diff --git a/core/model.py b/core/model.py
index 2d34b5b068b71ac480575b08e95d67d0208f3841..cfe71f2a5db76b2585b6df23915724909ef68033 100755
--- a/core/model.py
+++ b/core/model.py
@@ -87,9 +87,13 @@ class Model(object):
         if dataset_class == "DataLoader":
             self._init_dataloader()
 
-    def _init_dataloader(self):
+    def _init_dataloader(self, is_infer=False):
+        if is_infer:
+            data = self._infer_data_var
+        else:
+            data = self._data_var
         self._data_loader = fluid.io.DataLoader.from_generator(
-            feed_list=self._data_var,
+            feed_list=data,
             capacity=64,
             use_double_buffer=False,
             iterable=False)
diff --git a/core/trainers/single_trainer.py b/core/trainers/single_trainer.py
index 501afd477a671d054e86011e34d8fc39640e6f91..db1bc8efb43e937b9e5446f55b3ec1a6e5bf5544 100755
--- a/core/trainers/single_trainer.py
+++ b/core/trainers/single_trainer.py
@@ -93,7 +93,10 @@ class SingleTrainer(TranspileTrainer):
         for model_dict in self._env["executor"]:
             if model_dict["dataset_name"] == dataset_name:
                 model = self._model[model_dict["name"]][3]
-                inputs = model.get_inputs()
+                if model_dict["is_infer"]:
+                    inputs = model._infer_data_var
+                else:
+                    inputs = model._data_var
                 dataset.set_use_var(inputs)
                 break
         return dataset
@@ -158,23 +161,32 @@ class SingleTrainer(TranspileTrainer):
                             envs.path_adapter(self._env["workspace"]))
                         model = envs.lazy_instance_by_fliename(
                             model_path, "Model")(self._env)
-                        model._data_var = model.input_data(
-                            dataset_name=model_dict["dataset_name"])
+                        is_infer = model_dict.get("is_infer", False)
+                        if is_infer:
+                            model._infer_data_var = model.input_data(
+                                dataset_name=model_dict["dataset_name"])
+                        else:
+                            model._data_var = model.input_data(
+                                dataset_name=model_dict["dataset_name"])
                         if envs.get_global_env("dataset." + dataset_name +
                                                ".type") == "DataLoader":
-                            model._init_dataloader()
+                            model._init_dataloader(is_infer=is_infer)
                             self._get_dataloader(dataset_name,
                                                  model._data_loader)
-                        model.net(model._data_var,
-                                  is_infer=model_dict.get("is_infer", False))
-                        optimizer = model._build_optimizer(opt_name, opt_lr,
-                                                           opt_strategy)
-                        optimizer.minimize(model._cost)
+                        if is_infer:
+                            model.net(model._infer_data_var, True)
+                        else:
+                            model.net(model._data_var, False)
+                            optimizer = model._build_optimizer(opt_name, opt_lr,
+                                                               opt_strategy)
+                            optimizer.minimize(model._cost)
+                        model_dict["is_infer"] = is_infer
             self._model[model_dict["name"]][0] = train_program
             self._model[model_dict["name"]][1] = startup_program
             self._model[model_dict["name"]][2] = scope
             self._model[model_dict["name"]][3] = model
             self._model[model_dict["name"]][4] = train_program.clone()
+            
 
         for dataset in self._env["dataset"]:
             if dataset["type"] != "DataLoader":
@@ -223,7 +235,10 @@ class SingleTrainer(TranspileTrainer):
         fetch_vars = []
         fetch_alias = []
         fetch_period = 20
-        metrics = model_class.get_metrics()
+        if model_dict["is_infer"]:
+            metrics = model_class.get_infer_results()
+        else:
+            metrics = model_class.get_metrics()
         if metrics:
             fetch_vars = metrics.values()
             fetch_alias = metrics.keys()
@@ -231,24 +246,36 @@ class SingleTrainer(TranspileTrainer):
         program = self._model[model_name][0]
         reader = self._dataset[reader_name]
         with fluid.scope_guard(scope):
-            self._exe.train_from_dataset(
-                program=program,
-                dataset=reader,
-                fetch_list=fetch_vars,
-                fetch_info=fetch_alias,
-                print_period=fetch_period)
+            if model_dict["is_infer"]:
+                self._exe.infer_from_dataset(
+                    program=program,
+                    dataset=reader,
+                    fetch_list=fetch_vars,
+                    fetch_info=fetch_alias,
+                    print_period=fetch_period)
+            else:
+                self._exe.train_from_dataset(
+                    program=program,
+                    dataset=reader,
+                    fetch_list=fetch_vars,
+                    fetch_info=fetch_alias,
+                    print_period=fetch_period)
 
     def _executor_dataloader_train(self, model_dict):
         reader_name = model_dict["dataset_name"]
         model_name = model_dict["name"]
         model_class = self._model[model_name][3]
         program = self._model[model_name][0].clone()
-        program = fluid.compiler.CompiledProgram(
-            program).with_data_parallel(loss_name=model_class.get_avg_cost().name)
+        if not model_dict["is_infer"]:
+            program = fluid.compiler.CompiledProgram(
+                program).with_data_parallel(loss_name=model_class.get_avg_cost().name)
         fetch_vars = []
         fetch_alias = []
         fetch_period = 20
-        metrics = model_class.get_metrics()
+        if model_dict["is_infer"]:
+            metrics = model_class.get_infer_results()
+        else:
+            metrics = model_class.get_metrics()
         if metrics:
             fetch_vars = metrics.values()
             fetch_alias = metrics.keys()