Merge pull request #55 from Archaic-Atom/refator_code_in_background_jack

fix some bugs

Source/JackFramework/Core/build_training_graph.py

@@ -92,14 +92,18 @@ def __pass_data2device(self, data: list) -> list:
 
         return data
 
-    def __calculation_process(self, model_item: object, input_data: list,
-                              model_id: int) -> tuple:
-        output_data, _, _ = self.__init_calculation_result()
+    def __calculation_process(self, model_item: object, input_data: list, label_data: list,
+                              model_id: int, is_training: bool = True) -> tuple:
+        output_data, loss, acc = self.__init_calculation_result()
 
         # get ouput
         output_data = self.__jf_model.inference(model_item, input_data, model_id)
+        # loss and acc
+        if is_training:
+            loss = self.__jf_model.loss(output_data, label_data, model_id)
+            acc = self.__jf_model.accuary(output_data, label_data, model_id)
 
-        return output_data
+        return output_data, loss, acc
 
     def __variable2tensor(self, data: list) -> None:
         res = []
@@ -174,15 +178,9 @@ def train_model(self, input_data: list, label_data: list) -> list:
         input_data = self.__pass_data2device(input_data)
         label_data = self.__pass_data2device(label_data)
 
-        output_data_list = []
         for i, model_item in enumerate(self.__model):
             self.__opt[i].zero_grad()
-            output_data = self.__calculation_process(model_item, input_data, i)
-            output_data_list.append(output_data)
-
-        for i, output_data in enumerate(output_data_list):
-            loss = self.__jf_model.loss(output_data, label_data, i)
-            acc = self.__jf_model.accuary(output_data, label_data, i)
+            _, loss, acc = self.__calculation_process(model_item, input_data, label_data, i)
 
             loss[self.OPT_LOSS_ID].backward()
             self.__opt[i].step()
@@ -202,14 +200,8 @@ def val_model(self, input_data: list, label_data: list) -> list:
         label_data = self.__pass_data2device(label_data)
 
         with torch.no_grad():
-            output_data_list = []
             for i, model_item in enumerate(self.__model):
-                output_data = self.__calculation_process(model_item, input_data, i)
-                output_data_list.append(output_data)
-
-            for i, output_data in enumerate(output_data_list):
-                loss = self.__jf_model.loss(output_data, label_data, i)
-                acc = self.__jf_model.accuary(output_data, label_data, i)
+                _, loss, acc = self.__calculation_process(model_item, input_data, label_data, i)
                 tower_loss_iteration.append(self.__variable2tensor(loss))
                 tower_acc_iteration.append(self.__variable2tensor(acc))