fix demo details

demo/distillation/distillation_demo.py

@@ -140,43 +140,40 @@ def compress(args):
     # define teacher program
     teacher_program = fluid.Program()
     t_startup = fluid.Program()
-    teacher_scope = fluid.Scope()
-    with fluid.scope_guard(teacher_scope):
-        with fluid.program_guard(teacher_program, t_startup):
-            with fluid.unique_name.guard():
-                image = fluid.layers.data(
-                    name='image', shape=image_shape, dtype='float32')
-                predict = teacher_model.net(image, class_dim=class_dim)
-
-            #print("="*50+"teacher_model_params"+"="*50)
-            #for v in teacher_program.list_vars():
-            #    print(v.name, v.shape)
-
-        exe.run(t_startup)
-        _download('http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar', '.')
-        _decompress('./ResNet50_pretrained.tar')
-        assert args.teacher_pretrained_model and os.path.exists(
-            args.teacher_pretrained_model
-        ), "teacher_pretrained_model should be set when teacher_model is not None."
-
-        def if_exist(var):
-            return os.path.exists(
-                os.path.join(args.teacher_pretrained_model, var.name)
-            ) and var.name != 'fc_0.w_0' and var.name != 'fc_0.b_0'
-
-        fluid.io.load_vars(
-            exe,
-            args.teacher_pretrained_model,
-            main_program=teacher_program,
-            predicate=if_exist)
+    with fluid.program_guard(teacher_program, t_startup):
+        with fluid.unique_name.guard():
+            image = fluid.layers.data(
+                name='image', shape=image_shape, dtype='float32')
+            predict = teacher_model.net(image, class_dim=class_dim)
+
+        #print("="*50+"teacher_model_params"+"="*50)
+        #for v in teacher_program.list_vars():
+        #    print(v.name, v.shape)
+
+    exe.run(t_startup)
+    _download('http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar', '.')
+    _decompress('./ResNet50_pretrained.tar')
+    assert args.teacher_pretrained_model and os.path.exists(
+        args.teacher_pretrained_model
+    ), "teacher_pretrained_model should be set when teacher_model is not None."
+
+    def if_exist(var):
+        return os.path.exists(
+            os.path.join(args.teacher_pretrained_model, var.name)
+        ) and var.name != 'fc_0.w_0' and var.name != 'fc_0.b_0'
+
+    fluid.io.load_vars(
+        exe,
+        args.teacher_pretrained_model,
+        main_program=teacher_program,
+        predicate=if_exist)
 
     data_name_map = {'image': 'image'}
     main = merge(
         teacher_program,
         student_program,
         data_name_map,
-        place,
-        teacher_scope=teacher_scope)
+        place)
 
     with fluid.program_guard(main, s_startup):
         l2_loss_v = l2_loss("teacher_fc_0.tmp_0", "fc_0.tmp_0", main)