diff --git a/mindspore/train/callback/_summary_collector.py b/mindspore/train/callback/_summary_collector.py
index 17766f14049b46fad84252864da0f9d1755e87b8..fe5580bd25e2d13dc11c43ba8dc776cd18219a7a 100644
--- a/mindspore/train/callback/_summary_collector.py
+++ b/mindspore/train/callback/_summary_collector.py
@@ -182,7 +182,7 @@ class SummaryCollector(Callback):
         self._custom_lineage_data = custom_lineage_data
 
         self._temp_optimizer = None
-        self._has_saved_train_network = False
+        self._has_saved_graph = False
         self._has_saved_custom_data = False
         self._is_parse_loss_success = True
         self._first_step = True
@@ -287,32 +287,30 @@ class SummaryCollector(Callback):
                              'but got `{cb_params.mode}` mode.')
 
         self._record.set_mode(cb_params.mode)
-        if cb_params.mode == ModeEnum.TRAIN.value:
-            # Note: if model.init is not executed then the computed graph will not be obtained here
-            # The purpose of recording the graph here was to collect_freq if it was set to a large size,
-            # but also want to see the graph as soon after compilation.
-            self._collect_graphs(cb_params)
 
-            self._collect_dataset_graph(cb_params)
+        if cb_params.mode == ModeEnum.TRAIN.value:
             if self._collect_tensor_freq is None:
                 default_tensor_summary_limit = 20
                 total_step = cb_params.epoch_num * cb_params.batch_num
                 self._collect_tensor_freq = max(self._collect_freq, total_step // default_tensor_summary_limit)
 
-        if self._custom_lineage_data and not self._has_saved_custom_data:
-            packaged_custom_data = self._package_custom_lineage_data(self._custom_lineage_data)
-            self._record.add_value('custom_lineage_data', 'custom_lineage_data', packaged_custom_data)
-            self._has_saved_custom_data = True
-
-        # There's nothing special about setting step to 0 here, just to satisfy the interface call
-        self._record.record(step=0)
-
     def step_end(self, run_context):
         cb_params = run_context.original_args()
         if cb_params.mode != ModeEnum.TRAIN.value:
             return
-        if not self._has_saved_train_network:
+
+        if not self._has_saved_graph:
             self._collect_graphs(cb_params)
+            self._collect_dataset_graph(cb_params)
+            self._has_saved_graph = True
+            self._record.record(cb_params.cur_step_num)
+
+        if self._custom_lineage_data and not self._has_saved_custom_data:
+            packaged_custom_data = self._package_custom_lineage_data(self._custom_lineage_data)
+            self._record.add_value('custom_lineage_data', 'custom_lineage_data', packaged_custom_data)
+            self._has_saved_custom_data = True
+            self._record.record(cb_params.cur_step_num)
+
         if self._first_step:
             # Notice: This way of determining whether dataset sink mode is True does not work in the eval scenario
             self._dataset_sink_mode = cb_params.cur_step_num == cb_params.batch_num
@@ -327,14 +325,12 @@ class SummaryCollector(Callback):
             elif current % self._collect_freq == 0:
                 self._collect_at_step_end(cb_params, lambda plugin: plugin != PluginEnum.TENSOR.value)
 
-
     def _collect_at_step_end(self, cb_params, plugin_filter):
         self._collect_input_data(cb_params)
         self._collect_metric(cb_params)
         self._collect_histogram(cb_params)
         self._record.record(cb_params.cur_step_num, plugin_filter=plugin_filter)
 
-
     def end(self, run_context):
         cb_params = run_context.original_args()
         if cb_params.mode == ModeEnum.TRAIN.value:
@@ -428,7 +424,6 @@ class SummaryCollector(Callback):
         if graph_proto is None:
             return
 
-        self._has_saved_train_network = True
         self._record.add_value(PluginEnum.GRAPH.value, 'train_network/auto', graph_proto)
 
     def _collect_metric(self, cb_params):