Building our model with pytorch-geometric

PROTEINS_embedding.py

# Import everything we need to build our network:
from torch.nn import Linear
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.nn import global_mean_pool

# Define our GCN class as a pytorch Module
class GCN(torch.nn.Module):
    def __init__(self, hidden_channels):
        super(GCN, self).__init__()
        # We inherit from pytorch geometric's GCN class, and we initialize three layers
        self.conv1 = GCNConv(dataset.num_node_features, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, hidden_channels)
        self.conv3 = GCNConv(hidden_channels, hidden_channels)
        # Our final linear layer will define our output
        self.lin = Linear(hidden_channels, dataset.num_classes)
        
    def forward(self, x, edge_index, batch):
      # 1. Obtain node embeddings 
      x = self.conv1(x, edge_index)
      x = x.relu()
      x = self.conv2(x, edge_index)
      x = x.relu()
      x = self.conv3(x, edge_index)
      
      # 2. Readout layer
      x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]

      # 3. Apply a final classifier
      x = F.dropout(x, p=0.5, training=self.training)
      x = self.lin(x)
      return x
    
model = GCN(hidden_channels=64)
print(model)