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 adj_type_to_edge_tensor_type(layout: EdgeLayout,
edge_index: Adj) -> EdgeTensorType:
r"""Converts a PyG Adj tensor to an EdgeTensorType equivalent."""
if isinstance(edge_index, Tensor):
return (edge_index[0], edge_index[1]) # (row, col)
if layout == EdgeLayout.COO:
return edge_index.coo()[:-1] # (row, col)
elif layout == EdgeLayout.CSR:
return edge_index.csr()[:-1] # (rowptr, col)
else:
return edge_index.csr()[-2::-1] # (row, colptr)
def forward(self,
y: Tensor,
edge_index: Adj,
mask: Tensor,
edge_weight: OptTensor = None) -> Tensor:
""""""
if mask.dtype == torch.long:
idx = mask
mask = mask.new_zeros(y.size(0), dtype=torch.bool)
mask[idx] = True
y = F.one_hot(y.view(-1)).to(torch.float)
y[~mask] = 0.
if isinstance(edge_index, SparseTensor) and not edge_index.has_value():
edge_index = gcn_norm(edge_index, add_self_loops=False)
elif isinstance(edge_index, Tensor) and edge_weight is None:
edge_index, edge_weight = gcn_norm(edge_index,
num_nodes=y.size(0),
add_self_loops=False)
res = (1 - self.alpha) * y
for _ in range(self.num_layers):
# propagate_type: (y: Tensor, edge_weight: OptTensor)
y = self.propagate(edge_index,
y=y,
edge_weight=edge_weight,
size=None)
y.mul_(self.alpha).add_(res).clamp_(0, 1)
return y
def forward(self,
x: Union[Tensor, OptPairTensor],
edge_index: Adj,
edge_weight=None) -> Tensor:
""""""
if isinstance(x, Tensor):
x: OptPairTensor = (x, x)
# propagate_type: (x: OptPairTensor)
# start of modified code #########
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1), ),
dtype=x[0].dtype,
device=edge_index.device)
out = self.propagate(edge_index,
x=x,
size=None,
edge_weight=edge_weight)
# end of modified code #########
x_r = x[1]
if x_r is not None:
out += (1 + self.eps) * x_r
return self.nn(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, y: Tensor, edge_index: Adj, mask: Optional[Tensor] = None,
edge_weight: OptTensor = None,
post_step: Callable = lambda y: y.clamp_(0., 1.)
) -> Tensor:
""""""
if y.dtype == torch.long:
y = F.one_hot(y.view(-1)).to(torch.float)
out = y
if mask is not None:
out = torch.zeros_like(y)
out[mask] = y[mask]
if isinstance(edge_index, SparseTensor) and not edge_index.has_value():
edge_index = gcn_norm(edge_index, add_self_loops=False)
elif isinstance(edge_index, Tensor) and edge_weight is None:
edge_index, edge_weight = gcn_norm(edge_index, num_nodes=y.size(0),
add_self_loops=False)
res = (1 - self.alpha) * out
for _ in range(self.num_layers):
# propagate_type: (y: Tensor, edge_weight: OptTensor)
out = self.propagate(edge_index, x=out, edge_weight=edge_weight,
size=None)
out.mul_(self.alpha).add_(res)
out = post_step(out)
return out
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,
t,
x: Union[Tensor, OptPairTensor],
edge_index: Adj,
edge_attr: OptTensor = None,
size: Size = None) -> Tensor:
""""""
if isinstance(x, Tensor):
x: OptPairTensor = (x, x)
# Node and edge feature dimensionalites need to match.
if isinstance(edge_index, Tensor):
assert edge_attr is not None
assert x[0].size(-1) == edge_attr.size(-1)
elif isinstance(edge_index, SparseTensor):
assert x[0].size(-1) == edge_index.size(-1)
# propagate_type: (x: OptPairTensor, edge_attr: OptTensor)
out = self.propagate(edge_index, x=x, edge_attr=edge_attr, size=size)
x_r = x[1]
if x_r is not None:
out += (1 + self.eps) * x_r
return self.nn(t, out)
def forward(self,
x: Union[Tensor, OptPairTensor],
edge_index: Adj,
size: Size = None,
edge_weight=None) -> Tensor:
""""""
if isinstance(x, Tensor):
x: OptPairTensor = (x, x)
# propagate_type: (x: OptPairTensor)
# start of modified code #########
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1), ),
dtype=x[0].dtype,
device=edge_index.device)
out = self.propagate(edge_index,
x=x,
size=size,
edge_weight=edge_weight)
# start of modified code #########
out = self.lin_l(out)
x_r = x[1]
if x_r is not None:
out += self.lin_r(x_r)
if self.normalize:
out = F.normalize(out, p=2., dim=-1)
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: 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: 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 to_csc(
adj: Adj,
edge_attr: EdgeAttr,
device: Optional[torch.device] = None,
share_memory: bool = False,
) -> Tuple[Tensor, Tensor, OptTensor]:
# Convert the graph data into a suitable format for sampling (CSC format).
# Returns the `colptr` and `row` indices of the graph, as well as an
# `perm` vector that denotes the permutation of edges.
# Since no permutation of edges is applied when using `SparseTensor`,
# `perm` can be of type `None`.
perm: Optional[Tensor] = None
layout = edge_attr.layout
is_sorted = edge_attr.is_sorted
size = edge_attr.size
if layout == EdgeLayout.CSR:
colptr, row, _ = adj.csc()
elif layout == EdgeLayout.CSC:
colptr, row, _ = adj.csr()
else:
if size is None:
raise ValueError(
f"Edge {edge_attr.edge_type} cannot be converted "
f"to a different type without specifying 'size' for "
f"the source and destination node types (got {size}). "
f"Please specify these parameters for successful execution. ")
(row, col) = adj
if not is_sorted:
perm = (col * size[0]).add_(row).argsort()
row = row[perm]
colptr = torch.ops.torch_sparse.ind2ptr(col[perm], size[1])
colptr = colptr.to(device)
row = row.to(device)
perm = perm.to(device) if perm is not None else None
if not colptr.is_cuda and share_memory:
colptr.share_memory_()
row.share_memory_()
if perm is not None:
perm.share_memory_()
return colptr, row, perm
def homophily(edge_index: Adj, y: Tensor, method: str = 'edge'):
r"""The homophily of a graph characterizes how likely nodes with the same
label are near each other in a graph.
There are many measures of homophily that fits this definition.
In particular:
- In the `"Beyond Homophily in Graph Neural Networks: Current Limitations
and Effective Designs" <https://arxiv.org/abs/2006.11468>`_ paper, the
homophily is the fraction of edges in a graph which connects nodes
that have the same class label:
.. math::
\text{homophily} = \frac{| \{ (v,w) : (v,w) \in \mathcal{E} \wedge
y_v = y_w \} | } {|\mathcal{E}|}
That measure is called the *edge homophily ratio*.
- In the `"Geom-GCN: Geometric Graph Convolutional Networks"
<https://arxiv.org/abs/2002.05287>`_ paper, edge homophily is normalized
across neighborhoods:
.. math::
\text{homophily} = \frac{1}{|\mathcal{V}|} \sum_{v \in \mathcal{V}}
\frac{ | \{ (w,v) : w \in \mathcal{N}(v) \wedge y_v = y_w \} | }
{ |\mathcal{N}(v)| }
That measure is called the *node homophily ratio*.
Args:
edge_index (Tensor or SparseTensor): The graph connectivity.
y (Tensor): The labels.
method (str, optional): The method used to calculate the homophily,
either :obj:`"edge"` (first formula) or :obj:`"node"`
(second formula). (default: :obj:`"edge"`)
"""
assert method in ['edge', 'node']
y = y.squeeze(-1) if y.dim() > 1 else y
if isinstance(edge_index, SparseTensor):
col, row, _ = edge_index.coo()
else:
row, col = edge_index
if method == 'edge':
return int((y[row] == y[col]).sum()) / row.size(0)
else:
out = torch.zeros_like(row, dtype=float)
out[y[row] == y[col]] = 1.
out = scatter_mean(out, col, 0, dim_size=y.size(0))
return float(out.mean())
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) -> Tensor:
""""""
row, col, edge_attr = edge_index.coo()
edge_index = torch.stack([row, col], dim=0)
# calculate edge weights [edge_attr -> edge_weight]
encodings = F.one_hot(torch.flatten(edge_attr).long(), num_classes=self.num_edge_types).double()
alpha_m = (encodings * self.att_m).sum(dim=-1).reshape(-1, 1)
alpha_l = (x * self.att_l).sum(dim=-1).reshape(-1, 1)
alpha_r = (x * self.att_r).sum(dim=-1).reshape(-1, 1)
# propagate_type: (x: Tensor, x_norm: Tensor)
return self.propagate(edge_index,
x=x,
alpha=(alpha_l, alpha_r),
alpha_m=alpha_m,
size=None)
def forward(self,
x: Union[Tensor, List[Tensor]],
edge_index: Adj,
edge_attr: OptTensor = None,
size: Union[None, List[int]] = None) -> Tensor:
if edge_index.nelement() == 0:
out = torch.zeros([
x[1].shape[0],
(len(self.aggregators) * len(self.scalers)) * self.F_in[0]
]).to(device)
else:
out = self.propagate(edge_index,
x=x,
edge_attr=edge_attr,
size=size)
out = torch.cat([x[1], out], dim=-1)
out = self.post_nns(out)
return self.lin(out)
def message(self, x_i, x_j, edge_index: Adj, edge_attr: OptTensor,
size) -> Tensor:
if self.debug:
print('a x_j:', x_j.shape, 'x_i:', x_i.shape, 'edge_attr:',
edge_attr.shape)
if self.step == 0:
x_i = F.leaky_relu(self.atom_fc(x_i)) # code 3
# neighbor_feature => neighbor_fc
x_j = torch.cat([x_j, edge_attr], dim=-1) # code 8
if self.debug:
print('b neighbor_feature i = 0', x_j.shape)
x_j = F.leaky_relu(self.neighbor_fc(x_j)) # code 9
if self.debug:
print('c neighbor_feature i = 0', x_j.shape)
# align score
evu = F.leaky_relu(self.align(torch.cat([x_i, x_j],
dim=-1))) # code 10
if self.debug:
print('d align_score:', evu.shape)
avu = EdgePooling.compute_edge_score_softmax(evu, edge_index,
edge_index.max().item() +
1) # code 11
if self.debug:
print('e attention_weight:', avu.shape)
# to do downscaling 200 fp => 32
c_i = F.elu(torch.mul(avu, self.attend(self.dropout(x_i)))) # code 12
# to do upscaling 32 => 200 fp
if self.debug:
print('f context', c_i.shape)
x_i = self.rnn(c_i, x_i)
if self.debug:
print('g gru', c_i.shape)
return x_i
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, edge_index: Adj,
edge_label_index: OptTensor = None) -> Tensor:
r"""Computes rankings for pairs of nodes.
Args:
edge_index (Tensor or SparseTensor): Edge tensor specifying the
connectivity of the graph.
edge_label_index (Tensor, optional): Edge tensor specifying the
node pairs for which to compute rankings or probabilities.
If :obj:`edge_label_index` is set to :obj:`None`, all edges in
:obj:`edge_index` will be used instead. (default: :obj:`None`)
"""
if edge_label_index is None:
if isinstance(edge_index, SparseTensor):
edge_label_index = torch.stack(edge_index.coo()[:2], dim=0)
else:
edge_label_index = edge_index
out = self.get_embedding(edge_index)
out_src = out[edge_label_index[0]]
out_dst = out[edge_label_index[1]]
return (out_src * out_dst).sum(dim=-1)
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,
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: 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, 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 homophily(edge_index: Adj,
y: Tensor,
batch: OptTensor = None,
method: str = 'edge') -> Union[float, Tensor]:
r"""The homophily of a graph characterizes how likely nodes with the same
label are near each other in a graph.
There are many measures of homophily that fits this definition.
In particular:
- In the `"Beyond Homophily in Graph Neural Networks: Current Limitations
and Effective Designs" <https://arxiv.org/abs/2006.11468>`_ paper, the
homophily is the fraction of edges in a graph which connects nodes
that have the same class label:
.. math::
\frac{| \{ (v,w) : (v,w) \in \mathcal{E} \wedge y_v = y_w \} | }
{|\mathcal{E}|}
That measure is called the *edge homophily ratio*.
- In the `"Geom-GCN: Geometric Graph Convolutional Networks"
<https://arxiv.org/abs/2002.05287>`_ paper, edge homophily is normalized
across neighborhoods:
.. math::
\frac{1}{|\mathcal{V}|} \sum_{v \in \mathcal{V}} \frac{ | \{ (w,v) : w
\in \mathcal{N}(v) \wedge y_v = y_w \} | } { |\mathcal{N}(v)| }
That measure is called the *node homophily ratio*.
- In the `"Large-Scale Learning on Non-Homophilous Graphs: New Benchmarks
and Strong Simple Methods" <https://arxiv.org/abs/2110.14446>`_ paper,
edge homophily is modified to be insensitive to the number of classes
and size of each class:
.. math::
\frac{1}{C-1} \sum_{k=1}^{C} \max \left(0, h_k - \frac{|\mathcal{C}_k|}
{|\mathcal{V}|} \right),
where :math:`C` denotes the number of classes, :math:`|\mathcal{C}_k|`
denotes the number of nodes of class :math:`k`, and :math:`h_k` denotes
the edge homophily ratio of nodes of class :math:`k`.
Thus, that measure is called the *class insensitive edge homophily
ratio*.
Args:
edge_index (Tensor or SparseTensor): The graph connectivity.
y (Tensor): The labels.
batch (LongTensor, optional): Batch vector\
:math:`\mathbf{b} \in {\{ 0, \ldots,B-1\}}^N`, which assigns
each node to a specific example. (default: :obj:`None`)
method (str, optional): The method used to calculate the homophily,
either :obj:`"edge"` (first formula), :obj:`"node"` (second
formula) or :obj:`"edge_insensitive"` (third formula).
(default: :obj:`"edge"`)
"""
assert method in {'edge', 'node', 'edge_insensitive'}
y = y.squeeze(-1) if y.dim() > 1 else y
if isinstance(edge_index, SparseTensor):
row, col, _ = edge_index.coo()
else:
row, col = edge_index
if method == 'edge':
out = torch.zeros(row.size(0), device=row.device)
out[y[row] == y[col]] = 1.
if batch is None:
return float(out.mean())
else:
dim_size = int(batch.max()) + 1
return scatter_mean(out, batch[col], dim=0, dim_size=dim_size)
elif method == 'node':
out = torch.zeros(row.size(0), device=row.device)
out[y[row] == y[col]] = 1.
out = scatter_mean(out, col, 0, dim_size=y.size(0))
if batch is None:
return float(out.mean())
else:
return scatter_mean(out, batch, dim=0)
elif method == 'edge_insensitive':
assert y.dim() == 1
num_classes = int(y.max()) + 1
assert num_classes >= 2
batch = torch.zeros_like(y) if batch is None else batch
num_nodes = degree(batch, dtype=torch.int64)
num_graphs = num_nodes.numel()
batch = num_classes * batch + y
h = homophily(edge_index, y, batch, method='edge')
h = h.view(num_graphs, num_classes)
counts = batch.bincount(minlength=num_classes * num_graphs)
counts = counts.view(num_graphs, num_classes)
proportions = counts / num_nodes.view(-1, 1)
out = (h - proportions).clamp_(min=0).sum(dim=-1)
out /= num_classes - 1
return out if out.numel() > 1 else float(out)
else:
raise NotImplementedError
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_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, 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