some model bugs fixed

drug/models.py

@@ -29,6 +29,7 @@ class GCN(torch.nn.Module):
 
     def forward(self, x, edge_index):
         # First Message Passing Layer (Transformation)
+        x = x.to(torch.float32)
         x = self.conv1(x, edge_index)
         x = x.relu()
         x = F.dropout(x, p=0.5, training=self.training)

predictor/cross_validation.py

@@ -76,6 +76,7 @@ def train_model(model, optimizer, loss_func, train_loader, valid_loader, n_epoch
         trn_loss /= train_loader.dataset_len
         val_loss = eval_epoch(model, valid_loader, loss_func, gpu_id)
         val_loss /= valid_loader.dataset_len
+        print("loss epoch " + str(epoch) + ": " + str(trn_loss))
         if val_loss < min_loss:
             angry = 0
             min_loss = val_loss
@@ -104,7 +105,7 @@ def create_model(data, hidden_size, gpu_id=None):
 def cv(args, out_dir):
     save_args(args, os.path.join(out_dir, 'args.json'))
     test_loss_file = os.path.join(out_dir, 'test_loss.pkl')
-
+    print("cuda available: " + str(torch.cuda.is_available()))
     if torch.cuda.is_available() and (args.gpu is not None):
         gpu_id = args.gpu
     else:

predictor/model/models.py

@@ -26,10 +26,11 @@ class Connector(nn.Module):
         x = self.ddiDataset.get().x
         edge_index = self.ddiDataset.get().edge_index
         x = self.gcn(x, edge_index)
+        drug1_idx = torch.flatten(drug1_idx)
+        drug2_idx = torch.flatten(drug2_idx)
         drug1_feat = x[drug1_idx]
         drug2_feat = x[drug2_idx]
         feat = torch.cat([drug1_feat, drug2_feat, cell_feat], 1)
-
         return feat