fix the problem of not counting the ST case

tests/st/func/lineagemgr/test_model.py

@@ -21,8 +21,7 @@ Usage:
     pytest lineagemgr
 """
 import os
-from unittest import TestCase, mock
-import numpy as np
+from unittest import TestCase
 import pytest
 
 from mindinsight.lineagemgr.model import filter_summary_lineage, get_summary_lineage
@@ -32,12 +31,6 @@ from mindinsight.lineagemgr.common.exceptions.exceptions import (LineageFileNotF
 from mindinsight.datavisual.data_transform import data_manager
 from mindinsight.lineagemgr.cache_item_updater import LineageCacheItemUpdater
 from mindinsight.lineagemgr.model import get_flattened_lineage
-from mindspore.application.model_zoo.resnet import ResNet
-from mindspore.common.tensor import Tensor
-from mindspore.dataset.engine import MindDataset
-from mindspore.nn import Momentum, SoftmaxCrossEntropyWithLogits
-from mindspore.train.callback import RunContext
-from ....utils.lineage_writer.model_lineage import AnalyzeObject, TrainLineage
 
 from .conftest import BASE_SUMMARY_DIR, DATASET_GRAPH, SUMMARY_DIR, SUMMARY_DIR_2
 from ....ut.lineagemgr.querier import event_data
@@ -825,44 +818,15 @@ class TestModelApi(TestCase):
                 search_condition
             )
 
-
-class TestLineageTable:
-    """Test lineage table ."""
-
-    @classmethod
-    def setup_class(cls):
-        """Setup method"""
-        cls.run_context = dict(
-            train_network=ResNet(),
-            loss_fn=SoftmaxCrossEntropyWithLogits(),
-            net_outputs=Tensor(np.array([0.03])),
-            optimizer=Momentum(Tensor(0.12)),
-            train_dataset=MindDataset(dataset_size=32),
-            epoch_num=10,
-            cur_step_num=320,
-            parallel_mode="stand_alone",
-            device_number=2,
-            batch_num=32
-        )
-        cls.user_defined_info = {"info": "info1", "version": "v1"}
-
-    @pytest.mark.scene_train(2)
+    @pytest.mark.level1
     @pytest.mark.platform_arm_ascend_training
     @pytest.mark.platform_x86_gpu_training
     @pytest.mark.platform_x86_ascned_training
     @pytest.mark.platform_x86_cpu
     @pytest.mark.env_single
-    @mock.patch.object(AnalyzeObject, 'get_file_size')
-    def test_training_end(self):
+    def test_get_flattened_lineage(self):
         """Test the function of get_flattened_lineage"""
-        train_callback = TrainLineage(SUMMARY_DIR, True, self.user_defined_info)
-
-        train_callback.initial_learning_rate = 0.12
-        train_callback.begin(RunContext(self.run_context))
-        train_callback.end(RunContext(self.run_context))
-
-        summary_base_dir = SUMMARY_DIR
-        datamanager = data_manager.DataManager(summary_base_dir)
+        datamanager = data_manager.DataManager(SUMMARY_DIR)
         datamanager.register_brief_cache_item_updater(LineageCacheItemUpdater())
         datamanager.start_load_data().join()