Class GCNModel

src/models.py:11–135

The model for the single kind of deepgcn blocks. The model architecture likes: inputlayer(nfeat)--block(nbaselayer, nhid)--...--outputlayer(nclass)--softmax(nclass) |------ nhidlayer ----| The total layer is nhidlayer*nbaselayer + 2.

class GCNModel(nn.Module):
    """
       The model for the single kind of deepgcn blocks.

       The model architecture likes:
       inputlayer(nfeat)--block(nbaselayer, nhid)--...--outputlayer(nclass)--softmax(nclass)
                           |------  nhidlayer  ----|
       The total layer is nhidlayer*nbaselayer + 2.
       All options are configurable.
    """

    def __init__(self,
                 nfeat,
                 nhid,
                 nclass,
                 nhidlayer,
                 dropout,
                 baseblock="mutigcn",
                 inputlayer="gcn",
                 outputlayer="gcn",
                 nbaselayer=0,
                 activation=lambda x: x,
                 withbn=True,
                 withloop=True,
                 aggrmethod="add",
                 mixmode=False):
        """
        Initial function.
        :param nfeat: the input feature dimension.
        :param nhid:  the hidden feature dimension.
        :param nclass: the output feature dimension.
        :param nhidlayer: the number of hidden blocks.
        :param dropout:  the dropout ratio.
        :param baseblock: the baseblock type, can be "mutigcn", "resgcn", "densegcn" and "inceptiongcn".
        :param inputlayer: the input layer type, can be "gcn", "dense", "none".
        :param outputlayer: the input layer type, can be "gcn", "dense".
        :param nbaselayer: the number of layers in one hidden block.
        :param activation: the activation function, default is ReLu.
        :param withbn: using batch normalization in graph convolution.
        :param withloop: using self feature modeling in graph convolution.
        :param aggrmethod: the aggregation function for baseblock, can be "concat" and "add". For "resgcn", the default
                           is "add", for others the default is "concat".
        :param mixmode: enable cpu-gpu mix mode. If true, put the inputlayer to cpu.
        """
        super(GCNModel, self).__init__()
        self.mixmode = mixmode
        self.dropout = dropout

        if baseblock == "resgcn":
            self.BASEBLOCK = ResGCNBlock
        elif baseblock == "densegcn":
            self.BASEBLOCK = DenseGCNBlock
        elif baseblock == "mutigcn":
            self.BASEBLOCK = MultiLayerGCNBlock
        elif baseblock == "inceptiongcn":
            self.BASEBLOCK = InecptionGCNBlock
        else:
            raise NotImplementedError("Current baseblock %s is not supported." % (baseblock))