fix the name problem

paddleslim/dist/single_distiller.py

@@ -38,21 +38,25 @@ def merge(teacher_program,
         name_prefix(str): Name prefix added for all vars of the teacher program.
     Return(Program): Merged program.
     """
-    teacher_program = teacher_program.clone(for_test = True)
+    teacher_program = teacher_program.clone(for_test=True)
     for teacher_var in teacher_program.list_vars():
+        skip_rename = False
         if teacher_var.name != 'fetch' and teacher_var.name != 'feed':
             if teacher_var.name in data_name_map.keys():
                 new_name = data_name_map[teacher_var.name]
+                if new_name == teacher_var.name:
+                    skip_rename = True
             else:
                 new_name = name_prefix + teacher_var.name
-            # scope var rename
-            scope_var = teacher_scope.var(teacher_var.name).get_tensor()
-            renamed_scope_var = teacher_scope.var(new_name).get_tensor()
-            renamed_scope_var.set(np.array(scope_var), place)
-
-            # program var rename
-            renamed_var = teacher_program.global_block()._rename_var(
-                teacher_var.name, new_name)
+            if not skip_rename:
+                # scope var rename
+                scope_var = teacher_scope.var(teacher_var.name).get_tensor()
+                renamed_scope_var = teacher_scope.var(new_name).get_tensor()
+                renamed_scope_var.set(np.array(scope_var), place)
+    
+                # program var rename
+                renamed_var = teacher_program.global_block()._rename_var(
+                    teacher_var.name, new_name)
 
     for teacher_var in teacher_program.list_vars():
         if teacher_var.name != 'fetch' and teacher_var.name != 'feed':
@@ -148,7 +152,6 @@ def soft_label_loss(teacher_var_name,
             teacher_feature_map before softmax. default: 1.0
         student_temperature(float): Temperature used to divide 
             student_feature_map before softmax. default: 1.0
-
     Return(Variable): l2 distiller loss.
     """
     student_var = program.global_block().var(student_var_name)
@@ -162,13 +165,12 @@ def soft_label_loss(teacher_var_name,
     return soft_label_loss
 
 
-def self_defined_loss(program, loss_func, **kwargs):
+def loss(program, loss_func, **kwargs):
     """
     Combine variables from student model and teacher model by self defined loss.
     Args:
         program(Program): The input distiller program. 
         loss_func(function): The user self defined loss function. 
-
     Return(Variable): self defined distiller loss.
     """
     func_parameters = {}