def forward()

in experiments/codes/model/gat/layers.py [0:0]

• 24 lines of code
• 8 McCabe index (conditional complexity)

    def forward(self, x, edge_index, edge_attr, params, param_name_dict, size=None):
        self.att = get_param(params, param_name_dict, "att")
        self.edge_update = get_param(params, param_name_dict, "edge_update")
        self.bias = None
        if self.use_bias:
            self.bias = get_param(params, param_name_dict, "bias")
        if size is None and torch.is_tensor(x):
            edge_index, _ = remove_self_loops(edge_index)
            edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))

        # edge_index = add_self_loops(edge_index, num_nodes=x.size(0))
        self_loop_edges = torch.zeros(x.size(0), edge_attr.size(1)).to(
            edge_index.device
        )
        edge_attr = torch.cat([edge_attr, self_loop_edges], dim=0)  # (500, 10)
        # Note: we need to add blank edge attributes for self loops
        weight = get_param(params, param_name_dict, "weight")
        if torch.is_tensor(x):
            x = torch.matmul(x, weight)
        else:
            x = (
                None if x[0] is None else torch.matmul(x[0], weight),
                None if x[1] is None else torch.matmul(x[1], weight),
            )
        # x = x.view(-1, self.heads, self.out_channels)
        # x = torch.mm(x, weight).view(-1, self.heads, self.out_channels)
        return self.propagate(
            edge_index, size=size, x=x, num_nodes=x.size(0), edge_attr=edge_attr
        )