Using Customized GNN
Sometimes you would like to design your own GNN module or use GNN for other purposes. In this chapter, we introduce how to use GNN layer in CogDL to write your own GNN model and how to write a GNN layer from scratch.
GNN layers in CogDL to Define model
CogDL has implemented popular GNN layers in
cogdl.layers, and they can serve as modules to help design new GNNs.
Here is how we implement Jumping Knowledge Network (JKNet) with
GCNLayer in CogDL.
JKNet collects the output of all layers and concatenate them together to get the result:
import torch from cogdl.layers import GCNLayer from cogdl.models import BaseModel class JKNet(BaseModel): def __init__(self, in_feats, out_feats, hidden_size, num_layers): super(JKNet, self).__init__() shapes = [in_feats] + [hidden_size] * num_layers self.layers = nn.ModuleList([ GCNLayer(shapes[i], shapes[i+1]) for i in range(num_layers) ]) self.fc = nn.Linear(hidden_size * num_layers, out_feats) def forward(self, graph): # symmetric normalization of adjacency matrix graph.sym_norm() h = graph.x out =  for layer in self.layers: h = layer(graph,h) out.append(h) out = torch.cat(out, dim=1) return self.fc(out)
Define your GNN Module
In most cases, you may build a layer module with new message propagation and aggragation scheme. Here the code snippet
shows how to implement a GCNLayer using
Graph and efficient sparse matrix operators in CogDL.
import torch from cogdl.utils import spmm class GCNLayer(torch.nn.Module): """ Args: in_feats: int Input feature size out_feats: int Output feature size """ def __init__(self, in_feats, out_feats): super(GCNLayer, self).__init__() self.fc = torch.nn.Linear(in_feats, out_feats) def forward(self, graph, x): h = self.fc(x) h = spmm(graph, h) return h
spmm is sparse matrix multiplication operation frequently used in GNNs.
Sparse matrix is stored in
Graph and will be called automatically. Message-passing in spatial space is equivalent to
matrix operations. CogDL also supports other efficient operators like
multi_head_spmm, you can refer
to this page for usage.
Use Custom models with CogDL
Now that you have defined your own GNN, you can use dataset/task in CogDL to immediately train and evaluate the performance of your model.
data = build_dataset_from_name("cora") # Use the JKNet model as defined above model = JKNet(data.num_features, data.num_classes, 32, 4) experiment(model=model, dataset="cora", mw="node_classification_mw", dw="node_classification_dw")