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.bias is not None:
out += self.bias
if isinstance(return_attention_weights, bool):
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self,
x: Union[Tensor, OptPairTensor],
edge_index: Adj,
size: Size = None,
return_attention_weights=None,
edge_weight=None):
# type: (Union[Tensor, OptPairTensor], Tensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], Tensor, Size, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
H, C = self.heads, self.out_channels
x_l: OptTensor = None
x_r: OptTensor = None
alpha_l: OptTensor = None
alpha_r: OptTensor = None
if isinstance(x, Tensor):
assert x.dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = x_r = self.lin_l(x).view(-1, H, C)
alpha_l = alpha_r = (x_l * self.att_l).sum(dim=-1)
else:
x_l, x_r = x[0], x[1]
assert x[0].dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = self.lin_l(x_l).view(-1, H, C)
alpha_l = (x_l * self.att_l).sum(dim=-1)
if x_r is not None:
x_r = self.lin_r(x_r).view(-1, H, C)
alpha_r = (x_r * self.att_r).sum(dim=-1)
assert x_l is not None
assert alpha_l is not None
if self.add_self_loops:
if isinstance(edge_index, Tensor):
num_nodes = x_l.size(0)
num_nodes = size[1] if size is not None else num_nodes
num_nodes = x_r.size(0) if x_r is not None else num_nodes
edge_index, edge_weight = remove_self_loops(
edge_index, edge_attr=edge_weight)
edge_index, edge_weight = add_self_loops(
edge_index, edge_weight=edge_weight, num_nodes=num_nodes)
elif isinstance(edge_index, SparseTensor):
edge_index = set_diag(edge_index)
# propagate_type: (x: OptPairTensor, alpha: OptPairTensor)
# This is the modified part:
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1), ),
dtype=x.dtype,
device=edge_index.device)
out = self.propagate(edge_index,
x=(x_l, x_r),
alpha=(alpha_l, alpha_r),
size=size,
edge_weight=edge_weight)
alpha = self._alpha
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.bias is not None:
out += self.bias
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self,
x: Union[Tensor, OptPairTensor],
edge_index: Adj,
size: Size = None,
return_attention_weights=None,
layer=None):
# type: (Union[Tensor, OptPairTensor], Tensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], Tensor, Size, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
H, C = self.heads, self.out_channels
x_l: OptTensor = None
x_r: OptTensor = None
alpha_l: OptTensor = None
alpha_r: OptTensor = None
if isinstance(x, Tensor):
assert x.dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = x_r = self.lin_l(x).view(-1, H, C)
alpha_l = (x_l * self.att_l).sum(dim=-1)
alpha_r = (x_r * self.att_r).sum(dim=-1)
else:
x_l, x_r = x[0], x[1]
assert x[0].dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = self.lin_l(x_l).view(-1, H, C)
alpha_l = (x_l * self.att_l).sum(dim=-1)
if x_r is not None:
x_r = self.lin_r(x_r).view(-1, H, C)
alpha_r = (x_r * self.att_r).sum(dim=-1)
assert x_l is not None
assert alpha_l is not None
if self.add_self_loops:
if isinstance(edge_index, Tensor):
num_nodes = x_l.size(0)
if x_r is not None:
num_nodes = min(num_nodes, x_r.size(0))
if size is not None:
num_nodes = min(size[0], size[1])
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes)
elif isinstance(edge_index, SparseTensor):
edge_index = set_diag(edge_index)
# propagate_type: (x: OptPairTensor, alpha: OptPairTensor)
if layer != None and (layer > 1):
# propagate_type: (x: Tensor, edge_weight: OptTensor)
L = math.floor(self.out_channels / layer)
parts = []
for i in range(layer - 1):
parts.append(L)
parts.append(x_l.shape[-1] - L * (layer - 1))
parts = tuple(parts)
xl = x_l.split(parts, dim=-1)
xr = x_r.split(parts, dim=-1)
out = self.propagate(edge_index,
x=(xl[0], xr[0]),
alpha=(alpha_l, alpha_r),
size=size)
for i in range(1, layer):
out1 = self.propagate(edge_index,
x=(xl[i], xr[i]),
alpha=(alpha_l, alpha_r),
size=size)
out = torch.cat((out, out1), dim=-1)
else:
out = self.propagate(edge_index,
x=(x_l, x_r),
alpha=(alpha_l, alpha_r),
size=size)
alpha = self._alpha
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.bias is not None:
out += self.bias
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self,
x: Tensor,
x_0: Tensor,
edge_index: Adj,
edge_weight: OptTensor = None,
return_attention_weights=None):
# type: (Tensor, Tensor, Tensor, OptTensor, NoneType) -> Tensor # noqa
# type: (Tensor, Tensor, SparseTensor, OptTensor, NoneType) -> Tensor # noqa
# type: (Tensor, Tensor, Tensor, OptTensor, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Tensor, Tensor, SparseTensor, OptTensor, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
if self.normalize:
if isinstance(edge_index, Tensor):
assert edge_weight is None
cache = self._cached_edge_index
if cache is None:
edge_index, edge_weight = gcn_norm( # yapf: disable
edge_index,
None,
x.size(self.node_dim),
False,
self.add_self_loops,
dtype=x.dtype)
if self.cached:
self._cached_edge_index = (edge_index, edge_weight)
else:
edge_index, edge_weight = cache[0], cache[1]
elif isinstance(edge_index, SparseTensor):
assert not edge_index.has_value()
cache = self._cached_adj_t
if cache is None:
edge_index = gcn_norm( # yapf: disable
edge_index,
None,
x.size(self.node_dim),
False,
self.add_self_loops,
dtype=x.dtype)
if self.cached:
self._cached_adj_t = edge_index
else:
edge_index = cache
else:
if isinstance(edge_index, Tensor):
assert edge_weight is not None
elif isinstance(edge_index, SparseTensor):
assert edge_index.has_value()
alpha_l = self.att_l(x)
alpha_r = self.att_r(x)
# propagate_type: (x: Tensor, alpha: PairTensor, edge_weight: OptTensor) # noqa
out = self.propagate(edge_index,
x=x,
alpha=(alpha_l, alpha_r),
edge_weight=edge_weight,
size=None)
alpha = self._alpha
self._alpha = None
if self.eps != 0.0:
out += self.eps * x_0
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self, x: Union[Tensor, PairTensor], edge_index: Adj,
edge_attr: OptTensor = None, size: Size = None,
return_attention_weights: bool = None):
# type: (Union[Tensor, PairTensor], Tensor, OptTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, PairTensor], SparseTensor, OptTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, PairTensor], Tensor, OptTensor, Size, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Union[Tensor, PairTensor], SparseTensor, OptTensor, Size, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
H, C = self.heads, self.out_channels
x_l: OptTensor = None
x_r: OptTensor = None
if isinstance(x, Tensor):
assert x.dim() == 2
x_l = self.lin_l(x).view(-1, H, C)
if self.share_weights:
x_r = x_l
else:
x_r = self.lin_r(x).view(-1, H, C)
else:
x_l, x_r = x[0], x[1]
assert x[0].dim() == 2
x_l = self.lin_l(x_l).view(-1, H, C)
if x_r is not None:
x_r = self.lin_r(x_r).view(-1, H, C)
assert x_l is not None
assert x_r is not None
if self.add_self_loops:
if isinstance(edge_index, Tensor):
num_nodes = x_l.size(0)
if x_r is not None:
num_nodes = min(num_nodes, x_r.size(0))
if size is not None:
num_nodes = min(size[0], size[1])
edge_index, edge_attr = remove_self_loops(
edge_index, edge_attr)
edge_index, edge_attr = add_self_loops(
edge_index, edge_attr, fill_value=self.fill_value,
num_nodes=num_nodes)
elif isinstance(edge_index, SparseTensor):
if self.edge_dim is None:
edge_index = set_diag(edge_index)
else:
raise NotImplementedError(
"The usage of 'edge_attr' and 'add_self_loops' "
"simultaneously is currently not yet supported for "
"'edge_index' in a 'SparseTensor' form")
# propagate_type: (x: PairTensor, edge_attr: OptTensor)
out = self.propagate(edge_index, x=(x_l, x_r), edge_attr=edge_attr,
size=size)
alpha = self._alpha
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.bias is not None:
out += self.bias
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self,
x: Union[Tensor, OptPairTensor],
edge_index: Adj,
size: Size = None,
return_attention_weights=None):
# type: (Union[Tensor, OptPairTensor], Tensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], Tensor, Size, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
H, C = self.heads, self.out_channels
x_l: OptTensor = None
x_r: OptTensor = None
alpha_l: OptTensor = None
alpha_r: OptTensor = None
if isinstance(x, Tensor):
assert x.dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = x_r = self.lin_l(x).view(-1, H, C)
alpha_l = (x_l * self.att_l).sum(
dim=-1
) # (num_nodes, heads, out_channels) (1, heads, out_channels)
alpha_r = (x_r * self.att_r).sum(dim=-1)
if not self.gi_by_gcn:
alpha_gi = (x_r * self.global_importance_scorer).sum(dim=-1)
else:
x_l, x_r = x[0], x[1]
assert x[0].dim() == 2, 'Static graphs not supported in `GATConv`.'
x_l = self.lin_l(x_l).view(-1, H, C)
alpha_l = (x_l * self.att_l).sum(dim=-1)
if x_r is not None:
x_r = self.lin_r(x_r).view(-1, H, C)
alpha_r = (x_r * self.att_r).sum(dim=-1)
# if not self.gi_by_gcn:
# alpha_gi = (x_r * self.global_importance_scorer).sum(dim=-1)
assert x_l is not None
assert alpha_l is not None
if self.add_self_loops:
if isinstance(edge_index, Tensor):
num_nodes = x_l.size(0)
if x_r is not None:
num_nodes = min(num_nodes, x_r.size(0))
if size is not None:
num_nodes = min(size[0], size[1])
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes)
elif isinstance(edge_index, SparseTensor):
edge_index = set_diag(edge_index)
if self.gi_by_gcn:
alpha_gi = self.global_importance_scorer(
x_r, edge_index) # num_nodes, num_heads
# A_ij + GI_j 因此GI 应当像alpha_j 一样扩展， 我在思考 alpha_i 和alpha_j 的得到方式是不是可以通过这个思路
# propagate_type: (x: OptPairTensor, alpha: OptPairTensor)
out = self.propagate(edge_index,
x=(x_l, x_r),
alpha=(alpha_l, alpha_r),
size=size) # base class 的propagate 调用了message()
alpha = self._alpha
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.bias is not None:
out += self.bias
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self,
x: Union[Tensor, OptPairTensor],
edge_index: Adj,
size: Size = None,
return_attention_weights=None):
# type: (Union[Tensor, OptPairTensor], Tensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], Tensor, Size, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
H, C = self.heads, self.out_channels
# We first transform the input node features. If a tuple is passed, we
# transform source and target node features via separate weights:
if isinstance(x, Tensor):
assert x.dim() == 2, "Static graphs not supported in 'GATConv'"
x_src = x_dst = self.lin_src(x).view(-1, H, C)
else: # Tuple of source and target node features:
x_src, x_dst = x
assert x_src.dim() == 2, "Static graphs not supported in 'GATConv'"
x_src = self.lin_src(x_src).view(-1, H, C)
if x_dst is not None:
x_dst = self.lin_dst(x_dst).view(-1, H, C)
x = (x_src, x_dst)
# Next, we compute node-level attention coefficients, both for source
# and target nodes (if present):
alpha_src = (x_src * self.att_src).sum(dim=-1)
alpha_dst = None if x_dst is None else (x_dst * self.att_dst).sum(-1)
alpha = (alpha_src, alpha_dst)
if self.add_self_loops:
if isinstance(edge_index, Tensor):
# We only want to add self-loops for nodes that appear both as
# source and target nodes:
num_nodes = x_src.size(0)
if x_dst is not None:
num_nodes = min(num_nodes, x_dst.size(0))
num_nodes = min(size) if size is not None else num_nodes
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes)
elif isinstance(edge_index, SparseTensor):
edge_index = set_diag(edge_index)
# propagate_type: (x: OptPairTensor, alpha: OptPairTensor)
out = self.propagate(edge_index, x=x, alpha=alpha, size=size)
alpha = self._alpha
assert alpha is not None
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.bias is not None:
out += self.bias
if isinstance(return_attention_weights, bool):
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out
def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, cmd, batch, edge_attr=None,
size: Size = None, return_attention_weights=None):
# type: (Union[Tensor, OptPairTensor], Tensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, NoneType) -> Tensor # noqa
# type: (Union[Tensor, OptPairTensor], Tensor, Size, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
# type: (Union[Tensor, OptPairTensor], SparseTensor, Size, bool) -> Tuple[Tensor, SparseTensor] # noqa
r"""
Args:
return_attention_weights (bool, optional): If set to :obj:`True`,
will additionally return the tuple
:obj:`(edge_index, attention_weights)`, holding the computed
attention weights for each edge. (default: :obj:`None`)
"""
H, C = self.heads, self.out_channels
x_l: OptTensor = None
x_r: OptTensor = None
alpha_l: OptTensor = None
alpha_r: OptTensor = None
if isinstance(x, Tensor):
assert x.dim() == 2, 'Static graphs not supported in `GATConv`.'
# print(x.shape)
x_l = self.lin_l(x).view(-1, H, C)
x_r = self.lin_r(x).view(-1, H, C)
# print(x_l.shape)
# sleep
proj_cmd = self.proj_cmd(cmd) # (bs, H * C)
cal_cmd = self.cal_cmd(cmd) # (bs, H * C)
batch_onehot = F.one_hot(batch).float() # (num_node, bs)
# print(batch_onehot.shape, proj_cmd.shape)
proj_cmd = batch_onehot.matmul(proj_cmd).view(-1, H, C) # (num_node, H, C)
cal_cmd = batch_onehot.matmul(cal_cmd).view(-1, H, C) # (num_node, H, C)
x_mul = proj_cmd * x_r
alpha_l = x_l
alpha_r = x_mul
# else:
# x_l, x_r = x[0], x[1]
# assert x[0].dim() == 2, 'Static graphs not supported in `GATConv`.'
# x_l = self.lin_l(x_l).view(-1, H, C)
# alpha_l = (x_l * self.att_l).sum(dim=-1)
# if x_r is not None:
# x_r = self.lin_r(x_r).view(-1, H, C)
# alpha_r = (x_r * self.att_r).sum(dim=-1)
assert x_l is not None
assert alpha_l is not None
# # for edge features:
# e = self.lin_e(edge_attr).view(-1, H, C)
# alpha_e = (e * self.att_e).sum(dim=-1)
# propagate_type: (x: OptPairTensor, alpha: OptPairTensor)
out = self.propagate(edge_index, x=x, cmd=cmd, cal_cmd=(cal_cmd, cal_cmd),
alpha=(alpha_l, alpha_r), size=size)
alpha = self._alpha
self._alpha = None
if self.concat:
out = out.view(-1, self.heads * self.out_channels)
else:
out = out.mean(dim=1)
if self.bias is not None:
out += self.bias
if isinstance(return_attention_weights, bool):
assert alpha is not None
if isinstance(edge_index, Tensor):
return out, (edge_index, alpha)
elif isinstance(edge_index, SparseTensor):
return out, edge_index.set_value(alpha, layout='coo')
else:
return out