importtorch_geometric.transformsasTfromtorch_geometric.datasetsimportShapeNetdataset=ShapeNet(root='/tmp/ShapeNet',categories=['Airplane'],pre_transform=T.KNNGraph(k=6),transform=T.RandomTranslate(0.01))# dataset[0]: Data(edge_index=[2, 15108], pos=[2518, 3], y=[2518]) 模型搭建 这里只是展示...
在torch_geometric 的依赖库 torch_cluster 当中,已经实现了 knn 算法,如下图,我进行了如下操作: 随机生成了5个点的坐标 利用knn算法,找到了这些点之间的关系 根据pos 和 edge_index 构建 data 以上就是 torch_geometric.data.Data 的内容了, 后续本篇专栏还会不断的更新有关 Batch , Dataset , MessagePassing...
if knn_graph is None: raise ImportError('`DynamicEdgeConv` requires `torch-cluster`.') self.k = k def forward(self, x, batch=None): 9 changes: 8 additions & 1 deletion 9 torch_geometric/nn/conv/x_conv.py Original file line numberDiff line numberDiff line change @@ -5,11 +5,15...
kNN-Graph Computes graph edges to the nearestkpoints. Args: x(Tensor): Node feature matrix of shape[N, F]. k(int): The number of neighbors. batch(LongTensor, optional): Batch vector of shape[N], which assigns each node to a specific example.batchneeds to be sorted. (default:None) ...