models_sag.py

from torch_geometric.nn import GCNConv
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
from layers import SAGPool

import torch
import torch.nn.functional as F
import Analysis
import pickle


class Net(torch.nn.Module):
    def __init__(self,args):
        super(Net, self).__init__()
        self.args = args
        self.num_features = args.num_features
        self.nhid = args.nhid
        self.num_classes = args.num_classes
        self.pooling_ratio = args.pooling_ratio
        self.dropout_ratio = args.dropout_ratio
        
        self.conv1 = GCNConv(self.num_features, self.nhid)
        self.pool1 = SAGPool(self.nhid, ratio=self.pooling_ratio)
        self.conv2 = GCNConv(self.nhid, self.nhid)
        self.pool2 = SAGPool(self.nhid, ratio=self.pooling_ratio)
        self.conv3 = GCNConv(self.nhid, self.nhid)
        self.pool3 = SAGPool(self.nhid, ratio=self.pooling_ratio)

        self.lin1 = torch.nn.Linear(self.nhid*2, self.nhid)
        self.lin2 = torch.nn.Linear(self.nhid, self.nhid//2)
        self.lin3 = torch.nn.Linear(self.nhid//2, self. num_classes)

        self.LogSoftmax = torch.nn.LogSoftmax()
    def forward(self, data):
        x, edge_index, edge_attr, batch = data.x, data.edge_index, data.edge_attr, data.batch

        x = F.relu(self.conv1(x, edge_index, edge_weight = edge_attr))
        
        # Save weights of SAG Model
        if Analysis.is_trained:
          #w_dict = {"w": x}
          w_dict = {"w": self.conv1.weight}
          with open("SAG_W.pickle", 'wb') as f:
            pickle.dump(w_dict, f)
            print("SAG Weights are saved.")
            print(x)
          Analysis.is_trained = False
        
        
        x, edge_index, edge_attr, batch, _ = self.pool1(x, edge_index, edge_attr, batch)
        
        x1 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)

        x = F.relu(self.conv2(x.float(), edge_index, edge_weight=edge_attr))
        x, edge_index, edge_attr, batch, _ = self.pool2(x, edge_index, edge_attr, batch)
        x2 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)

        x = F.relu(self.conv3(x.float(), edge_index, edge_weight=edge_attr))
        x, edge_index, edge_attr, batch, _ = self.pool3(x, edge_index, edge_attr, batch)
        x3 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)

        x = x1 + x2 + x3

        x = F.relu(self.lin1(x.float()))
        x = F.dropout(x, p=self.dropout_ratio, training=self.training)
        x = F.relu(self.lin2(x))
        
        x = F.log_softmax(self.lin3(x), dim=-1) # original
        #x = F.relu(self.lin3(x))
        return x