import torch from torch_geometric.nn import MessagePassing from torch_geometric.utils import add_self_loops, degree class GCNConv(MessagePassing): def __init__(self, in_channels, out_channels): super(GCNConv, self).__init__(aggr='add') self.lin = torch.nn.Linear(in_channels, out_channe...
1.torch-scatter2.torch-sparse3.torch-cluster4.torch-spline-conv5.torch-geometric 其中1-4的步骤是利用离线的安装包在本地进行安装,命令为 pip install +本地的路径+文件名称,最后一个安装包是利用镜像源下载,命令为 pip install torch-geometric +镜像源;到此本次的安装就全部结束。 Ps: 1. 镜像源: -i...
1.torch-scatter 2.torch-sparse 3.torch-cluster 4.torch-spline-conv 5.torch-geometric 其中1-4的步骤是利用离线的安装包在本地进行安装,命令为 pip install +本地的路径+文件名称,最后一个安装包是利用镜像源下载,命令为 pip install torch-geometric +镜像源;到此本次的安装就全部结束。 Ps: 镜像源: -...
获取并分析数据集 from torch_geometric.datasets import Planetoid from torch_geometric.transforms import NormalizeFeatures dataset = Planetoid(root='dataset', name='Cora', transform=NormalizeFeatures()) print() print(f'Dataset: {dataset}:') print('===') print(f'Number of graphs: {len(dataset)}...
7 6 from torch_geometric.data import Data 8 7 from torch_geometric.utils import degree, segregate_self_loops 9 8 from torch_geometric.utils.repeat import repeat 10 9 11 10 from .data import size_repr 12 11 12 + try: 13 + from torch_cluster import neighbor_sampler 14 + exc...
Source File: test_glob.py From pytorch_geometric with MIT License 6 votes def test_permuted_global_pool(): N_1, N_2 = 4, 6 x = torch.randn(N_1 + N_2, 4) batch = torch.cat([torch.zeros(N_1), torch.ones(N_2)]).to(torch.long) perm = torch.randperm(N_1 + N_2) px...
Source File: local_degree_profile.py From pytorch_geometric with MIT License 6 votes def __call__(self, data): row, col = data.edge_index N = data.num_nodes deg = degree(row, N, dtype=torch.float) deg_col = deg[col] min_deg, _ = scatter_min(deg_col, row, dim_size=N) ...
class torch.distributions.geometric.Geometric(probs=None, logits=None, validate_args=None)[source] Bases: torch.distributions.distribution.Distribution Creates a Geometric distribution parameterized by probs, where probs is the probability of success of Bernoulli trials. It represents the probability that...
Quiver is a distributed graph learning library forPyTorch Geometric(PyG). The goal of Quiver is to make distributed graph learning easy-to-use and achieve high-performance. Release 0.2.0 is out! In the latest releasetorch-quiver==0.2.0, we have added support for efficient GNN serving and fa...
As will be apparent, the present torch cutting machine is desiged to flame cut structural steel shapes and plates in straight line, geometric or intricate shapes. Solid state electronic tracer 20 may follow a black line or paper template 40, so as to automatically guide the torches, cutting ...