padding added

GraphTransformer.py

     print('Loaded')
     return graphs
 
+
 def feature_matrix(g):
     '''
     constructs the feautre matrix (N x 3) for the enzymes datasets
         piazche[i][v-1] = 1
     return piazche
 
+
 # def remove_random_node(graph, max_size=40, min_size=10):
 #     '''
 #     removes a random node from the gragh
 def prepare_graph_data(graph, max_size=40, min_size=10):
   '''
   gets a graph as an input
-  returns a graph with a randomly removed node adj matrix [0], its feature matrix [0], the removed node true links [2]
+  returns a graph with a randomly removed node adj matrix [0], its feature matrix [1], the removed node true links [2]
   '''
   if len(graph.nodes()) >= max_size or len(graph.nodes()) < min_size:
     return None
         y = torch.matmul(y, self.weight.double())
         return y
 
+
 class GraphAttn(nn.Module):
   def __init__(self, heads, model_dim, dropout=0.1):
     super().__init__()
 
     return output
 
+
 class FeedForward(nn.Module):
     def __init__(self, input_size, hidden_size):
         super().__init__()
         output = self.fully_connected2(relu)
         output = self.sigmoid(output)
         return output
+
+
+class Hydra(nn.Module):
+  def __init__(self, gcn_input, model_dim, head):
+    super().__init__()
+
+    self.GCN = GraphConv(input_dim=gcn_input, output_dim=model_dim).cuda()
+    self.GAT = GraphAttn(heads=head, model_dim=model_dim).cuda()
+    self.MLP = FeedForward(input_size=model_dim, hidden_size=gcn_input).cuda()
+
+  def forward(self, x, adj):
+    gcn_outputs = self.GCN(x, adj)
+    gat_output = self.GAT(gcn_outputs)
+    mlp_output = self.MLP(gat_output)
+
+    return output