def __init__()

in experiments/codes/model/rgcn/rgcn.py [0:0]

• 54 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self, config):
        super(CompositionRGCNEncoder, self).__init__(config)
        self.name = "CompositionRGCNConv"
        self.rgcn_layers = []
        for l in range(config.model.rgcn.num_layers):
            in_channels = config.model.relation_embedding_dim
            out_channels = config.model.relation_embedding_dim
            num_bases = config.model.relation_embedding_dim
            uniform_size = num_bases * in_channels

            basis = torch.Tensor(size=(num_bases, in_channels, out_channels)).to(
                config.general.device
            )
            basis.requires_grad = True
            self.add_weight(
                basis,
                "{}.{}.basis".format(self.name, l),
                initializer=(uniform, uniform_size),
                weight_norm=config.model.weight_norm,
            )

            if config.model.rgcn.root_weight:
                root = torch.Tensor(size=(in_channels, out_channels)).to(
                    config.general.device
                )
                root.requires_grad = True
                self.add_weight(
                    root,
                    "{}.{}.root".format(self.name, l),
                    initializer=(uniform, uniform_size),
                    weight_norm=config.model.weight_norm,
                )

            if config.model.rgcn.bias:
                bias = torch.Tensor(size=(out_channels,)).to(config.general.device)
                bias.requires_grad = True
                self.add_weight(
                    bias,
                    "{}.{}.bias".format(self.name, l),
                    initializer=(uniform, uniform_size),
                    weight_norm=config.model.weight_norm,
                )

            self.rgcn_layers.append(
                RGCNConv(
                    in_channels,
                    out_channels,
                    config.model.num_classes,
                    num_bases,
                    root_weight=config.model.rgcn.root_weight,
                    bias=config.model.rgcn.bias,
                )
            )

        ## Add classify params
        in_class_dim = (
            config.model.relation_embedding_dim * 2
            + config.model.relation_embedding_dim
        )
        self.add_classify_weights(in_class_dim)