Fixed a bug with batch_forward

Issue: #3

libs/core/core_base.py

@@ -393,7 +393,7 @@ def train(self, retrain=False):
 
                 with torch.set_grad_enabled(True):
                     # If training, forward with loss, and no top 5 accuracy calculation 
-                    self.batch_forward(inputs, labels, phase="train", retrain=retrain)
+                    self.batch_forward(inputs)
                     self.batch_loss(labels)                   
                     self.batch_backward()
 
@@ -654,7 +654,7 @@ def eval(self, phase='val'):
             with torch.set_grad_enabled(False):
 
                 # In validation or testing
-                self.batch_forward(inputs, labels, phase=phase)
+                self.batch_forward(inputs)
                 self.batch_loss(labels)
                 minibatch_loss_total.append(self.loss.item())
 
@@ -856,4 +856,4 @@ def resume_run(self, saved_dict):
 
 # This is there so that we can use source_import from the utils to import model
 def get_core(*args):
-    return model(*args)
+    return model(*args)