InMemoryDataset基类简介 在PyG中,我们通过继承InMemoryDataset类来自定义一个数据可全部存储到内存的数据集类。 classInMemoryDataset(root:Optional[str]=None,transform:Optional[Callable]=None,pre_transform:Optional[Callable]=None,pre_filter:Optional[Callable]=None) InMemoryDataset类初始化方法参数说明: root:字...
此外要将样本分为训练集、验证集和测试集三个集合。 PyG中为我们提供了现成的方法,train_test_split_edges(data, val_ratio=0.05, test_ratio=0.1),其第一个参数为torch_geometric.data.Data对象,第二参数为验证集所占比例,第三个参数为测试集所占比例。该函数将自动地采样得到负样本,并将正负样本分成训练集、...
🐛 Describe the bug import torch_geometric.datasets as pyg dataset = pyg.QM9('./qm9') error message: Downloading https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/molnet_publish/qm9.zip Extracting qm9/raw/qm9.zip Downloading https:...
train/ 包含训练集的图像和对应的掩膜文件。valid/ 包含验证集的图像和对应的掩膜文件。数据配置文件 虽...
🐛 Describe the bug I am following the examples in PyG documentation to download some benchmarking datasets. In particular, I tried to download the ENZYMES dataset by followin the code from torch_geometric.datasets import TUDataset datase...
以公开数据集PubMed为例子。PubMed数据集存储的是文章引用网络,文章对应图的结点,如果两篇文章存在引用关系(无论引用与被引),则这两篇文章对应的结点之间存在边。该数据集来源于论文Revisiting Semi-Supervised Learning with Graph Embeddings。我们直接基于PyG中的Planetoid类修改得到下面的PlanetoidPubMed数据集类。
For benchmarking, please refer to its variant UPFD-POL and UPFD-GOS. The dataset has been integrated with Pytorch Geometric (PyG) and Deep Graph Library (DGL). You can load the dataset after installing the latest versions of PyG or DGL. The UPFD datas
这个目录通常包含已经处理过的数据集文件,这些文件可能不兼容当前版本的PyG库。 bash rm -rf /path/to/dataset/processed/ 请将/path/to/dataset/替换为你的数据集实际存储路径。 尝试重新加载数据集: 在删除processed/目录后,尝试重新加载数据集。如果你使用的是PyG的torch_geometric.datasets模块来加载数据集,...
This repository contains three graph datasets for the UE traffic assignment problem on Sioux-Falls, Eastern-Massachusetts and Anaheim networks in both dgl and pyg formats. The datasets are generated and used to train and evaluate models for solving the User Equilibrium (UE) problem on three transpo...
The goal of this project is to reproduce the work done in ProtienMPNN while tying it as closely to the existing GNN+LLM frameworks in PyG. We recommend using as many existing features as possible from PyG. Additional features which you feel will be reusable for other workflows should be add...