def forward(self, x, edge_index, edge_weight=None):
""""""
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached:
x = self.lin(x)
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
edge_index, norm = GCNConv.norm(edge_index,
x.size(0),
edge_weight,
dtype=x.dtype)
for k in range(self.K):
x = self.propagate(edge_index, x=x, norm=norm)
self.cached_result = x
if self.cached:
x = self.lin(self.cached_result)
return x
def forward(self, x, edge_index, edge_weight=None):
edge_index, norm = GCNConv.norm(edge_index, x.size(0), edge_weight,
dtype=x.dtype)
for k in range(self.K):
x = self.propagate(edge_index, x=x, norm=norm)
return x
def forward(self, x, edge_index, edge_weight=None):
""""""
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached:
x = self.lin(x)
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
edge_index, norm = GCNConv.norm(edge_index,
x.size(0),
edge_weight,
dtype=x.dtype)
for k in range(self.K):
x = self.propagate(edge_index, x=x, norm=norm)
self.cached_result = x
if self.cached:
x = self.lin(self.cached_result)
return x
def forward(self, x, edge_index, edge_weight=None):
""""""
# x: [num_nodes, num_layers, channels]
# edge_index: [2, num_edges]
# edge_weight: [num_edges]
if x.dim() != 3:
raise ValueError('Feature shape must be [num_nodes, num_layers, '
'channels].')
num_nodes, num_layers, channels = x.size()
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
edge_index, norm = GCNConv.norm(edge_index,
x.size(self.node_dim),
edge_weight,
dtype=x.dtype)
self.cached_result = edge_index, norm
edge_index, norm = self.cached_result
return self.propagate(edge_index, x=x, norm=norm)
def forward(self, x, edge_index, edge_weight=None):
edge_index, norm = GCNConv.norm(edge_index, x.size(0), edge_weight,
dtype=x.dtype)
xs = [x]
for k in range(self.K):
xs.append(self.propagate(edge_index, x=xs[-1], norm=norm))
return torch.cat(xs, dim = 1)
def forward(self, x, edge_index, edge_weight=None):
""""""
if not self.cached or self.cached_result is None:
edge_index, norm = GCNConv.norm(
edge_index, x.size(0), edge_weight, dtype=x.dtype)
for k in range(self.K):
x = self.propagate(edge_index, x=x, norm=norm)
self.cached_result = x
return self.lin(self.cached_result)
def forward(self, x, edge_index, edge_weight=None):
""""""
edge_index, norm = GCNConv.norm(
edge_index, x.size(0), edge_weight, dtype=x.dtype)
hidden = x
for k in range(self.K):
x = self.propagate('add', edge_index, x=x, norm=norm)
x = x * (1 - self.alpha)
x = x + self.alpha * hidden
return x
def forward(self, x, edge_index, edge_weight=None):
edge_index, norm = GCNConv.norm(edge_index, x.size(0), edge_weight, dtype=x.dtype)
preds = []
preds.append(x)
for k in range(self.K):
x = self.propagate(edge_index, x=x, norm=norm)
preds.append(x)
pps = torch.stack(preds, dim=1)
retain_score = self.proj(pps)
retain_score = retain_score.squeeze()
retain_score = torch.sigmoid(retain_score)
retain_score = retain_score.unsqueeze(1)
out = torch.matmul(retain_score, pps).squeeze()
return out
def forward(self, x, edge_index, edge_weight=None):
""""""
# X: [num_nodes, num_layers, channels]
# Edge Index: [2, num_edges]
# Edge Weight: [num_edges]
if x.dim() != 3:
raise ValueError('Feature shape must be [num_nodes, num_layers, '
'channels].')
num_nodes, num_layers, channels = x.size()
if not self.cached or self.cached_result is None:
edge_index, norm = GCNConv.norm(
edge_index, x.size(0), edge_weight, dtype=x.dtype)
self.cached_result = edge_index, norm
edge_index, norm = self.cached_result
return self.propagate(edge_index, x=x, norm=norm)