Merge branch 'master' of gitee.com:mindspore/mindinsight into phdev

.gitee/PULL_REQUEST_TEMPLATE.md

+<!--  Thanks for sending a pull request!  Here are some tips for you:
+
+If this is your first time, please read our contributor guidelines: https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md
+-->
+
+**What type of PR is this?**
+> Uncomment only one ` /kind <>` line, hit enter to put that in a new line, and remove leading whitespaces from that line:
+>
+> /kind bug
+> /kind task
+> /kind feature
+
+
+**What does this PR do / why do we need it**:
+
+
+**Which issue(s) this PR fixes**:
+<!-- 
+*Automatically closes linked issue when PR is merged.
+Usage: `Fixes #<issue number>`, or `Fixes (paste link of issue)`.
+-->
+Fixes #
+
+**Special notes for your reviewers**:
+
+

.github/ISSUE_TEMPLATE/RFC.md

+---
+name: RFC
+about: Use this template for the new feature or enhancement
+labels: kind/feature or kind/enhancement
+
+---
+
+## Background
+- Describe the status of the problem you wish to solve
+- Attach the relevant issue if have
+
+## Introduction
+- Describe the general solution, design and/or pseudo-code
+
+## Trail
+| No. | Task Description | Related Issue(URL) |
+| --- | ---------------- | ------------------ |
+| 1   |                  |                    |
+| 2   |                  |                    |

.github/ISSUE_TEMPLATE/bug-report.md

+---
+name: Bug Report
+about: Use this template for reporting a bug
+labels: kind/bug
+
+---
+
+<!--  Thanks for sending an issue!  Here are some tips for you:
+
+If this is your first time, please read our contributor guidelines: https://github.com/mindspore-ai/mindspore/blob/master/CONTRIBUTING.md
+-->
+
+## Environment
+### Hardware Environment(`Ascend`/`GPU`/`CPU`): 
+> Uncomment only one ` /device <>` line, hit enter to put that in a new line, and remove leading whitespaces from that line:
+>
+> `/device ascend`</br>
+> `/device gpu`</br>
+> `/device cpu`</br>
+
+### Software Environment:
+- **MindSpore version (source or binary)**:
+- **Python version (e.g., Python 3.7.5)**:
+- **OS platform and distribution (e.g., Linux Ubuntu 16.04)**:
+- **GCC/Compiler version (if compiled from source)**: 
+
+## Describe the current behavior
+
+
+## Describe the expected behavior
+
+
+## Steps to reproduce the issue
+1. 
+2. 
+3.
+
+## Related log / screenshot
+
+
+## Special notes for this issue
+
+

.github/ISSUE_TEMPLATE/task-tracking.md

+---
+name: Task
+about: Use this template for task tracking
+labels: kind/task
+
+---
+
+## Task Description
+
+
+## Task Goal
+
+
+## Sub Task
+| No. | Task Description | Issue ID |
+| --- | ---------------- | -------- |
+| 1   |                  |          |
+| 2   |                  |          |
+

.github/PULL_REQUEST_TEMPLATE.md

+<!--  Thanks for sending a pull request!  Here are some tips for you:
+
+If this is your first time, please read our contributor guidelines: https://github.com/mindspore-ai/mindspore/blob/master/CONTRIBUTING.md
+-->
+
+**What type of PR is this?**
+> Uncomment only one ` /kind <>` line, hit enter to put that in a new line, and remove leading whitespaces from that line:
+>
+> `/kind bug`</br>
+> `/kind task`</br>
+> `/kind feature`</br>
+
+**What does this PR do / why do we need it**:
+
+
+**Which issue(s) this PR fixes**:
+<!-- 
+*Automatically closes linked issue when PR is merged.
+Usage: `Fixes #<issue number>`, or `Fixes (paste link of issue)`.
+-->
+Fixes #
+
+**Special notes for your reviewers**:
+

mindinsight/datavisual/data_transform/graph/graph.py

@@ -18,19 +18,21 @@ This file is used to define the basic graph.
 import copy
 import time
 
+from enum import Enum
+
 from mindinsight.datavisual.common.log import logger
 from mindinsight.datavisual.common import exceptions
 from .node import NodeTypeEnum
 from .node import Node
 
 
-class EdgeTypeEnum:
+class EdgeTypeEnum(Enum):
     """Node edge type enum."""
-    control = 'control'
-    data = 'data'
+    CONTROL = 'control'
+    DATA = 'data'
 
 
-class DataTypeEnum:
+class DataTypeEnum(Enum):
     """Data type enum."""
     DT_TENSOR = 13
 
@@ -292,70 +294,65 @@ class Graph:
                     output_attr['scope'] = NodeTypeEnum.POLYMERIC_SCOPE.value
                     node.update_output({dst_name: output_attr})
 
-    def _calc_polymeric_input_output(self):
+    def _update_polymeric_input_output(self):
         """Calc polymeric input and output after build polymeric node."""
-        for name, node in self._normal_nodes.items():
-            polymeric_input = {}
-            for src_name in node.input:
-                src_node = self._polymeric_nodes.get(src_name)
-                if node.node_type == NodeTypeEnum.POLYMERIC_SCOPE.value:
-                    src_name = src_name if not src_node else src_node.polymeric_scope_name
-                    output_name = self._calc_dummy_node_name(name, src_name)
-                    polymeric_input.update({output_name: {'edge_type': EdgeTypeEnum.data}})
-                    continue
-
-                if not src_node:
-                    continue
-
-                if not node.name_scope and src_node.name_scope:
-                    # if current node is in first layer, and the src node is not in
-                    # the first layer, the src node will not be the polymeric input of current node.
-                    continue
-
-                if node.name_scope == src_node.name_scope \
-                        or node.name_scope.startswith(src_node.name_scope):
-                    polymeric_input.update(
-                        {src_node.polymeric_scope_name: {'edge_type': EdgeTypeEnum.data}})
-
+        for node in self._normal_nodes.values():
+            polymeric_input = self._calc_polymeric_attr(node, 'input')
             node.update_polymeric_input(polymeric_input)
 
-            polymeric_output = {}
-            for dst_name in node.output:
-                dst_node = self._polymeric_nodes.get(dst_name)
-
-                if node.node_type == NodeTypeEnum.POLYMERIC_SCOPE.value:
-                    dst_name = dst_name if not dst_node else dst_node.polymeric_scope_name
-                    output_name = self._calc_dummy_node_name(name, dst_name)
-                    polymeric_output.update({output_name: {'edge_type': EdgeTypeEnum.data}})
-                    continue
-
-                if not dst_node:
-                    continue
-
-                if not node.name_scope and dst_node.name_scope:
-                    continue
-
-                if node.name_scope == dst_node.name_scope \
-                        or node.name_scope.startswith(dst_node.name_scope):
-                    polymeric_output.update(
-                        {dst_node.polymeric_scope_name: {'edge_type': EdgeTypeEnum.data}})
-
+            polymeric_output = self._calc_polymeric_attr(node, 'output')
             node.update_polymeric_output(polymeric_output)
 
         for name, node in self._polymeric_nodes.items():
             polymeric_input = {}
             for src_name in node.input:
                 output_name = self._calc_dummy_node_name(name, src_name)
-                polymeric_input.update({output_name: {'edge_type': EdgeTypeEnum.data}})
+                polymeric_input.update({output_name: {'edge_type': EdgeTypeEnum.DATA.value}})
             node.update_polymeric_input(polymeric_input)
 
             polymeric_output = {}
             for dst_name in node.output:
                 polymeric_output = {}
                 output_name = self._calc_dummy_node_name(name, dst_name)
-                polymeric_output.update({output_name: {'edge_type': EdgeTypeEnum.data}})
+                polymeric_output.update({output_name: {'edge_type': EdgeTypeEnum.DATA.value}})
             node.update_polymeric_output(polymeric_output)
 
+    def _calc_polymeric_attr(self, node, attr):
+        """
+        Calc polymeric input or polymeric output after build polymeric node.
+
+        Args:
+            node (Node): Computes the polymeric input for a given node.
+            attr (str): The polymeric attr, optional value is `input` or `output`.
+
+        Returns:
+            dict, return polymeric input or polymeric output of the given node.
+        """
+        polymeric_attr = {}
+        for node_name in getattr(node, attr):
+            polymeric_node = self._polymeric_nodes.get(node_name)
+            if node.node_type == NodeTypeEnum.POLYMERIC_SCOPE.value:
+                node_name = node_name if not polymeric_node else polymeric_node.polymeric_scope_name
+                dummy_node_name = self._calc_dummy_node_name(node.name, node_name)
+                polymeric_attr.update({dummy_node_name: {'edge_type': EdgeTypeEnum.DATA.value}})
+                continue
+
+            if not polymeric_node:
+                continue
+
+            if not node.name_scope and polymeric_node.name_scope:
+                # If current node is in top-level layer, and the polymeric_node node is not in
+                # the top-level layer, the polymeric node will not be the polymeric input
+                # or polymeric output of current node.
+                continue
+
+            if node.name_scope == polymeric_node.name_scope \
+                    or node.name_scope.startswith(polymeric_node.name_scope + '/'):
+                polymeric_attr.update(
+                    {polymeric_node.polymeric_scope_name: {'edge_type': EdgeTypeEnum.DATA.value}})
+
+        return polymeric_attr
+
     def _calc_dummy_node_name(self, current_node_name, other_node_name):
         """
         Calc dummy node name.

mindinsight/datavisual/data_transform/graph/msgraph.py

@@ -39,7 +39,7 @@ class MSGraph(Graph):
         self._build_leaf_nodes(graph_proto)
         self._build_polymeric_nodes()
         self._build_name_scope_nodes()
-        self._calc_polymeric_input_output()
+        self._update_polymeric_input_output()
         logger.info("Build graph end, normal node count: %s, polymeric node "
                     "count: %s.", len(self._normal_nodes), len(self._polymeric_nodes))
 
@@ -90,9 +90,9 @@ class MSGraph(Graph):
             node_name = leaf_node_id_map_name[node_def.name]
             node = self._leaf_nodes[node_name]
             for input_def in node_def.input:
-                edge_type = EdgeTypeEnum.data
+                edge_type = EdgeTypeEnum.DATA.value
                 if input_def.type == "CONTROL_EDGE":
-                    edge_type = EdgeTypeEnum.control
+                    edge_type = EdgeTypeEnum.CONTROL.value
 
                 if const_nodes_map.get(input_def.name):
                     const_node = copy.deepcopy(const_nodes_map[input_def.name])
@@ -218,7 +218,7 @@ class MSGraph(Graph):
         node = Node(name=const.key, node_id=const_node_id)
         node.node_type = NodeTypeEnum.CONST.value
         node.update_attr({const.key: str(const.value)})
-        if const.value.dtype == DataTypeEnum.DT_TENSOR:
+        if const.value.dtype == DataTypeEnum.DT_TENSOR.value:
             shape = []
             for dim in const.value.tensor_val.dims:
                 shape.append(dim)

mindinsight/datavisual/data_transform/graph/node.py

@@ -172,7 +172,7 @@ class Node:
 
         Args:
             polymeric_output (dict[str, dict): Format is {dst_node.polymeric_scope_name:
-                                                {'edge_type': EdgeTypeEnum.data}}).
+                                                {'edge_type': EdgeTypeEnum.DATA.value}}).
 
         """
         self._polymeric_output.update(polymeric_output)

mindinsight/lineagemgr/collection/model/model_lineage.py

@@ -168,7 +168,7 @@ class TrainLineage(Callback):
         train_lineage = AnalyzeObject.get_network_args(
             run_context_args, train_lineage
         )
-        
+
         train_dataset = run_context_args.get('train_dataset')
         callbacks = run_context_args.get('list_callback')
         list_callback = getattr(callbacks, '_callbacks', [])
@@ -601,7 +601,7 @@ class AnalyzeObject:
             loss = None
         else:
             loss = run_context_args.get('net_outputs')
-        
+
         if loss:
             log.info('Calculating loss...')
             loss_numpy = loss.asnumpy()
@@ -610,7 +610,7 @@ class AnalyzeObject:
             train_lineage[Metadata.loss] = loss
         else:
             train_lineage[Metadata.loss] = None
-        
+
         # Analyze classname of optimizer, loss function and training network.
         train_lineage[Metadata.optimizer] = type(optimizer).__name__ \
             if optimizer else None

tests/st/func/datavisual/conftest.py

@@ -18,13 +18,10 @@ Description: This file is used for some common util.
 import os
 import shutil
 from unittest.mock import Mock
+
 import pytest
 from flask import Response
 
-from tests.st.func.datavisual import constants
-from tests.st.func.datavisual.utils.log_operations import LogOperations
-from tests.st.func.datavisual.utils.utils import check_loading_done
-from tests.st.func.datavisual.utils import globals as gbl
 from mindinsight.conf import settings
 from mindinsight.datavisual.data_transform import data_manager
 from mindinsight.datavisual.data_transform.data_manager import DataManager
@@ -32,6 +29,11 @@ from mindinsight.datavisual.data_transform.loader_generators.data_loader_generat
 from mindinsight.datavisual.data_transform.loader_generators.loader_generator import MAX_DATA_LOADER_SIZE
 from mindinsight.datavisual.utils import tools
 
+from ....utils.log_operations import LogOperations
+from ....utils.tools import check_loading_done
+from . import constants
+from . import globals as gbl
+
 summaries_metadata = None
 mock_data_manager = None
 summary_base_dir = constants.SUMMARY_BASE_DIR
@@ -55,17 +57,21 @@ def init_summary_logs():
             os.mkdir(summary_base_dir, mode=mode)
         global summaries_metadata, mock_data_manager
         log_operations = LogOperations()
-        summaries_metadata = log_operations.create_summary_logs(summary_base_dir, constants.SUMMARY_DIR_NUM_FIRST)
+        summaries_metadata = log_operations.create_summary_logs(summary_base_dir, constants.SUMMARY_DIR_NUM_FIRST,
+                                                                constants.SUMMARY_DIR_PREFIX)
         mock_data_manager = DataManager([DataLoaderGenerator(summary_base_dir)])
         mock_data_manager.start_load_data(reload_interval=0)
         check_loading_done(mock_data_manager)
 
-        summaries_metadata.update(log_operations.create_summary_logs(
-            summary_base_dir, constants.SUMMARY_DIR_NUM_SECOND, constants.SUMMARY_DIR_NUM_FIRST))
-        summaries_metadata.update(log_operations.create_multiple_logs(
-            summary_base_dir, constants.MULTIPLE_DIR_NAME, constants.MULTIPLE_LOG_NUM))
-        summaries_metadata.update(log_operations.create_reservoir_log(
-            summary_base_dir, constants.RESERVOIR_DIR_NAME, constants.RESERVOIR_STEP_NUM))
+        summaries_metadata.update(
+            log_operations.create_summary_logs(summary_base_dir, constants.SUMMARY_DIR_NUM_SECOND,
+                                               constants.SUMMARY_DIR_NUM_FIRST))
+        summaries_metadata.update(
+            log_operations.create_multiple_logs(summary_base_dir, constants.MULTIPLE_DIR_NAME,
+                                                constants.MULTIPLE_LOG_NUM))
+        summaries_metadata.update(
+            log_operations.create_reservoir_log(summary_base_dir, constants.RESERVOIR_DIR_NAME,
+                                                constants.RESERVOIR_STEP_NUM))
         mock_data_manager.start_load_data(reload_interval=0)
 
         # Sleep 1 sec to make sure the status of mock_data_manager changed to LOADING.
@@ -73,7 +79,7 @@ def init_summary_logs():
 
         # Maximum number of loads is `MAX_DATA_LOADER_SIZE`.
         for i in range(len(summaries_metadata) - MAX_DATA_LOADER_SIZE):
-            summaries_metadata.pop("./%s%d" % (constants.SUMMARY_PREFIX, i))
+            summaries_metadata.pop("./%s%d" % (constants.SUMMARY_DIR_PREFIX, i))
 
         yield
     finally:

tests/st/func/datavisual/constants.py

@@ -16,7 +16,7 @@
 import tempfile
 
 SUMMARY_BASE_DIR = tempfile.NamedTemporaryFile().name
-SUMMARY_PREFIX = "summary"
+SUMMARY_DIR_PREFIX = "summary"
 
 SUMMARY_DIR_NUM_FIRST = 5
 SUMMARY_DIR_NUM_SECOND = 11

tests/st/func/datavisual/graph/test_query_nodes_restful_api.py

@@ -19,11 +19,11 @@ Usage:
     pytest tests/st/func/datavisual
 """
 import os
-import json
+
 import pytest
 
-from tests.st.func.datavisual.utils import globals as gbl
-from tests.st.func.datavisual.utils.utils import get_url
+from .. import globals as gbl
+from .....utils.tools import get_url, compare_result_with_file
 
 BASE_URL = '/v1/mindinsight/datavisual/graphs/nodes'
 
@@ -33,12 +33,6 @@ class TestQueryNodes:
 
     graph_results_dir = os.path.join(os.path.dirname(__file__), 'graph_results')
 
-    def compare_result_with_file(self, result, filename):
-        """Compare result with file which contain the expected results."""
-        with open(os.path.join(self.graph_results_dir, filename), 'r') as fp:
-            expected_results = json.load(fp)
-            assert result == expected_results
-
     @pytest.mark.level0
     @pytest.mark.env_single
     @pytest.mark.platform_x86_cpu
@@ -65,4 +59,5 @@ class TestQueryNodes:
         url = get_url(BASE_URL, params)
         response = client.get(url)
         assert response.status_code == 200
-        self.compare_result_with_file(response.get_json(), result_file)
+        file_path = os.path.join(self.graph_results_dir, result_file)
+        compare_result_with_file(response.get_json(), file_path)

tests/st/func/datavisual/graph/test_query_single_nodes_restful_api.py

@@ -19,12 +19,11 @@ Usage:
     pytest tests/st/func/datavisual
 """
 import os
-import json
 
 import pytest
 
-from tests.st.func.datavisual.utils import globals as gbl
-from tests.st.func.datavisual.utils.utils import get_url
+from .. import globals as gbl
+from .....utils.tools import get_url, compare_result_with_file
 
 BASE_URL = '/v1/mindinsight/datavisual/graphs/single-node'
 
@@ -34,12 +33,6 @@ class TestQuerySingleNode:
 
     graph_results_dir = os.path.join(os.path.dirname(__file__), 'graph_results')
 
-    def compare_result_with_file(self, result, filename):
-        """Compare result with file which contain the expected results."""
-        with open(os.path.join(self.graph_results_dir, filename), 'r') as fp:
-            expected_results = json.load(fp)
-            assert result == expected_results
-
     @pytest.mark.level0
     @pytest.mark.env_single
     @pytest.mark.platform_x86_cpu
@@ -59,4 +52,5 @@ class TestQuerySingleNode:
         url = get_url(BASE_URL, params)
         response = client.get(url)
         assert response.status_code == 200
-        self.compare_result_with_file(response.get_json(), result_file)
+        file_path = os.path.join(self.graph_results_dir, result_file)
+        compare_result_with_file(response.get_json(), file_path)

tests/st/func/datavisual/graph/test_search_nodes_restful_api.py

@@ -19,25 +19,20 @@ Usage:
     pytest tests/st/func/datavisual
 """
 import os
-import json
 
 import pytest
-from tests.st.func.datavisual.utils import globals as gbl
-from tests.st.func.datavisual.utils.utils import get_url
+
+from .. import globals as gbl
+from .....utils.tools import get_url, compare_result_with_file
 
 BASE_URL = '/v1/mindinsight/datavisual/graphs/nodes/names'
 
+
 class TestSearchNodes:
-    """Test search nodes restful APIs."""
+    """Test searching nodes restful APIs."""
 
     graph_results_dir = os.path.join(os.path.dirname(__file__), 'graph_results')
 
-    def compare_result_with_file(self, result, filename):
-        """Compare result with file which contain the expected results."""
-        with open(os.path.join(self.graph_results_dir, filename), 'r') as fp:
-            expected_results = json.load(fp)
-            assert result == expected_results
-
     @pytest.mark.level0
     @pytest.mark.env_single
     @pytest.mark.platform_x86_cpu
@@ -58,4 +53,5 @@ class TestSearchNodes:
         url = get_url(BASE_URL, params)
         response = client.get(url)
         assert response.status_code == 200
-        self.compare_result_with_file(response.get_json(), result_file)
+        file_path = os.path.join(self.graph_results_dir, result_file)
+        compare_result_with_file(response.get_json(), file_path)

tests/st/func/datavisual/image/test_metadata_restful_api.py

@@ -20,13 +20,13 @@ Usage:
 """
 import pytest
 
-from tests.st.func.datavisual.constants import MULTIPLE_TRAIN_ID, RESERVOIR_TRAIN_ID
-from tests.st.func.datavisual.utils import globals as gbl
-from tests.st.func.datavisual.utils.utils import get_url
-
 from mindinsight.conf import settings
 from mindinsight.datavisual.common.enums import PluginNameEnum
 
+from .....utils.tools import get_url
+from .. import globals as gbl
+from ..constants import MULTIPLE_TRAIN_ID, RESERVOIR_TRAIN_ID
+
 BASE_URL = '/v1/mindinsight/datavisual/image/metadata'
 
 

tests/st/func/datavisual/image/test_single_image_restful_api.py

@@ -20,11 +20,11 @@ Usage:
 """
 import pytest
 
-from tests.st.func.datavisual.utils import globals as gbl
-from tests.st.func.datavisual.utils.utils import get_url, get_image_tensor_from_bytes
-
 from mindinsight.datavisual.common.enums import PluginNameEnum
 
+from .....utils.tools import get_image_tensor_from_bytes, get_url
+from .. import globals as gbl
+
 BASE_URL = '/v1/mindinsight/datavisual/image/single-image'
 
 

tests/st/func/datavisual/scalar/test_metadata_restful_api.py

@@ -19,11 +19,12 @@ Usage:
     pytest tests/st/func/datavisual
 """
 import pytest
-from tests.st.func.datavisual.utils import globals as gbl
-from tests.st.func.datavisual.utils.utils import get_url
 
 from mindinsight.datavisual.common.enums import PluginNameEnum
 
+from .....utils.tools import get_url
+from .. import globals as gbl
+
 BASE_URL = '/v1/mindinsight/datavisual/scalar/metadata'
 
 

tests/st/func/datavisual/taskmanager/test_plugins_restful_api.py

@@ -20,11 +20,11 @@ Usage:
 """
 import pytest
 
-from tests.st.func.datavisual.utils import globals as gbl
-from tests.st.func.datavisual.utils.utils import get_url
-
 from mindinsight.datavisual.common.enums import PluginNameEnum
 
+from .....utils.tools import get_url
+from .. import globals as gbl
+
 BASE_URL = '/v1/mindinsight/datavisual/plugins'
 
 

tests/st/func/datavisual/taskmanager/test_query_single_train_task_restful_api.py

@@ -19,11 +19,12 @@ Usage:
     pytest tests/st/func/datavisual
 """
 import pytest
-from tests.st.func.datavisual.utils import globals as gbl
-from tests.st.func.datavisual.utils.utils import get_url
 
 from mindinsight.datavisual.common.enums import PluginNameEnum
 
+from .....utils.tools import get_url
+from .. import globals as gbl
+
 BASE_URL = '/v1/mindinsight/datavisual/single-job'
 
 

tests/st/func/datavisual/taskmanager/test_query_train_jobs_restful_api.py

@@ -20,8 +20,8 @@ Usage:
 """
 import pytest
 
-from tests.st.func.datavisual.constants import SUMMARY_DIR_NUM
-from tests.st.func.datavisual.utils.utils import get_url
+from ..constants import SUMMARY_DIR_NUM
+from .....utils.tools import get_url
 
 BASE_URL = '/v1/mindinsight/datavisual/train-jobs'
 

tests/st/func/datavisual/utils/log_generators/graph_log_generator.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Log generator for graph."""
-import json
-import os
-import time
-
-from google.protobuf import json_format
-from tests.st.func.datavisual.utils.log_generators.log_generator import LogGenerator
-
-from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
-
-
-class GraphLogGenerator(LogGenerator):
-    """
-    Log generator for graph.
-
-    This is a log generator writing graph. User can use it to generate fake
-    summary logs about graph.
-    """
-
-    def generate_log(self, file_path, graph_dict):
-        """
-        Generate log for external calls.
-
-        Args:
-            file_path (str): Path to write logs.
-            graph_dict (dict): A dict consists of graph node information.
-
-        Returns:
-            dict, generated scalar metadata.
-
-        """
-
-        graph_event = self.generate_event(dict(graph=graph_dict))
-
-        self._write_log_from_event(file_path, graph_event)
-
-        return graph_dict
-
-    def generate_event(self, values):
-        """
-        Method for generating graph event.
-
-        Args:
-            values (dict): Graph values. e.g. {'graph': graph_dict}.
-
-        Returns:
-            summary_pb2.Event.
-
-        """
-        graph_json = {
-            'wall_time': time.time(),
-            'graph_def': values.get('graph'),
-        }
-        graph_event = json_format.Parse(json.dumps(graph_json), summary_pb2.Event())
-
-        return graph_event
-
-
-if __name__ == "__main__":
-    graph_log_generator = GraphLogGenerator()
-    test_file_name = '%s.%s.%s' % ('graph', 'summary', str(time.time()))
-    graph_base_path = os.path.join(os.path.dirname(__file__), os.pardir, "log_generators", "graph_base.json")
-    with open(graph_base_path, 'r') as load_f:
-        graph = json.load(load_f)
-    graph_log_generator.generate_log(test_file_name, graph)

tests/st/func/datavisual/workflow/test_image_metadata.py

@@ -20,11 +20,11 @@ Usage:
 """
 import pytest
 
-from tests.st.func.datavisual.utils import globals as gbl
-from tests.st.func.datavisual.utils.utils import get_url
-
 from mindinsight.datavisual.common.enums import PluginNameEnum
 
+from .....utils.tools import get_url
+from .. import globals as gbl
+
 TRAIN_JOB_URL = '/v1/mindinsight/datavisual/train-jobs'
 PLUGIN_URL = '/v1/mindinsight/datavisual/plugins'
 METADATA_URL = '/v1/mindinsight/datavisual/image/metadata'

tests/st/func/datavisual/workflow/test_single_image.py

@@ -20,11 +20,11 @@ Usage:
 """
 import pytest
 
-from tests.st.func.datavisual.utils import globals as gbl
-from tests.st.func.datavisual.utils.utils import get_url, get_image_tensor_from_bytes
-
 from mindinsight.datavisual.common.enums import PluginNameEnum
 
+from .....utils.tools import get_image_tensor_from_bytes, get_url
+from .. import globals as gbl
+
 TRAIN_JOB_URL = '/v1/mindinsight/datavisual/train-jobs'
 PLUGIN_URL = '/v1/mindinsight/datavisual/plugins'
 METADATA_URL = '/v1/mindinsight/datavisual/image/metadata'

tests/st/func/lineagemgr/api/test_model_api.py

@@ -26,11 +26,101 @@ from unittest import TestCase
 
 import pytest
 
-from mindinsight.lineagemgr import get_summary_lineage, filter_summary_lineage
-from mindinsight.lineagemgr.common.exceptions.exceptions import \
-    LineageParamSummaryPathError, LineageParamValueError, LineageParamTypeError, \
-    LineageSearchConditionParamError, LineageFileNotFoundError
-from ..conftest import BASE_SUMMARY_DIR, SUMMARY_DIR, SUMMARY_DIR_2, DATASET_GRAPH
+from mindinsight.lineagemgr import filter_summary_lineage, get_summary_lineage
+from mindinsight.lineagemgr.common.exceptions.exceptions import (LineageFileNotFoundError, LineageParamSummaryPathError,
+                                                                 LineageParamTypeError, LineageParamValueError,
+                                                                 LineageSearchConditionParamError)
+
+from ..conftest import BASE_SUMMARY_DIR, DATASET_GRAPH, SUMMARY_DIR, SUMMARY_DIR_2
+
+LINEAGE_INFO_RUN1 = {
+    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
+    'metric': {
+        'accuracy': 0.78
+    },
+    'hyper_parameters': {
+        'optimizer': 'Momentum',
+        'learning_rate': 0.11999999731779099,
+        'loss_function': 'SoftmaxCrossEntropyWithLogits',
+        'epoch': 14,
+        'parallel_mode': 'stand_alone',
+        'device_num': 2,
+        'batch_size': 32
+    },
+    'algorithm': {
+        'network': 'ResNet'
+    },
+    'train_dataset': {
+        'train_dataset_size': 731
+    },
+    'valid_dataset': {
+        'valid_dataset_size': 10240
+    },
+    'model': {
+        'path': '{"ckpt": "'
+                + BASE_SUMMARY_DIR + '/run1/CKPtest_model.ckpt"}',
+        'size': 64
+    },
+    'dataset_graph': DATASET_GRAPH
+}
+LINEAGE_FILTRATION_EXCEPT_RUN = {
+    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'except_run'),
+    'loss_function': 'SoftmaxCrossEntropyWithLogits',
+    'train_dataset_path': None,
+    'train_dataset_count': 1024,
+    'test_dataset_path': None,
+    'test_dataset_count': None,
+    'network': 'ResNet',
+    'optimizer': 'Momentum',
+    'learning_rate': 0.11999999731779099,
+    'epoch': 10,
+    'batch_size': 32,
+    'loss': 0.029999999329447746,
+    'model_size': 64,
+    'metric': {},
+    'dataset_graph': DATASET_GRAPH,
+    'dataset_mark': 2
+}
+LINEAGE_FILTRATION_RUN1 = {
+    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
+    'loss_function': 'SoftmaxCrossEntropyWithLogits',
+    'train_dataset_path': None,
+    'train_dataset_count': 731,
+    'test_dataset_path': None,
+    'test_dataset_count': 10240,
+    'network': 'ResNet',
+    'optimizer': 'Momentum',
+    'learning_rate': 0.11999999731779099,
+    'epoch': 14,
+    'batch_size': 32,
+    'loss': None,
+    'model_size': 64,
+    'metric': {
+        'accuracy': 0.78
+    },
+    'dataset_graph': DATASET_GRAPH,
+    'dataset_mark': 2
+}
+LINEAGE_FILTRATION_RUN2 = {
+    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run2'),
+    'loss_function': None,
+    'train_dataset_path': None,
+    'train_dataset_count': None,
+    'test_dataset_path': None,
+    'test_dataset_count': 10240,
+    'network': None,
+    'optimizer': None,
+    'learning_rate': None,
+    'epoch': None,
+    'batch_size': None,
+    'loss': None,
+    'model_size': None,
+    'metric': {
+        'accuracy': 2.7800000000000002
+    },
+    'dataset_graph': {},
+    'dataset_mark': 3
+}
 
 
 @pytest.mark.usefixtures("create_summary_dir")
@@ -67,36 +157,7 @@ class TestModelApi(TestCase):
         total_res = get_summary_lineage(SUMMARY_DIR)
         partial_res1 = get_summary_lineage(SUMMARY_DIR, ['hyper_parameters'])
         partial_res2 = get_summary_lineage(SUMMARY_DIR, ['metric', 'algorithm'])
-        expect_total_res = {
-            'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
-            'metric': {
-                'accuracy': 0.78
-            },
-            'hyper_parameters': {
-                'optimizer': 'Momentum',
-                'learning_rate': 0.11999999731779099,
-                'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                'epoch': 14,
-                'parallel_mode': 'stand_alone',
-                'device_num': 2,
-                'batch_size': 32
-            },
-            'algorithm': {
-                'network': 'ResNet'
-            },
-            'train_dataset': {
-                'train_dataset_size': 731
-            },
-            'valid_dataset': {
-                'valid_dataset_size': 10240
-            },
-            'model': {
-                'path': '{"ckpt": "'
-                        + BASE_SUMMARY_DIR + '/run1/CKPtest_model.ckpt"}',
-                'size': 64
-            },
-            'dataset_graph': DATASET_GRAPH
-        }
+        expect_total_res = LINEAGE_INFO_RUN1
         expect_partial_res1 = {
             'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
             'hyper_parameters': {
@@ -139,7 +200,7 @@ class TestModelApi(TestCase):
     @pytest.mark.platform_x86_ascend_training
     @pytest.mark.platform_x86_cpu
     @pytest.mark.env_single
-    def test_get_summary_lineage_exception(self):
+    def test_get_summary_lineage_exception_1(self):
         """Test the interface of get_summary_lineage with exception."""
         # summary path does not exist
         self.assertRaisesRegex(
@@ -183,6 +244,14 @@ class TestModelApi(TestCase):
             keys=None
         )
 
+    @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_get_summary_lineage_exception_2(self):
+        """Test the interface of get_summary_lineage with exception."""
         # keys is invalid
         self.assertRaisesRegex(
             LineageParamValueError,
@@ -250,64 +319,9 @@ class TestModelApi(TestCase):
         """Test the interface of filter_summary_lineage."""
         expect_result = {
             'object': [
-                {
-                    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'except_run'),
-                    'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                    'train_dataset_path': None,
-                    'train_dataset_count': 1024,
-                    'test_dataset_path': None,
-                    'test_dataset_count': None,
-                    'network': 'ResNet',
-                    'optimizer': 'Momentum',
-                    'learning_rate': 0.11999999731779099,
-                    'epoch': 10,
-                    'batch_size': 32,
-                    'loss': 0.029999999329447746,
-                    'model_size': 64,
-                    'metric': {},
-                    'dataset_graph': DATASET_GRAPH,
-                    'dataset_mark': 2
-                },
-                {
-                    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
-                    'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                    'train_dataset_path': None,
-                    'train_dataset_count': 731,
-                    'test_dataset_path': None,
-                    'test_dataset_count': 10240,
-                    'network': 'ResNet',
-                    'optimizer': 'Momentum',
-                    'learning_rate': 0.11999999731779099,
-                    'epoch': 14,
-                    'batch_size': 32,
-                    'loss': None,
-                    'model_size': 64,
-                    'metric': {
-                        'accuracy': 0.78
-                    },
-                    'dataset_graph': DATASET_GRAPH,
-                    'dataset_mark': 2
-                },
-                {
-                    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run2'),
-                    'loss_function': None,
-                    'train_dataset_path': None,
-                    'train_dataset_count': None,
-                    'test_dataset_path': None,
-                    'test_dataset_count': 10240,
-                    'network': None,
-                    'optimizer': None,
-                    'learning_rate': None,
-                    'epoch': None,
-                    'batch_size': None,
-                    'loss': None,
-                    'model_size': None,
-                    'metric': {
-                        'accuracy': 2.7800000000000002
-                    },
-                    'dataset_graph': {},
-                    'dataset_mark': 3
-                }
+                LINEAGE_FILTRATION_EXCEPT_RUN,
+                LINEAGE_FILTRATION_RUN1,
+                LINEAGE_FILTRATION_RUN2
             ],
             'count': 3
         }
@@ -357,46 +371,8 @@ class TestModelApi(TestCase):
         }
         expect_result = {
             'object': [
-                {
-                    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run2'),
-                    'loss_function': None,
-                    'train_dataset_path': None,
-                    'train_dataset_count': None,
-                    'test_dataset_path': None,
-                    'test_dataset_count': 10240,
-                    'network': None,
-                    'optimizer': None,
-                    'learning_rate': None,
-                    'epoch': None,
-                    'batch_size': None,
-                    'loss': None,
-                    'model_size': None,
-                    'metric': {
-                        'accuracy': 2.7800000000000002
-                    },
-                    'dataset_graph': {},
-                    'dataset_mark': 3
-                },
-                {
-                    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
-                    'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                    'train_dataset_path': None,
-                    'train_dataset_count': 731,
-                    'test_dataset_path': None,
-                    'test_dataset_count': 10240,
-                    'network': 'ResNet',
-                    'optimizer': 'Momentum',
-                    'learning_rate': 0.11999999731779099,
-                    'epoch': 14,
-                    'batch_size': 32,
-                    'loss': None,
-                    'model_size': 64,
-                    'metric': {
-                        'accuracy': 0.78
-                    },
-                    'dataset_graph': DATASET_GRAPH,
-                    'dataset_mark': 2
-                }
+                LINEAGE_FILTRATION_RUN2,
+                LINEAGE_FILTRATION_RUN1
             ],
             'count': 2
         }
@@ -432,46 +408,8 @@ class TestModelApi(TestCase):
         }
         expect_result = {
             'object': [
-                {
-                    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run2'),
-                    'loss_function': None,
-                    'train_dataset_path': None,
-                    'train_dataset_count': None,
-                    'test_dataset_path': None,
-                    'test_dataset_count': 10240,
-                    'network': None,
-                    'optimizer': None,
-                    'learning_rate': None,
-                    'epoch': None,
-                    'batch_size': None,
-                    'loss': None,
-                    'model_size': None,
-                    'metric': {
-                        'accuracy': 2.7800000000000002
-                    },
-                    'dataset_graph': {},
-                    'dataset_mark': 3
-                },
-                {
-                    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
-                    'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                    'train_dataset_path': None,
-                    'train_dataset_count': 731,
-                    'test_dataset_path': None,
-                    'test_dataset_count': 10240,
-                    'network': 'ResNet',
-                    'optimizer': 'Momentum',
-                    'learning_rate': 0.11999999731779099,
-                    'epoch': 14,
-                    'batch_size': 32,
-                    'loss': None,
-                    'model_size': 64,
-                    'metric': {
-                        'accuracy': 0.78
-                    },
-                    'dataset_graph': DATASET_GRAPH,
-                    'dataset_mark': 2
-                }
+                LINEAGE_FILTRATION_RUN2,
+                LINEAGE_FILTRATION_RUN1
             ],
             'count': 2
         }
@@ -498,44 +436,8 @@ class TestModelApi(TestCase):
         }
         expect_result = {
             'object': [
-                {
-                    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'except_run'),
-                    'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                    'train_dataset_path': None,
-                    'train_dataset_count': 1024,
-                    'test_dataset_path': None,
-                    'test_dataset_count': None,
-                    'network': 'ResNet',
-                    'optimizer': 'Momentum',
-                    'learning_rate': 0.11999999731779099,
-                    'epoch': 10,
-                    'batch_size': 32,
-                    'loss': 0.029999999329447746,
-                    'model_size': 64,
-                    'metric': {},
-                    'dataset_graph': DATASET_GRAPH,
-                    'dataset_mark': 2
-                },
-                {
-                    'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
-                    'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                    'train_dataset_path': None,
-                    'train_dataset_count': 731,
-                    'test_dataset_path': None,
-                    'test_dataset_count': 10240,
-                    'network': 'ResNet',
-                    'optimizer': 'Momentum',
-                    'learning_rate': 0.11999999731779099,
-                    'epoch': 14,
-                    'batch_size': 32,
-                    'loss': None,
-                    'model_size': 64,
-                    'metric': {
-                        'accuracy': 0.78
-                    },
-                    'dataset_graph': DATASET_GRAPH,
-                    'dataset_mark': 2
-                }
+                LINEAGE_FILTRATION_EXCEPT_RUN,
+                LINEAGE_FILTRATION_RUN1
             ],
             'count': 2
         }
@@ -674,6 +576,14 @@ class TestModelApi(TestCase):
             search_condition
         )
 
+    @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_filter_summary_lineage_exception_3(self):
+        """Test the abnormal execution of the filter_summary_lineage interface."""
         # the condition of offset is invalid
         search_condition = {
             'offset': 1.0
@@ -712,6 +622,14 @@ class TestModelApi(TestCase):
             search_condition
         )
 
+    @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_filter_summary_lineage_exception_4(self):
+        """Test the abnormal execution of the filter_summary_lineage interface."""
         # the sorted_type not supported
         search_condition = {
             'sorted_name': 'summary_dir',
@@ -753,6 +671,14 @@ class TestModelApi(TestCase):
             search_condition
         )
 
+    @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_filter_summary_lineage_exception_5(self):
+        """Test the abnormal execution of the filter_summary_lineage interface."""
         # the summary dir is invalid in search condition
         search_condition = {
             'summary_dir': {
@@ -811,7 +737,7 @@ class TestModelApi(TestCase):
     @pytest.mark.platform_x86_ascend_training
     @pytest.mark.platform_x86_cpu
     @pytest.mark.env_single
-    def test_filter_summary_lineage_exception_3(self):
+    def test_filter_summary_lineage_exception_6(self):
         """Test the abnormal execution of the filter_summary_lineage interface."""
         # gt > lt
         search_condition1 = {

tests/st/func/lineagemgr/conftest.py

@@ -21,7 +21,8 @@ import tempfile
 
 import pytest
 
-from .collection.model import mindspore
+from ....utils import mindspore
+from ....utils.mindspore.dataset.engine.serializer_deserializer import SERIALIZED_PIPELINE
 
 sys.modules['mindspore'] = mindspore
 
@@ -32,52 +33,7 @@ SUMMARY_DIR_3 = os.path.join(BASE_SUMMARY_DIR, 'except_run')
 
 COLLECTION_MODULE = 'TestModelLineage'
 API_MODULE = 'TestModelApi'
-DATASET_GRAPH = {
-    'op_type': 'BatchDataset',
-    'op_module': 'minddata.dataengine.datasets',
-    'num_parallel_workers': None,
-    'drop_remainder': True,
-    'batch_size': 10,
-    'children': [
-        {
-            'op_type': 'MapDataset',
-            'op_module': 'minddata.dataengine.datasets',
-            'num_parallel_workers': None,
-            'input_columns': [
-                'label'
-            ],
-            'output_columns': [
-                None
-            ],
-            'operations': [
-                {
-                    'tensor_op_module': 'minddata.transforms.c_transforms',
-                    'tensor_op_name': 'OneHot',
-                    'num_classes': 10
-                }
-            ],
-            'children': [
-                {
-                    'op_type': 'MnistDataset',
-                    'shard_id': None,
-                    'num_shards': None,
-                    'op_module': 'minddata.dataengine.datasets',
-                    'dataset_dir': '/home/anthony/MindData/tests/dataset/data/testMnistData',
-                    'num_parallel_workers': None,
-                    'shuffle': None,
-                    'num_samples': 100,
-                    'sampler': {
-                        'sampler_module': 'minddata.dataengine.samplers',
-                        'sampler_name': 'RandomSampler',
-                        'replacement': True,
-                        'num_samples': 100
-                    },
-                    'children': []
-                }
-            ]
-        }
-    ]
-}
+DATASET_GRAPH = SERIALIZED_PIPELINE
 
 def get_module_name(nodeid):
     """Get the module name from nodeid."""

tests/ut/__init__.py

@@ -14,6 +14,6 @@
 # ============================================================================
 """Import the mocked mindspore."""
 import sys
-from .lineagemgr.collection.model import mindspore
+from ..utils import mindspore
 
 sys.modules['mindspore'] = mindspore

tests/ut/backend/datavisual/test_task_manager_api.py

@@ -21,14 +21,15 @@ Usage:
 from unittest.mock import patch
 
 import pytest
-from tests.ut.backend.datavisual.conftest import TRAIN_ROUTES
-from tests.ut.datavisual.utils.log_generators.images_log_generator import ImagesLogGenerator
-from tests.ut.datavisual.utils.log_generators.scalars_log_generator import ScalarsLogGenerator
-from tests.ut.datavisual.utils.utils import get_url
 
 from mindinsight.datavisual.common.enums import PluginNameEnum
 from mindinsight.datavisual.processors.train_task_manager import TrainTaskManager
 
+from ....utils.log_generators.images_log_generator import ImagesLogGenerator
+from ....utils.log_generators.scalars_log_generator import ScalarsLogGenerator
+from ....utils.tools import get_url
+from .conftest import TRAIN_ROUTES
+
 
 class TestTrainTask:
     """Test train task api."""
@@ -36,9 +37,7 @@ class TestTrainTask:
     _scalar_log_generator = ScalarsLogGenerator()
     _image_log_generator = ImagesLogGenerator()
 
-    @pytest.mark.parametrize(
-        "plugin_name",
-        ['no_plugin_name', 'not_exist_plugin_name'])
+    @pytest.mark.parametrize("plugin_name", ['no_plugin_name', 'not_exist_plugin_name'])
     def test_query_single_train_task_with_plugin_name_not_exist(self, client, plugin_name):
         """
         Parsing unavailable plugin name to single train task.

tests/ut/backend/datavisual/test_train_visual_api.py

@@ -21,14 +21,15 @@ Usage:
 from unittest.mock import Mock, patch
 
 import pytest
-from tests.ut.backend.datavisual.conftest import TRAIN_ROUTES
-from tests.ut.datavisual.utils.utils import get_url
 
 from mindinsight.datavisual.data_transform.graph import NodeTypeEnum
 from mindinsight.datavisual.processors.graph_processor import GraphProcessor
 from mindinsight.datavisual.processors.images_processor import ImageProcessor
 from mindinsight.datavisual.processors.scalars_processor import ScalarsProcessor
 
+from ....utils.tools import get_url
+from .conftest import TRAIN_ROUTES
+
 
 class TestTrainVisual:
     """Test Train Visual APIs."""
@@ -95,14 +96,7 @@ class TestTrainVisual:
 
         assert response.status_code == 200
         response = response.get_json()
-        expected_response = {
-            "metadatas": [{
-                "height": 224,
-                "step": 1,
-                "wall_time": 1572058058.1175,
-                "width": 448
-            }]
-        }
+        expected_response = {"metadatas": [{"height": 224, "step": 1, "wall_time": 1572058058.1175, "width": 448}]}
         assert expected_response == response
 
     def test_single_image_with_params_miss(self, client):
@@ -254,8 +248,10 @@ class TestTrainVisual:
     @patch.object(GraphProcessor, 'get_nodes')
     def test_graph_nodes_success(self, mock_graph_processor, mock_graph_processor_1, client):
         """Test getting graph nodes successfully."""
+
         def mock_get_nodes(name, node_type):
             return dict(name=name, node_type=node_type)
+
         mock_graph_processor.side_effect = mock_get_nodes
 
         mock_init = Mock(return_value=None)
@@ -327,10 +323,7 @@ class TestTrainVisual:
         assert results['error_msg'] == "Invalid parameter value. 'offset' should " \
                                        "be greater than or equal to 0."
 
-    @pytest.mark.parametrize(
-        "limit",
-        [-1, 0, 1001]
-    )
+    @pytest.mark.parametrize("limit", [-1, 0, 1001])
     @patch.object(GraphProcessor, '__init__')
     def test_graph_node_names_with_invalid_limit(self, mock_graph_processor, client, limit):
         """Test getting graph node names with invalid limit."""
@@ -348,14 +341,10 @@ class TestTrainVisual:
         assert results['error_msg'] == "Invalid parameter value. " \
                                        "'limit' should in [1, 1000]."
 
-    @pytest.mark.parametrize(
-        " offset, limit",
-        [(0, 100), (1, 1), (0, 1000)]
-    )
+    @pytest.mark.parametrize(" offset, limit", [(0, 100), (1, 1), (0, 1000)])
     @patch.object(GraphProcessor, '__init__')
     @patch.object(GraphProcessor, 'search_node_names')
-    def test_graph_node_names_success(self, mock_graph_processor, mock_graph_processor_1, client,
-                                      offset, limit):
+    def test_graph_node_names_success(self, mock_graph_processor, mock_graph_processor_1, client, offset, limit):
         """
         Parsing unavailable params to get image metadata.
 
@@ -367,8 +356,10 @@ class TestTrainVisual:
         response status code: 200.
         response json: dict, contains search_content, offset, and limit.
         """
+
         def mock_search_node_names(search_content, offset, limit):
             return dict(search_content=search_content, offset=int(offset), limit=int(limit))
+
         mock_graph_processor.side_effect = mock_search_node_names
 
         mock_init = Mock(return_value=None)
@@ -376,15 +367,12 @@ class TestTrainVisual:
 
         test_train_id = "aaa"
         test_search_content = "bbb"
-        params = dict(train_id=test_train_id, search=test_search_content,
-                      offset=offset, limit=limit)
+        params = dict(train_id=test_train_id, search=test_search_content, offset=offset, limit=limit)
         url = get_url(TRAIN_ROUTES['graph_nodes_names'], params)
         response = client.get(url)
         assert response.status_code == 200
         results = response.get_json()
-        assert results == dict(search_content=test_search_content,
-                               offset=int(offset),
-                               limit=int(limit))
+        assert results == dict(search_content=test_search_content, offset=int(offset), limit=int(limit))
 
     def test_graph_search_single_node_with_params_is_wrong(self, client):
         """Test searching graph single node with params is wrong."""
@@ -427,8 +415,10 @@ class TestTrainVisual:
         response status code: 200.
         response json: name.
         """
+
         def mock_search_single_node(name):
             return name
+
         mock_graph_processor.side_effect = mock_search_single_node
 
         mock_init = Mock(return_value=None)

tests/ut/backend/lineagemgr/test_lineage_api.py

@@ -20,8 +20,42 @@ from unittest import TestCase, mock
 from flask import Response
 
 from mindinsight.backend.application import APP
-from mindinsight.lineagemgr.common.exceptions.exceptions import \
-    LineageQuerySummaryDataError
+from mindinsight.lineagemgr.common.exceptions.exceptions import LineageQuerySummaryDataError
+
+LINEAGE_FILTRATION_BASE = {
+    'accuracy': None,
+    'mae': None,
+    'mse': None,
+    'loss_function': 'SoftmaxCrossEntropyWithLogits',
+    'train_dataset_path': None,
+    'train_dataset_count': 64,
+    'test_dataset_path': None,
+    'test_dataset_count': None,
+    'network': 'str',
+    'optimizer': 'Momentum',
+    'learning_rate': 0.11999999731779099,
+    'epoch': 12,
+    'batch_size': 32,
+    'loss': 0.029999999329447746,
+    'model_size': 128
+}
+LINEAGE_FILTRATION_RUN1 = {
+    'accuracy': 0.78,
+    'mae': None,
+    'mse': None,
+    'loss_function': 'SoftmaxCrossEntropyWithLogits',
+    'train_dataset_path': None,
+    'train_dataset_count': 64,
+    'test_dataset_path': None,
+    'test_dataset_count': 64,
+    'network': 'str',
+    'optimizer': 'Momentum',
+    'learning_rate': 0.11999999731779099,
+    'epoch': 14,
+    'batch_size': 32,
+    'loss': 0.029999999329447746,
+    'model_size': 128
+}
 
 
 class TestSearchModel(TestCase):
@@ -42,39 +76,11 @@ class TestSearchModel(TestCase):
             'object': [
                 {
                     'summary_dir': base_dir,
-                    'accuracy': None,
-                    'mae': None,
-                    'mse': None,
-                    'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                    'train_dataset_path': None,
-                    'train_dataset_count': 64,
-                    'test_dataset_path': None,
-                    'test_dataset_count': None,
-                    'network': 'str',
-                    'optimizer': 'Momentum',
-                    'learning_rate': 0.11999999731779099,
-                    'epoch': 12,
-                    'batch_size': 32,
-                    'loss': 0.029999999329447746,
-                    'model_size': 128
+                    **LINEAGE_FILTRATION_BASE
                 },
                 {
                     'summary_dir': os.path.join(base_dir, 'run1'),
-                    'accuracy': 0.78,
-                    'mae': None,
-                    'mse': None,
-                    'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                    'train_dataset_path': None,
-                    'train_dataset_count': 64,
-                    'test_dataset_path': None,
-                    'test_dataset_count': 64,
-                    'network': 'str',
-                    'optimizer': 'Momentum',
-                    'learning_rate': 0.11999999731779099,
-                    'epoch': 14,
-                    'batch_size': 32,
-                    'loss': 0.029999999329447746,
-                    'model_size': 128
+                    **LINEAGE_FILTRATION_RUN1
                 }
             ],
             'count': 2
@@ -93,39 +99,11 @@ class TestSearchModel(TestCase):
             'object': [
                 {
                     'summary_dir': './',
-                    'accuracy': None,
-                    'mae': None,
-                    'mse': None,
-                    'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                    'train_dataset_path': None,
-                    'train_dataset_count': 64,
-                    'test_dataset_path': None,
-                    'test_dataset_count': None,
-                    'network': 'str',
-                    'optimizer': 'Momentum',
-                    'learning_rate': 0.11999999731779099,
-                    'epoch': 12,
-                    'batch_size': 32,
-                    'loss': 0.029999999329447746,
-                    'model_size': 128
+                    **LINEAGE_FILTRATION_BASE
                 },
                 {
                     'summary_dir': './run1',
-                    'accuracy': 0.78,
-                    'mae': None,
-                    'mse': None,
-                    'loss_function': 'SoftmaxCrossEntropyWithLogits',
-                    'train_dataset_path': None,
-                    'train_dataset_count': 64,
-                    'test_dataset_path': None,
-                    'test_dataset_count': 64,
-                    'network': 'str',
-                    'optimizer': 'Momentum',
-                    'learning_rate': 0.11999999731779099,
-                    'epoch': 14,
-                    'batch_size': 32,
-                    'loss': 0.029999999329447746,
-                    'model_size': 128
+                    **LINEAGE_FILTRATION_RUN1
                 }
             ],
             'count': 2

tests/ut/datavisual/common/test_error_handler.py

@@ -19,15 +19,16 @@ Usage:
     pytest tests/ut/datavisual
 """
 from unittest.mock import patch
-from werkzeug.exceptions import MethodNotAllowed, NotFound
 
-from tests.ut.backend.datavisual.conftest import TRAIN_ROUTES
-from tests.ut.datavisual.mock import MockLogger
-from tests.ut.datavisual.utils.utils import get_url
+from werkzeug.exceptions import MethodNotAllowed, NotFound
 
 from mindinsight.datavisual.processors import scalars_processor
 from mindinsight.datavisual.processors.scalars_processor import ScalarsProcessor
 
+from ....utils.tools import get_url
+from ...backend.datavisual.conftest import TRAIN_ROUTES
+from ..mock import MockLogger
+
 
 class TestErrorHandler:
     """Test train visual api."""

tests/ut/datavisual/data_transform/loader_generators/test_data_loader_generator.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """
 Function:
-    Test mindinsight.datavisual.data_transform.log_generators.data_loader_generator
+    Test mindinsight.datavisual.data_transform.loader_generators.data_loader_generator
 Usage:
     pytest tests/ut/datavisual
 """
@@ -22,18 +22,19 @@ import datetime
 import os
 import shutil
 import tempfile
-
 from unittest.mock import patch
-import pytest
 
-from tests.ut.datavisual.mock import MockLogger
+import pytest
 
 from mindinsight.datavisual.data_transform.loader_generators import data_loader_generator
 from mindinsight.utils.exceptions import ParamValueError
 
+from ...mock import MockLogger
+
 
 class TestDataLoaderGenerator:
     """Test data_loader_generator."""
+
     @classmethod
     def setup_class(cls):
         data_loader_generator.logger = MockLogger
@@ -88,8 +89,9 @@ class TestDataLoaderGenerator:
         mock_data_loader.return_value = True
 
         loader_dict = generator.generate_loaders(loader_pool=dict())
-        expected_ids = [summary.get('relative_path')
-                        for summary in summaries[-data_loader_generator.MAX_DATA_LOADER_SIZE:]]
+        expected_ids = [
+            summary.get('relative_path') for summary in summaries[-data_loader_generator.MAX_DATA_LOADER_SIZE:]
+        ]
         assert sorted(loader_dict.keys()) == sorted(expected_ids)
 
         shutil.rmtree(summary_base_dir)

tests/ut/datavisual/data_transform/test_data_loader.py

@@ -23,12 +23,13 @@ import shutil
 import tempfile
 
 import pytest
-from tests.ut.datavisual.mock import MockLogger
 
 from mindinsight.datavisual.common.exceptions import SummaryLogPathInvalid
 from mindinsight.datavisual.data_transform import data_loader
 from mindinsight.datavisual.data_transform.data_loader import DataLoader
 
+from ..mock import MockLogger
+
 
 class TestDataLoader:
     """Test data_loader."""
@@ -37,13 +38,13 @@ class TestDataLoader:
     def setup_class(cls):
         data_loader.logger = MockLogger
 
-    def setup_method(self, method):
+    def setup_method(self):
         self._summary_dir = tempfile.mkdtemp()
         if os.path.exists(self._summary_dir):
             shutil.rmtree(self._summary_dir)
         os.mkdir(self._summary_dir)
 
-    def teardown_method(self, method):
+    def teardown_method(self):
         if os.path.exists(self._summary_dir):
             shutil.rmtree(self._summary_dir)
 

tests/ut/datavisual/data_transform/test_data_manager.py

@@ -18,32 +18,29 @@ Function:
 Usage:
     pytest tests/ut/datavisual
 """
-import time
 import os
 import shutil
 import tempfile
+import time
 from unittest import mock
-from unittest.mock import Mock
-from unittest.mock import patch
+from unittest.mock import Mock, patch
 
 import pytest
-from tests.ut.datavisual.mock import MockLogger
-from tests.ut.datavisual.utils.utils import check_loading_done
 
 from mindinsight.datavisual.common.enums import DataManagerStatus, PluginNameEnum
 from mindinsight.datavisual.data_transform import data_manager, ms_data_loader
 from mindinsight.datavisual.data_transform.data_loader import DataLoader
 from mindinsight.datavisual.data_transform.data_manager import DataManager
 from mindinsight.datavisual.data_transform.events_data import EventsData
-from mindinsight.datavisual.data_transform.loader_generators.data_loader_generator import \
-    DataLoaderGenerator
-from mindinsight.datavisual.data_transform.loader_generators.loader_generator import \
-    MAX_DATA_LOADER_SIZE
-from mindinsight.datavisual.data_transform.loader_generators.loader_struct import \
-    LoaderStruct
+from mindinsight.datavisual.data_transform.loader_generators.data_loader_generator import DataLoaderGenerator
+from mindinsight.datavisual.data_transform.loader_generators.loader_generator import MAX_DATA_LOADER_SIZE
+from mindinsight.datavisual.data_transform.loader_generators.loader_struct import LoaderStruct
 from mindinsight.datavisual.data_transform.ms_data_loader import MSDataLoader
 from mindinsight.utils.exceptions import ParamValueError
 
+from ....utils.tools import check_loading_done
+from ..mock import MockLogger
+
 
 class TestDataManager:
     """Test data_manager."""
@@ -101,11 +98,17 @@ class TestDataManager:
                                              "and loader pool size is '3'."
         shutil.rmtree(summary_base_dir)
 
-    @pytest.mark.parametrize('params',
-                             [{'reload_interval': '30'},
-                              {'reload_interval': -1},
-                              {'reload_interval': 30, 'max_threads_count': '20'},
-                              {'reload_interval': 30, 'max_threads_count': 0}])
+    @pytest.mark.parametrize('params', [{
+        'reload_interval': '30'
+    }, {
+        'reload_interval': -1
+    }, {
+        'reload_interval': 30,
+        'max_threads_count': '20'
+    }, {
+        'reload_interval': 30,
+        'max_threads_count': 0
+    }])
     def test_start_load_data_with_invalid_params(self, params):
         """Test start_load_data with invalid reload_interval or invalid max_threads_count."""
         summary_base_dir = tempfile.mkdtemp()

tests/ut/datavisual/data_transform/test_events_data.py

@@ -22,20 +22,24 @@ import threading
 from collections import namedtuple
 
 import pytest
-from tests.ut.datavisual.mock import MockLogger
 
 from mindinsight.conf import settings
 from mindinsight.datavisual.data_transform import events_data
 from mindinsight.datavisual.data_transform.events_data import EventsData, TensorEvent, _Tensor
 
+from ..mock import MockLogger
+
 
 class MockReservoir:
     """Use this class to replace reservoir.Reservoir in test."""
 
     def __init__(self, size):
         self.size = size
-        self._samples = [_Tensor('wall_time1', 1, 'value1'), _Tensor('wall_time2', 2, 'value2'),
-                         _Tensor('wall_time3', 3, 'value3')]
+        self._samples = [
+            _Tensor('wall_time1', 1, 'value1'),
+            _Tensor('wall_time2', 2, 'value2'),
+            _Tensor('wall_time3', 3, 'value3')
+        ]
 
     def samples(self):
         """Replace the samples function."""
@@ -63,11 +67,12 @@ class TestEventsData:
     def setup_method(self):
         """Mock original logger, init a EventsData object for use."""
         self._ev_data = EventsData()
-        self._ev_data._tags_by_plugin = {'plugin_name1': [f'tag{i}' for i in range(10)],
-                                         'plugin_name2': [f'tag{i}' for i in range(20, 30)]}
+        self._ev_data._tags_by_plugin = {
+            'plugin_name1': [f'tag{i}' for i in range(10)],
+            'plugin_name2': [f'tag{i}' for i in range(20, 30)]
+        }
         self._ev_data._tags_by_plugin_mutex_lock.update({'plugin_name1': threading.Lock()})
-        self._ev_data._reservoir_by_tag = {'tag0': MockReservoir(500),
-                                           'new_tag': MockReservoir(500)}
+        self._ev_data._reservoir_by_tag = {'tag0': MockReservoir(500), 'new_tag': MockReservoir(500)}
         self._ev_data._tags = [f'tag{i}' for i in range(settings.MAX_TAG_SIZE_PER_EVENTS_DATA)]
 
     def get_ev_data(self):
@@ -102,8 +107,7 @@ class TestEventsData:
         """Test add_tensor_event success."""
 
         ev_data = self.get_ev_data()
-        t_event = TensorEvent(wall_time=1, step=4, tag='new_tag', plugin_name='plugin_name1',
-                              value='value1')
+        t_event = TensorEvent(wall_time=1, step=4, tag='new_tag', plugin_name='plugin_name1', value='value1')
 
         ev_data.add_tensor_event(t_event)
         assert 'tag0' not in ev_data._tags
@@ -111,6 +115,5 @@ class TestEventsData:
         assert 'tag0' not in ev_data._tags_by_plugin['plugin_name1']
         assert 'tag0' not in ev_data._reservoir_by_tag
         assert 'new_tag' in ev_data._tags_by_plugin['plugin_name1']
-        assert ev_data._reservoir_by_tag['new_tag'].samples()[-1] == _Tensor(t_event.wall_time,
-                                                                             t_event.step,
+        assert ev_data._reservoir_by_tag['new_tag'].samples()[-1] == _Tensor(t_event.wall_time, t_event.step,
                                                                              t_event.value)

tests/ut/datavisual/data_transform/test_ms_data_loader.py

@@ -19,16 +19,17 @@ Usage:
     pytest tests/ut/datavisual
 """
 import os
-import tempfile
 import shutil
+import tempfile
 from unittest.mock import Mock
 
 import pytest
-from tests.ut.datavisual.mock import MockLogger
 
 from mindinsight.datavisual.data_transform import ms_data_loader
 from mindinsight.datavisual.data_transform.ms_data_loader import MSDataLoader
 
+from ..mock import MockLogger
+
 # bytes of 3 scalar events
 SCALAR_RECORD = (b'\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\t\x96\xe1\xeb)>}\xd7A\x10\x01*'
                  b'\x11\n\x0f\n\x08tag_name\x1d\r\x06V>\x00\x00\x00\x00\x1e\x00\x00\x00\x00\x00\x00'
@@ -74,7 +75,8 @@ class TestMsDataLoader:
                                                 "we will reload all files in path {}.".format(summary_dir)
         shutil.rmtree(summary_dir)
 
-    def test_load_success_with_crc_pass(self, crc_pass):
+    @pytest.mark.usefixtures('crc_pass')
+    def test_load_success_with_crc_pass(self):
         """Test load success."""
         summary_dir = tempfile.mkdtemp()
         file1 = os.path.join(summary_dir, 'summary.01')
@@ -88,7 +90,8 @@ class TestMsDataLoader:
         tensors = ms_loader.get_events_data().tensors(tag[0])
         assert len(tensors) == 3
 
-    def test_load_with_crc_fail(self, crc_fail):
+    @pytest.mark.usefixtures('crc_fail')
+    def test_load_with_crc_fail(self):
         """Test when crc_fail and will not go to func _event_parse."""
         summary_dir = tempfile.mkdtemp()
         file2 = os.path.join(summary_dir, 'summary.02')
@@ -100,8 +103,10 @@ class TestMsDataLoader:
 
     def test_filter_event_files(self):
         """Test filter_event_files function ok."""
-        file_list = ['abc.summary', '123sumary0009abc', 'summary1234', 'aaasummary.5678',
-                     'summary.0012', 'hellosummary.98786', 'mysummary.123abce', 'summay.4567']
+        file_list = [
+            'abc.summary', '123sumary0009abc', 'summary1234', 'aaasummary.5678', 'summary.0012', 'hellosummary.98786',
+            'mysummary.123abce', 'summay.4567'
+        ]
         summary_dir = tempfile.mkdtemp()
         for file in file_list:
             with open(os.path.join(summary_dir, file), 'w'):
@@ -113,6 +118,7 @@ class TestMsDataLoader:
 
         shutil.rmtree(summary_dir)
 
+
 def write_file(filename, record):
     """Write bytes strings to file."""
     with open(filename, 'wb') as file:

tests/ut/datavisual/processors/test_graph_processor.py

@@ -19,18 +19,11 @@ Usage:
     pytest tests/ut/datavisual
 """
 import os
-import json
 import tempfile
-
-from unittest.mock import Mock
-from unittest.mock import patch
+from unittest.mock import Mock, patch
 
 import pytest
 
-from tests.ut.datavisual.mock import MockLogger
-from tests.ut.datavisual.utils.log_operations import LogOperations
-from tests.ut.datavisual.utils.utils import check_loading_done, delete_files_or_dirs
-
 from mindinsight.datavisual.common import exceptions
 from mindinsight.datavisual.common.enums import PluginNameEnum
 from mindinsight.datavisual.data_transform import data_manager
@@ -40,6 +33,10 @@ from mindinsight.datavisual.processors.graph_processor import GraphProcessor
 from mindinsight.datavisual.utils import crc32
 from mindinsight.utils.exceptions import ParamValueError
 
+from ....utils.log_operations import LogOperations
+from ....utils.tools import check_loading_done, compare_result_with_file, delete_files_or_dirs
+from ..mock import MockLogger
+
 
 class TestGraphProcessor:
     """Test Graph Processor api."""
@@ -70,18 +67,13 @@ class TestGraphProcessor:
         """Load graph record."""
         summary_base_dir = tempfile.mkdtemp()
         log_dir = tempfile.mkdtemp(dir=summary_base_dir)
-
         self._train_id = log_dir.replace(summary_base_dir, ".")
 
-        graph_base_path = os.path.join(os.path.dirname(__file__),
-                                       os.pardir, "utils", "log_generators", "graph_base.json")
-        self._temp_path, self._graph_dict = LogOperations.generate_log(
-            PluginNameEnum.GRAPH.value, log_dir, dict(graph_base_path=graph_base_path))
-
+        log_operation = LogOperations()
+        self._temp_path, self._graph_dict = log_operation.generate_log(PluginNameEnum.GRAPH.value, log_dir)
         self._generated_path.append(summary_base_dir)
 
-        self._mock_data_manager = data_manager.DataManager(
-            [DataLoaderGenerator(summary_base_dir)])
+        self._mock_data_manager = data_manager.DataManager([DataLoaderGenerator(summary_base_dir)])
         self._mock_data_manager.start_load_data(reload_interval=0)
 
         # wait for loading done
@@ -94,33 +86,29 @@ class TestGraphProcessor:
         log_dir = tempfile.mkdtemp(dir=summary_base_dir)
         self._train_id = log_dir.replace(summary_base_dir, ".")
 
-        self._temp_path, _, _ = LogOperations.generate_log(
-            PluginNameEnum.IMAGE.value, log_dir, dict(steps=self._steps_list, tag="image"))
+        log_operation = LogOperations()
+        self._temp_path, _, _ = log_operation.generate_log(PluginNameEnum.IMAGE.value, log_dir,
+                                                           dict(steps=self._steps_list, tag="image"))
 
         self._generated_path.append(summary_base_dir)
 
-        self._mock_data_manager = data_manager.DataManager(
-            [DataLoaderGenerator(summary_base_dir)])
+        self._mock_data_manager = data_manager.DataManager([DataLoaderGenerator(summary_base_dir)])
         self._mock_data_manager.start_load_data(reload_interval=0)
 
         # wait for loading done
         check_loading_done(self._mock_data_manager, time_limit=5)
 
-    def compare_result_with_file(self, result, filename):
-        """Compare result with file which contain the expected results."""
-        with open(os.path.join(self.graph_results_dir, filename), 'r') as fp:
-            expected_results = json.load(fp)
-        assert result == expected_results
-
-    def test_get_nodes_with_not_exist_train_id(self, load_graph_record):
+    @pytest.mark.usefixtures('load_graph_record')
+    def test_get_nodes_with_not_exist_train_id(self):
         """Test getting nodes with not exist train id."""
         test_train_id = "not_exist_train_id"
         with pytest.raises(ParamValueError) as exc_info:
             GraphProcessor(test_train_id, self._mock_data_manager)
         assert "Can not find the train job in data manager." in exc_info.value.message
 
+    @pytest.mark.usefixtures('load_graph_record')
     @patch.object(DataManager, 'get_train_job_by_plugin')
-    def test_get_nodes_with_loader_is_none(self, mock_get_train_job_by_plugin, load_graph_record):
+    def test_get_nodes_with_loader_is_none(self, mock_get_train_job_by_plugin):
         """Test get nodes with loader is None."""
         mock_get_train_job_by_plugin.return_value = None
         with pytest.raises(exceptions.SummaryLogPathInvalid):
@@ -128,15 +116,12 @@ class TestGraphProcessor:
 
         assert mock_get_train_job_by_plugin.called
 
-    @pytest.mark.parametrize("name, node_type", [
-        ("not_exist_name", "name_scope"),
-        ("", "polymeric_scope")
-    ])
-    def test_get_nodes_with_not_exist_name(self, load_graph_record, name, node_type):
+    @pytest.mark.usefixtures('load_graph_record')
+    @pytest.mark.parametrize("name, node_type", [("not_exist_name", "name_scope"), ("", "polymeric_scope")])
+    def test_get_nodes_with_not_exist_name(self, name, node_type):
         """Test getting nodes with not exist name."""
         with pytest.raises(ParamValueError) as exc_info:
-            graph_processor = GraphProcessor(self._train_id,
-                                             self._mock_data_manager)
+            graph_processor = GraphProcessor(self._train_id, self._mock_data_manager)
             graph_processor.get_nodes(name, node_type)
 
         if name:
@@ -144,105 +129,99 @@ class TestGraphProcessor:
         else:
             assert f'The node name "{name}" not in graph, node type is {node_type}.' in exc_info.value.message
 
-    @pytest.mark.parametrize("name, node_type, result_file", [
-        (None, 'name_scope', 'test_get_nodes_success_expected_results1.json'),
-        ('Default/conv1-Conv2d', 'name_scope', 'test_get_nodes_success_expected_results2.json'),
-        ('Default/bn1/Reshape_1_[12]', 'polymeric_scope', 'test_get_nodes_success_expected_results3.json')
-    ])
-    def test_get_nodes_success(self, load_graph_record, name, node_type, result_file):
+    @pytest.mark.usefixtures('load_graph_record')
+    @pytest.mark.parametrize(
+        "name, node_type, result_file",
+        [(None, 'name_scope', 'test_get_nodes_success_expected_results1.json'),
+         ('Default/conv1-Conv2d', 'name_scope', 'test_get_nodes_success_expected_results2.json'),
+         ('Default/bn1/Reshape_1_[12]', 'polymeric_scope', 'test_get_nodes_success_expected_results3.json')])
+    def test_get_nodes_success(self, name, node_type, result_file):
         """Test getting nodes successfully."""
 
-        graph_processor = GraphProcessor(self._train_id,
-                                         self._mock_data_manager)
+        graph_processor = GraphProcessor(self._train_id, self._mock_data_manager)
         results = graph_processor.get_nodes(name, node_type)
-        self.compare_result_with_file(results, result_file)
-
-    @pytest.mark.parametrize("search_content, result_file", [
-        (None, 'test_search_node_names_with_search_content_expected_results1.json'),
-        ('Default/bn1', 'test_search_node_names_with_search_content_expected_results2.json'),
-        ('not_exist_search_content', None)
-    ])
-    def test_search_node_names_with_search_content(self, load_graph_record,
-                                                   search_content,
-                                                   result_file):
+
+        expected_file_path = os.path.join(self.graph_results_dir, result_file)
+        compare_result_with_file(results, expected_file_path)
+
+    @pytest.mark.usefixtures('load_graph_record')
+    @pytest.mark.parametrize("search_content, result_file",
+                             [(None, 'test_search_node_names_with_search_content_expected_results1.json'),
+                              ('Default/bn1', 'test_search_node_names_with_search_content_expected_results2.json'),
+                              ('not_exist_search_content', None)])
+    def test_search_node_names_with_search_content(self, search_content, result_file):
         """Test search node names with search content."""
         test_offset = 0
         test_limit = 1000
 
-        graph_processor = GraphProcessor(self._train_id,
-                                         self._mock_data_manager)
-        results = graph_processor.search_node_names(search_content,
-                                                    test_offset,
-                                                    test_limit)
+        graph_processor = GraphProcessor(self._train_id, self._mock_data_manager)
+        results = graph_processor.search_node_names(search_content, test_offset, test_limit)
         if search_content == 'not_exist_search_content':
             expected_results = {'names': []}
             assert results == expected_results
         else:
-            self.compare_result_with_file(results, result_file)
+            expected_file_path = os.path.join(self.graph_results_dir, result_file)
+            compare_result_with_file(results, expected_file_path)
 
+    @pytest.mark.usefixtures('load_graph_record')
     @pytest.mark.parametrize("offset", [-100, -1])
-    def test_search_node_names_with_negative_offset(self, load_graph_record, offset):
+    def test_search_node_names_with_negative_offset(self, offset):
         """Test search node names with negative offset."""
         test_search_content = ""
         test_limit = 3
 
-        graph_processor = GraphProcessor(self._train_id,
-                                         self._mock_data_manager)
+        graph_processor = GraphProcessor(self._train_id, self._mock_data_manager)
         with pytest.raises(ParamValueError) as exc_info:
             graph_processor.search_node_names(test_search_content, offset, test_limit)
         assert "'offset' should be greater than or equal to 0." in exc_info.value.message
 
-    @pytest.mark.parametrize("offset, result_file", [
-        (1, 'test_search_node_names_with_offset_expected_results1.json')
-    ])
-    def test_search_node_names_with_offset(self, load_graph_record, offset, result_file):
+    @pytest.mark.usefixtures('load_graph_record')
+    @pytest.mark.parametrize("offset, result_file", [(1, 'test_search_node_names_with_offset_expected_results1.json')])
+    def test_search_node_names_with_offset(self, offset, result_file):
         """Test search node names with offset."""
         test_search_content = "Default/bn1"
         test_offset = offset
         test_limit = 3
 
-        graph_processor = GraphProcessor(self._train_id,
-                                         self._mock_data_manager)
-        results = graph_processor.search_node_names(test_search_content,
-                                                    test_offset,
-                                                    test_limit)
-        self.compare_result_with_file(results, result_file)
+        graph_processor = GraphProcessor(self._train_id, self._mock_data_manager)
+        results = graph_processor.search_node_names(test_search_content, test_offset, test_limit)
+        expected_file_path = os.path.join(self.graph_results_dir, result_file)
+        compare_result_with_file(results, expected_file_path)
 
-    def test_search_node_names_with_wrong_limit(self, load_graph_record):
+    @pytest.mark.usefixtures('load_graph_record')
+    def test_search_node_names_with_wrong_limit(self):
         """Test search node names with wrong limit."""
         test_search_content = ""
         test_offset = 0
         test_limit = 0
 
-        graph_processor = GraphProcessor(self._train_id,
-                                         self._mock_data_manager)
+        graph_processor = GraphProcessor(self._train_id, self._mock_data_manager)
         with pytest.raises(ParamValueError) as exc_info:
-            graph_processor.search_node_names(test_search_content, test_offset,
-                                              test_limit)
+            graph_processor.search_node_names(test_search_content, test_offset, test_limit)
         assert "'limit' should in [1, 1000]." in exc_info.value.message
 
-    @pytest.mark.parametrize("name, result_file", [
-        ('Default/bn1', 'test_search_single_node_success_expected_results1.json')
-    ])
-    def test_search_single_node_success(self, load_graph_record, name, result_file):
+    @pytest.mark.usefixtures('load_graph_record')
+    @pytest.mark.parametrize("name, result_file",
+                             [('Default/bn1', 'test_search_single_node_success_expected_results1.json')])
+    def test_search_single_node_success(self, name, result_file):
         """Test searching single node successfully."""
 
-        graph_processor = GraphProcessor(self._train_id,
-                                         self._mock_data_manager)
+        graph_processor = GraphProcessor(self._train_id, self._mock_data_manager)
         results = graph_processor.search_single_node(name)
-        self.compare_result_with_file(results, result_file)
-
+        expected_file_path = os.path.join(self.graph_results_dir, result_file)
+        compare_result_with_file(results, expected_file_path)
 
-    def test_search_single_node_with_not_exist_name(self, load_graph_record):
+    @pytest.mark.usefixtures('load_graph_record')
+    def test_search_single_node_with_not_exist_name(self):
         """Test searching single node with not exist name."""
         test_name = "not_exist_name"
 
         with pytest.raises(exceptions.NodeNotInGraphError):
-            graph_processor = GraphProcessor(self._train_id,
-                                             self._mock_data_manager)
+            graph_processor = GraphProcessor(self._train_id, self._mock_data_manager)
             graph_processor.search_single_node(test_name)
 
-    def test_check_graph_status_no_graph(self, load_no_graph_record):
+    @pytest.mark.usefixtures('load_no_graph_record')
+    def test_check_graph_status_no_graph(self):
         """Test checking graph status no graph."""
         with pytest.raises(ParamValueError) as exc_info:
             GraphProcessor(self._train_id, self._mock_data_manager)

tests/ut/datavisual/processors/test_images_processor.py

@@ -22,9 +22,6 @@ import tempfile
 from unittest.mock import Mock
 
 import pytest
-from tests.ut.datavisual.mock import MockLogger
-from tests.ut.datavisual.utils.log_operations import LogOperations
-from tests.ut.datavisual.utils.utils import check_loading_done, delete_files_or_dirs
 
 from mindinsight.datavisual.common.enums import PluginNameEnum
 from mindinsight.datavisual.data_transform import data_manager
@@ -33,6 +30,10 @@ from mindinsight.datavisual.processors.images_processor import ImageProcessor
 from mindinsight.datavisual.utils import crc32
 from mindinsight.utils.exceptions import ParamValueError
 
+from ....utils.log_operations import LogOperations
+from ....utils.tools import check_loading_done, delete_files_or_dirs, get_image_tensor_from_bytes
+from ..mock import MockLogger
+
 
 class TestImagesProcessor:
     """Test images processor api."""
@@ -73,12 +74,11 @@ class TestImagesProcessor:
         """
         summary_base_dir = tempfile.mkdtemp()
         log_dir = tempfile.mkdtemp(dir=summary_base_dir)
-
         self._train_id = log_dir.replace(summary_base_dir, ".")
 
-        self._temp_path, self._images_metadata, self._images_values = LogOperations.generate_log(
+        log_operation = LogOperations()
+        self._temp_path, self._images_metadata, self._images_values = log_operation.generate_log(
             PluginNameEnum.IMAGE.value, log_dir, dict(steps=steps_list, tag=self._tag_name))
-
         self._generated_path.append(summary_base_dir)
 
         self._mock_data_manager = data_manager.DataManager([DataLoaderGenerator(summary_base_dir)])
@@ -102,7 +102,8 @@ class TestImagesProcessor:
         """Load image record."""
         self._init_data_manager(self._cross_steps_list)
 
-    def test_get_metadata_list_with_not_exist_id(self, load_image_record):
+    @pytest.mark.usefixtures('load_image_record')
+    def test_get_metadata_list_with_not_exist_id(self):
         """Test getting metadata list with not exist id."""
         test_train_id = 'not_exist_id'
         image_processor = ImageProcessor(self._mock_data_manager)
@@ -112,7 +113,8 @@ class TestImagesProcessor:
         assert exc_info.value.error_code == '50540002'
         assert "Can not find any data in loader pool about the train job." in exc_info.value.message
 
-    def test_get_metadata_list_with_not_exist_tag(self, load_image_record):
+    @pytest.mark.usefixtures('load_image_record')
+    def test_get_metadata_list_with_not_exist_tag(self):
         """Test get metadata list with not exist tag."""
         test_tag_name = 'not_exist_tag_name'
 
@@ -124,7 +126,8 @@ class TestImagesProcessor:
         assert exc_info.value.error_code == '50540002'
         assert "Can not find any data in this train job by given tag." in exc_info.value.message
 
-    def test_get_metadata_list_success(self, load_image_record):
+    @pytest.mark.usefixtures('load_image_record')
+    def test_get_metadata_list_success(self):
         """Test getting metadata list success."""
         test_tag_name = self._complete_tag_name
 
@@ -133,7 +136,8 @@ class TestImagesProcessor:
 
         assert results == self._images_metadata
 
-    def test_get_single_image_with_not_exist_id(self, load_image_record):
+    @pytest.mark.usefixtures('load_image_record')
+    def test_get_single_image_with_not_exist_id(self):
         """Test getting single image with not exist id."""
         test_train_id = 'not_exist_id'
         test_tag_name = self._complete_tag_name
@@ -146,7 +150,8 @@ class TestImagesProcessor:
         assert exc_info.value.error_code == '50540002'
         assert "Can not find any data in loader pool about the train job." in exc_info.value.message
 
-    def test_get_single_image_with_not_exist_tag(self, load_image_record):
+    @pytest.mark.usefixtures('load_image_record')
+    def test_get_single_image_with_not_exist_tag(self):
         """Test getting single image with not exist tag."""
         test_tag_name = 'not_exist_tag_name'
         test_step = self._steps_list[0]
@@ -159,7 +164,8 @@ class TestImagesProcessor:
         assert exc_info.value.error_code == '50540002'
         assert "Can not find any data in this train job by given tag." in exc_info.value.message
 
-    def test_get_single_image_with_not_exist_step(self, load_image_record):
+    @pytest.mark.usefixtures('load_image_record')
+    def test_get_single_image_with_not_exist_step(self):
         """Test getting single image with not exist step."""
         test_tag_name = self._complete_tag_name
         test_step = 10000
@@ -172,24 +178,22 @@ class TestImagesProcessor:
         assert exc_info.value.error_code == '50540002'
         assert "Can not find the step with given train job id and tag." in exc_info.value.message
 
-    def test_get_single_image_success(self, load_image_record):
+    @pytest.mark.usefixtures('load_image_record')
+    def test_get_single_image_success(self):
         """Test getting single image successfully."""
         test_tag_name = self._complete_tag_name
         test_step_index = 0
         test_step = self._steps_list[test_step_index]
 
+        expected_image_tensor = self._images_values.get(test_step)
         image_processor = ImageProcessor(self._mock_data_manager)
-
         results = image_processor.get_single_image(self._train_id, test_tag_name, test_step)
-
-        expected_image_tensor = self._images_values.get(test_step)
-
-        image_generator = LogOperations.get_log_generator(PluginNameEnum.IMAGE.value)
-        recv_image_tensor = image_generator.get_image_tensor_from_bytes(results)
+        recv_image_tensor = get_image_tensor_from_bytes(results)
 
         assert recv_image_tensor.any() == expected_image_tensor.any()
 
-    def test_reservoir_add_sample(self, load_more_than_limit_image_record):
+    @pytest.mark.usefixtures('load_more_than_limit_image_record')
+    def test_reservoir_add_sample(self):
         """Test adding sample in reservoir."""
         test_tag_name = self._complete_tag_name
 
@@ -206,7 +210,8 @@ class TestImagesProcessor:
                 cnt += 1
         assert len(self._more_steps_list) - cnt == 10
 
-    def test_reservoir_remove_sample(self, load_reservoir_remove_sample_image_record):
+    @pytest.mark.usefixtures('load_reservoir_remove_sample_image_record')
+    def test_reservoir_remove_sample(self):
         """
         Test removing sample in reservoir.
 

tests/ut/datavisual/processors/test_scalars_processor.py

@@ -22,9 +22,6 @@ import tempfile
 from unittest.mock import Mock
 
 import pytest
-from tests.ut.datavisual.mock import MockLogger
-from tests.ut.datavisual.utils.log_operations import LogOperations
-from tests.ut.datavisual.utils.utils import check_loading_done, delete_files_or_dirs
 
 from mindinsight.datavisual.common.enums import PluginNameEnum
 from mindinsight.datavisual.data_transform import data_manager
@@ -33,6 +30,10 @@ from mindinsight.datavisual.processors.scalars_processor import ScalarsProcessor
 from mindinsight.datavisual.utils import crc32
 from mindinsight.utils.exceptions import ParamValueError
 
+from ....utils.log_operations import LogOperations
+from ....utils.tools import check_loading_done, delete_files_or_dirs
+from ..mock import MockLogger
+
 
 class TestScalarsProcessor:
     """Test scalar processor api."""
@@ -65,12 +66,11 @@ class TestScalarsProcessor:
         """Load scalar record."""
         summary_base_dir = tempfile.mkdtemp()
         log_dir = tempfile.mkdtemp(dir=summary_base_dir)
-
         self._train_id = log_dir.replace(summary_base_dir, ".")
 
-        self._temp_path, self._scalars_metadata, self._scalars_values = LogOperations.generate_log(
+        log_operation = LogOperations()
+        self._temp_path, self._scalars_metadata, self._scalars_values = log_operation.generate_log(
             PluginNameEnum.SCALAR.value, log_dir, dict(step=self._steps_list, tag=self._tag_name))
-
         self._generated_path.append(summary_base_dir)
 
         self._mock_data_manager = data_manager.DataManager([DataLoaderGenerator(summary_base_dir)])
@@ -79,7 +79,8 @@ class TestScalarsProcessor:
         # wait for loading done
         check_loading_done(self._mock_data_manager, time_limit=5)
 
-    def test_get_metadata_list_with_not_exist_id(self, load_scalar_record):
+    @pytest.mark.usefixtures('load_scalar_record')
+    def test_get_metadata_list_with_not_exist_id(self):
         """Get metadata list with not exist id."""
         test_train_id = 'not_exist_id'
         scalar_processor = ScalarsProcessor(self._mock_data_manager)
@@ -89,7 +90,8 @@ class TestScalarsProcessor:
         assert exc_info.value.error_code == '50540002'
         assert "Can not find any data in loader pool about the train job." in exc_info.value.message
 
-    def test_get_metadata_list_with_not_exist_tag(self, load_scalar_record):
+    @pytest.mark.usefixtures('load_scalar_record')
+    def test_get_metadata_list_with_not_exist_tag(self):
         """Get metadata list with not exist tag."""
         test_tag_name = 'not_exist_tag_name'
 
@@ -101,7 +103,8 @@ class TestScalarsProcessor:
         assert exc_info.value.error_code == '50540002'
         assert "Can not find any data in this train job by given tag." in exc_info.value.message
 
-    def test_get_metadata_list_success(self, load_scalar_record):
+    @pytest.mark.usefixtures('load_scalar_record')
+    def test_get_metadata_list_success(self):
         """Get metadata list success."""
         test_tag_name = self._complete_tag_name
 

tests/ut/datavisual/processors/test_train_task_manager.py

@@ -18,15 +18,11 @@ Function:
 Usage:
     pytest tests/ut/datavisual
 """
-import os
 import tempfile
 import time
 from unittest.mock import Mock
 
 import pytest
-from tests.ut.datavisual.mock import MockLogger
-from tests.ut.datavisual.utils.log_operations import LogOperations
-from tests.ut.datavisual.utils.utils import check_loading_done, delete_files_or_dirs
 
 from mindinsight.datavisual.common.enums import PluginNameEnum
 from mindinsight.datavisual.data_transform import data_manager
@@ -35,6 +31,10 @@ from mindinsight.datavisual.processors.train_task_manager import TrainTaskManage
 from mindinsight.datavisual.utils import crc32
 from mindinsight.utils.exceptions import ParamValueError
 
+from ....utils.log_operations import LogOperations
+from ....utils.tools import check_loading_done, delete_files_or_dirs
+from ..mock import MockLogger
+
 
 class TestTrainTaskManager:
     """Test train task manager."""
@@ -70,39 +70,30 @@ class TestTrainTaskManager:
     @pytest.fixture(scope='function')
     def load_data(self):
         """Load data."""
+        log_operation = LogOperations()
         self._plugins_id_map = {'image': [], 'scalar': [], 'graph': []}
         self._events_names = []
         self._train_id_list = []
 
-        graph_base_path = os.path.join(os.path.dirname(__file__),
-                                       os.pardir, "utils", "log_generators", "graph_base.json")
-
         self._root_dir = tempfile.mkdtemp()
         for i in range(self._dir_num):
             dir_path = tempfile.mkdtemp(dir=self._root_dir)
-
             tmp_tag_name = self._tag_name + '_' + str(i)
             event_name = str(i) + "_name"
             train_id = dir_path.replace(self._root_dir, ".")
 
             # Pass timestamp to write to the same file.
-            log_settings = dict(
-                steps=self._steps_list,
-                tag=tmp_tag_name,
-                graph_base_path=graph_base_path,
-                time=time.time())
-
+            log_settings = dict(steps=self._steps_list, tag=tmp_tag_name, time=time.time())
             if i % 3 != 0:
-                LogOperations.generate_log(PluginNameEnum.IMAGE.value, dir_path, log_settings)
+                log_operation.generate_log(PluginNameEnum.IMAGE.value, dir_path, log_settings)
                 self._plugins_id_map['image'].append(train_id)
             if i % 3 != 1:
-                LogOperations.generate_log(PluginNameEnum.SCALAR.value, dir_path, log_settings)
+                log_operation.generate_log(PluginNameEnum.SCALAR.value, dir_path, log_settings)
                 self._plugins_id_map['scalar'].append(train_id)
             if i % 3 != 2:
-                LogOperations.generate_log(PluginNameEnum.GRAPH.value, dir_path, log_settings)
+                log_operation.generate_log(PluginNameEnum.GRAPH.value, dir_path, log_settings)
                 self._plugins_id_map['graph'].append(train_id)
             self._events_names.append(event_name)
-
             self._train_id_list.append(train_id)
 
         self._generated_path.append(self._root_dir)
@@ -112,7 +103,8 @@ class TestTrainTaskManager:
 
         check_loading_done(self._mock_data_manager, time_limit=30)
 
-    def test_get_single_train_task_with_not_exists_train_id(self, load_data):
+    @pytest.mark.usefixtures('load_data')
+    def test_get_single_train_task_with_not_exists_train_id(self):
         """Test getting single train task with not exists train_id."""
         train_task_manager = TrainTaskManager(self._mock_data_manager)
         for plugin_name in PluginNameEnum.list_members():
@@ -124,7 +116,8 @@ class TestTrainTaskManager:
                                              "the train job in data manager."
             assert exc_info.value.error_code == '50540002'
 
-    def test_get_single_train_task_with_params(self, load_data):
+    @pytest.mark.usefixtures('load_data')
+    def test_get_single_train_task_with_params(self):
         """Test getting single train task with params."""
         train_task_manager = TrainTaskManager(self._mock_data_manager)
         for plugin_name in PluginNameEnum.list_members():
@@ -138,7 +131,8 @@ class TestTrainTaskManager:
                 else:
                     assert test_train_id not in self._plugins_id_map.get(plugin_name)
 
-    def test_get_plugins_with_train_id(self, load_data):
+    @pytest.mark.usefixtures('load_data')
+    def test_get_plugins_with_train_id(self):
         """Test getting plugins with train id."""
         train_task_manager = TrainTaskManager(self._mock_data_manager)
 

tests/ut/datavisual/utils/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================

tests/ut/datavisual/utils/crc32.py

-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mask string to crc32."""
-CRC_TABLE_32 = (
-    0x00000000, 0xF26B8303, 0xE13B70F7, 0x1350F3F4, 0xC79A971F, 0x35F1141C, 0x26A1E7E8, 0xD4CA64EB, 0x8AD958CF,
-    0x78B2DBCC, 0x6BE22838, 0x9989AB3B, 0x4D43CFD0, 0xBF284CD3, 0xAC78BF27, 0x5E133C24, 0x105EC76F, 0xE235446C,
-    0xF165B798, 0x030E349B, 0xD7C45070, 0x25AFD373, 0x36FF2087, 0xC494A384, 0x9A879FA0, 0x68EC1CA3, 0x7BBCEF57,
-    0x89D76C54, 0x5D1D08BF, 0xAF768BBC, 0xBC267848, 0x4E4DFB4B, 0x20BD8EDE, 0xD2D60DDD, 0xC186FE29, 0x33ED7D2A,
-    0xE72719C1, 0x154C9AC2, 0x061C6936, 0xF477EA35, 0xAA64D611, 0x580F5512, 0x4B5FA6E6, 0xB93425E5, 0x6DFE410E,
-    0x9F95C20D, 0x8CC531F9, 0x7EAEB2FA, 0x30E349B1, 0xC288CAB2, 0xD1D83946, 0x23B3BA45, 0xF779DEAE, 0x05125DAD,
-    0x1642AE59, 0xE4292D5A, 0xBA3A117E, 0x4851927D, 0x5B016189, 0xA96AE28A, 0x7DA08661, 0x8FCB0562, 0x9C9BF696,
-    0x6EF07595, 0x417B1DBC, 0xB3109EBF, 0xA0406D4B, 0x522BEE48, 0x86E18AA3, 0x748A09A0, 0x67DAFA54, 0x95B17957,
-    0xCBA24573, 0x39C9C670, 0x2A993584, 0xD8F2B687, 0x0C38D26C, 0xFE53516F, 0xED03A29B, 0x1F682198, 0x5125DAD3,
-    0xA34E59D0, 0xB01EAA24, 0x42752927, 0x96BF4DCC, 0x64D4CECF, 0x77843D3B, 0x85EFBE38, 0xDBFC821C, 0x2997011F,
-    0x3AC7F2EB, 0xC8AC71E8, 0x1C661503, 0xEE0D9600, 0xFD5D65F4, 0x0F36E6F7, 0x61C69362, 0x93AD1061, 0x80FDE395,
-    0x72966096, 0xA65C047D, 0x5437877E, 0x4767748A, 0xB50CF789, 0xEB1FCBAD, 0x197448AE, 0x0A24BB5A, 0xF84F3859,
-    0x2C855CB2, 0xDEEEDFB1, 0xCDBE2C45, 0x3FD5AF46, 0x7198540D, 0x83F3D70E, 0x90A324FA, 0x62C8A7F9, 0xB602C312,
-    0x44694011, 0x5739B3E5, 0xA55230E6, 0xFB410CC2, 0x092A8FC1, 0x1A7A7C35, 0xE811FF36, 0x3CDB9BDD, 0xCEB018DE,
-    0xDDE0EB2A, 0x2F8B6829, 0x82F63B78, 0x709DB87B, 0x63CD4B8F, 0x91A6C88C, 0x456CAC67, 0xB7072F64, 0xA457DC90,
-    0x563C5F93, 0x082F63B7, 0xFA44E0B4, 0xE9141340, 0x1B7F9043, 0xCFB5F4A8, 0x3DDE77AB, 0x2E8E845F, 0xDCE5075C,
-    0x92A8FC17, 0x60C37F14, 0x73938CE0, 0x81F80FE3, 0x55326B08, 0xA759E80B, 0xB4091BFF, 0x466298FC, 0x1871A4D8,
-    0xEA1A27DB, 0xF94AD42F, 0x0B21572C, 0xDFEB33C7, 0x2D80B0C4, 0x3ED04330, 0xCCBBC033, 0xA24BB5A6, 0x502036A5,
-    0x4370C551, 0xB11B4652, 0x65D122B9, 0x97BAA1BA, 0x84EA524E, 0x7681D14D, 0x2892ED69, 0xDAF96E6A, 0xC9A99D9E,
-    0x3BC21E9D, 0xEF087A76, 0x1D63F975, 0x0E330A81, 0xFC588982, 0xB21572C9, 0x407EF1CA, 0x532E023E, 0xA145813D,
-    0x758FE5D6, 0x87E466D5, 0x94B49521, 0x66DF1622, 0x38CC2A06, 0xCAA7A905, 0xD9F75AF1, 0x2B9CD9F2, 0xFF56BD19,
-    0x0D3D3E1A, 0x1E6DCDEE, 0xEC064EED, 0xC38D26C4, 0x31E6A5C7, 0x22B65633, 0xD0DDD530, 0x0417B1DB, 0xF67C32D8,
-    0xE52CC12C, 0x1747422F, 0x49547E0B, 0xBB3FFD08, 0xA86F0EFC, 0x5A048DFF, 0x8ECEE914, 0x7CA56A17, 0x6FF599E3,
-    0x9D9E1AE0, 0xD3D3E1AB, 0x21B862A8, 0x32E8915C, 0xC083125F, 0x144976B4, 0xE622F5B7, 0xF5720643, 0x07198540,
-    0x590AB964, 0xAB613A67, 0xB831C993, 0x4A5A4A90, 0x9E902E7B, 0x6CFBAD78, 0x7FAB5E8C, 0x8DC0DD8F, 0xE330A81A,
-    0x115B2B19, 0x020BD8ED, 0xF0605BEE, 0x24AA3F05, 0xD6C1BC06, 0xC5914FF2, 0x37FACCF1, 0x69E9F0D5, 0x9B8273D6,
-    0x88D28022, 0x7AB90321, 0xAE7367CA, 0x5C18E4C9, 0x4F48173D, 0xBD23943E, 0xF36E6F75, 0x0105EC76, 0x12551F82,
-    0xE03E9C81, 0x34F4F86A, 0xC69F7B69, 0xD5CF889D, 0x27A40B9E, 0x79B737BA, 0x8BDCB4B9, 0x988C474D, 0x6AE7C44E,
-    0xBE2DA0A5, 0x4C4623A6, 0x5F16D052, 0xAD7D5351
-)
-
-_CRC = 0
-_MASK = 0xFFFFFFFF
-
-
-def _uint32(x):
-    """Transform x's type to uint32."""
-    return x & 0xFFFFFFFF
-
-
-def _get_crc_checksum(crc, data):
-    """Get crc checksum."""
-    crc ^= _MASK
-    for d in data:
-        crc_table_index = (crc ^ d) & 0xFF
-        crc = (CRC_TABLE_32[crc_table_index] ^ (crc >> 8)) & _MASK
-
-    crc ^= _MASK
-
-    return crc
-
-
-def get_mask_from_string(data):
-    """
-    Get masked crc from data.
-
-    Args:
-        data (byte): Byte string of data.
-
-    Returns:
-        uint32, masked crc.
-
-    """
-
-    crc = _get_crc_checksum(_CRC, data)
-    crc = _uint32(crc & _MASK)
-    crc = _uint32(((crc >> 15) | _uint32(crc << 17)) + 0xA282EAD8)
-
-    return crc

tests/ut/datavisual/utils/log_generators/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================

tests/ut/datavisual/utils/log_generators/graph_base.json

-{
-  "node": [
-    {
-      "input": [
-        {
-          "name": "x",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x1",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x2",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x3",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x4",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x5",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x6",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x7",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x8",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x9",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x10",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "conv1.weight",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "1",
-      "opType": "Conv2D",
-      "scope": "Default/conv1-Conv2d",
-      "attribute": [
-        {
-          "name": "output_names",
-          "value": {
-            "dtype": "DT_GRAPHS",
-            "values": [
-              {
-                "dtype": "DT_FLOAT64",
-                "strVal": "output"
-              }
-            ]
-          }
-        },
-        {
-          "name": "pad_mode",
-          "value": {
-            "dtype": "DT_FLOAT64",
-            "strVal": "same"
-          }
-        }
-      ],
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "64"
-              },
-              {
-                "size": "112"
-              },
-              {
-                "size": "112"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x1",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x2",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x3",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x4",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x5",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x6",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x7",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x8",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x9",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x10",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "cst13",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "53",
-      "opType": "tuple_getitem",
-      "scope": "Default/bn1-BatchNorm2d",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "128"
-              },
-              {
-                "size": "28"
-              },
-              {
-                "size": "28"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x11",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x12",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x13",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x14",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x15",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x16",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x17",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x18",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x19",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "x20",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "conv1.weight",
-          "type": "DATA_EDGE"
-        },
-        {
-          "name": "cst25",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "105",
-      "opType": "tuple_getitem",
-      "scope": "Default/bn1-BatchNorm2d",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "1024"
-              },
-              {
-                "size": "14"
-              },
-              {
-                "size": "14"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x11",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "50",
-      "opType": "Add",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "1024"
-              },
-              {
-                "size": "14"
-              },
-              {
-                "size": "14"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x11",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "51",
-      "opType": "Add",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "1024"
-              },
-              {
-                "size": "14"
-              },
-              {
-                "size": "14"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x11",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "52",
-      "opType": "Add",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "1024"
-              },
-              {
-                "size": "14"
-              },
-              {
-                "size": "14"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x11",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "53",
-      "opType": "Add",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "1024"
-              },
-              {
-                "size": "14"
-              },
-              {
-                "size": "14"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x11",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "54",
-      "opType": "Add",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "1024"
-              },
-              {
-                "size": "14"
-              },
-              {
-                "size": "14"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "50",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "1",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "128"
-              },
-              {
-                "size": "28"
-              },
-              {
-                "size": "28"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "51",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "2",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "128"
-              },
-              {
-                "size": "28"
-              },
-              {
-                "size": "28"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "52",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "3",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "128"
-              },
-              {
-                "size": "28"
-              },
-              {
-                "size": "28"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "53",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "4",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "128"
-              },
-              {
-                "size": "28"
-              },
-              {
-                "size": "28"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "54",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "5",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "128"
-              },
-              {
-                "size": "28"
-              },
-              {
-                "size": "28"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "6",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "128"
-              },
-              {
-                "size": "28"
-              },
-              {
-                "size": "28"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "7",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "128"
-              },
-              {
-                "size": "28"
-              },
-              {
-                "size": "28"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "8",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "128"
-              },
-              {
-                "size": "28"
-              },
-              {
-                "size": "28"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "9",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "128"
-              },
-              {
-                "size": "28"
-              },
-              {
-                "size": "28"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "input": [
-        {
-          "name": "x",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "10",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
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-          "name": "x",
-          "type": "DATA_EDGE"
-        }
-      ],
-      "name": "11",
-      "opType": "Reshape",
-      "scope": "Default/bn1",
-      "outputType": {
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-        "tensorType": {
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-          "shape": {
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-              },
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-              },
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-              },
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-        }
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-        "tensorType": {
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-          "shape": {
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-              },
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-              },
-              {
-                "size": "7"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "name": "x1",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
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-        "tensorType": {
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-      "type": {
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-      "name": "x8",
-      "type": {
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-        "tensorType": {
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-              }
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-          }
-        }
-      }
-    },
-    {
-      "name": "x9",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "3"
-              },
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-              },
-              {
-                "size": "224"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "name": "x10",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
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-              },
-              {
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-                "size": "224"
-              },
-              {
-                "size": "224"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "name": "x11",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "3"
-              },
-              {
-                "size": "224"
-              },
-              {
-                "size": "224"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "name": "x12",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
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-              },
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-              }
-            ]
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-        }
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-    },
-    {
-      "name": "x13",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
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-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
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-              },
-              {
-                "size": "224"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "name": "x15",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
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-                "size": "3"
-              },
-              {
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-              },
-              {
-                "size": "224"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "name": "x16",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
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-              },
-              {
-                "size": "3"
-              },
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-              },
-              {
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-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "name": "x17",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "3"
-              },
-              {
-                "size": "224"
-              },
-              {
-                "size": "224"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "name": "x18",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
-              {
-                "size": "3"
-              },
-              {
-                "size": "224"
-              },
-              {
-                "size": "224"
-              }
-            ]
-          }
-        }
-      }
-    },
-    {
-      "name": "x19",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
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-              },
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-                "size": "224"
-              }
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-        }
-      }
-    },
-    {
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-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
-              {
-                "size": "1"
-              },
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-                "size": "3"
-              },
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-                "size": "224"
-              },
-              {
-                "size": "224"
-              }
-            ]
-          }
-        }
-      }
-    }
-  ],
-  "outputs": [
-    {
-      "name": "228",
-      "type": {
-        "dataType": "DT_STRING",
-        "tensorType": {
-          "elemType": "DT_FLOAT16",
-          "shape": {
-            "dim": [
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-              },
-              {
-                "size": "10"
-              }
-            ]
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-        }
-      }
-    }
-  ],
-  "constVals": [
-    {
-      "key": "cst12",
-      "value": {
-        "dtype": "DT_INT32",
-        "intVal": "0"
-      }
-    },
-    {
-      "key": "cst13",
-      "value": {
-        "dtype": "DT_INT32",
-        "intVal": "0"
-      }
-    },
-    {
-      "key": "cst25",
-      "value": {
-        "dtype": "DT_INT32",
-        "intVal": "0"
-      }
-    }
-  ]
-}

tests/ut/datavisual/utils/log_generators/images_log_generator.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Log generator for images."""
-import io
-import time
-
-import numpy as np
-from PIL import Image
-
-from tests.ut.datavisual.utils.log_generators.log_generator import LogGenerator
-
-from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
-
-
-class ImagesLogGenerator(LogGenerator):
-    """
-    Log generator for images.
-
-    This is a log generator writing images. User can use it to generate fake
-    summary logs about images.
-    """
-
-    def generate_event(self, values):
-        """
-        Method for generating image event.
-
-        Args:
-            values (dict): A dict contains:
-                {
-                    wall_time (float): Timestamp.
-                    step (int): Train step.
-                    image (np.array): Pixels tensor.
-                    tag (str): Tag name.
-                }
-
-        Returns:
-            summary_pb2.Event.
-
-        """
-
-        image_event = summary_pb2.Event()
-        image_event.wall_time = values.get('wall_time')
-        image_event.step = values.get('step')
-
-        height, width, channel, image_string = self._get_image_string(values.get('image'))
-        value = image_event.summary.value.add()
-        value.tag = values.get('tag')
-        value.image.height = height
-        value.image.width = width
-        value.image.colorspace = channel
-        value.image.encoded_image = image_string
-
-        return image_event
-
-    def _get_image_string(self, image_tensor):
-        """
-        Generate image string from tensor.
-
-        Args:
-            image_tensor (np.array): Pixels tensor.
-
-        Returns:
-            int, height.
-            int, width.
-            int, channel.
-            bytes, image_string.
-
-        """
-        height, width, channel = image_tensor.shape
-        scaled_height = int(height)
-        scaled_width = int(width)
-
-        image = Image.fromarray(image_tensor)
-        image = image.resize((scaled_width, scaled_height), Image.ANTIALIAS)
-        output = io.BytesIO()
-        image.save(output, format='PNG')
-        image_string = output.getvalue()
-        output.close()
-        return height, width, channel, image_string
-
-    def _make_image_tensor(self, shape):
-        """
-        Make image tensor according to shape.
-
-        Args:
-            shape (list): Shape of image, consists of height, width, channel.
-
-        Returns:
-            np.array, image tensor.
-
-        """
-        image = np.prod(shape)
-        image_tensor = (np.arange(image, dtype=float)).reshape(shape)
-        image_tensor = image_tensor / np.max(image_tensor) * 255
-        image_tensor = image_tensor.astype(np.uint8)
-
-        return image_tensor
-
-    def generate_log(self, file_path, steps_list, tag_name):
-        """
-        Generate log for external calls.
-
-        Args:
-            file_path (str): Path to write logs.
-            steps_list (list): A list consists of step.
-            tag_name (str): Tag name.
-
-        Returns:
-            list[dict], generated image metadata.
-            dict, generated image tensors.
-
-        """
-
-        images_values = dict()
-        images_metadata = []
-        for step in steps_list:
-            wall_time = time.time()
-
-            # height, width, channel
-            image_tensor = self._make_image_tensor([5, 5, 3])
-
-            image_metadata = dict()
-            image_metadata.update({'wall_time': wall_time})
-            image_metadata.update({'step': step})
-            image_metadata.update({'height': image_tensor.shape[0]})
-            image_metadata.update({'width': image_tensor.shape[1]})
-            images_metadata.append(image_metadata)
-            images_values.update({step: image_tensor})
-
-            values = dict(
-                wall_time=wall_time,
-                step=step,
-                image=image_tensor,
-                tag=tag_name
-            )
-
-            self._write_log_one_step(file_path, values)
-
-        return images_metadata, images_values
-
-    def get_image_tensor_from_bytes(self, image_string):
-        """Get image tensor from bytes."""
-        img = Image.open(io.BytesIO(image_string))
-        image_tensor = np.array(img)
-
-        return image_tensor
-
-
-if __name__ == "__main__":
-    images_log_generator = ImagesLogGenerator()
-    test_file_name = '%s.%s.%s' % ('image', 'summary', str(time.time()))
-    test_steps = [1, 3, 5]
-    test_tags = "test_image_tag_name"
-    images_log_generator.generate_log(test_file_name, test_steps, test_tags)

tests/ut/datavisual/utils/log_generators/log_generator.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Base log Generator."""
-
-import struct
-from abc import abstractmethod
-
-from tests.ut.datavisual.utils import crc32
-
-
-class LogGenerator:
-    """
-    Base log generator.
-
-    This is a base class for log generators. User can use it to generate fake
-    summary logs.
-    """
-
-    @abstractmethod
-    def generate_event(self, values):
-        """
-        Abstract method for generating event.
-
-        Args:
-            values (dict): Values.
-
-        Returns:
-            summary_pb2.Event.
-
-        """
-
-    def _write_log_one_step(self, file_path, values):
-        """
-        Write log one step.
-
-        Args:
-            file_path (str): File path to write.
-            values (dict): Values.
-
-        """
-        event = self.generate_event(values)
-        self._write_log_from_event(file_path, event)
-
-    @staticmethod
-    def _write_log_from_event(file_path, event):
-        """
-        Write log by event.
-
-        Args:
-            file_path (str): File path to write.
-            event (summary_pb2.Event): Event object in proto.
-
-        """
-        send_msg = event.SerializeToString()
-
-        header = struct.pack('<Q', len(send_msg))
-        header_crc = struct.pack('<I', crc32.get_mask_from_string(header))
-        footer_crc = struct.pack('<I', crc32.get_mask_from_string(send_msg))
-
-        write_event = header + header_crc + send_msg + footer_crc
-
-        with open(file_path, "ab") as f:
-            f.write(write_event)

tests/ut/datavisual/utils/log_generators/scalars_log_generator.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Log generator for scalars."""
-import time
-
-import numpy as np
-
-from tests.ut.datavisual.utils.log_generators.log_generator import LogGenerator
-
-from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
-
-
-class ScalarsLogGenerator(LogGenerator):
-    """
-    Log generator for scalars.
-
-    This is a log generator writing scalars. User can use it to generate fake
-    summary logs about scalar.
-    """
-
-    def generate_event(self, values):
-        """
-        Method for generating scalar event.
-
-        Args:
-            values (dict): A dict contains:
-                {
-                    wall_time (float): Timestamp.
-                    step (int): Train step.
-                    value (float): Scalar value.
-                    tag (str): Tag name.
-                }
-
-
-        Returns:
-            summary_pb2.Event.
-
-        """
-        scalar_event = summary_pb2.Event()
-        scalar_event.wall_time = values.get('wall_time')
-        scalar_event.step = values.get('step')
-
-        value = scalar_event.summary.value.add()
-        value.tag = values.get('tag')
-        value.scalar_value = values.get('value')
-
-        return scalar_event
-
-    def generate_log(self, file_path, steps_list, tag_name):
-        """
-        Generate log for external calls.
-
-        Args:
-            file_path (str): Path to write logs.
-            steps_list (list): A list consists of step.
-            tag_name (str): Tag name.
-
-        Returns:
-            list[dict], generated scalar metadata.
-            None, to be consistent with return value of ImageGenerator.
-
-        """
-        scalars_metadata = []
-        for step in steps_list:
-            scalar_metadata = dict()
-
-            wall_time = time.time()
-            value = np.random.rand()
-
-            scalar_metadata.update({'wall_time': wall_time})
-            scalar_metadata.update({'step': step})
-            scalar_metadata.update({'value': value})
-
-            scalars_metadata.append(scalar_metadata)
-
-            scalar_metadata.update({"tag": tag_name})
-
-            self._write_log_one_step(file_path, scalar_metadata)
-
-        return scalars_metadata, None
-
-
-if __name__ == "__main__":
-    scalars_log_generator = ScalarsLogGenerator()
-    test_file_name = '%s.%s.%s' % ('scalar', 'summary', str(time.time()))
-    test_steps = [1, 3, 5]
-    test_tag = "test_scalar_tag_name"
-    scalars_log_generator.generate_log(test_file_name, test_steps, test_tag)

tests/ut/datavisual/utils/log_operations.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""log operations module."""
-import json
-import os
-import time
-
-from tests.ut.datavisual.utils.log_generators.graph_log_generator import GraphLogGenerator
-from tests.ut.datavisual.utils.log_generators.images_log_generator import ImagesLogGenerator
-from tests.ut.datavisual.utils.log_generators.scalars_log_generator import ScalarsLogGenerator
-
-from mindinsight.datavisual.common.enums import PluginNameEnum
-
-log_generators = {
-    PluginNameEnum.GRAPH.value: GraphLogGenerator(),
-    PluginNameEnum.IMAGE.value: ImagesLogGenerator(),
-    PluginNameEnum.SCALAR.value: ScalarsLogGenerator()
-}
-
-
-class LogOperations:
-    """Log Operations class."""
-
-    @staticmethod
-    def generate_log(plugin_name, log_dir, log_settings, valid=True):
-        """
-        Generate log.
-
-        Args:
-            plugin_name (str): Plugin name, contains 'graph', 'image', and 'scalar'.
-            log_dir (str): Log path to write log.
-            log_settings (dict): Info about the log, e.g.:
-                {
-                    current_time (int): Timestamp in summary file name, not necessary.
-                    graph_base_path (str): Path of graph_bas.json, necessary for `graph`.
-                    steps (list[int]): Steps for `image` and `scalar`, default is [1].
-                    tag (str): Tag name, default is 'default_tag'.
-                }
-            valid (bool): If true, summary name will be valid.
-
-        Returns:
-            str, Summary log path.
-
-
-        """
-        current_time = log_settings.get('time', int(time.time()))
-        current_time = int(current_time)
-
-        log_generator = log_generators.get(plugin_name)
-        if valid:
-            temp_path = os.path.join(log_dir, '%s.%s' % ('test.summary', str(current_time)))
-        else:
-            temp_path = os.path.join(log_dir, '%s.%s' % ('test.invalid', str(current_time)))
-
-        if plugin_name == PluginNameEnum.GRAPH.value:
-            graph_base_path = log_settings.get('graph_base_path')
-            with open(graph_base_path, 'r') as load_f:
-                graph_dict = json.load(load_f)
-
-            graph_dict = log_generator.generate_log(temp_path, graph_dict)
-            return temp_path, graph_dict
-
-        steps_list = log_settings.get('steps', [1])
-        tag_name = log_settings.get('tag', 'default_tag')
-        metadata, values = log_generator.generate_log(temp_path, steps_list, tag_name)
-        return temp_path, metadata, values
-
-    @staticmethod
-    def get_log_generator(plugin_name):
-        """Get log generator."""
-        return log_generators.get(plugin_name)

tests/ut/datavisual/utils/utils.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""
-Description: This file is used for some common util.
-"""
-import os
-import shutil
-import time
-from urllib.parse import urlencode
-
-from mindinsight.datavisual.common.enums import DataManagerStatus
-
-
-def get_url(url, params):
-    """
-    Concatenate the URL and params.
-
-    Args:
-        url (str): A link requested. For example, http://example.com.
-        params (dict): A dict consists of params. For example, {'offset': 1, 'limit':'100}.
-
-    Returns:
-        str, like http://example.com?offset=1&limit=100
-
-    """
-
-    return url + '?' + urlencode(params)
-
-
-def delete_files_or_dirs(path_list):
-    """Delete files or dirs in path_list."""
-    for path in path_list:
-        if os.path.isdir(path):
-            shutil.rmtree(path)
-        else:
-            os.remove(path)
-
-
-def check_loading_done(data_manager, time_limit=15):
-    """If loading data for more than `time_limit` seconds, exit."""
-    start_time = time.time()
-    while data_manager.status != DataManagerStatus.DONE.value:
-        time_used = time.time() - start_time
-        if time_used > time_limit:
-            break
-        time.sleep(0.1)
-        continue

tests/ut/lineagemgr/__init__.py

@@ -14,6 +14,6 @@
 # ============================================================================
 """Import the mocked mindspore."""
 import sys
-from .collection.model import mindspore
+from ...utils import mindspore
 
 sys.modules['mindspore'] = mindspore

tests/ut/lineagemgr/collection/model/mindspore/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock MindSpore Interface."""
-from .application.model_zoo.resnet import ResNet
-from .common.tensor import Tensor
-from .dataset import MindDataset
-from .nn import *
-from .train.callback import _ListCallback, Callback, RunContext, ModelCheckpoint, SummaryStep
-from .train.summary import SummaryRecord

tests/ut/lineagemgr/collection/model/mindspore/application/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================

tests/ut/lineagemgr/collection/model/mindspore/application/model_zoo/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================

tests/ut/lineagemgr/collection/model/mindspore/application/model_zoo/resnet.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock the MindSpore ResNet class."""
-from ...nn.cell import Cell
-
-
-class ResNet(Cell):
-    """Mocked ResNet."""
-
-    def __init__(self):
-        super(ResNet, self).__init__()
-        self._cells = {}

tests/ut/lineagemgr/collection/model/mindspore/common/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================

tests/ut/lineagemgr/collection/model/mindspore/common/tensor.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock the MindSpore mindspore/common/tensor.py."""
-import numpy as np
-
-
-class Tensor:
-    """Mock the MindSpore Tensor class."""
-
-    def __init__(self, value=0):
-        self._value = value
-
-    def asnumpy(self):
-        """Get value in numpy format."""
-        return np.array(self._value)
-
-    def __repr__(self):
-        return str(self.asnumpy())

tests/ut/lineagemgr/collection/model/mindspore/context.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""MindSpore Mock Interface"""
-
-
-def get_context(key):
-    """Get key in context."""
-    context = {"device_id": 1}
-    return context.get(key)

tests/ut/lineagemgr/collection/model/mindspore/dataset/__init__.py

-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock mindspore.dataset."""
-from .engine import MindDataset

tests/ut/lineagemgr/collection/model/mindspore/dataset/engine/__init__.py

-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock mindspore.dataset.engine."""
-from .datasets import MindDataset, Dataset

tests/ut/lineagemgr/collection/model/mindspore/dataset/engine/datasets.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock the MindSpore mindspore/dataset/engine/datasets.py."""
-
-
-class Dataset:
-    """Mock the MindSpore Dataset class."""
-
-    def __init__(self, dataset_size=None, dataset_path=None):
-        self.dataset_size = dataset_size
-        self.dataset_path = dataset_path
-        self.input = []
-
-    def get_dataset_size(self):
-        """Mocked get_dataset_size."""
-        return self.dataset_size
-
-
-class MindDataset(Dataset):
-    """Mock the MindSpore MindDataset class."""
-
-    def __init__(self, dataset_size=None, dataset_file=None):
-        super(MindDataset, self).__init__(dataset_size)
-        self.dataset_file = dataset_file

tests/ut/lineagemgr/collection/model/mindspore/nn/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock the mindspore.nn package."""
-from .optim import Optimizer, Momentum
-from .loss.loss import SoftmaxCrossEntropyWithLogits, _Loss
-from .cell import Cell, WithLossCell, TrainOneStepWithLossScaleCell
-
-__all__ = ['Optimizer', 'Momentum', 'SoftmaxCrossEntropyWithLogits',
-           '_Loss', 'Cell', 'WithLossCell',
-           'TrainOneStepWithLossScaleCell']

tests/ut/lineagemgr/collection/model/mindspore/nn/cell.py

-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock the MindSpore mindspore/train/callback.py."""
-
-
-class Cell:
-    """Mock the Cell class."""
-
-    def __init__(self, auto_prefix=True, pips=None):
-        if pips is None:
-            pips = dict()
-        self._auto_prefix = auto_prefix
-        self._pips = pips
-
-    @property
-    def auto_prefix(self):
-        """The property of auto_prefix."""
-        return self._auto_prefix
-
-    @property
-    def pips(self):
-        """The property of pips."""
-        return self._pips
-
-
-class WithLossCell(Cell):
-    """Mocked WithLossCell class."""
-
-    def __init__(self, backbone, loss_fn):
-        super(WithLossCell, self).__init__(auto_prefix=False, pips=backbone.pips)
-        self._backbone = backbone
-        self._loss_fn = loss_fn
-
-
-class TrainOneStepWithLossScaleCell(Cell):
-    """Mocked TrainOneStepWithLossScaleCell."""
-
-    def __init__(self):
-        super(TrainOneStepWithLossScaleCell, self).__init__()

tests/ut/lineagemgr/collection/model/mindspore/nn/loss/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================

tests/ut/lineagemgr/collection/model/mindspore/nn/loss/loss.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock the MindSpore SoftmaxCrossEntropyWithLogits class."""
-from ..cell import Cell
-
-
-class _Loss(Cell):
-    """Mocked _Loss."""
-
-    def __init__(self, reduction='mean'):
-        super(_Loss, self).__init__()
-        self.reduction = reduction
-
-    def construct(self, base, target):
-        """Mocked construct function."""
-        raise NotImplementedError
-
-
-class SoftmaxCrossEntropyWithLogits(_Loss):
-    """Mocked SoftmaxCrossEntropyWithLogits."""
-
-    def __init__(self, weight=None):
-        super(SoftmaxCrossEntropyWithLogits, self).__init__(weight)
-
-    def construct(self, base, target):
-        """Mocked construct."""
-        return 1

tests/ut/lineagemgr/collection/model/mindspore/nn/optim.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock the MindSpore mindspore/nn/optim.py."""
-from .cell import Cell
-
-
-class Parameter:
-    """Mock the MindSpore Parameter class."""
-
-    def __init__(self, learning_rate):
-        self._name = "Parameter"
-        self.default_input = learning_rate
-
-    @property
-    def name(self):
-        """The property of name."""
-        return self._name
-
-    def __repr__(self):
-        format_str = 'Parameter (name={name})'
-        return format_str.format(name=self._name)
-
-
-class Optimizer(Cell):
-    """Mock the MindSpore Optimizer class."""
-
-    def __init__(self, learning_rate):
-        super(Optimizer, self).__init__()
-        self.learning_rate = Parameter(learning_rate)
-
-
-class Momentum(Optimizer):
-    """Mock the MindSpore Momentum class."""
-
-    def __init__(self, learning_rate):
-        super(Momentum, self).__init__(learning_rate)
-        self.dynamic_lr = False

tests/ut/lineagemgr/collection/model/mindspore/nn/wrap/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock MindSpore wrap package."""
-from .loss_scale import TrainOneStepWithLossScaleCell
-from .cell_wrapper import WithLossCell

tests/ut/lineagemgr/collection/model/mindspore/nn/wrap/cell_wrapper.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock MindSpore cell_wrapper.py."""
-from ..cell import Cell
-
-
-class WithLossCell(Cell):
-    """Mock the WithLossCell class."""
-
-    def __init__(self, backbone, loss_fn):
-        super(WithLossCell, self).__init__()
-        self._backbone = backbone
-        self._loss_fn = loss_fn

tests/ut/lineagemgr/collection/model/mindspore/nn/wrap/loss_scale.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock MindSpore loss_scale.py."""
-from ..cell import Cell
-
-
-class TrainOneStepWithLossScaleCell(Cell):
-    """Mock the TrainOneStepWithLossScaleCell class."""
-
-    def __init__(self, network, optimizer):
-        super(TrainOneStepWithLossScaleCell, self).__init__()
-        self.network = network
-        self.optimizer = optimizer
-
-    def construct(self, data, label):
-        """Mock the construct method."""
-        raise NotImplementedError

tests/ut/lineagemgr/collection/model/mindspore/train/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================

tests/ut/lineagemgr/collection/model/mindspore/train/callback.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Mock the MindSpore mindspore/train/callback.py."""
-import os
-
-
-class RunContext:
-    """Mock the RunContext class."""
-
-    def __init__(self, original_args=None):
-        self._original_args = original_args
-        self._stop_requested = False
-
-    def original_args(self):
-        """Mock original_args."""
-        return self._original_args
-
-    def stop_requested(self):
-        """Mock stop_requested method."""
-        return self._stop_requested
-
-
-class Callback:
-    """Mock the Callback class."""
-
-    def __init__(self):
-        pass
-
-    def begin(self, run_context):
-        """Called once before network training."""
-
-    def epoch_begin(self, run_context):
-        """Called before each epoch begin."""
-
-
-class _ListCallback(Callback):
-    """Mock the _ListCallabck class."""
-
-    def __init__(self, callbacks):
-        super(_ListCallback, self).__init__()
-        self._callbacks = callbacks
-
-
-class ModelCheckpoint(Callback):
-    """Mock the ModelCheckpoint class."""
-
-    def __init__(self, prefix='CKP', directory=None, config=None):
-        super(ModelCheckpoint, self).__init__()
-        self._prefix = prefix
-        self._directory = directory
-        self._config = config
-        self._latest_ckpt_file_name = os.path.join(directory, prefix + 'test_model.ckpt')
-
-    @property
-    def model_file_name(self):
-        """Get the file name of model."""
-        return self._model_file_name
-
-    @property
-    def latest_ckpt_file_name(self):
-        """Get the latest file name fo checkpoint."""
-        return self._latest_ckpt_file_name
-
-
-class SummaryStep(Callback):
-    """Mock the SummaryStep class."""
-
-    def __init__(self, summary, flush_step=10):
-        super(SummaryStep, self).__init__()
-        self._sumamry = summary
-        self._flush_step = flush_step
-        self.summary_file_name = summary.full_file_name

tests/ut/lineagemgr/collection/model/mindspore/train/summary/__init__.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""MindSpore Mock Interface"""
-from .summary_record import SummaryRecord
-
-__all__ = ["SummaryRecord"]

tests/ut/lineagemgr/collection/model/mindspore/train/summary/summary_record.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""MindSpore Mock Interface"""
-import os
-import time
-
-
-class SummaryRecord:
-    """Mock the MindSpore SummaryRecord class."""
-
-    def __init__(self,
-                 log_dir: str,
-                 file_prefix: str = "events.",
-                 file_suffix: str = ".MS",
-                 create_time=int(time.time())):
-        self.log_dir = log_dir
-        self.prefix = file_prefix
-        self.suffix = file_suffix
-        file_name = file_prefix + 'summary.' + str(create_time) + file_suffix
-        self.full_file_name = os.path.join(log_dir, file_name)
-
-    def flush(self):
-        """Mock flush method."""
-
-    def close(self):
-        """Mock close method."""

tests/ut/lineagemgr/collection/model/test_model_lineage.py

@@ -16,21 +16,21 @@
 import os
 import shutil
 import unittest
-from unittest import mock, TestCase
+from unittest import TestCase, mock
 from unittest.mock import MagicMock
 
-from mindinsight.lineagemgr.collection.model.model_lineage import TrainLineage, EvalLineage, \
-    AnalyzeObject
-from mindinsight.lineagemgr.common.exceptions.exceptions import \
-    LineageLogError, LineageGetModelFileError, MindInsightException
+from mindinsight.lineagemgr.collection.model.model_lineage import AnalyzeObject, EvalLineage, TrainLineage
+from mindinsight.lineagemgr.common.exceptions.exceptions import (LineageGetModelFileError, LineageLogError,
+                                                                 MindInsightException)
 from mindspore.common.tensor import Tensor
-from mindspore.dataset.engine import MindDataset, Dataset
-from mindspore.nn import Optimizer, WithLossCell, TrainOneStepWithLossScaleCell, \
-    SoftmaxCrossEntropyWithLogits
-from mindspore.train.callback import RunContext, ModelCheckpoint, SummaryStep
+from mindspore.dataset.engine import Dataset, MindDataset
+from mindspore.nn import Optimizer, SoftmaxCrossEntropyWithLogits, TrainOneStepWithLossScaleCell, WithLossCell
+from mindspore.train.callback import ModelCheckpoint, RunContext, SummaryStep
 from mindspore.train.summary import SummaryRecord
 
 
+@mock.patch('builtins.open')
+@mock.patch('os.makedirs')
 class TestModelLineage(TestCase):
     """Test TrainLineage and EvalLineage class in model_lineage.py."""
 
@@ -51,23 +51,19 @@ class TestModelLineage(TestCase):
         cls.summary_log_path = '/path/to/summary_log'
 
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_summary_record')
-    def test_summary_record_exception(self, mock_validate_summary):
+    def test_summary_record_exception(self, *args):
         """Test SummaryRecord with exception."""
-        mock_validate_summary.return_value = None
+        args[0].return_value = None
         summary_record = self.my_summary_record(self.summary_log_path)
         with self.assertRaises(MindInsightException) as context:
             self.my_train_module(summary_record=summary_record, raise_exception=1)
         self.assertTrue(f'Invalid value for raise_exception.' in str(context.exception))
 
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.ds')
-    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.'
-                'LineageSummary.record_dataset_graph')
-    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.'
-                'validate_summary_record')
-    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.'
-                'AnalyzeObject.get_optimizer_by_network')
-    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.'
-                'AnalyzeObject.analyze_optimizer')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.LineageSummary.record_dataset_graph')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_summary_record')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_optimizer_by_network')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_optimizer')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_network')
     def test_begin(self, *args):
         """Test TrainLineage.begin method."""
@@ -82,14 +78,10 @@ class TestModelLineage(TestCase):
         args[4].assert_called()
 
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.ds')
-    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.'
-                'LineageSummary.record_dataset_graph')
-    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.'
-                'validate_summary_record')
-    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.'
-                'AnalyzeObject.get_optimizer_by_network')
-    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.'
-                'AnalyzeObject.analyze_optimizer')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.LineageSummary.record_dataset_graph')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_summary_record')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_optimizer_by_network')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_optimizer')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_network')
     def test_begin_error(self, *args):
         """Test TrainLineage.begin method."""
@@ -122,15 +114,11 @@ class TestModelLineage(TestCase):
             train_lineage.begin(self.my_run_context(run_context))
         self.assertTrue('The parameter optimizer is invalid.' in str(context.exception))
 
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_model_size')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_model_size')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_file_path')
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.LineageSummary.record_train_lineage')
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_dataset')
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_optimizer')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.LineageSummary.record_train_lineage')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_dataset')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_optimizer')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_network')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_train_run_context')
     @mock.patch('builtins.float')
@@ -150,23 +138,19 @@ class TestModelLineage(TestCase):
         args[6].assert_called()
 
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_summary_record')
-    def test_train_end_exception(self, mock_validate_summary):
+    def test_train_end_exception(self, *args):
         """Test TrainLineage.end method when exception."""
-        mock_validate_summary.return_value = True
+        args[0].return_value = True
         train_lineage = self.my_train_module(self.my_summary_record(self.summary_log_path), True)
         with self.assertRaises(Exception) as context:
             train_lineage.end(self.run_context)
         self.assertTrue('Invalid TrainLineage run_context.' in str(context.exception))
 
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_model_size')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_model_size')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_file_path')
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.LineageSummary.record_train_lineage')
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_dataset')
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_optimizer')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.LineageSummary.record_train_lineage')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_dataset')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_optimizer')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_network')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_train_run_context')
     @mock.patch('builtins.float')
@@ -186,15 +170,11 @@ class TestModelLineage(TestCase):
             train_lineage.end(self.my_run_context(self.run_context))
         self.assertTrue('End error in TrainLineage:' in str(context.exception))
 
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_model_size')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_model_size')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.get_file_path')
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.LineageSummary.record_train_lineage')
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_dataset')
-    @mock.patch(
-        'mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_optimizer')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.LineageSummary.record_train_lineage')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_dataset')
+    @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.AnalyzeObject.analyze_optimizer')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_network')
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_train_run_context')
     @mock.patch('builtins.float')
@@ -218,9 +198,9 @@ class TestModelLineage(TestCase):
         self.assertTrue('End error in TrainLineage:' in str(context.exception))
 
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_summary_record')
-    def test_eval_exception_train_id_none(self, mock_validate_summary):
+    def test_eval_exception_train_id_none(self, *args):
         """Test EvalLineage.end method with initialization error."""
-        mock_validate_summary.return_value = True
+        args[0].return_value = True
         with self.assertRaises(MindInsightException) as context:
             self.my_eval_module(self.my_summary_record(self.summary_log_path), raise_exception=2)
         self.assertTrue('Invalid value for raise_exception.' in str(context.exception))
@@ -242,9 +222,9 @@ class TestModelLineage(TestCase):
         args[0].assert_called()
 
     @mock.patch('mindinsight.lineagemgr.collection.model.model_lineage.validate_summary_record')
-    def test_eval_end_except_run_context(self, mock_validate_summary):
+    def test_eval_end_except_run_context(self, *args):
         """Test EvalLineage.end method when run_context is invalid.."""
-        mock_validate_summary.return_value = True
+        args[0].return_value = True
         eval_lineage = self.my_eval_module(self.my_summary_record(self.summary_log_path), True)
         with self.assertRaises(Exception) as context:
             eval_lineage.end(self.run_context)
@@ -284,8 +264,9 @@ class TestModelLineage(TestCase):
             eval_lineage.end(self.my_run_context(self.run_context))
         self.assertTrue('End error in EvalLineage' in str(context.exception))
 
-    def test_epoch_is_zero(self):
+    def test_epoch_is_zero(self, *args):
         """Test TrainLineage.end method."""
+        args[0].return_value = None
         run_context = self.run_context
         run_context['epoch_num'] = 0
         with self.assertRaises(MindInsightException):
@@ -345,7 +326,7 @@ class TestAnalyzer(TestCase):
         )
         res1 = self.analyzer.analyze_dataset(dataset, {'step_num': 10, 'epoch': 2}, 'train')
         res2 = self.analyzer.analyze_dataset(dataset, {'step_num': 5}, 'valid')
-        assert res1 == {'step_num': 10, 
+        assert res1 == {'step_num': 10,
                         'train_dataset_path': '/path/to',
                         'train_dataset_size': 50,
                         'epoch': 2}

tests/ut/lineagemgr/common/test_path_parser.py

@@ -20,23 +20,44 @@ from mindinsight.datavisual.data_transform.summary_watcher import SummaryWatcher
 from mindinsight.lineagemgr.common.path_parser import SummaryPathParser
 
 
+MOCK_SUMMARY_DIRS = [
+    {
+        'relative_path': './relative_path0'
+    },
+    {
+        'relative_path': './'
+    },
+    {
+        'relative_path': './relative_path1'
+    }
+]
+MOCK_SUMMARIES = [
+    {
+        'file_name': 'file0',
+        'create_time': datetime.fromtimestamp(1582031970)
+    },
+    {
+        'file_name': 'file0_lineage',
+        'create_time': datetime.fromtimestamp(1582031970)
+    },
+    {
+        'file_name': 'file1',
+        'create_time': datetime.fromtimestamp(1582031971)
+    },
+    {
+        'file_name': 'file1_lineage',
+        'create_time': datetime.fromtimestamp(1582031971)
+    }
+]
+
+
 class TestSummaryPathParser(TestCase):
     """Test the class of SummaryPathParser."""
 
     @mock.patch.object(SummaryWatcher, 'list_summary_directories')
     def test_get_summary_dirs(self, *args):
         """Test the function of get_summary_dirs."""
-        args[0].return_value = [
-            {
-                'relative_path': './relative_path0'
-            },
-            {
-                'relative_path': './'
-            },
-            {
-                'relative_path': './relative_path1'
-            }
-        ]
+        args[0].return_value = MOCK_SUMMARY_DIRS
 
         expected_result = [
             '/path/to/base/relative_path0',
@@ -54,24 +75,7 @@ class TestSummaryPathParser(TestCase):
     @mock.patch.object(SummaryWatcher, 'list_summaries')
     def test_get_latest_lineage_summary(self, *args):
         """Test the function of get_latest_lineage_summary."""
-        args[0].return_value = [
-            {
-                'file_name': 'file0',
-                'create_time': datetime.fromtimestamp(1582031970)
-            },
-            {
-                'file_name': 'file0_lineage',
-                'create_time': datetime.fromtimestamp(1582031970)
-            },
-            {
-                'file_name': 'file1',
-                'create_time': datetime.fromtimestamp(1582031971)
-            },
-            {
-                'file_name': 'file1_lineage',
-                'create_time': datetime.fromtimestamp(1582031971)
-            }
-        ]
+        args[0].return_value = MOCK_SUMMARIES
         summary_dir = '/path/to/summary_dir'
         result = SummaryPathParser.get_latest_lineage_summary(summary_dir)
         self.assertEqual('/path/to/summary_dir/file1_lineage', result)
@@ -119,35 +123,8 @@ class TestSummaryPathParser(TestCase):
     @mock.patch.object(SummaryWatcher, 'list_summary_directories')
     def test_get_latest_lineage_summaries(self, *args):
         """Test the function of get_latest_lineage_summaries."""
-        args[0].return_value = [
-            {
-                'relative_path': './relative_path0'
-            },
-            {
-                'relative_path': './'
-            },
-            {
-                'relative_path': './relative_path1'
-            }
-        ]
-        args[1].return_value = [
-            {
-                'file_name': 'file0',
-                'create_time': datetime.fromtimestamp(1582031970)
-            },
-            {
-                'file_name': 'file0_lineage',
-                'create_time': datetime.fromtimestamp(1582031970)
-            },
-            {
-                'file_name': 'file1',
-                'create_time': datetime.fromtimestamp(1582031971)
-            },
-            {
-                'file_name': 'file1_lineage',
-                'create_time': datetime.fromtimestamp(1582031971)
-            }
-        ]
+        args[0].return_value = MOCK_SUMMARY_DIRS
+        args[1].return_value = MOCK_SUMMARIES
 
         expected_result = [
             '/path/to/base/relative_path0/file1_lineage',

tests/ut/lineagemgr/common/validator/test_validate.py

@@ -15,38 +15,31 @@
 """Test the validate module."""
 from unittest import TestCase
 
-from mindinsight.lineagemgr.common.exceptions.exceptions import \
-    LineageParamValueError, LineageParamTypeError
-from mindinsight.lineagemgr.common.validator.model_parameter import \
-    SearchModelConditionParameter
-from mindinsight.lineagemgr.common.validator.validate import \
-    validate_search_model_condition
+from mindinsight.lineagemgr.common.exceptions.exceptions import LineageParamTypeError, LineageParamValueError
+from mindinsight.lineagemgr.common.validator.model_parameter import SearchModelConditionParameter
+from mindinsight.lineagemgr.common.validator.validate import validate_search_model_condition
 from mindinsight.utils.exceptions import MindInsightException
 
 
 class TestValidateSearchModelCondition(TestCase):
     """Test the mothod of validate_search_model_condition."""
-    def test_validate_search_model_condition(self):
-        """Test the mothod of validate_search_model_condition."""
+    def test_validate_search_model_condition_param_type_error(self):
+        """Test the mothod of validate_search_model_condition with LineageParamTypeError."""
         condition = {
             'summary_dir': 'xxx'
         }
-        self.assertRaisesRegex(
-            LineageParamTypeError,
+        self._assert_raise_of_lineage_param_type_error(
             'The search_condition element summary_dir should be dict.',
-            validate_search_model_condition,
-            SearchModelConditionParameter,
             condition
         )
 
+    def test_validate_search_model_condition_param_value_error(self):
+        """Test the mothod of validate_search_model_condition with LineageParamValueError."""
         condition = {
             'xxx': 'xxx'
         }
-        self.assertRaisesRegex(
-            LineageParamValueError,
+        self._assert_raise_of_lineage_param_value_error(
             'The search attribute not supported.',
-            validate_search_model_condition,
-            SearchModelConditionParameter,
             condition
         )
 
@@ -55,22 +48,38 @@ class TestValidateSearchModelCondition(TestCase):
                 'xxx': 'xxx'
             }
         }
-        self.assertRaisesRegex(
-            LineageParamValueError,
+        self._assert_raise_of_lineage_param_value_error(
             "The compare condition should be in",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
             condition
         )
 
+        condition = {
+            1: {
+                "ge": 8.0
+            }
+        }
+        self._assert_raise_of_lineage_param_value_error(
+            "The search attribute not supported.",
+            condition
+        )
+
+        condition = {
+            'metric_': {
+                "ge": 8.0
+            }
+        }
+        self._assert_raise_of_lineage_param_value_error(
+            "The search attribute not supported.",
+            condition
+        )
+
+    def test_validate_search_model_condition_mindinsight_exception_1(self):
+        """Test the mothod of validate_search_model_condition with MindinsightException."""
         condition = {
             "offset": 100001
         }
-        self.assertRaisesRegex(
-            MindInsightException,
+        self._assert_raise_of_mindinsight_exception(
             "Invalid input offset. 0 <= offset <= 100000",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
             condition
         )
 
@@ -80,11 +89,9 @@ class TestValidateSearchModelCondition(TestCase):
             },
             'limit': 10
         }
-        self.assertRaisesRegex(
-            MindInsightException,
-            "The parameter summary_dir is invalid. It should be a dict and the value should be a string",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
+        self._assert_raise_of_mindinsight_exception(
+            "The parameter summary_dir is invalid. It should be a dict and "
+            "the value should be a string",
             condition
         )
 
@@ -93,11 +100,9 @@ class TestValidateSearchModelCondition(TestCase):
                 'in': 1.0
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
-            "The parameter learning_rate is invalid. It should be a dict and the value should be a float or a integer",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
+        self._assert_raise_of_mindinsight_exception(
+            "The parameter learning_rate is invalid. It should be a dict and "
+            "the value should be a float or a integer",
             condition
         )
 
@@ -106,24 +111,22 @@ class TestValidateSearchModelCondition(TestCase):
                 'lt': True
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
-            "The parameter learning_rate is invalid. It should be a dict and the value should be a float or a integer",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
+        self._assert_raise_of_mindinsight_exception(
+            "The parameter learning_rate is invalid. It should be a dict and "
+            "the value should be a float or a integer",
             condition
         )
 
+    def test_validate_search_model_condition_mindinsight_exception_2(self):
+        """Test the mothod of validate_search_model_condition with MindinsightException."""
         condition = {
             'learning_rate': {
                 'gt': [1.0]
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
-            "The parameter learning_rate is invalid. It should be a dict and the value should be a float or a integer",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
+        self._assert_raise_of_mindinsight_exception(
+            "The parameter learning_rate is invalid. It should be a dict and "
+            "the value should be a float or a integer",
             condition
         )
 
@@ -132,11 +135,9 @@ class TestValidateSearchModelCondition(TestCase):
                 'ge': 1
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
-            "The parameter loss_function is invalid. It should be a dict and the value should be a string",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
+        self._assert_raise_of_mindinsight_exception(
+            "The parameter loss_function is invalid. It should be a dict and "
+            "the value should be a string",
             condition
         )
 
@@ -145,12 +146,9 @@ class TestValidateSearchModelCondition(TestCase):
                 'in': 2
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
+        self._assert_raise_of_mindinsight_exception(
             "The parameter train_dataset_count is invalid. It should be a dict "
             "and the value should be a integer between 0",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
             condition
         )
 
@@ -162,14 +160,14 @@ class TestValidateSearchModelCondition(TestCase):
                 'eq': 'xxx'
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
-            "The parameter network is invalid. It should be a dict and the value should be a string",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
+        self._assert_raise_of_mindinsight_exception(
+            "The parameter network is invalid. It should be a dict and "
+            "the value should be a string",
             condition
         )
 
+    def test_validate_search_model_condition_mindinsight_exception_3(self):
+        """Test the mothod of validate_search_model_condition with MindinsightException."""
         condition = {
             'batch_size': {
                 'lt': 2,
@@ -179,11 +177,8 @@ class TestValidateSearchModelCondition(TestCase):
                 'eq': 222
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
+        self._assert_raise_of_mindinsight_exception(
             "The parameter batch_size is invalid. It should be a non-negative integer.",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
             condition
         )
 
@@ -192,12 +187,9 @@ class TestValidateSearchModelCondition(TestCase):
                 'lt': -2
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
+        self._assert_raise_of_mindinsight_exception(
             "The parameter test_dataset_count is invalid. It should be a dict "
             "and the value should be a integer between 0",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
             condition
         )
 
@@ -206,11 +198,8 @@ class TestValidateSearchModelCondition(TestCase):
                 'lt': False
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
+        self._assert_raise_of_mindinsight_exception(
             "The parameter epoch is invalid. It should be a positive integer.",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
             condition
         )
 
@@ -219,65 +208,79 @@ class TestValidateSearchModelCondition(TestCase):
                 "ge": ""
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
-            "The parameter learning_rate is invalid. It should be a dict and the value should be a float or a integer",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
+        self._assert_raise_of_mindinsight_exception(
+            "The parameter learning_rate is invalid. It should be a dict and "
+            "the value should be a float or a integer",
             condition
         )
 
+    def test_validate_search_model_condition_mindinsight_exception_4(self):
+        """Test the mothod of validate_search_model_condition with MindinsightException."""
         condition = {
             "train_dataset_count": {
                 "ge": 8.0
             }
         }
-        self.assertRaisesRegex(
-            MindInsightException,
+        self._assert_raise_of_mindinsight_exception(
             "The parameter train_dataset_count is invalid. It should be a dict "
             "and the value should be a integer between 0",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
             condition
         )
 
         condition = {
-            1: {
-                "ge": 8.0
+            'metric_attribute': {
+                'ge': 'xxx'
             }
         }
-        self.assertRaisesRegex(
-            LineageParamValueError,
-            "The search attribute not supported.",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
+        self._assert_raise_of_mindinsight_exception(
+            "The parameter metric_attribute is invalid. "
+            "It should be a dict and the value should be a float or a integer",
             condition
         )
 
-        condition = {
-            'metric_': {
-                "ge": 8.0
-            }
-        }
-        LineageParamValueError('The search attribute not supported.')
-        self.assertRaisesRegex(
-            LineageParamValueError,
-            "The search attribute not supported.",
-            validate_search_model_condition,
-            SearchModelConditionParameter,
-            condition
-        )
+    def _assert_raise(self, exception, msg, condition):
+        """
+        Assert raise by unittest.
 
-        condition = {
-            'metric_attribute': {
-                'ge': 'xxx'
-            }
-        }
+        Args:
+            exception (Type): Exception class expected to be raised.
+            msg (msg): Expected error message.
+            condition (dict): The parameter of search condition.
+        """
         self.assertRaisesRegex(
-            MindInsightException,
-            "The parameter metric_attribute is invalid. "
-            "It should be a dict and the value should be a float or a integer",
+            exception,
+            msg,
             validate_search_model_condition,
             SearchModelConditionParameter,
             condition
         )
+
+    def _assert_raise_of_mindinsight_exception(self, msg, condition):
+        """
+        Assert raise of MindinsightException by unittest.
+
+        Args:
+            msg (msg): Expected error message.
+            condition (dict): The parameter of search condition.
+        """
+        self._assert_raise(MindInsightException, msg, condition)
+
+    def _assert_raise_of_lineage_param_value_error(self, msg, condition):
+        """
+        Assert raise of LineageParamValueError by unittest.
+
+        Args:
+            msg (msg): Expected error message.
+            condition (dict): The parameter of search condition.
+        """
+        self._assert_raise(LineageParamValueError, msg, condition)
+
+    def _assert_raise_of_lineage_param_type_error(self, msg, condition):
+        """
+        Assert raise of LineageParamTypeError by unittest.
+
+        Args:
+            msg (msg): Expected error message.
+            condition (dict): The parameter of search condition.
+        """
+        self._assert_raise(LineageParamTypeError, msg, condition)

tests/ut/lineagemgr/querier/event_data.py

@@ -15,6 +15,8 @@
 """The event data in querier test."""
 import json
 
+from ....utils.mindspore.dataset.engine.serializer_deserializer import SERIALIZED_PIPELINE
+
 EVENT_TRAIN_DICT_0 = {
     'wall_time': 1581499557.7017336,
     'train_lineage': {
@@ -373,49 +375,4 @@ EVENT_DATASET_DICT_0 = {
     }
 }
 
-DATASET_DICT_0 = {
-    'op_type': 'BatchDataset',
-    'op_module': 'minddata.dataengine.datasets',
-    'num_parallel_workers': None,
-    'drop_remainder': True,
-    'batch_size': 10,
-    'children': [
-        {
-            'op_type': 'MapDataset',
-            'op_module': 'minddata.dataengine.datasets',
-            'num_parallel_workers': None,
-            'input_columns': [
-                'label'
-            ],
-            'output_columns': [
-                None
-            ],
-            'operations': [
-                {
-                    'tensor_op_module': 'minddata.transforms.c_transforms',
-                    'tensor_op_name': 'OneHot',
-                    'num_classes': 10
-                }
-            ],
-            'children': [
-                {
-                    'op_type': 'MnistDataset',
-                    'shard_id': None,
-                    'num_shards': None,
-                    'op_module': 'minddata.dataengine.datasets',
-                    'dataset_dir': '/home/anthony/MindData/tests/dataset/data/testMnistData',
-                    'num_parallel_workers': None,
-                    'shuffle': None,
-                    'num_samples': 100,
-                    'sampler': {
-                        'sampler_module': 'minddata.dataengine.samplers',
-                        'sampler_name': 'RandomSampler',
-                        'replacement': True,
-                        'num_samples': 100
-                    },
-                    'children': []
-                }
-            ]
-        }
-    ]
-}
+DATASET_DICT_0 = SERIALIZED_PIPELINE

tests/ut/lineagemgr/querier/test_querier.py

@@ -18,12 +18,12 @@ from unittest import TestCase, mock
 from google.protobuf.json_format import ParseDict
 
 import mindinsight.datavisual.proto_files.mindinsight_summary_pb2 as summary_pb2
-from mindinsight.lineagemgr.common.exceptions.exceptions import \
-    LineageQuerierParamException, LineageParamTypeError, \
-    LineageSummaryAnalyzeException, LineageSummaryParseException
+from mindinsight.lineagemgr.common.exceptions.exceptions import (LineageParamTypeError, LineageQuerierParamException,
+                                                                 LineageSummaryAnalyzeException,
+                                                                 LineageSummaryParseException)
 from mindinsight.lineagemgr.querier.querier import Querier
-from mindinsight.lineagemgr.summary.lineage_summary_analyzer import \
-    LineageInfo
+from mindinsight.lineagemgr.summary.lineage_summary_analyzer import LineageInfo
+
 from . import event_data
 
 
@@ -140,6 +140,98 @@ def get_lineage_infos():
     return lineage_infos
 
 
+LINEAGE_INFO_0 = {
+    'summary_dir': '/path/to/summary0',
+    **event_data.EVENT_TRAIN_DICT_0['train_lineage'],
+    'metric': event_data.METRIC_0,
+    'valid_dataset': event_data.EVENT_EVAL_DICT_0['evaluation_lineage']['valid_dataset'],
+    'dataset_graph': event_data.DATASET_DICT_0
+}
+LINEAGE_INFO_1 = {
+    'summary_dir': '/path/to/summary1',
+    **event_data.EVENT_TRAIN_DICT_1['train_lineage'],
+    'metric': event_data.METRIC_1,
+    'valid_dataset': event_data.EVENT_EVAL_DICT_1['evaluation_lineage']['valid_dataset'],
+    'dataset_graph': event_data.DATASET_DICT_0
+}
+LINEAGE_FILTRATION_0 = create_filtration_result(
+    '/path/to/summary0',
+    event_data.EVENT_TRAIN_DICT_0,
+    event_data.EVENT_EVAL_DICT_0,
+    event_data.METRIC_0,
+    event_data.DATASET_DICT_0
+)
+LINEAGE_FILTRATION_1 = create_filtration_result(
+    '/path/to/summary1',
+    event_data.EVENT_TRAIN_DICT_1,
+    event_data.EVENT_EVAL_DICT_1,
+    event_data.METRIC_1,
+    event_data.DATASET_DICT_0
+)
+LINEAGE_FILTRATION_2 = create_filtration_result(
+    '/path/to/summary2',
+    event_data.EVENT_TRAIN_DICT_2,
+    event_data.EVENT_EVAL_DICT_2,
+    event_data.METRIC_2,
+    event_data.DATASET_DICT_0
+)
+LINEAGE_FILTRATION_3 = create_filtration_result(
+    '/path/to/summary3',
+    event_data.EVENT_TRAIN_DICT_3,
+    event_data.EVENT_EVAL_DICT_3,
+    event_data.METRIC_3,
+    event_data.DATASET_DICT_0
+)
+LINEAGE_FILTRATION_4 = create_filtration_result(
+    '/path/to/summary4',
+    event_data.EVENT_TRAIN_DICT_4,
+    event_data.EVENT_EVAL_DICT_4,
+    event_data.METRIC_4,
+    event_data.DATASET_DICT_0
+)
+LINEAGE_FILTRATION_5 = {
+    "summary_dir": '/path/to/summary5',
+    "loss_function":
+        event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['loss_function'],
+    "train_dataset_path": None,
+    "train_dataset_count":
+        event_data.EVENT_TRAIN_DICT_5['train_lineage']['train_dataset']['train_dataset_size'],
+    "test_dataset_path": None,
+    "test_dataset_count": None,
+    "network": event_data.EVENT_TRAIN_DICT_5['train_lineage']['algorithm']['network'],
+    "optimizer": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['optimizer'],
+    "learning_rate":
+        event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['learning_rate'],
+    "epoch": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['epoch'],
+    "batch_size": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['batch_size'],
+    "loss": event_data.EVENT_TRAIN_DICT_5['train_lineage']['algorithm']['loss'],
+    "model_size": event_data.EVENT_TRAIN_DICT_5['train_lineage']['model']['size'],
+    "metric": {},
+    "dataset_graph": event_data.DATASET_DICT_0,
+    "dataset_mark": '2'
+}
+LINEAGE_FILTRATION_6 = {
+    "summary_dir": '/path/to/summary6',
+    "loss_function": None,
+    "train_dataset_path": None,
+    "train_dataset_count": None,
+    "test_dataset_path":
+        event_data.EVENT_EVAL_DICT_5['evaluation_lineage']['valid_dataset']['valid_dataset_path'],
+    "test_dataset_count":
+        event_data.EVENT_EVAL_DICT_5['evaluation_lineage']['valid_dataset']['valid_dataset_size'],
+    "network": None,
+    "optimizer": None,
+    "learning_rate": None,
+    "epoch": None,
+    "batch_size": None,
+    "loss": None,
+    "model_size": None,
+    "metric": event_data.METRIC_5,
+    "dataset_graph": event_data.DATASET_DICT_0,
+    "dataset_mark": '2'
+}
+
+
 class TestQuerier(TestCase):
     """Test the class of `Querier`."""
     @mock.patch('mindinsight.lineagemgr.querier.querier.LineageSummaryAnalyzer.get_summary_infos')
@@ -169,31 +261,13 @@ class TestQuerier(TestCase):
 
     def test_get_summary_lineage_success_1(self):
         """Test the success of get_summary_lineage."""
-        expected_result = [
-            {
-                'summary_dir': '/path/to/summary0',
-                **event_data.EVENT_TRAIN_DICT_0['train_lineage'],
-                'metric': event_data.METRIC_0,
-                'valid_dataset': event_data.EVENT_EVAL_DICT_0['evaluation_lineage']['valid_dataset'],
-                'dataset_graph': event_data.DATASET_DICT_0
-            }
-        ]
+        expected_result = [LINEAGE_INFO_0]
         result = self.single_querier.get_summary_lineage()
         self.assertListEqual(expected_result, result)
 
     def test_get_summary_lineage_success_2(self):
         """Test the success of get_summary_lineage."""
-        expected_result = [
-            {
-                'summary_dir': '/path/to/summary0',
-                **event_data.EVENT_TRAIN_DICT_0['train_lineage'],
-                'metric': event_data.METRIC_0,
-                'valid_dataset':
-                    event_data.EVENT_EVAL_DICT_0['evaluation_lineage'][
-                        'valid_dataset'],
-                'dataset_graph': event_data.DATASET_DICT_0
-            }
-        ]
+        expected_result = [LINEAGE_INFO_0]
         result = self.single_querier.get_summary_lineage(
             summary_dir='/path/to/summary0'
         )
@@ -216,20 +290,8 @@ class TestQuerier(TestCase):
     def test_get_summary_lineage_success_4(self):
         """Test the success of get_summary_lineage."""
         expected_result = [
-            {
-                'summary_dir': '/path/to/summary0',
-                **event_data.EVENT_TRAIN_DICT_0['train_lineage'],
-                'metric': event_data.METRIC_0,
-                'valid_dataset': event_data.EVENT_EVAL_DICT_0['evaluation_lineage']['valid_dataset'],
-                'dataset_graph': event_data.DATASET_DICT_0
-            },
-            {
-                'summary_dir': '/path/to/summary1',
-                **event_data.EVENT_TRAIN_DICT_1['train_lineage'],
-                'metric': event_data.METRIC_1,
-                'valid_dataset': event_data.EVENT_EVAL_DICT_1['evaluation_lineage']['valid_dataset'],
-                'dataset_graph': event_data.DATASET_DICT_0
-            },
+            LINEAGE_INFO_0,
+            LINEAGE_INFO_1,
             {
                 'summary_dir': '/path/to/summary2',
                 **event_data.EVENT_TRAIN_DICT_2['train_lineage'],
@@ -274,15 +336,7 @@ class TestQuerier(TestCase):
 
     def test_get_summary_lineage_success_5(self):
         """Test the success of get_summary_lineage."""
-        expected_result = [
-            {
-                'summary_dir': '/path/to/summary1',
-                **event_data.EVENT_TRAIN_DICT_1['train_lineage'],
-                'metric': event_data.METRIC_1,
-                'valid_dataset': event_data.EVENT_EVAL_DICT_1['evaluation_lineage']['valid_dataset'],
-                'dataset_graph': event_data.DATASET_DICT_0
-            }
-        ]
+        expected_result = [LINEAGE_INFO_1]
         result = self.multi_querier.get_summary_lineage(
             summary_dir='/path/to/summary1'
         )
@@ -341,20 +395,8 @@ class TestQuerier(TestCase):
         }
         expected_result = {
             'object': [
-                create_filtration_result(
-                    '/path/to/summary1',
-                    event_data.EVENT_TRAIN_DICT_1,
-                    event_data.EVENT_EVAL_DICT_1,
-                    event_data.METRIC_1,
-                    event_data.DATASET_DICT_0,
-                ),
-                create_filtration_result(
-                    '/path/to/summary2',
-                    event_data.EVENT_TRAIN_DICT_2,
-                    event_data.EVENT_EVAL_DICT_2,
-                    event_data.METRIC_2,
-                    event_data.DATASET_DICT_0
-                )
+                LINEAGE_FILTRATION_1,
+                LINEAGE_FILTRATION_2
             ],
             'count': 2,
         }
@@ -377,20 +419,8 @@ class TestQuerier(TestCase):
         }
         expected_result = {
             'object': [
-                create_filtration_result(
-                    '/path/to/summary2',
-                    event_data.EVENT_TRAIN_DICT_2,
-                    event_data.EVENT_EVAL_DICT_2,
-                    event_data.METRIC_2,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary3',
-                    event_data.EVENT_TRAIN_DICT_3,
-                    event_data.EVENT_EVAL_DICT_3,
-                    event_data.METRIC_3,
-                    event_data.DATASET_DICT_0
-                )
+                LINEAGE_FILTRATION_2,
+                LINEAGE_FILTRATION_3
             ],
             'count': 2,
         }
@@ -405,20 +435,8 @@ class TestQuerier(TestCase):
         }
         expected_result = {
             'object': [
-                create_filtration_result(
-                    '/path/to/summary2',
-                    event_data.EVENT_TRAIN_DICT_2,
-                    event_data.EVENT_EVAL_DICT_2,
-                    event_data.METRIC_2,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary3',
-                    event_data.EVENT_TRAIN_DICT_3,
-                    event_data.EVENT_EVAL_DICT_3,
-                    event_data.METRIC_3,
-                    event_data.DATASET_DICT_0
-                )
+                LINEAGE_FILTRATION_2,
+                LINEAGE_FILTRATION_3
             ],
             'count': 7,
         }
@@ -429,82 +447,13 @@ class TestQuerier(TestCase):
         """Test the success of filter_summary_lineage."""
         expected_result = {
             'object': [
-                create_filtration_result(
-                    '/path/to/summary0',
-                    event_data.EVENT_TRAIN_DICT_0,
-                    event_data.EVENT_EVAL_DICT_0,
-                    event_data.METRIC_0,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary1',
-                    event_data.EVENT_TRAIN_DICT_1,
-                    event_data.EVENT_EVAL_DICT_1,
-                    event_data.METRIC_1,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary2',
-                    event_data.EVENT_TRAIN_DICT_2,
-                    event_data.EVENT_EVAL_DICT_2,
-                    event_data.METRIC_2,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary3',
-                    event_data.EVENT_TRAIN_DICT_3,
-                    event_data.EVENT_EVAL_DICT_3,
-                    event_data.METRIC_3,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary4',
-                    event_data.EVENT_TRAIN_DICT_4,
-                    event_data.EVENT_EVAL_DICT_4,
-                    event_data.METRIC_4,
-                    event_data.DATASET_DICT_0
-                ),
-                {
-                    "summary_dir": '/path/to/summary5',
-                    "loss_function":
-                        event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['loss_function'],
-                    "train_dataset_path": None,
-                    "train_dataset_count":
-                        event_data.EVENT_TRAIN_DICT_5['train_lineage']['train_dataset']['train_dataset_size'],
-                    "test_dataset_path": None,
-                    "test_dataset_count": None,
-                    "network": event_data.EVENT_TRAIN_DICT_5['train_lineage']['algorithm']['network'],
-                    "optimizer": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['optimizer'],
-                    "learning_rate":
-                        event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['learning_rate'],
-                    "epoch": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['epoch'],
-                    "batch_size": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['batch_size'],
-                    "loss": event_data.EVENT_TRAIN_DICT_5['train_lineage']['algorithm']['loss'],
-                    "model_size": event_data.EVENT_TRAIN_DICT_5['train_lineage']['model']['size'],
-                    "metric": {},
-                    "dataset_graph": event_data.DATASET_DICT_0,
-                    "dataset_mark": '2'
-                },
-                {
-                    "summary_dir": '/path/to/summary6',
-                    "loss_function": None,
-                    "train_dataset_path": None,
-                    "train_dataset_count": None,
-                    "test_dataset_path":
-                        event_data.EVENT_EVAL_DICT_5['evaluation_lineage']['valid_dataset']['valid_dataset_path'],
-                    "test_dataset_count":
-                        event_data.EVENT_EVAL_DICT_5['evaluation_lineage']['valid_dataset']['valid_dataset_size'],
-                    "network": None,
-                    "optimizer": None,
-                    "learning_rate": None,
-                    "epoch": None,
-                    "batch_size": None,
-                    "loss": None,
-                    "model_size": None,
-                    "metric": event_data.METRIC_5,
-                    "dataset_graph": event_data.DATASET_DICT_0,
-                    "dataset_mark": '2'
-                }
+                LINEAGE_FILTRATION_0,
+                LINEAGE_FILTRATION_1,
+                LINEAGE_FILTRATION_2,
+                LINEAGE_FILTRATION_3,
+                LINEAGE_FILTRATION_4,
+                LINEAGE_FILTRATION_5,
+                LINEAGE_FILTRATION_6
             ],
             'count': 7,
         }
@@ -519,15 +468,7 @@ class TestQuerier(TestCase):
             }
         }
         expected_result = {
-            'object': [
-                create_filtration_result(
-                    '/path/to/summary4',
-                    event_data.EVENT_TRAIN_DICT_4,
-                    event_data.EVENT_EVAL_DICT_4,
-                    event_data.METRIC_4,
-                    event_data.DATASET_DICT_0
-                ),
-            ],
+            'object': [LINEAGE_FILTRATION_4],
             'count': 1,
         }
         result = self.multi_querier.filter_summary_lineage(condition=condition)
@@ -541,82 +482,13 @@ class TestQuerier(TestCase):
         }
         expected_result = {
             'object': [
-                create_filtration_result(
-                    '/path/to/summary0',
-                    event_data.EVENT_TRAIN_DICT_0,
-                    event_data.EVENT_EVAL_DICT_0,
-                    event_data.METRIC_0,
-                    event_data.DATASET_DICT_0
-                ),
-                {
-                    "summary_dir": '/path/to/summary5',
-                    "loss_function":
-                        event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['loss_function'],
-                    "train_dataset_path": None,
-                    "train_dataset_count":
-                        event_data.EVENT_TRAIN_DICT_5['train_lineage']['train_dataset']['train_dataset_size'],
-                    "test_dataset_path": None,
-                    "test_dataset_count": None,
-                    "network": event_data.EVENT_TRAIN_DICT_5['train_lineage']['algorithm']['network'],
-                    "optimizer": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['optimizer'],
-                    "learning_rate":
-                        event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['learning_rate'],
-                    "epoch": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['epoch'],
-                    "batch_size": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['batch_size'],
-                    "loss": event_data.EVENT_TRAIN_DICT_5['train_lineage']['algorithm']['loss'],
-                    "model_size": event_data.EVENT_TRAIN_DICT_5['train_lineage']['model']['size'],
-                    "metric": {},
-                    "dataset_graph": event_data.DATASET_DICT_0,
-                    "dataset_mark": '2'
-                },
-                create_filtration_result(
-                    '/path/to/summary1',
-                    event_data.EVENT_TRAIN_DICT_1,
-                    event_data.EVENT_EVAL_DICT_1,
-                    event_data.METRIC_1,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary2',
-                    event_data.EVENT_TRAIN_DICT_2,
-                    event_data.EVENT_EVAL_DICT_2,
-                    event_data.METRIC_2,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary3',
-                    event_data.EVENT_TRAIN_DICT_3,
-                    event_data.EVENT_EVAL_DICT_3,
-                    event_data.METRIC_3,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary4',
-                    event_data.EVENT_TRAIN_DICT_4,
-                    event_data.EVENT_EVAL_DICT_4,
-                    event_data.METRIC_4,
-                    event_data.DATASET_DICT_0
-                ),
-                {
-                    "summary_dir": '/path/to/summary6',
-                    "loss_function": None,
-                    "train_dataset_path": None,
-                    "train_dataset_count": None,
-                    "test_dataset_path":
-                        event_data.EVENT_EVAL_DICT_5['evaluation_lineage']['valid_dataset']['valid_dataset_path'],
-                    "test_dataset_count":
-                        event_data.EVENT_EVAL_DICT_5['evaluation_lineage']['valid_dataset']['valid_dataset_size'],
-                    "network": None,
-                    "optimizer": None,
-                    "learning_rate": None,
-                    "epoch": None,
-                    "batch_size": None,
-                    "loss": None,
-                    "model_size": None,
-                    "metric": event_data.METRIC_5,
-                    "dataset_graph": event_data.DATASET_DICT_0,
-                    "dataset_mark": '2'
-                }
+                LINEAGE_FILTRATION_0,
+                LINEAGE_FILTRATION_5,
+                LINEAGE_FILTRATION_1,
+                LINEAGE_FILTRATION_2,
+                LINEAGE_FILTRATION_3,
+                LINEAGE_FILTRATION_4,
+                LINEAGE_FILTRATION_6
             ],
             'count': 7,
         }
@@ -631,82 +503,13 @@ class TestQuerier(TestCase):
         }
         expected_result = {
             'object': [
-                {
-                    "summary_dir": '/path/to/summary6',
-                    "loss_function": None,
-                    "train_dataset_path": None,
-                    "train_dataset_count": None,
-                    "test_dataset_path":
-                        event_data.EVENT_EVAL_DICT_5['evaluation_lineage']['valid_dataset']['valid_dataset_path'],
-                    "test_dataset_count":
-                        event_data.EVENT_EVAL_DICT_5['evaluation_lineage']['valid_dataset']['valid_dataset_size'],
-                    "network": None,
-                    "optimizer": None,
-                    "learning_rate": None,
-                    "epoch": None,
-                    "batch_size": None,
-                    "loss": None,
-                    "model_size": None,
-                    "metric": event_data.METRIC_5,
-                    "dataset_graph": event_data.DATASET_DICT_0,
-                    "dataset_mark": '2'
-                },
-                create_filtration_result(
-                    '/path/to/summary4',
-                    event_data.EVENT_TRAIN_DICT_4,
-                    event_data.EVENT_EVAL_DICT_4,
-                    event_data.METRIC_4,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary3',
-                    event_data.EVENT_TRAIN_DICT_3,
-                    event_data.EVENT_EVAL_DICT_3,
-                    event_data.METRIC_3,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary2',
-                    event_data.EVENT_TRAIN_DICT_2,
-                    event_data.EVENT_EVAL_DICT_2,
-                    event_data.METRIC_2,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary1',
-                    event_data.EVENT_TRAIN_DICT_1,
-                    event_data.EVENT_EVAL_DICT_1,
-                    event_data.METRIC_1,
-                    event_data.DATASET_DICT_0
-                ),
-                create_filtration_result(
-                    '/path/to/summary0',
-                    event_data.EVENT_TRAIN_DICT_0,
-                    event_data.EVENT_EVAL_DICT_0,
-                    event_data.METRIC_0,
-                    event_data.DATASET_DICT_0
-                ),
-                {
-                    "summary_dir": '/path/to/summary5',
-                    "loss_function":
-                        event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['loss_function'],
-                    "train_dataset_path": None,
-                    "train_dataset_count":
-                        event_data.EVENT_TRAIN_DICT_5['train_lineage']['train_dataset']['train_dataset_size'],
-                    "test_dataset_path": None,
-                    "test_dataset_count": None,
-                    "network": event_data.EVENT_TRAIN_DICT_5['train_lineage']['algorithm']['network'],
-                    "optimizer": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['optimizer'],
-                    "learning_rate":
-                        event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['learning_rate'],
-                    "epoch": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['epoch'],
-                    "batch_size": event_data.EVENT_TRAIN_DICT_5['train_lineage']['hyper_parameters']['batch_size'],
-                    "loss": event_data.EVENT_TRAIN_DICT_5['train_lineage']['algorithm']['loss'],
-                    "model_size": event_data.EVENT_TRAIN_DICT_5['train_lineage']['model']['size'],
-                    "metric": {},
-                    "dataset_graph": event_data.DATASET_DICT_0,
-                    "dataset_mark": '2'
-                }
+                LINEAGE_FILTRATION_6,
+                LINEAGE_FILTRATION_4,
+                LINEAGE_FILTRATION_3,
+                LINEAGE_FILTRATION_2,
+                LINEAGE_FILTRATION_1,
+                LINEAGE_FILTRATION_0,
+                LINEAGE_FILTRATION_5
             ],
             'count': 7,
         }
@@ -722,15 +525,7 @@ class TestQuerier(TestCase):
             }
         }
         expected_result = {
-            'object': [
-                create_filtration_result(
-                    '/path/to/summary4',
-                    event_data.EVENT_TRAIN_DICT_4,
-                    event_data.EVENT_EVAL_DICT_4,
-                    event_data.METRIC_4,
-                    event_data.DATASET_DICT_0
-                ),
-            ],
+            'object': [LINEAGE_FILTRATION_4],
             'count': 1,
         }
         result = self.multi_querier.filter_summary_lineage(condition=condition)
@@ -809,20 +604,8 @@ class TestQuerier(TestCase):
         querier = Querier(summary_path)
         querier._parse_failed_paths.append('/path/to/summary1/log1')
         expected_result = [
-            {
-                'summary_dir': '/path/to/summary0',
-                **event_data.EVENT_TRAIN_DICT_0['train_lineage'],
-                'metric': event_data.METRIC_0,
-                'valid_dataset': event_data.EVENT_EVAL_DICT_0['evaluation_lineage']['valid_dataset'],
-                'dataset_graph': event_data.DATASET_DICT_0
-            },
-            {
-                'summary_dir': '/path/to/summary1',
-                **event_data.EVENT_TRAIN_DICT_1['train_lineage'],
-                'metric': event_data.METRIC_1,
-                'valid_dataset': event_data.EVENT_EVAL_DICT_1['evaluation_lineage']['valid_dataset'],
-                'dataset_graph': event_data.DATASET_DICT_0
-            }
+            LINEAGE_INFO_0,
+            LINEAGE_INFO_1
         ]
         result = querier.get_summary_lineage()
         self.assertListEqual(expected_result, result)
@@ -842,17 +625,7 @@ class TestQuerier(TestCase):
         querier._parse_failed_paths.append('/path/to/summary1/log1')
 
         args[0].return_value = create_lineage_info(None, None, None)
-        expected_result = [
-            {
-                'summary_dir': '/path/to/summary0',
-                **event_data.EVENT_TRAIN_DICT_0['train_lineage'],
-                'metric': event_data.METRIC_0,
-                'valid_dataset':
-                    event_data.EVENT_EVAL_DICT_0['evaluation_lineage'][
-                        'valid_dataset'],
-                'dataset_graph': event_data.DATASET_DICT_0
-            }
-        ]
+        expected_result = [LINEAGE_INFO_0]
         result = querier.get_summary_lineage()
         self.assertListEqual(expected_result, result)
         self.assertListEqual(

tests/ut/lineagemgr/querier/test_query_model.py

@@ -15,11 +15,12 @@
 """Test the query_model module."""
 from unittest import TestCase
 
-from mindinsight.lineagemgr.common.exceptions.exceptions import \
-    LineageEventNotExistException, LineageEventFieldNotExistException
+from mindinsight.lineagemgr.common.exceptions.exceptions import (LineageEventFieldNotExistException,
+                                                                 LineageEventNotExistException)
 from mindinsight.lineagemgr.querier.query_model import LineageObj
+
 from . import event_data
-from .test_querier import create_lineage_info, create_filtration_result
+from .test_querier import create_filtration_result, create_lineage_info
 
 
 class TestLineageObj(TestCase):

tests/utils/__init__.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================

tests/ut/datavisual/utils/log_generators/graph_log_generator.py → tests/utils/log_generators/graph_log_generator.py

@@ -19,10 +19,10 @@ import time
 
 from google.protobuf import json_format
 
-from tests.ut.datavisual.utils.log_generators.log_generator import LogGenerator
-
 from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
 
+from .log_generator import LogGenerator
+
 
 class GraphLogGenerator(LogGenerator):
     """
@@ -74,7 +74,7 @@ class GraphLogGenerator(LogGenerator):
 if __name__ == "__main__":
     graph_log_generator = GraphLogGenerator()
     test_file_name = '%s.%s.%s' % ('graph', 'summary', str(time.time()))
-    graph_base_path = os.path.join(os.path.dirname(__file__), os.pardir, "log_generators", "graph_base.json")
+    graph_base_path = os.path.join(os.path.dirname(__file__), os.pardir, "log_generators--", "graph_base.json")
     with open(graph_base_path, 'r') as load_f:
         graph = json.load(load_f)
     graph_log_generator.generate_log(test_file_name, graph)

tests/st/func/datavisual/utils/log_generators/images_log_generator.py → tests/utils/log_generators/images_log_generator.py

@@ -18,10 +18,11 @@ import time
 
 import numpy as np
 from PIL import Image
-from tests.st.func.datavisual.utils.log_generators.log_generator import LogGenerator
 
 from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
 
+from .log_generator import LogGenerator
+
 
 class ImagesLogGenerator(LogGenerator):
     """
@@ -138,12 +139,7 @@ class ImagesLogGenerator(LogGenerator):
             images_metadata.append(image_metadata)
             images_values.update({step: image_tensor})
 
-            values = dict(
-                wall_time=wall_time,
-                step=step,
-                image=image_tensor,
-                tag=tag_name
-            )
+            values = dict(wall_time=wall_time, step=step, image=image_tensor, tag=tag_name)
 
             self._write_log_one_step(file_path, values)
 

tests/st/func/datavisual/utils/log_generators/log_generator.py → tests/utils/log_generators/log_generator.py

@@ -17,7 +17,7 @@
 import struct
 from abc import abstractmethod
 
-from tests.st.func.datavisual.utils import crc32
+from ...utils import crc32
 
 
 class LogGenerator:

tests/st/func/datavisual/utils/log_generators/scalars_log_generator.py → tests/utils/log_generators/scalars_log_generator.py

@@ -16,10 +16,11 @@
 import time
 
 import numpy as np
-from tests.st.func.datavisual.utils.log_generators.log_generator import LogGenerator
 
 from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
 
+from .log_generator import LogGenerator
+
 
 class ScalarsLogGenerator(LogGenerator):
     """

tests/st/func/datavisual/utils/log_operations.py → tests/utils/log_operations.py

@@ -19,13 +19,12 @@ import json
 import os
 import time
 
-from tests.st.func.datavisual.constants import SUMMARY_PREFIX
-from tests.st.func.datavisual.utils.log_generators.graph_log_generator import GraphLogGenerator
-from tests.st.func.datavisual.utils.log_generators.images_log_generator import ImagesLogGenerator
-from tests.st.func.datavisual.utils.log_generators.scalars_log_generator import ScalarsLogGenerator
-
 from mindinsight.datavisual.common.enums import PluginNameEnum
 
+from .log_generators.graph_log_generator import GraphLogGenerator
+from .log_generators.images_log_generator import ImagesLogGenerator
+from .log_generators.scalars_log_generator import ScalarsLogGenerator
+
 log_generators = {
     PluginNameEnum.GRAPH.value: GraphLogGenerator(),
     PluginNameEnum.IMAGE.value: ImagesLogGenerator(),
@@ -35,10 +34,12 @@ log_generators = {
 
 class LogOperations:
     """Log Operations."""
+
     def __init__(self):
         self._step_num = 3
         self._tag_num = 2
         self._time_count = 0
+        self._graph_base_path = os.path.join(os.path.dirname(__file__), "log_generators", "graph_base.json")
 
     def _get_steps(self):
         """Get steps."""
@@ -61,9 +62,7 @@ class LogOperations:
             metadata_dict["plugins"].update({plugin_name: list()})
             log_generator = log_generators.get(plugin_name)
             if plugin_name == PluginNameEnum.GRAPH.value:
-                graph_base_path = os.path.join(os.path.dirname(__file__),
-                                               os.pardir, "utils", "log_generators", "graph_base.json")
-                with open(graph_base_path, 'r') as load_f:
+                with open(self._graph_base_path, 'r') as load_f:
                     graph_dict = json.load(load_f)
                 values = log_generator.generate_log(file_path, graph_dict)
                 metadata_dict["actual_values"].update({plugin_name: values})
@@ -82,13 +81,13 @@ class LogOperations:
         self._time_count += 1
         return metadata_dict
 
-    def create_summary_logs(self, summary_base_dir, summary_dir_num, start_index=0):
+    def create_summary_logs(self, summary_base_dir, summary_dir_num, dir_prefix, start_index=0):
         """Create summary logs in summary_base_dir."""
         summary_metadata = dict()
         steps_list = self._get_steps()
         tag_name_list = self._get_tags()
         for i in range(start_index, summary_dir_num + start_index):
-            log_dir = os.path.join(summary_base_dir, f'{SUMMARY_PREFIX}{i}')
+            log_dir = os.path.join(summary_base_dir, f'{dir_prefix}{i}')
             os.makedirs(log_dir)
             train_id = log_dir.replace(summary_base_dir, ".")
 
@@ -120,3 +119,47 @@ class LogOperations:
         metadata_dict = self.create_summary(log_dir, steps_list, tag_name_list)
 
         return {train_id: metadata_dict}
+
+    def generate_log(self, plugin_name, log_dir, log_settings=None, valid=True):
+        """
+        Generate log for ut.
+
+        Args:
+            plugin_name (str): Plugin name, contains 'graph', 'image', and 'scalar'.
+            log_dir (str): Log path to write log.
+            log_settings (dict): Info about the log, e.g.:
+                {
+                    current_time (int): Timestamp in summary file name, not necessary.
+                    graph_base_path (str): Path of graph_bas.json, necessary for `graph`.
+                    steps (list[int]): Steps for `image` and `scalar`, default is [1].
+                    tag (str): Tag name, default is 'default_tag'.
+                }
+            valid (bool): If true, summary name will be valid.
+
+        Returns:
+            str, Summary log path.
+
+
+        """
+        if log_settings is None:
+            log_settings = dict()
+        current_time = log_settings.get('time', int(time.time()))
+        current_time = int(current_time)
+
+        log_generator = log_generators.get(plugin_name)
+        if valid:
+            temp_path = os.path.join(log_dir, '%s.%s' % ('test.summary', str(current_time)))
+        else:
+            temp_path = os.path.join(log_dir, '%s.%s' % ('test.invalid', str(current_time)))
+
+        if plugin_name == PluginNameEnum.GRAPH.value:
+            with open(self._graph_base_path, 'r') as load_f:
+                graph_dict = json.load(load_f)
+
+            graph_dict = log_generator.generate_log(temp_path, graph_dict)
+            return temp_path, graph_dict
+
+        steps_list = log_settings.get('steps', [1])
+        tag_name = log_settings.get('tag', 'default_tag')
+        metadata, values = log_generator.generate_log(temp_path, steps_list, tag_name)
+        return temp_path, metadata, values

tests/st/func/lineagemgr/collection/model/mindspore/nn/cell.py → tests/utils/mindspore/nn/cell.py

@@ -48,7 +48,7 @@ class WithLossCell(Cell):
 
 class TrainOneStepWithLossScaleCell(Cell):
     """Mocked TrainOneStepWithLossScaleCell."""
-    def __init__(self, network, optimizer):
+    def __init__(self, network=None, optimizer=None):
         super(TrainOneStepWithLossScaleCell, self).__init__()
         self.network = network
         self.optimizer = optimizer

tests/st/func/datavisual/utils/utils.py → tests/utils/tools.py

-# Copyright 2019 Huawei Technologies Co., Ltd
+# Copyright 2020 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -19,9 +19,13 @@ import io
 import os
 import shutil
 import time
+import json
+
 from urllib.parse import urlencode
+
 import numpy as np
 from PIL import Image
+
 from mindinsight.datavisual.common.enums import DataManagerStatus
 
 
@@ -69,3 +73,10 @@ def get_image_tensor_from_bytes(image_string):
     image_tensor = np.array(img)
 
     return image_tensor
+
+
+def compare_result_with_file(result, expected_file_path):
+    """Compare result with file which contain the expected results."""
+    with open(expected_file_path, 'r') as file:
+        expected_results = json.load(file)
+        assert result == expected_results