graph.edge_index,graph.edge_attr=remove_self_loops(graph.edge_index,graph.edge_attr) graph 可以看到,原来是存在7条自环的。 然后有向图转无向图: graph.edge_index,graph.edge_attr=to_undirected(graph.edge_index,graph.edge_attr,reduce='add') graph 最后加入自环: graph.edge_index,graph.edge_at...
to_undirected: 转换成无向图还是有向图(True/False,默认False), remove_self_loops: 是否将图中的loop移除(True/False,默认False), ) ■■Case1:转换时,不指定 node_attrs、edge_attrs、graph_attrs参数。 从输出结果来看,这种情况to_networkx()只会把PyG对象的节点(nodes)和边(edges)转换...
RemoveTrainingClasses。根据train_mask将训练集中的标签抹去,创造zero-shot learning的场景; RandomNodeSplit。随机对节点样本进行划分,创建train_mask、valid_mask、test_mask属性给到原本的Dataset对象; RandomLinkSplit。同节点划分; AddMetaPath。增加一个类型为“metapath”的边,操作源于论文"Heterogenous Graph Attenti...
from torch_geometric.utils import remove_self_loops import numpy as np dim=64 from torch_geometric.nn import DataParallel class MyTransform(object): def call(self, data): # Specify target. data.y = data.y[:, target] return data class Complete(object): def call(self, data): device = da...
'add_self_loops': <function add_self_loops at 0x7f6deb483b00>, 'is_torch_sparse_tensor': <function is_torch_sparse_tensor at 0x7f6deb492ac0>, 'remove_self_loops': <function remove_self_loops at 0x7f6deb493560>, 'softmax': <function softmax at 0x7f6deb490e00>, 'set_sparse_val...
Support for heterogeneous graph transformations intransforms.AddSelfLoopstransforms.ToSparseTensortransforms.ToUndirected datasets.OGB_MAG Support for converting heterogeneous graphs to "typed" homogeneous ones viadata.HeteroData.to_homogeneous examplePyTorch Lightning ...
remove_edge_index() edge_index从 中 同步删除一个元组GraphStore。返回删除是否成功 remove_tensor() 从 中删除一个张量FeatureStore。返回删除是否成功 requires_grad_() 跟踪梯度计算,无论是针对所有属性还是仅针对 中给出的属性*args share_memory_() 将所有属性或仅 中给出的属性移动到共享内存*args ...
(self, key: str) -> Any: if '_store' not in self.__dict__: raise RuntimeError( "The 'data' object was created by an older version of PyG. " "If this error occurred while loading an already existing " "dataset, remove the 'processed/' directory in the dataset's " "root ...
It looks like your GNN layer adds self-loops to the graph, that's why you have x_j.size(0) = 25 + 56 = 81. Can you remove this logic? Thanks dear @rusty1s : I think that's the point, so x_j is the aggregated message from node j, then concatenate it with the edge_attr ...