diff --git a/mindinsight/datavisual/data_transform/ms_data_loader.py b/mindinsight/datavisual/data_transform/ms_data_loader.py
index afe42fe6b492d8b5d5346eedd7ed458680c22466..b3d87e34fb1561cc5e056f28d8f7c3eb60ffa86d 100644
--- a/mindinsight/datavisual/data_transform/ms_data_loader.py
+++ b/mindinsight/datavisual/data_transform/ms_data_loader.py
@@ -416,6 +416,44 @@ class _SummaryParser(_Parser):
 
         return event_str
 
+    @staticmethod
+    def _parse_summary_value(value, plugin):
+        """
+        Parse summary value and create corresponding container according to plugin.
+
+        Args:
+            value (Summary.Value): Value message in summary file.
+            plugin (str): Plugin value.
+
+        Returns:
+            Union[Summary.Value, HistogramContainer, TensorContainer, ImageContainer], original summary value
+            or an instance of  HistogramContainer or TensorContainer or ImageContainer.
+        """
+        tensor_event_value = getattr(value, plugin)
+        if plugin == PluginNameEnum.HISTOGRAM.value:
+            tensor_event_value = HistogramContainer(tensor_event_value)
+            # Drop steps if original_buckets_count exceeds HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT
+            # to avoid time-consuming re-sample process.
+            if tensor_event_value.histogram.original_buckets_count > Histogram.MAX_ORIGINAL_BUCKETS_COUNT:
+                logger.info('original_buckets_count exceeds '
+                            'HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT')
+                return None
+
+        elif plugin == PluginNameEnum.TENSOR.value:
+            tensor_event_value = TensorContainer(tensor_event_value)
+            tensor_count = 1
+            for d in tensor_event_value.dims:
+                tensor_count *= d
+            if tensor_count > MAX_TENSOR_COUNT:
+                logger.warning('tag: %s/tensor, dims: %s, tensor count: %d exceeds %d and drop it.',
+                               value.tag, tensor_event_value.dims, tensor_count, MAX_TENSOR_COUNT)
+                return None
+
+        elif plugin == PluginNameEnum.IMAGE.value:
+            tensor_event_value = ImageContainer(tensor_event_value)
+
+        return tensor_event_value
+
     @staticmethod
     def _event_parse(event_str, latest_file_name):
         """
@@ -424,7 +462,7 @@ class _SummaryParser(_Parser):
         This method is static to avoid sending unnecessary objects to other processes.
 
         Args:
-            event (str): Message event string in summary proto, data read from file handler.
+            event_str (str): Message event string in summary proto, data read from file handler.
             latest_file_name (str): Latest file name.
         """
 
@@ -445,30 +483,10 @@ class _SummaryParser(_Parser):
                     if not value.HasField(plugin):
                         continue
                     plugin_name_enum = plugins[plugin]
-                    tensor_event_value = getattr(value, plugin)
                     logger.debug("Processing plugin value: %s.", plugin_name_enum)
-
-                    if plugin == PluginNameEnum.HISTOGRAM.value:
-                        tensor_event_value = HistogramContainer(tensor_event_value)
-                        # Drop steps if original_buckets_count exceeds HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT
-                        # to avoid time-consuming re-sample process.
-                        if tensor_event_value.histogram.original_buckets_count > Histogram.MAX_ORIGINAL_BUCKETS_COUNT:
-                            logger.info('original_buckets_count exceeds '
-                                        'HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT')
-                            continue
-
-                    elif plugin == PluginNameEnum.TENSOR.value:
-                        tensor_event_value = TensorContainer(tensor_event_value)
-                        tensor_count = 1
-                        for d in tensor_event_value.dims:
-                            tensor_count *= d
-                        if tensor_count > MAX_TENSOR_COUNT:
-                            logger.warning('tag: %s/tensor, tensor count: %d exceeds %d and drop it.',
-                                           value.tag, tensor_count, MAX_TENSOR_COUNT)
-                            continue
-
-                    elif plugin == PluginNameEnum.IMAGE.value:
-                        tensor_event_value = ImageContainer(tensor_event_value)
+                    tensor_event_value = _SummaryParser._parse_summary_value(value, plugin)
+                    if tensor_event_value is None:
+                        continue
 
                     tensor_event = TensorEvent(wall_time=event.wall_time,
                                                step=event.step,