[docs]classHGNN(nn.Module):r"""The HGNN model proposed in `Hypergraph Neural Networks <https://arxiv.org/pdf/1809.09401>`_ paper (AAAI 2019). Parameters: ``in_channels`` (``int``): :math:`C_{in}` is the number of input channels. ``hid_channels`` (``int``): :math:`C_{hid}` is the number of hidden channels. ``num_classes`` (``int``): The Number of class of the classification task. ``use_bn`` (``bool``): If set to ``True``, use batch normalization. Defaults to ``False``. ``drop_rate`` (``float``, optional): Dropout ratio. Defaults to 0.5. """def__init__(self,in_channels:int,hid_channels:int,num_classes:int,use_bn:bool=False,drop_rate:float=0.5,)->None:super().__init__()self.layers=nn.ModuleList()self.layers.append(HGNNConv(in_channels,hid_channels,use_bn=use_bn,drop_rate=drop_rate))self.layers.append(HGNNConv(hid_channels,num_classes,use_bn=use_bn,is_last=True))
[docs]defforward(self,X:torch.Tensor,hg:"Hypergraph")->torch.Tensor:r"""The forward function. Parameters: ``X`` (``torch.Tensor``): Input vertex feature matrix. Size :math:`(N, C_{in})`. ``hg`` (``eg.Hypergraph``): The hypergraph structure that contains :math:`N` vertices. """# import time# start = time.time()# X = self.laplacian @ self.Theta1(self.dropout(X))# end = time.time()# # print("lal:",end-start)# X = self.act(X)# X = self.laplacian @ self.Theta2(X)# return Xforlayerinself.layers:X=layer(X,hg)returnX