AFGraphLib/GraphLib/model.py at main · lmlearning/AFGraphLib · GitHub

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import dgl
import dgl.function as fn
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.pytorch import GraphConv

class GCN(nn.Module):
    def __init__(self,
                 g,
                 in_feats,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 dropout):
        super(GCN, self).__init__()
        self.g = g
        self.layers = nn.ModuleList()
        # input layer
        self.layers.append(GraphConv(in_feats, n_hidden, activation=activation))
        # hidden layers
        for i in range(n_layers - 1):
            self.layers.append(GraphConv(n_hidden, n_hidden, activation=activation))
        # output layer
        self.layers.append(GraphConv(n_hidden, n_classes))
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, features):
        h = features
        for i, layer in enumerate(self.layers):
            if i != 0:
                h = self.dropout(h)
            h = layer(self.g, h)
        return h

class GCNResNet(nn.Module):

    def __init__(self,
                 g,
                 in_feats,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 dropout):
        super(GCNResNet, self).__init__()
        self.orig_features = None
        self.g = g
        self.layers = nn.ModuleList()
        # input layer
        self.layers.append(GraphConv(in_feats, n_hidden, activation=activation))
        # hidden layers
        for i in range(n_layers - 1):
            self.layers.append(GraphConv(n_hidden, n_hidden, activation=activation))
        # output layer
        self.layers.append(GraphConv(n_hidden, n_classes))
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, features):
        add_orig = True
        if self.orig_features is None:
            self.orig_features = features
            add_org = False

        h = features
        for i, layer in enumerate(self.layers):
            if i != 0:
                #print(self.orig_features[,])
                #h += self.orig_features[:,1]
                h = self.dropout(h)
            h = layer(self.g, h)
        #print(h.size())
        if add_orig:
            #print(h)
            h[:,0].add_(  self.orig_features[:,1] )
            #print(h)
            return h
        else:
            return h