tutorial of PyG

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Some simple knowledge of PyG

Basic

Data

A single graph in PyG is described by an instance of torch_geometric.data.Data, which holds the following attributes by default:

  1. data.x: Node feature matrix [num_nodes,num_node_features_dim]

  2. data.edge_index: Graph connectivity in COO format with shape [2, num_edges] and type torch.long

  3. data.edge_attr: Edge feature matrix with shape [num_edges, num_edge_features_dim]

  4. data.y: Target to train(label). e.g., node-level targets of shape [num_nodes, *] or graph-level targets of shape [1, *]

    ...

example:

image-20230523172031612 
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import torch
from torch_geometric.data import Data

edge_index = torch.tensor([[0, 1],
                           [1, 0],
                           [1, 2],
                           [2, 1]], dtype=torch.long)
x = torch.tensor([[-1], [0], [1]], dtype=torch.float)

##t()会让原变量和新变量直接有依赖（类似浅拷贝），contiguous()断开依赖
data = Data(x=x, edge_index=edge_index.t().contiguous())
>>> Data(edge_index=[2, 4], x=[3, 1])

Note： Although the graph has only two edges, we need to define four index tuples to account for both directions of an edge.

operation

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print(data.keys)
>>> ['x', 'edge_index']

print(data['x'])
>>> tensor([[-1.0],
            [0.0],
            [1.0]])

for key, item in data:
    print(f'{key} found in data')
>>> x found in data
>>> edge_index found in data

'edge_attr' in data
>>> False

data.num_nodes
>>> 3

data.num_edges
>>> 4

data.num_node_features
>>> 1

data.has_isolated_nodes()
>>> False

data.has_self_loops()
>>> False

data.is_directed()
>>> False

# Transfer data object to GPU.
device = torch.device('cuda')
data = data.to(device)

Minibatch

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from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader

dataset = TUDataset(root='/tmp/ENZYMES', name='ENZYMES', use_node_attr=True)
loader = DataLoader(dataset, batch_size=32, shuffle=True)

for batch in loader:
    batch
    >>> DataBatch(batch=[1082], edge_index=[2, 4066], x=[1082, 21], y=[32])

    batch.num_graphs
    >>> 32

Message Passing Network