resolve NeighborLoader exit with no error message

predictor/cross_validation.py

@@ -20,13 +20,14 @@ from torch_geometric.loader import NeighborLoader
 
 
 def find_subgraph_mask(ddi_graph, batch):
-    print('----------------------------------')
+    # print('----------------------------------')
     drug1_id, drug2_id, cell_feat, y_true = batch
     drug1_id = torch.flatten(drug1_id)
     drug2_id = torch.flatten(drug2_id)
     drug_ids = torch.cat((drug1_id, drug2_id), 0)
     drug_ids, count = drug_ids.unique(return_counts=True)
-    print(drug_ids)
+    # Removing negative ids
+    drug_ids = (drug_ids >= 0).nonzero(as_tuple=True)[0]
     return drug_ids
 
  
@@ -62,26 +63,34 @@ def step_batch(model, batch, loss_func, subgraph, gpu_id=None, train=True):
 
 
 def train_epoch(model, loader, loss_func, optimizer, ddi_graph, gpu_id=None):
-    print('this is in train epoch: ', ddi_graph)
+    # print('this is in train epoch: ', ddi_graph)
     model.train()
     epoch_loss = 0
     
     for _, batch in enumerate(loader):
         # TODO: for batch ... desired subgraph
         subgraph_mask = find_subgraph_mask(ddi_graph, batch)
-        print(ddi_graph)
+        # print("subgraph_mask: ",subgraph_mask) ---> subgraph_mask is OK
+        # print("This is ddi_graph:")
+        # print(ddi_graph) ---> ddi_graph is OK 
         neighbor_loader  = NeighborLoader(ddi_graph,
-                              num_neighbors=[3,2], batch_size=100,
+                              num_neighbors=[3,2], batch_size=10,
                               input_nodes=subgraph_mask,
                               directed=False, shuffle=True)
         
+
+        # sampled_data = next(iter(neighbor_loader))
+        # print("Sampled_data: ")
+        # print(sampled_data.batch_size)
+        
         for subgraph in neighbor_loader:
             optimizer.zero_grad()
             loss = step_batch(model, batch, loss_func, subgraph, gpu_id)
             loss.backward()
             optimizer.step()
             epoch_loss += loss.item()
-            
+    
+    print("epoch_loss: ", epoch_loss)
     return epoch_loss
 
 
@@ -98,11 +107,13 @@ def eval_epoch(model, loader, loss_func, gpu_id=None):
 def train_model(model, optimizer, loss_func, train_loader, valid_loader, n_epoch, patience, gpu_id,
                 ddi_graph,
                 sl=False, mdl_dir=None):
-    print('this is in train: ', ddi_graph)
+    # print('this is in train: ', ddi_graph)
     min_loss = float('inf')
     angry = 0
     for epoch in range(1, n_epoch + 1):
+        print("epoch: ",str(epoch))
         trn_loss = train_epoch(model, train_loader, loss_func, optimizer, ddi_graph, gpu_id)
+        print("train loss: ", trn_loss)
         trn_loss /= train_loader.dataset_len
         val_loss = eval_epoch(model, valid_loader, loss_func, gpu_id)
         val_loss /= valid_loader.dataset_len

predictor/model/datasets.py

@@ -111,8 +111,8 @@ class FastSynergyDataset(Dataset):
         # print('-----------------------------')
         # print(self.samples)
         # print('-----------------')
-        print(self.samples[0])
-        print(self.samples[i][0] and self.samples[i][1] for i in indices)
+        # print(self.samples[0])
+        # print(self.samples[i][0] and self.samples[i][1] for i in indices)
         d1 = torch.cat([torch.unsqueeze(self.samples[i][0], 0) for i in indices], dim=0)
         d2 = torch.cat([torch.unsqueeze(self.samples[i][1], 0) for i in indices], dim=0)
         c = torch.cat([torch.unsqueeze(self.samples[i][2], 0) for i in indices], dim=0)

predictor/model/models.py

@@ -25,8 +25,13 @@ class Connector(nn.Module):
 
     def forward(self, drug1_idx, drug2_idx, cell_feat, subgraph):
         if self.gcn == None:
+            print("here is for initializing the GCN. num_features: ", subgraph.num_features)
             self.gcn = GCN(subgraph.num_features, subgraph.num_features // 2)
-        x = subgraph.get().x
+        print("this is subgraph: --------------")
+        print(subgraph)
+        # graph.get().x --> DDInteractionDataset
+        # subgraph = graph.get() --> Data 
+        x = subgraph.x
         edge_index = subgraph.edge_index
         x = self.gcn(x, edge_index)
         drug1_idx = torch.flatten(drug1_idx)