lineagemgr: add test case for lineage decouple

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

@@ -31,6 +31,7 @@ import pytest
 from mindinsight.lineagemgr import get_summary_lineage
 from mindinsight.lineagemgr.collection.model.model_lineage import TrainLineage, EvalLineage, \
     AnalyzeObject
+from mindinsight.lineagemgr.common.utils import make_directory
 from mindinsight.lineagemgr.common.exceptions.error_code import LineageErrors
 from mindinsight.lineagemgr.common.exceptions.exceptions import LineageParamRunContextError
 from mindinsight.utils.exceptions import MindInsightException
@@ -198,6 +199,7 @@ class TestModelLineage(TestCase):
     @pytest.mark.platform_x86_ascend_training
     @pytest.mark.platform_x86_cpu
     @pytest.mark.env_single
+    @mock.patch('mindinsight.lineagemgr.summary.summary_record.get_lineage_file_name')
     @mock.patch('os.path.getsize')
     def test_multiple_trains(self, *args):
         """
@@ -208,22 +210,116 @@ class TestModelLineage(TestCase):
         """
         args[0].return_value = 10
         for i in range(2):
-            summary_record = SummaryRecord(SUMMARY_DIR_2, create_time=int(time.time()))
-            eval_record = SummaryRecord(SUMMARY_DIR_2, create_time=int(time.time()) + 1)
+            summary_record = SummaryRecord(
+                SUMMARY_DIR_2,
+                create_time=int(time.time()) + i)
+            eval_record = SummaryRecord(
+                SUMMARY_DIR_2,
+                create_time=int(time.time() + 10) + i)
+            args[1].return_value = os.path.join(
+                SUMMARY_DIR_2,
+                f'train_out.events.summary.{str(int(time.time()) + 2*i)}.ubuntu_lineage'
+            )
             train_callback = TrainLineage(summary_record, True)
             train_callback.begin(RunContext(self.run_context))
             train_callback.end(RunContext(self.run_context))
-            time.sleep(1)
+
+            args[1].return_value = os.path.join(
+                SUMMARY_DIR_2,
+                f'eval_out.events.summary.{str(int(time.time())+ 2*i + 1)}.ubuntu_lineage'
+            )
             eval_callback = EvalLineage(eval_record, True)
             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']
             eval_run_context['step_num'] = 32
             eval_callback.end(RunContext(eval_run_context))
-            time.sleep(1)
         file_num = os.listdir(SUMMARY_DIR_2)
         assert len(file_num) == 8
 
+    @pytest.mark.scene_train(2)
+    @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
+    @mock.patch('mindinsight.lineagemgr.summary.summary_record.get_lineage_file_name')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_file_size')
+    def test_train_eval(self, *args):
+        """Callback for train once and eval once."""
+        args[0].return_value = 10
+        summary_dir = os.path.join(BASE_SUMMARY_DIR, 'train_eval')
+        make_directory(summary_dir)
+        args[1].return_value = os.path.join(
+            summary_dir,
+            f'train_out.events.summary.{str(int(time.time()))}.ubuntu_lineage'
+        )
+        train_callback = TrainLineage(summary_dir)
+        train_callback.begin(RunContext(self.run_context))
+        train_callback.end(RunContext(self.run_context))
+        args[1].return_value = os.path.join(
+            summary_dir,
+            f'eval_out.events.summary.{str(int(time.time())+1)}.ubuntu_lineage'
+        )
+        eval_callback = EvalLineage(summary_dir)
+        eval_run_context = self.run_context
+        eval_run_context['metrics'] = {'accuracy': 0.78}
+        eval_run_context['valid_dataset'] = self.run_context['train_dataset']
+        eval_run_context['step_num'] = 32
+        eval_callback.end(RunContext(eval_run_context))
+        res = get_summary_lineage(summary_dir)
+        assert res.get('hyper_parameters', {}).get('loss_function') \
+            == 'SoftmaxCrossEntropyWithLogits'
+        assert res.get('algorithm', {}).get('network') == 'ResNet'
+        if os.path.exists(summary_dir):
+            shutil.rmtree(summary_dir)
+
+    @pytest.mark.scene_train(2)
+    @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
+    @mock.patch('mindinsight.lineagemgr.summary.summary_record.get_lineage_file_name')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_file_size')
+    def test_train_multi_eval(self, *args):
+        """Callback for train once and eval twice."""
+        args[0].return_value = 10
+        summary_dir = os.path.join(BASE_SUMMARY_DIR, 'train_multi_eval')
+        make_directory(summary_dir)
+        args[1].return_value = os.path.join(
+            summary_dir,
+            'train_out.events.summary.1590107366.ubuntu_lineage')
+        train_callback = TrainLineage(summary_dir, True)
+        train_callback.begin(RunContext(self.run_context))
+        train_callback.end(RunContext(self.run_context))
+
+        args[1].return_value = os.path.join(
+            summary_dir,
+            'eval_out.events.summary.1590107367.ubuntu_lineage')
+        eval_callback = EvalLineage(summary_dir, True)
+        eval_run_context = self.run_context
+        eval_run_context['valid_dataset'] = self.run_context['train_dataset']
+        eval_run_context['metrics'] = {'accuracy': 0.79}
+        eval_callback.end(RunContext(eval_run_context))
+        res = get_summary_lineage(summary_dir)
+        assert res.get('metric', {}).get('accuracy') == 0.79
+
+        args[1].return_value = os.path.join(
+            summary_dir,
+            'eval_out.events.summary.1590107368.ubuntu_lineage')
+        eval_callback = EvalLineage(summary_dir, True)
+        eval_run_context = self.run_context
+        eval_run_context['valid_dataset'] = self.run_context['train_dataset']
+        eval_run_context['metrics'] = {'accuracy': 0.80}
+        eval_callback.end(RunContext(eval_run_context))
+        res = get_summary_lineage(summary_dir)
+        assert res.get('metric', {}).get('accuracy') == 0.80
+        if os.path.exists(summary_dir):
+            shutil.rmtree(summary_dir)
+
     @pytest.mark.scene_train(2)
     @pytest.mark.level0
     @pytest.mark.platform_arm_ascend_training