using new load api (#2372)

dygraph/mnist/mnist_dygraph.py

@@ -148,7 +148,8 @@ def inference_mnist():
     with fluid.dygraph.guard(place):
         mnist_infer = MNIST("mnist")
         # load checkpoint
-        mnist_infer.load_dict(fluid.dygraph.load_persistables("save_dir"))
+        model_dict, _ = fluid.dygraph.load_persistables("save_dir")
+        mnist_infer.load_dict(model_dict)
         print("checkpoint loaded")
 
         # start evaluate mode

dygraph/reinforcement_learning/test_actor_critic_load.py

@@ -174,7 +174,8 @@ with fluid.dygraph.guard():
         return returns
 
     running_reward = 10
-    policy.load_dict(fluid.dygraph.load_persistables(args.save_dir))
+    model_dict, _ = fluid.dygraph.load_persistables(args.save_dir)
+    policy.load_dict(model_dict)
 
     state, ep_reward = env.reset(), 0
     for t in range(1, 10000):  # Don't infinite loop while learning

dygraph/reinforcement_learning/test_reinforce_load.py

@@ -161,7 +161,8 @@ with fluid.dygraph.guard():
 
     running_reward = 10
     state, ep_reward = env.reset(), 0
-    policy.load_dict(fluid.dygraph.load_persistables(args.save_dir))
+    model_dict, _ = fluid.dygraph.load_persistables(args.save_dir)
+    policy.load_dict(model_dict)
 
     for t in range(1, 10000):  # Don't infinite loop while learning
         state = np.array(state).astype("float32")

dygraph/sentiment/main.py

@@ -231,7 +231,7 @@ def infer():
         print('Do inferring ...... ')
         total_acc, total_num_seqs = [], []
 
-        restore = fluid.dygraph.load_persistables(args.checkpoints)
+        restore, _ = fluid.dygraph.load_persistables(args.checkpoints)
         cnn_net_infer.load_dict(restore)
         cnn_net_infer.eval()