KNNGraph(k=6), transform=T.RandomJitter(0.01)) dataset[0] >>> Data(edge_index=[2, 15108], pos=[2518, 3], y=[2518]) 3 GCN实现 3.1 谱方法GCN实现(Cora引文节点分类例子) 3.1.1 加载数据。 from torch_geometric.datasets import Planetoid dataset = Planetoid(root='/tmp/Cora', name='...
def forward(self, x, batch=None): # 每次都会重新计算KNN近邻图,调用torch_cluster库函数 edge_index = knn_graph(x, self.k, batch, loop=False, flow=self.flow) # 调用父类的forward函数 return super(DynamicEdgeConv, self).forward(x, edge_index) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. ...
KNNGraph(k=6)) # dataset[0]: Data(edge_index=[2, 15108], pos=[2518, 3], y=[2518]) 还可以通过transform在一定范围内随机平移每个点,增加坐标上的扰动,做数据增强: import torch_geometric.transforms as T from torch_geometric.datasets import ShapeNet dataset = ShapeNet(root='/tmp/ShapeNet', ...
dataset = ShapeNet(root='Airplane', categories=['Airplane'], pre_transform=T.KNNGraph(k=6)) # 进行 KNN 聚类操作 1. 2. 3. 结果: Data(x=[2518, 3], y=[2518], pos=[2518, 3], category=[1]) Process finished with exit code 0 1. 2. 3. 当然,仅仅打印dataset[0]无法可视化两者的差...
pre_transform=T.KNNGraph(k=6))# dataset[0]: Data(edge_index=[2, 15108], pos=[2518, 3], y=[2518]) 还可以通过transform在一定范围内随机平移每个点,增加坐标上的扰动,做数据增强: importtorch_geometric.transformsasTfromtorch_geometric.datasetsimportShapeNet ...
❓ Questions & Help Hi, I'm new to pytorch_geometric, while trying to call knn_graph() I encountered the following error: TypeError: knn_graph() takes from 2 to 6 positional arguments but 7 were given It is raises from the initialization ...
可以发现,虽然还没有做任何特征提取的工作,但 MNIST 的数据已经呈现出聚类的效果,相同数字之间距离更近一些(有没有想到 KNN 分类器)。我们还可以点击左下方的T-SNE,用 t-SNE 的方法进行可视化。add_embedding方法需要注意的几点: mat是二维 MxN,metadata是一维 N,label_img是四维 NxCxHxW!
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) ...
import torch from torch_cluster import knn_graph x = torch.tensor([[-1., -1.], [-1., 1.], [1., -1.], [1., 1.]]) batch = torch.tensor([0, 0, 0, 0]) edge_index = knn_graph(x, k=2, batch=batch, loop=False) print(edge_index) tensor([[1, 2, 0, 3, 0, 3...
内容提示: Pytorch复现STGCN:基于图卷积时空神经⽹络在交通速度中的预测1:论⽂信息来⾃IJCAI 2018的⼀篇论⽂:《Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for TrafficForecasting 》1.1:论⽂思路使⽤Kipf & Welling 2017的近似谱图卷积得到的图卷积作为空间上的卷积操作,...