fix the probabilistic failure of st caused by the different order of generating data

tests/st/func/lineagemgr/collection/model/test_model_lineage.py

@@ -108,16 +108,7 @@ class TestModelLineage(TestCase):
         res = filter_summary_lineage(data_manager=LINEAGE_DATA_MANAGER, search_condition=self._search_condition)
         assert res.get('object')[0].get('model_lineage', {}).get('epoch') == 14
 
-    @pytest.mark.scene_eval(3)
-    @pytest.mark.level0
-    @pytest.mark.platform_arm_ascend_training
-    @pytest.mark.platform_x86_gpu_training
-    @pytest.mark.platform_x86_ascend_training
-    @pytest.mark.platform_x86_cpu
-    @pytest.mark.env_single
-    def test_eval_end(self):
-        """Test the end function in EvalLineage."""
-        eval_callback = EvalLineage(self.summary_record, True, {'eval_version': 'version2'})
+        eval_callback = EvalLineage(self.summary_record, True, self.user_defined_info)
         eval_run_context = self.run_context
         eval_run_context['metrics'] = {'accuracy': 0.78}
         eval_run_context['valid_dataset'] = self.run_context['train_dataset']
@@ -152,7 +143,7 @@ class TestModelLineage(TestCase):
                 SUMMARY_DIR_2,
                 f'train_out.events.summary.{str(int(time.time()) + 2*i)}.ubuntu_lineage'
             )
-            train_callback = TrainLineage(summary_record, True)
+            train_callback = TrainLineage(summary_record, True, self.user_defined_info)
             train_callback.begin(RunContext(self.run_context))
             train_callback.end(RunContext(self.run_context))
 
@@ -160,7 +151,7 @@ class TestModelLineage(TestCase):
                 SUMMARY_DIR_2,
                 f'eval_out.events.summary.{str(int(time.time())+ 2*i + 1)}.ubuntu_lineage'
             )
-            eval_callback = EvalLineage(eval_record, True)
+            eval_callback = EvalLineage(eval_record, True, {'eval_version': 'version2'})
             eval_run_context = self.run_context
             eval_run_context['metrics'] = {'accuracy': 0.78 + i + 1}
             eval_run_context['valid_dataset'] = self.run_context['train_dataset']
@@ -169,7 +160,7 @@ class TestModelLineage(TestCase):
         file_num = os.listdir(SUMMARY_DIR_2)
         assert len(file_num) == 8
 
-    @pytest.mark.scene_train(2)
+    @pytest.mark.scene_train(3)
     @pytest.mark.level0
     @pytest.mark.platform_arm_ascend_training
     @pytest.mark.platform_x86_gpu_training

tests/st/func/lineagemgr/test_model.py

@@ -97,8 +97,7 @@ LINEAGE_FILTRATION_RUN1 = {
         'test_dataset_count': 1024,
         'user_defined': {
             'info': 'info1',
-            'version': 'v1',
-            'eval_version': 'version2'
+            'version': 'v1'
         },
         'network': 'ResNet',
         'optimizer': 'Momentum',
@@ -124,7 +123,11 @@ LINEAGE_FILTRATION_RUN2 = {
         'train_dataset_count': 1024,
         'test_dataset_path': None,
         'test_dataset_count': 1024,
-        'user_defined': {},
+        'user_defined': {
+            'info': 'info1',
+            'version': 'v1',
+            'eval_version': 'version2'
+        },
         'network': "ResNet",
         'optimizer': "Momentum",
         'learning_rate': 0.12,

tests/ut/lineagemgr/test_model.py

@@ -20,7 +20,8 @@ from mindinsight.lineagemgr.model import filter_summary_lineage, get_flattened_l
 from mindinsight.lineagemgr.common.exceptions.exceptions import LineageSummaryParseException, \
     LineageQuerierParamException, LineageQuerySummaryDataError, LineageSearchConditionParamError, LineageParamTypeError
 from mindinsight.lineagemgr.common.path_parser import SummaryPathParser
-from ...st.func.lineagemgr.test_model import LINEAGE_FILTRATION_RUN1, LINEAGE_FILTRATION_RUN2
+from tests.st.func.lineagemgr.test_model import LINEAGE_FILTRATION_EXCEPT_RUN, \
+    LINEAGE_FILTRATION_RUN1, LINEAGE_FILTRATION_RUN2
 
 
 class TestFilterAPI(TestCase):
@@ -84,9 +85,9 @@ class TestFilterAPI(TestCase):
     def test_get_lineage_table(self, mock_filter_summary_lineage):
         """Test get_flattened_lineage with valid param."""
         mock_data = {
-            'object': [LINEAGE_FILTRATION_RUN1, LINEAGE_FILTRATION_RUN2]
+            'object': [LINEAGE_FILTRATION_EXCEPT_RUN, LINEAGE_FILTRATION_RUN1, LINEAGE_FILTRATION_RUN2]
         }
         mock_data_manager = MagicMock()
         mock_filter_summary_lineage.return_value = mock_data
         result = get_flattened_lineage(mock_data_manager)
-        assert result.get('[U]info') == ['info1', None]
+        assert result.get('[U]info') == [None, 'info1', 'info1']