DrugEncoder.GCN

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class GCN(nn.Module):
    def __init__(self, x_num_embedding: int = _x_num_embedding, embedding_dim: int = _drug_dim, ft_dim: int = _drug_dim,
                 hid_dim: int = 384, num_layers: int = 2):
        """hid_dim, num_layers"""
        super(GCN, self).__init__()
        assert num_layers >= 2
        self.x_embedding = nn.Embedding(x_num_embedding, embedding_dim)
        self.reset_parameters()
        self.input = GCNConv(embedding_dim, hid_dim)
        self.encode_gcn = nn.ModuleList([GCNConv(hid_dim, hid_dim) for _ in range(num_layers - 2)])
        self.output = GCNConv(hid_dim, ft_dim)

    def reset_parameters(self):
        torch.nn.init.xavier_uniform_(self.x_embedding.weight.data)

    def forward(self, g):
        x, edge_index = g.x, g.edge_index
        x = self.x_embedding(x).sum(1)
        x = F.relu(self.input(x, edge_index))
        for layer in self.encode_gcn:
            x = x + F.relu(layer(x, edge_index))
        x = self.output(x, edge_index)
        return x, g