fix save distillation model (#567)

ppcls/utils/save_load.py

@@ -73,13 +73,13 @@ def load_dygraph_pretrain(model, path=None, load_static_weights=False):
 def load_distillation_model(model, pretrained_model, load_static_weights):
     logger.info("In distillation mode, teacher model will be "
                 "loaded firstly before student model.")
-    assert len(pretrained_model
-               ) == 2, "pretrained_model length should be 2 but got {}".format(
-                   len(pretrained_model))
-    assert len(
-        load_static_weights
-    ) == 2, "load_static_weights length should be 2 but got {}".format(
-        len(load_static_weights))
+
+    if not isinstance(pretrained_model, list):
+        pretrained_model = [pretrained_model]
+
+    if not isinstance(load_static_weights, list):
+        load_static_weights = [load_static_weights]
+
     teacher = model.teacher if hasattr(model,
                                        "teacher") else model._layers.teacher
     student = model.student if hasattr(model,
@@ -88,16 +88,16 @@ def load_distillation_model(model, pretrained_model, load_static_weights):
         teacher,
         path=pretrained_model[0],
         load_static_weights=load_static_weights[0])
-    logger.info(
-        logger.coloring("Finish initing teacher model from {}".format(
-            pretrained_model), "HEADER"))
+    logger.info("Finish initing teacher model from {}".format(
+        pretrained_model))
+    # load student model
+    if len(pretrained_model) >= 2:
         load_dygraph_pretrain(
             student,
             path=pretrained_model[1],
             load_static_weights=load_static_weights[1])
-    logger.info(
-        logger.coloring("Finish initing student model from {}".format(
-            pretrained_model), "HEADER"))
+        logger.info("Finish initing student model from {}".format(
+            pretrained_model))
 
 
 def init_model(config, net, optimizer=None):
@@ -123,11 +123,7 @@ def init_model(config, net, optimizer=None):
     load_static_weights = config.get('load_static_weights', False)
     use_distillation = config.get('use_distillation', False)
     if pretrained_model:
-        if isinstance(pretrained_model,
-                      list):  # load distillation pretrained model
-            if not isinstance(load_static_weights, list):
-                load_static_weights = [load_static_weights] * len(
-                    pretrained_model)
+        if use_distillation:
             load_distillation_model(net, pretrained_model, load_static_weights)
         else:  # common load
             load_dygraph_pretrain(