import math import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.parameter import Parameter from .. import BaseModel, register_model class GraphConvolution(nn.Module): """ Simple GCN layer, similar to https://arxiv.org/abs/1609.02907 """ def __init__(self, in_features, out_features, bias=True): super(GraphConvolution, self).__init__() self.in_features = in_features self.out_features = out_features self.weight = Parameter(torch.FloatTensor(in_features, out_features)) if bias: self.bias = Parameter(torch.FloatTensor(out_features)) else: self.register_parameter("bias", None) self.reset_parameters() def reset_parameters(self): stdv = 1.0 / math.sqrt(self.weight.size(1)) self.weight.data.normal_(-stdv, stdv) if self.bias is not None: self.bias.data.normal_(-stdv, stdv) def forward(self, input, edge_index): adj = torch.sparse_coo_tensor( edge_index, torch.ones(edge_index.shape[1]).float(), (input.shape[0], input.shape[0]), ).to(input.device) support = torch.mm(input, self.weight) output = torch.spmm(adj, support) if self.bias is not None: return output + self.bias else: return output def __repr__(self): return ( self.__class__.__name__ + " (" + str(self.in_features) + " -> " + str(self.out_features) + ")" ) @register_model("gcn") class TKipfGCN(BaseModel): @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" # fmt: off parser.add_argument("--num-features", type=int) parser.add_argument("--num-classes", type=int) parser.add_argument("--hidden-size", type=int, default=64) parser.add_argument("--dropout", type=float, default=0.5) # fmt: on @classmethod def build_model_from_args(cls, args): return cls(args.num_features, args.hidden_size, args.num_classes, args.dropout) def __init__(self, nfeat, nhid, nclass, dropout): super(TKipfGCN, self).__init__() self.gc1 = GraphConvolution(nfeat, nhid) self.gc2 = GraphConvolution(nhid, nclass) self.dropout = dropout # self.nonlinear = nn.SELU() def forward(self, x, adj): x = F.relu(self.gc1(x, adj)) # h1 = x x = F.dropout(x, self.dropout, training=self.training) x = self.gc2(x, adj) # x = F.relu(x) # x = torch.sigmoid(x) # return x # h2 = x return F.log_softmax(x, dim=-1) def loss(self, data): return F.nll_loss( self.forward(data.x, data.edge_index)[data.train_mask], data.y[data.train_mask], ) def predict(self, data): return self.forward(data.x, data.edge_index)