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 edge_softmax
and 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")[0]
# 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")