def __init__(self,
edges,
num_nodes=None,
node_feat=None,
edge_feat=None,
**kwargs):
if node_feat is not None:
self._node_feat = node_feat
else:
self._node_feat = {}
if edge_feat is not None:
self._edge_feat = edge_feat
else:
self._edge_feat = {}
if not check_is_tensor(edges):
if isinstance(edges, np.ndarray):
if edges.dtype != "int64":
edges = edges.astype("int64")
else:
edges = np.array(edges, dtype="int64")
self._edges = edges
if num_nodes is None:
self._num_nodes = maybe_num_nodes(self._edges)
else:
self._num_nodes = num_nodes
max_edge_id = maybe_num_nodes(self._edges)
if not isinstance(max_edge_id, paddle.fluid.framework.Variable
) and self._num_nodes < max_edge_id:
raise ValueError("The max edge ID should be less than the number of nodes. "
"But got max edge ID [%s] >= num_nodes [%s]" \
% (max_edge_id-1, self._num_nodes))
self._adj_src_index = kwargs.get("adj_src_index", None)
self._adj_dst_index = kwargs.get("adj_dst_index", None)
if check_is_tensor(self._num_nodes, self._edges,
*list(self._node_feat.values()),
*list(self._edge_feat.values())):
self._is_tensor = True
elif self._adj_src_index is not None and self._adj_src_index.is_tensor(
):
self._is_tensor = True
elif self._adj_dst_index is not None and self._adj_dst_index.is_tensor(
):
self._is_tensor = True
else:
self._is_tensor = False
if self._is_tensor:
# ensure all variable is tenosr
if not check_is_tensor(self._num_nodes):
self._num_nodes = paddle.to_tensor(self._num_nodes)
if not check_is_tensor(self._edges):
self._edges = paddle.to_tensor(self._edges)
for key in self._node_feat:
if not check_is_tensor(self._node_feat[key]):
self._node_feat[key] = paddle.to_tensor(
self._node_feat[key])
for key in self._edge_feat:
if not check_is_tensor(self._edge_feat[key]):
self._edge_feat[key] = paddle.to_tensor(
self._edge_feat[key])
if self._adj_src_index is not None:
if not self._adj_src_index.is_tensor():
self._adj_src_index.tensor(inplace=True)
if self._adj_dst_index is not None:
if not self._adj_dst_index.is_tensor():
self._adj_dst_index.tensor(inplace=True)
# preprocess graph level informations
self._process_graph_info(**kwargs)
self._nodes = None
def __init__(self,
edges,
num_nodes=None,
node_feat=None,
edge_feat=None,
**kwargs):
if node_feat is not None:
self._node_feat = node_feat
else:
self._node_feat = {}
if edge_feat is not None:
self._edge_feat = edge_feat
else:
self._edge_feat = {}
if not check_is_tensor(edges):
if isinstance(edges, np.ndarray):
if edges.dtype != "int64":
edges = edges.astype("int64")
else:
edges = np.array(edges, dtype="int64")
self._edges = edges
if num_nodes is None:
self._num_nodes = maybe_num_nodes(self._edges)
else:
self._num_nodes = num_nodes
self._adj_src_index = kwargs.get("adj_src_index", None)
self._adj_dst_index = kwargs.get("adj_dst_index", None)
if check_is_tensor(self._num_nodes, self._edges,
*list(self._node_feat.values()),
*list(self._edge_feat.values())):
self._is_tensor = True
elif self._adj_src_index is not None and self._adj_src_index.is_tensor(
):
self._is_tensor = True
elif self._adj_dst_index is not None and self._adj_dst_index.is_tensor(
):
self._is_tensor = True
else:
self._is_tensor = False
if self._is_tensor:
# ensure all variable is tenosr
if not check_is_tensor(self._num_nodes):
self._num_nodes = paddle.to_tensor(self._num_nodes)
if not check_is_tensor(self._edges):
self._edges = paddle.to_tensor(self._edges)
for key in self._node_feat:
if not check_is_tensor(self._node_feat[key]):
self._node_feat[key] = paddle.to_tensor(
self._node_feat[key])
for key in self._edge_feat:
if not check_is_tensor(self._edge_feat[key]):
self._edge_feat[key] = paddle.to_tensor(
self._edge_feat[key])
if self._adj_src_index is not None:
if not self._adj_src_index.is_tensor():
self._adj_src_index.tensor(inplace=True)
if self._adj_dst_index is not None:
if not self._adj_dst_index.is_tensor():
self._adj_dst_index.tensor(inplace=True)
# preprocess graph level informations
self._process_graph_info(**kwargs)
self._nodes = None