Optimize train_CSL_graph_classification.py

train/train_CSL_graph_classification.py

@@ -16,108 +16,120 @@ def train_epoch_sparse(model, optimizer, device, data_loader, epoch):
     epoch_loss = 0
     epoch_train_acc = 0
     nb_data = 0
-    gpu_mem = 0
+
+    optimizer.zero_grad()
+
     for iter, (batch_graphs, batch_labels) in enumerate(data_loader):
-        batch_x = batch_graphs.ndata['feat'].to(device)  # num x feat
+        batch_x = batch_graphs.ndata['feat'].to(device)
         batch_e = batch_graphs.edata['feat'].to(device)
         batch_graphs = batch_graphs.to(device)
         batch_labels = batch_labels.to(device)
-        optimizer.zero_grad()
+
         try:
             batch_pos_enc = batch_graphs.ndata['pos_enc'].to(device)
             sign_flip = torch.rand(batch_pos_enc.size(1)).to(device)
-            sign_flip[sign_flip>=0.5] = 1.0; sign_flip[sign_flip<0.5] = -1.0
+            sign_flip[sign_flip >= 0.5] = 1.0
+            sign_flip[sign_flip < 0.5] = -1.0
             batch_pos_enc = batch_pos_enc * sign_flip.unsqueeze(0)
             batch_scores = model.forward(batch_graphs, batch_x, batch_e, batch_pos_enc)
         except:
             batch_scores = model.forward(batch_graphs, batch_x, batch_e)
-        loss = model.loss(batch_scores, batch_labels) 
+
+        loss = model.loss(batch_scores, batch_labels)
         loss.backward()
-        optimizer.step()
-        epoch_loss += loss.detach().item()
+
+        epoch_loss += loss.item()
         epoch_train_acc += accuracy(batch_scores, batch_labels)
         nb_data += batch_labels.size(0)
+
+        optimizer.step()
+        optimizer.zero_grad()
+
     epoch_loss /= (iter + 1)
     epoch_train_acc /= nb_data
-    
+
     return epoch_loss, epoch_train_acc, optimizer
 
+
 def evaluate_network_sparse(model, device, data_loader, epoch):
     model.eval()
     epoch_test_loss = 0
     epoch_test_acc = 0
     nb_data = 0
+
     with torch.no_grad():
         for iter, (batch_graphs, batch_labels) in enumerate(data_loader):
             batch_x = batch_graphs.ndata['feat'].to(device)
             batch_e = batch_graphs.edata['feat'].to(device)
             batch_graphs = batch_graphs.to(device)
             batch_labels = batch_labels.to(device)
+
             try:
                 batch_pos_enc = batch_graphs.ndata['pos_enc'].to(device)
                 batch_scores = model.forward(batch_graphs, batch_x, batch_e, batch_pos_enc)
             except:
                 batch_scores = model.forward(batch_graphs, batch_x, batch_e)
-            loss = model.loss(batch_scores, batch_labels) 
-            epoch_test_loss += loss.detach().item()
+
+            loss = model.loss(batch_scores, batch_labels)
+            epoch_test_loss += loss.item()
             epoch_test_acc += accuracy(batch_scores, batch_labels)
             nb_data += batch_labels.size(0)
-        epoch_test_loss /= (iter + 1)
-        epoch_test_acc /= nb_data
-
-    return epoch_test_loss, epoch_test_acc
 
+    epoch_test_loss /= (iter + 1)
+    epoch_test_acc /= nb_data
 
+    return epoch_test_loss, epoch_test_acc
 
 
-"""
-    For WL-GNNs 
-"""
-def train_epoch_dense(model, optimizer, device, data_loader, epoch, batch_size):
+def train_epoch_dense(model, optimizer, device, data_loader, epoch):
     model.train()
     epoch_loss = 0
     epoch_train_acc = 0
     nb_data = 0
-    gpu_mem = 0
+
     optimizer.zero_grad()
+
     for iter, (x_with_node_feat, labels) in enumerate(data_loader):
         x_with_node_feat = x_with_node_feat.to(device)
         labels = labels.to(device)
-        
+
         scores = model.forward(x_with_node_feat)
-        loss = model.loss(scores, labels) 
+        loss = model.loss(scores, labels)
         loss.backward()
-
-        if not (iter%batch_size):
-            optimizer.step()
-            optimizer.zero_grad()
-
-        epoch_loss += loss.detach().item()
+
+        epoch_loss += loss.item()
         epoch_train_acc += accuracy(scores, labels)
         nb_data += labels.size(0)
+
+        optimizer.step()
+        optimizer.zero_grad()
+
     epoch_loss /= (iter + 1)
     epoch_train_acc /= nb_data
-    
+
     return epoch_loss, epoch_train_acc, optimizer
 
+
 def evaluate_network_dense(model, device, data_loader, epoch):
     model.eval()
     epoch_test_loss = 0
     epoch_test_acc = 0
     nb_data = 0
+
     with torch.no_grad():
         for iter, (x_with_node_feat, labels) in enumerate(data_loader):
             x_with_node_feat = x_with_node_feat.to(device)
             labels = labels.to(device)
-            
+
             scores = model.forward(x_with_node_feat)
-            loss = model.loss(scores, labels) 
-            epoch_test_loss += loss.detach().item()
+            loss = model.loss(scores, labels)
+            epoch_test_loss += loss.item()
             epoch_test_acc += accuracy(scores, labels)
             nb_data += labels.size(0)
-        epoch_test_loss /= (iter + 1)
-        epoch_test_acc /= nb_data
-
+
+    epoch_test_loss /= (iter + 1)
+    epoch_test_acc /= nb_data
+
     return epoch_test_loss, epoch_test_acc
 
 
@@ -128,4 +140,5 @@ def check_patience(all_losses, best_loss, best_epoch, curr_loss, curr_epoch, cou
         best_epoch = curr_epoch
     else:
         counter += 1
+
     return best_loss, best_epoch, counter