Pytorch Geometric has a really great documentation. It has helper functions for data loading, data transformers, batching specific to graph data structures, ...
class Sequential (input_args: str, modules: List [Union [Tuple [Callable, str], Callable]]) [source] ¶. An extension of the torch.nn.Sequential container in order to define a sequential GNN model. Since GNN operators take in multiple input arguments, torch_geometric.nn.Sequential expects both global input arguments, and function header definitions of individual operators.
Advanced Mini-Batching — pytorch_geometric 2.0.4 documentation Advanced Mini-Batching ¶ The creation of mini-batching is crucial for letting the training of a deep learning model scale to huge amounts of data.
PyG Documentation ¶ PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to ...
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to ...
PyTorch Geometric Temporal Documentation ¶ PyTorch Geometric Temporal is a temporal graph neural network extension library for PyTorch Geometric. It builds on open-source deep-learning and graph processing libraries.
In general, Data tries to mimic the behaviour of a regular Python dictionary. In addition, it provides useful functionality for analyzing graph structures, and provides basic PyTorch tensor functionalities. See here for the accompanying tutorial. from torch_geometric.data import Data data = Data(x=x, edge_index=edge_index, ...)
PyG Documentation ¶ PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
Installation via Pip Wheels¶. We have outsourced a lot of functionality of PyG to other packages, which needs to be installed in advance. These packages come with their own CPU and GPU kernel implementations based on the PyTorch C++/CUDA extension interface.We provide pip wheels for these packages for all major OS/PyTorch/CUDA combinations: