def edge_index_from_dict(graph_dict, num_nodes=None):
row, col = [], []
for key, value in graph_dict.items():
row.append(np.repeat(key, len(value)))
col.append(value)
_row = np.concatenate(np.array(row))
_col = np.concatenate(np.array(col))
edge_index = np.stack([_row, _col], axis=0)
row_dom = edge_index[:, _row > _col]
col_dom = edge_index[:, _col > _row][[1, 0]]
edge_index = np.concatenate([row_dom, col_dom], axis=1)
_row, _col = edge_index
edge_index = np.stack([_row, _col], axis=0)
order = np.lexsort((_col, _row))
edge_index = edge_index[:, order]
edge_index = torch.tensor(edge_index, dtype=torch.long)
# There may be duplicated edges and self loops in the datasets.
edge_index, _ = remove_self_loops(edge_index)
row = torch.cat([edge_index[0], edge_index[1]])
col = torch.cat([edge_index[1], edge_index[0]])
row, col, _ = coalesce(row, col)
edge_index = torch.stack([row, col])
return edge_index
def forward(self, A):
filter = F.softmax(self.weight, dim=1)
num_channels = filter.shape[0]
results = []
for i in range(num_channels):
for j, (edge_index, edge_value) in enumerate(A):
if j == 0:
total_edge_index = edge_index
total_edge_value = edge_value * filter[i][j]
else:
total_edge_index = torch.cat((total_edge_index, edge_index), dim=1)
total_edge_value = torch.cat((total_edge_value, edge_value * filter[i][j]))
row, col = total_edge_index.detach()
row, col, value = coalesce(row, col, total_edge_value)
index = torch.stack([row, col])
results.append((index, value))
return results