lineagemgr: model lineage support user defined info

mindinsight/datavisual/proto_files/mindinsight_lineage.proto

+// 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.
+
+syntax = "proto2";
+
+package mindinsight;
+option cc_enable_arenas = true;
+
+
+// Event Protocol buffer, Top define
+message LineageEvent {
+  // Timestamp
+  required double wall_time = 1;
+
+  // The step of train.
+  optional int64 step = 2;
+
+  oneof what {
+    // An event file was started, with the specified version.
+    // Now version is "Mindspore.Event:1"
+    string version = 3;
+
+    // Train lineage
+    TrainLineage train_lineage = 4;
+
+    // Evaluation lineage
+    EvaluationLineage evaluation_lineage = 5;
+
+    // Dataset graph
+    DatasetGraph dataset_graph = 6;
+
+    // User defined info
+    UserDefinedInfo user_defined_info = 7;
+  }
+}
+
+// User defined info
+message UserDefinedInfo{
+    // repeated user defined info
+    repeated UserDefinedInfo user_info = 1;
+
+    // key/value which contains both scalar and dict
+    map<string, UserDefinedInfo> map_dict = 2;
+    map<string, int32> map_int32 = 3;
+    map<string, string>  map_str = 4;
+    map<string, double>  map_double = 5;
+}
+
+// TrainLineage records infos of a train.
+message TrainLineage{
+    message HyperParameters{
+        optional string optimizer = 1;
+        optional float learning_rate = 2;
+        optional string loss_function = 3;
+        optional int32 epoch = 4;
+        optional string parallel_mode = 5;
+        optional int32 device_num = 6;
+        optional int32 batch_size = 8;
+    }
+
+    message TrainDataset{
+        optional string train_dataset_path = 1;
+        optional int32 train_dataset_size = 2;
+    }
+
+    message Algorithm{
+        optional string network = 1;
+        optional float loss = 2;
+    }
+
+    message Model{
+        optional string path = 3;
+        optional int64 size = 4;
+    }
+
+    optional HyperParameters hyper_parameters = 1;
+    optional TrainDataset train_dataset = 2;
+    optional Algorithm algorithm = 3;
+    optional Model model = 4;
+}
+
+//EvalLineage records infos of evaluation.
+message EvaluationLineage{
+    message ValidDataset{
+        optional string valid_dataset_path = 1;
+        optional int32 valid_dataset_size = 2;
+    }
+
+    optional string metric = 2;
+    optional ValidDataset valid_dataset = 3;
+}
+
+
+// DatasetGraph
+message DatasetGraph {
+    repeated DatasetGraph children = 1;
+    optional OperationParameter parameter = 2;
+    repeated Operation operations = 3;
+    optional Operation sampler = 4;
+}
+
+message Operation {
+    optional OperationParameter operationParam = 1;
+    repeated int32 size = 2;
+    repeated float weights = 3;
+}
+
+message OperationParameter{
+    map<string, string> mapStr = 1;
+    map<string, StrList> mapStrList = 2;
+    map<string, bool> mapBool = 3;
+    map<string, int32> mapInt = 4;
+    map<string, double> mapDouble = 5;
+}
+
+message StrList {
+    repeated string strValue = 1;
+}

mindinsight/datavisual/proto_files/mindinsight_summary.proto

@@ -39,60 +39,9 @@ message Event {
     // Summary data
     Summary summary = 5;
 
-    // Train lineage
-    TrainLineage train_lineage = 6;
-
-    // Evaluation lineage
-    EvaluationLineage evaluation_lineage = 7;
-
-    // dataset graph
-    DatasetGraph dataset_graph = 9;
   }
 }
 
-// TrainLineage records infos of a train.
-message TrainLineage{
-    message HyperParameters{
-        optional string optimizer = 1;
-        optional float learning_rate = 2;
-        optional string loss_function = 3;
-        optional int32 epoch = 4;
-        optional string parallel_mode = 5;
-        optional int32 device_num = 6;
-        optional int32 batch_size = 8;
-    }
-
-    message TrainDataset{
-        optional string train_dataset_path = 1;
-        optional int32 train_dataset_size = 2;
-    }
-
-    message Algorithm{
-        optional string network = 1;
-        optional float loss = 2;
-    }
-
-    message Model{
-        optional string path = 3;
-        optional int64 size = 4;
-    }
-
-    optional HyperParameters hyper_parameters = 1;
-    optional TrainDataset train_dataset = 2;
-    optional Algorithm algorithm = 3;
-    optional Model model = 4;
-}
-
-//EvalLineage records infos of evaluation.
-message EvaluationLineage{
-    message ValidDataset{
-        optional string valid_dataset_path = 1;
-        optional int32 valid_dataset_size = 2;
-    }
-
-    optional string metric = 2;
-    optional ValidDataset valid_dataset = 3;
-}
 
 // A Summary is a set of named values that be produced regularly during training
 message Summary {
@@ -127,29 +76,3 @@ message Summary {
     // Set of values for the summary.
     repeated Value value = 1;
 }
-
-// DatasetGraph
-message DatasetGraph {
-    repeated DatasetGraph children = 1;
-    optional OperationParameter parameter = 2;
-    repeated Operation operations = 3;
-    optional Operation sampler = 4;
-}
-
-message Operation {
-    optional OperationParameter operationParam = 1;
-    repeated int32 size = 2;
-    repeated float weights = 3;
-}
-
-message OperationParameter{
-    map<string, string> mapStr = 1;
-    map<string, StrList> mapStrList = 2;
-    map<string, bool> mapBool = 3;
-    map<string, int32> mapInt = 4;
-    map<string, double> mapDouble = 5;
-}
-
-message StrList {
-    repeated string strValue = 1;
-}

mindinsight/lineagemgr/api/model.py

@@ -112,10 +112,10 @@ def filter_summary_lineage(summary_base_dir, search_condition=None):
             directories generated by training.
         search_condition (dict): The search condition. When filtering and
             sorting, in addition to the following supported fields, fields
-            prefixed with `metric_` are also supported. The fields prefixed with
-            `metric_` are related to the `metrics` parameter in the training
+            prefixed with `metric/` are also supported. The fields prefixed with
+            `metric/` are related to the `metrics` parameter in the training
             script. For example, if the key of `metrics` parameter is
-            `accuracy`, the field should be `metric_accuracy`. Default: None.
+            `accuracy`, the field should be `metric/accuracy`. Default: None.
 
             - summary_dir (dict): The filter condition of summary directory.
 
@@ -187,7 +187,7 @@ def filter_summary_lineage(summary_base_dir, search_condition=None):
         >>>         'ge': 128,
         >>>         'le': 256
         >>>     },
-        >>>     'metric_accuracy': {
+        >>>     'metric/accuracy': {
         >>>         'lt': 0.1
         >>>     },
         >>>     'sorted_name': 'summary_dir',

mindinsight/lineagemgr/collection/model/model_lineage.py

@@ -23,7 +23,8 @@ from mindinsight.utils.exceptions import \
     MindInsightException
 from mindinsight.lineagemgr.common.validator.validate import validate_train_run_context, \
     validate_eval_run_context, validate_file_path, validate_network, \
-    validate_int_params, validate_summary_record, validate_raise_exception
+    validate_int_params, validate_summary_record, validate_raise_exception,\
+    validate_user_defined_info
 from mindinsight.lineagemgr.common.exceptions.error_code import LineageErrors, LineageErrorMsg
 from mindinsight.lineagemgr.common.exceptions.exceptions import LineageParamRunContextError, \
     LineageGetModelFileError, LineageLogError
@@ -71,7 +72,7 @@ class TrainLineage(Callback):
         >>> lineagemgr = TrainLineage(summary_record=summary_writer)
         >>> model.train(epoch_num, dataset, callbacks=[model_ckpt, summary_callback, lineagemgr])
     """
-    def __init__(self, summary_record, raise_exception=False):
+    def __init__(self, summary_record, raise_exception=False, user_defined_info=None):
         super(TrainLineage, self).__init__()
         try:
             validate_raise_exception(raise_exception)
@@ -85,6 +86,11 @@ class TrainLineage(Callback):
             self.lineage_log_path = summary_log_path + '_lineage'
 
             self.initial_learning_rate = None
+
+            self.user_defined_info = user_defined_info
+            if user_defined_info:
+                validate_user_defined_info(user_defined_info)
+
         except MindInsightException as err:
             log.error(err)
             if raise_exception:
@@ -104,6 +110,10 @@ class TrainLineage(Callback):
         """
         log.info('Initialize training lineage collection...')
 
+        if self.user_defined_info:
+            lineage_summary = LineageSummary(summary_log_path=self.lineage_log_path)
+            lineage_summary.record_user_defined_info(self.user_defined_info)
+
         if not isinstance(run_context, RunContext):
             error_msg = f'Invalid TrainLineage run_context.'
             log.error(error_msg)
@@ -239,7 +249,7 @@ class EvalLineage(Callback):
         >>> lineagemgr = EvalLineage(summary_record=summary_writer)
         >>> model.eval(epoch_num, dataset, callbacks=[model_ckpt, summary_callback, lineagemgr])
     """
-    def __init__(self, summary_record, raise_exception=False):
+    def __init__(self, summary_record, raise_exception=False, user_defined_info=None):
         super(EvalLineage, self).__init__()
         try:
             validate_raise_exception(raise_exception)
@@ -251,6 +261,11 @@ class EvalLineage(Callback):
             summary_log_path = summary_record.full_file_name
             validate_file_path(summary_log_path)
             self.lineage_log_path = summary_log_path + '_lineage'
+
+            self.user_defined_info = user_defined_info
+            if user_defined_info:
+                validate_user_defined_info(user_defined_info)
+
         except MindInsightException as err:
             log.error(err)
             if raise_exception:
@@ -269,6 +284,10 @@ class EvalLineage(Callback):
             MindInsightException: If validating parameter fails.
             LineageLogError: If recording lineage information fails.
         """
+        if self.user_defined_info:
+            lineage_summary = LineageSummary(summary_log_path=self.lineage_log_path)
+            lineage_summary.record_user_defined_info(self.user_defined_info)
+
         if not isinstance(run_context, RunContext):
             error_msg = f'Invalid EvalLineage run_context.'
             log.error(error_msg)

mindinsight/lineagemgr/common/validator/model_parameter.py

@@ -226,7 +226,7 @@ class SearchModelConditionParameter(Schema):
             if not isinstance(attr, str):
                 raise LineageParamValueError('The search attribute not supported.')
 
-            if attr not in FIELD_MAPPING and not attr.startswith('metric_'):
+            if attr not in FIELD_MAPPING and not attr.startswith(('metric/','user_defined/')):
                 raise LineageParamValueError('The search attribute not supported.')
 
             if not isinstance(condition, dict):
@@ -238,7 +238,7 @@ class SearchModelConditionParameter(Schema):
                     raise LineageParamValueError("The compare condition should be in "
                                                  "('eq', 'lt', 'gt', 'le', 'ge', 'in').")
 
-            if attr.startswith('metric_'):
+            if attr.startswith('metric/'):
                 if len(attr) == 7:
                     raise LineageParamValueError(
                         'The search attribute not supported.'

mindinsight/lineagemgr/common/validator/validate.py

@@ -349,12 +349,14 @@ def validate_condition(search_condition):
     if "sorted_name" in search_condition:
         sorted_name = search_condition.get("sorted_name")
         err_msg = "The sorted_name must be in {} or start with " \
-                  "`metric_`.".format(list(FIELD_MAPPING.keys()))
+                  "`metric/` or `user_defined/`.".format(list(FIELD_MAPPING.keys()))
         if not isinstance(sorted_name, str):
             log.error(err_msg)
             raise LineageParamValueError(err_msg)
-        if sorted_name not in FIELD_MAPPING and not (
-                sorted_name.startswith('metric_') and len(sorted_name) > 7):
+        if not (sorted_name in FIELD_MAPPING
+            or (sorted_name.startswith('metric/') and len(sorted_name) > 7)
+            or (sorted_name.startswith('user_defined/') and len(sorted_name) > 13)
+        ):
             log.error(err_msg)
             raise LineageParamValueError(err_msg)
 
@@ -393,3 +395,38 @@ def validate_path(summary_path):
         raise LineageDirNotExistError("The summary path does not exist or is not a dir.")
 
     return summary_path
+
+
+def validate_user_defined_info(user_defined_info):
+    """
+    Validate user defined info.
+
+    Args:
+        user_defined_info (dict): The user defined info.
+
+    Raises:
+        LineageParamTypeError: If the type of parameters is invalid.
+        LineageParamValueError: If user defined keys have been defined in lineage.
+
+    """
+    if not isinstance(user_defined_info, dict):
+        log.error("Invalid user defined info. It should be a dict.")
+        raise LineageParamTypeError("Invalid user defined info. It should be dict.")
+    for key, value in user_defined_info:
+        if not isinstance(key, str):
+            error_msg = "Dict key type {} is not supported in user defined info." \
+                        "Only str is permitted now.".format(type(key))
+            log.error(error_msg)
+            raise LineageParamTypeError(error_msg)
+        if not isinstance(key, (int, str, float)):
+            error_msg = "Dict value type {} is not supported in user defined info." \
+                        "Only str, int and float are permitted now.".format(type(value))
+            log.error(error_msg)
+            raise LineageParamTypeError(error_msg)
+
+    field_map = set(FIELD_MAPPING.keys())
+    user_defined_keys = set(user_defined_info.keys())
+    all_keys = field_map | user_defined_keys
+
+    if len(field_map) + len(user_defined_keys) != len(all_keys):
+        raise LineageParamValueError("There are some keys have defined in lineage.")
\ No newline at end of file

mindinsight/lineagemgr/querier/querier.py

@@ -236,7 +236,7 @@ class Querier:
 
         See `ConditionType` and `ExpressionType` class for the rule of filtering
         and sorting. The filtering and sorting fields are defined in
-        `FIELD_MAPPING` or prefixed with `metric_`.
+        `FIELD_MAPPING` or prefixed with `metric/` or 'user_defined/'.
 
         If the condition is `None`, all model lineage information will be
         returned.
@@ -288,7 +288,7 @@ class Querier:
 
         if condition is None:
             condition = {}
-        result = list(filter(_filter, self._lineage_objects))
+        results = list(filter(_filter, self._lineage_objects))
 
         if ConditionParam.SORTED_NAME.value in condition:
             sorted_name = condition.get(ConditionParam.SORTED_NAME.value)
@@ -299,19 +299,33 @@ class Querier:
                 )
             sorted_type = condition.get(ConditionParam.SORTED_TYPE.value)
             reverse = sorted_type == 'descending'
-            result = sorted(
-                result, key=functools.cmp_to_key(_cmp), reverse=reverse
+            results = sorted(
+                results, key=functools.cmp_to_key(_cmp), reverse=reverse
             )
 
-        offset_result = self._handle_limit_and_offset(condition, result)
+        offset_results = self._handle_limit_and_offset(condition, results)
+
+        customized = dict()
+        for offset_result in offset_results:
+            for obj_name in ["metric", "user_defined"]:
+                obj = getattr(offset_result, obj_name)
+                if obj and isinstance(obj, dict):
+                    for key, value in obj.items():
+                        label = obj_name + "/" + key
+                        customized[label] = dict()
+                        customized[label]["label"] = label
+                        # user defined info is default displayed
+                        customized[label]["required"] = True
+                        customized[label]["type"] = type(value).__name__
 
         search_type = condition.get(ConditionParam.LINEAGE_TYPE.value)
         lineage_info = {
+            'customized': customized,
             'object': [
                 item.to_dataset_lineage_dict() if search_type == LineageType.DATASET.value
-                else item.to_filtration_dict() for item in offset_result
+                else item.to_filtration_dict() for item in offset_results
             ],
-            'count': len(result)
+            'count': len(results)
         }
 
         return lineage_info
@@ -326,7 +340,8 @@ class Querier:
         Returns:
             bool, `True` if the field name is valid, else `False`.
         """
-        return field_name not in FIELD_MAPPING and not field_name.startswith('metric_')
+        return field_name not in FIELD_MAPPING and \
+               not field_name.startswith(('metric/', 'user_defined/'))
 
     def _handle_limit_and_offset(self, condition, result):
         """
@@ -397,11 +412,13 @@ class Querier:
         log_dir = os.path.dirname(log_path)
         try:
             lineage_info = LineageSummaryAnalyzer.get_summary_infos(log_path)
+            user_defined_info = LineageSummaryAnalyzer.get_user_defined_info(log_path)
             lineage_obj = LineageObj(
                 log_dir,
                 train_lineage=lineage_info.train_lineage,
                 evaluation_lineage=lineage_info.eval_lineage,
-                dataset_graph=lineage_info.dataset_graph
+                dataset_graph=lineage_info.dataset_graph,
+                user_defined_info=user_defined_info
             )
             self._lineage_objects.append(lineage_obj)
             self._add_dataset_mark()

mindinsight/lineagemgr/querier/query_model.py

@@ -57,6 +57,8 @@ class LineageObj:
 
             - dataset_graph (Event): Dataset graph object.
 
+            - user_defined_info (Event): User defined info object.
+
     Raises:
         LineageEventNotExistException: If train and evaluation event not exist.
         LineageEventFieldNotExistException: If the special event field not exist.
@@ -72,16 +74,19 @@ class LineageObj:
     _name_valid_dataset = 'valid_dataset'
     _name_dataset_graph = 'dataset_graph'
     _name_dataset_mark = 'dataset_mark'
+    _name_user_defined = 'user_defined'
 
     def __init__(self, summary_dir, **kwargs):
         self._lineage_info = {
             self._name_summary_dir: summary_dir
         }
+        user_defined_info_list = kwargs.get('user_defined_info', [])
         train_lineage = kwargs.get('train_lineage')
         evaluation_lineage = kwargs.get('evaluation_lineage')
         dataset_graph = kwargs.get('dataset_graph')
         if not any([train_lineage, evaluation_lineage, dataset_graph]):
             raise LineageEventNotExistException()
+        self._parse_user_defined_info(user_defined_info_list)
         self._parse_train_lineage(train_lineage)
         self._parse_evaluation_lineage(evaluation_lineage)
         self._parse_dataset_graph(dataset_graph)
@@ -107,6 +112,16 @@ class LineageObj:
         """
         return self._lineage_info.get(self._name_metric)
 
+    @property
+    def user_defined(self):
+        """
+        Get user defined information.
+
+        Returns:
+            dict, the user defined information.
+        """
+        return self._lineage_info.get(self._name_user_defined)
+
     @property
     def hyper_parameters(self):
         """
@@ -237,19 +252,22 @@ class LineageObj:
 
     def get_value_by_key(self, key):
         """
-        Get the value based on the key in `FIELD_MAPPING` or the key prefixed with `metric_`.
+        Get the value based on the key in `FIELD_MAPPING` or
+            the key prefixed with `metric/` or `user_defined/`.
 
         Args:
-            key (str): The key in `FIELD_MAPPING` or prefixed with `metric_`.
+            key (str): The key in `FIELD_MAPPING`
+                or prefixed with `metric/` or `user_defined/`.
 
         Returns:
             object, the value.
         """
-        if key.startswith('metric_'):
-            metric_key = key.split('_', 1)[1]
-            metric = self._filtration_result.get(self._name_metric)
-            if metric:
-                return metric.get(metric_key)
+        if key.startswith(('metric/', 'user_defined/')):
+            key_name, sub_key = key.split('/', 1)
+            sub_value_name = self._name_metric if key_name == 'metric' else self._name_user_defined
+            sub_value = self._filtration_result.get(sub_value_name)
+            if sub_value:
+                return sub_value.get(sub_key)
         return self._filtration_result.get(key)
 
     def _organize_filtration_result(self):
@@ -267,6 +285,8 @@ class LineageObj:
                     if field.sub_name else base_attr
         # add metric into filtration result
         result[self._name_metric] = self.metric
+
+        result[self._name_user_defined] = self.user_defined
         # add dataset_graph into filtration result
         result[self._name_dataset_graph] = getattr(self, self._name_dataset_graph)
         return result
@@ -342,3 +362,15 @@ class LineageObj:
             if event_dict is None:
                 raise LineageEventFieldNotExistException(self._name_evaluation_lineage)
             self._lineage_info[self._name_dataset_graph] = event_dict if event_dict else {}
+
+    def _parse_user_defined_info(self, user_defined_info_list):
+        """
+        Parse user defined info.
+
+        Args:
+            user_defined_info_list (list): user defined info list.
+        """
+        user_defined_infos = dict()
+        for user_defined_info in user_defined_info_list:
+            user_defined_infos.update(user_defined_info)
+        self._lineage_info[self._name_user_defined] = user_defined_infos

mindinsight/lineagemgr/summary/_summary_adapter.py

@@ -15,8 +15,9 @@
 """The converter between proto format event of lineage and dict."""
 import time
 
-from mindinsight.datavisual.proto_files.mindinsight_summary_pb2 import Event
-from mindinsight.lineagemgr.common.exceptions.exceptions import LineageParamTypeError
+from mindinsight.datavisual.proto_files.mindinsight_lineage_pb2 import LineageEvent, UserDefinedInfo
+from mindinsight.lineagemgr.common.exceptions.exceptions import LineageParamTypeError,\
+    LineageParamValueError
 from mindinsight.lineagemgr.common.log import logger as log
 
 
@@ -28,9 +29,9 @@ def package_dataset_graph(graph):
         graph (dict): Dataset graph.
 
     Returns:
-        Event, the proto message event contains dataset graph.
+        LineageEvent, the proto message event contains dataset graph.
     """
-    dataset_graph_event = Event()
+    dataset_graph_event = LineageEvent()
     dataset_graph_event.wall_time = time.time()
 
     dataset_graph = dataset_graph_event.dataset_graph
@@ -291,3 +292,57 @@ def _organize_parameter(parameter):
     parameter_result.update(result_str_list_para)
 
     return parameter_result
+
+
+def package_user_defined_info(user_dict):
+    """
+    Package user defined info.
+
+    Args:
+        user_dict(dict): User defined info dict.
+
+    Returns:
+        LineageEvent, the proto message event contains user defined info.
+
+    """
+    user_event = LineageEvent()
+    user_event.wall_time = time.time()
+    user_defined_info = user_event.user_defined_info
+    _package_user_defined_info(user_dict, user_defined_info)
+
+    return user_event
+
+
+def _package_user_defined_info(user_defined_dict, user_defined_message):
+    """
+    Setting attribute in user defined proto message.
+
+    Args:
+        user_defined_dict (dict): User define info dict.
+        user_defined_message (LineageEvent): Proto message of user defined info.
+
+    Raises:
+        LineageParamValueError: When the value is out of range.
+        LineageParamTypeError: When given a type not support yet.
+    """
+    for key, value in user_defined_dict.items():
+        if not isinstance(key, str):
+            raise LineageParamTypeError("The key must be str.")
+
+        if isinstance(value, int):
+            attr_name = "map_int32"
+        elif isinstance(value, float):
+            attr_name = "map_double"
+        elif isinstance(value, str):
+            attr_name = "map_str"
+        else:
+            error_msg = "Value type {} is not supported in user defined event package." \
+                        "Only str, int and float are permitted now.".format(type(value))
+            log.error(error_msg)
+            raise LineageParamTypeError(error_msg)
+
+        add_user_defined_info = user_defined_message.user_info.add()
+        try:
+            getattr(add_user_defined_info, attr_name)[key] = value
+        except ValueError:
+            raise LineageParamValueError("Value is out of range or not be supported yet.")
\ No newline at end of file

mindinsight/lineagemgr/summary/lineage_summary_analyzer.py

@@ -17,7 +17,9 @@ import struct
 from collections import namedtuple
 from enum import Enum
 
-from mindinsight.datavisual.proto_files.mindinsight_summary_pb2 import Event
+from google.protobuf.json_format import MessageToDict
+
+from mindinsight.datavisual.proto_files.mindinsight_lineage_pb2 import LineageEvent
 from mindinsight.datavisual.utils import crc32
 from mindinsight.lineagemgr.common.exceptions.exceptions import MindInsightException, \
     LineageVerificationException, LineageSummaryAnalyzeException
@@ -87,11 +89,11 @@ class SummaryAnalyzer:
         Read event.
 
         Returns:
-            Event, the event body.
+            LineageEvent, the event body.
         """
         body_size = self._read_header()
         body_str = self._read_body(body_size)
-        event = Event().FromString(body_str)
+        event = LineageEvent().FromString(body_str)
         return event
 
     def _read_header(self):
@@ -206,3 +208,51 @@ class LineageSummaryAnalyzer(SummaryAnalyzer):
             raise LineageSummaryAnalyzeException()
 
         return lineage_info
+
+    @staticmethod
+    def get_user_defined_info(file_path):
+        """
+        Get user defined info.
+        Args:
+            file_path (str): The file path of summary log.
+
+        Returns:
+            list, the list of dict format user defined information
+                which converted from proto message.
+        """
+        all_user_message = []
+        summary_analyzer = SummaryAnalyzer(file_path)
+
+        for event in summary_analyzer.load_events():
+            if event.HasField("user_defined_info"):
+                user_defined_info = MessageToDict(
+                    event,
+                    preserving_proto_field_name=True
+                ).get("user_defined_info")
+                user_dict = LineageSummaryAnalyzer._get_dict_from_proto(user_defined_info)
+                all_user_message.append(user_dict)
+
+        return all_user_message
+
+    @staticmethod
+    def _get_dict_from_proto(user_defined_info):
+        """
+        Convert the proto message UserDefinedInfo to its dict format.
+
+        Args:
+            user_defined_info (UserDefinedInfo): The proto message of user defined info.
+
+        Returns:
+            dict, the converted dict.
+        """
+        user_dict = dict()
+        proto_dict = user_defined_info.get("user_info")
+        for proto_item in proto_dict:
+            if proto_item and isinstance(proto_item, dict):
+                key, value = list(list(proto_item.values())[0].items())[0]
+                if isinstance(value, dict):
+                    user_dict[key] = LineageSummaryAnalyzer._get_dict_from_proto(value)
+                else:
+                    user_dict[key] = value
+
+        return user_dict

mindinsight/lineagemgr/summary/summary_record.py

@@ -15,9 +15,9 @@
 """Record message to summary log."""
 import time
 
-from mindinsight.datavisual.proto_files.mindinsight_summary_pb2 import Event
+from mindinsight.datavisual.proto_files.mindinsight_lineage_pb2 import LineageEvent
 from mindinsight.lineagemgr.summary.event_writer import EventWriter
-from ._summary_adapter import package_dataset_graph
+from ._summary_adapter import package_dataset_graph, package_user_defined_info
 
 
 class LineageSummary:
@@ -50,9 +50,9 @@ class LineageSummary:
             run_context_args (dict): The train lineage info to log.
 
         Returns:
-            Event, the proto message event contains train lineage.
+            LineageEvent, the proto message event contains train lineage.
         """
-        train_lineage_event = Event()
+        train_lineage_event = LineageEvent()
         train_lineage_event.wall_time = time.time()
 
         # Init train_lineage message.
@@ -124,9 +124,9 @@ class LineageSummary:
             run_context_args (dict): The evaluation lineage info to log.
 
         Returns:
-            Event, the proto message event contains evaluation lineage.
+            LineageEvent, the proto message event contains evaluation lineage.
         """
-        train_lineage_event = Event()
+        train_lineage_event = LineageEvent()
         train_lineage_event.wall_time = time.time()
 
         # Init evaluation_lineage message.
@@ -165,3 +165,18 @@ class LineageSummary:
         self.event_writer.write_event_to_file(
             package_dataset_graph(dataset_graph).SerializeToString()
         )
+
+    def record_user_defined_info(self, user_dict):
+        """
+        Write user defined info to summary log.
+
+        Note:
+            The type of references must be dict, the value should be
+            int32, float, string. Nested dict is not supported now.
+
+        Args:
+            user_dict (dict): The value user defined to be recorded.
+        """
+        self.event_writer.write_event_to_file(
+            package_user_defined_info(user_dict).SerializeToString()
+        )
\ No newline at end of file

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

@@ -32,7 +32,8 @@ from mindinsight.lineagemgr.common.exceptions.exceptions import (LineageFileNotF
                                                                  LineageSearchConditionParamError)
 
 from ..conftest import BASE_SUMMARY_DIR, DATASET_GRAPH, SUMMARY_DIR, SUMMARY_DIR_2
-
+from .....ut.lineagemgr.querier import event_data
+from os import environ
 LINEAGE_INFO_RUN1 = {
     'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
     'metric': {
@@ -68,6 +69,7 @@ LINEAGE_FILTRATION_EXCEPT_RUN = {
     'loss_function': 'SoftmaxCrossEntropyWithLogits',
     'train_dataset_path': None,
     'train_dataset_count': 1024,
+    'user_defined': {},
     'test_dataset_path': None,
     'test_dataset_count': None,
     'network': 'ResNet',
@@ -87,6 +89,7 @@ LINEAGE_FILTRATION_RUN1 = {
     'train_dataset_path': None,
     'train_dataset_count': 731,
     'test_dataset_path': None,
+    'user_defined': {},
     'test_dataset_count': 10240,
     'network': 'ResNet',
     'optimizer': 'Momentum',
@@ -106,6 +109,7 @@ LINEAGE_FILTRATION_RUN2 = {
     'loss_function': None,
     'train_dataset_path': None,
     'train_dataset_count': None,
+    'user_defined': {},
     'test_dataset_path': None,
     'test_dataset_count': 10240,
     'network': None,
@@ -318,6 +322,7 @@ class TestModelApi(TestCase):
     def test_filter_summary_lineage(self):
         """Test the interface of filter_summary_lineage."""
         expect_result = {
+            'customized': event_data.CUSTOMIZED__0,
             'object': [
                 LINEAGE_FILTRATION_EXCEPT_RUN,
                 LINEAGE_FILTRATION_RUN1,
@@ -360,16 +365,17 @@ class TestModelApi(TestCase):
                     SUMMARY_DIR_2
                 ]
             },
-            'metric_accuracy': {
+            'metric/accuracy': {
                 'lt': 3.0,
                 'gt': 0.5
             },
-            'sorted_name': 'metric_accuracy',
+            'sorted_name': 'metric/accuracy',
             'sorted_type': 'descending',
             'limit': 3,
             'offset': 0
         }
         expect_result = {
+            'customized': event_data.CUSTOMIZED__0,
             'object': [
                 LINEAGE_FILTRATION_RUN2,
                 LINEAGE_FILTRATION_RUN1
@@ -397,16 +403,17 @@ class TestModelApi(TestCase):
                     './run2'
                 ]
             },
-            'metric_accuracy': {
+            'metric/accuracy': {
                 'lt': 3.0,
                 'gt': 0.5
             },
-            'sorted_name': 'metric_accuracy',
+            'sorted_name': 'metric/accuracy',
             'sorted_type': 'descending',
             'limit': 3,
             'offset': 0
         }
         expect_result = {
+            'customized': event_data.CUSTOMIZED__0,
             'object': [
                 LINEAGE_FILTRATION_RUN2,
                 LINEAGE_FILTRATION_RUN1
@@ -431,10 +438,11 @@ class TestModelApi(TestCase):
             'batch_size': {
                 'ge': 30
             },
-            'sorted_name': 'metric_accuracy',
+            'sorted_name': 'metric/accuracy',
             'lineage_type': None
         }
         expect_result = {
+            'customized': event_data.CUSTOMIZED__0,
             'object': [
                 LINEAGE_FILTRATION_EXCEPT_RUN,
                 LINEAGE_FILTRATION_RUN1
@@ -454,6 +462,7 @@ class TestModelApi(TestCase):
             'lineage_type': 'model'
         }
         expect_result = {
+            'customized': {},
             'object': [],
             'count': 0
         }
@@ -479,6 +488,7 @@ class TestModelApi(TestCase):
             'lineage_type': 'dataset'
         }
         expect_result = {
+            'customized': {},
             'object': [
                 {
                     'summary_dir': summary_dir,
@@ -659,13 +669,13 @@ class TestModelApi(TestCase):
 
         # the search condition type error
         search_condition = {
-            'metric_accuracy': {
+            'metric/accuracy': {
                 'lt': 'xxx'
             }
         }
         self.assertRaisesRegex(
             LineageSearchConditionParamError,
-            'The parameter metric_accuracy is invalid.',
+            'The parameter metric/accuracy is invalid.',
             filter_summary_lineage,
             BASE_SUMMARY_DIR,
             search_condition
@@ -741,12 +751,13 @@ class TestModelApi(TestCase):
         """Test the abnormal execution of the filter_summary_lineage interface."""
         # gt > lt
         search_condition1 = {
-            'metric_accuracy': {
+            'metric/accuracy': {
                 'gt': 1,
                 'lt': 0.5
             }
         }
         expect_result = {
+            'customized': {},
             'object': [],
             'count': 0
         }
@@ -762,6 +773,7 @@ class TestModelApi(TestCase):
             'offset': 4
         }
         expect_result = {
+            'customized': {},
             'object': [],
             'count': 1
         }

tests/ut/backend/lineagemgr/test_lineage_api.py

@@ -29,6 +29,7 @@ LINEAGE_FILTRATION_BASE = {
     'loss_function': 'SoftmaxCrossEntropyWithLogits',
     'train_dataset_path': None,
     'train_dataset_count': 64,
+    'user_defined': {},
     'test_dataset_path': None,
     'test_dataset_count': None,
     'network': 'str',
@@ -46,6 +47,7 @@ LINEAGE_FILTRATION_RUN1 = {
     'loss_function': 'SoftmaxCrossEntropyWithLogits',
     'train_dataset_path': None,
     'train_dataset_count': 64,
+    'user_defined': {},
     'test_dataset_path': None,
     'test_dataset_count': 64,
     'network': 'str',

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

@@ -228,12 +228,12 @@ class TestValidateSearchModelCondition(TestCase):
         )
 
         condition = {
-            'metric_attribute': {
+            'metric/attribute': {
                 'ge': 'xxx'
             }
         }
         self._assert_raise_of_mindinsight_exception(
-            "The parameter metric_attribute is invalid. "
+            "The parameter metric/attribute is invalid. "
             "It should be a dict and the value should be a float or a integer",
             condition
         )

tests/ut/lineagemgr/querier/event_data.py

@@ -188,6 +188,22 @@ METRIC_0 = {
     'mse': 3.00000001
 }
 
+CUSTOMIZED__0 = {
+    'metric/accuracy': {'label': 'metric/accuracy', 'required': True, 'type': 'float'},
+}
+
+CUSTOMIZED_0 = {
+    **CUSTOMIZED__0,
+    'metric/mae': {'label': 'metric/mae', 'required': True, 'type': 'float'},
+    'metric/mse': {'label': 'metric/mse', 'required': True, 'type': 'float'}
+}
+
+CUSTOMIZED_1 = {
+    'metric/accuracy': {'label': 'metric/accuracy', 'required': True, 'type': 'NoneType'},
+    'metric/mae': {'label': 'metric/mae', 'required': True, 'type': 'float'},
+    'metric/mse': {'label': 'metric/mse', 'required': True, 'type': 'float'}
+}
+
 METRIC_1 = {
     'accuracy': 1.0000002,
     'mae': 2.00000002,

tests/ut/lineagemgr/querier/test_querier.py

@@ -17,7 +17,7 @@ from unittest import TestCase, mock
 
 from google.protobuf.json_format import ParseDict
 
-import mindinsight.datavisual.proto_files.mindinsight_summary_pb2 as summary_pb2
+import mindinsight.datavisual.proto_files.mindinsight_lineage_pb2 as summary_pb2
 from mindinsight.lineagemgr.common.exceptions.exceptions import (LineageParamTypeError, LineageQuerierParamException,
                                                                  LineageSummaryAnalyzeException,
                                                                  LineageSummaryParseException)
@@ -40,19 +40,19 @@ def create_lineage_info(train_event_dict, eval_event_dict, dataset_event_dict):
         namedtuple, parsed lineage info.
     """
     if train_event_dict is not None:
-        train_event = summary_pb2.Event()
+        train_event = summary_pb2.LineageEvent()
         ParseDict(train_event_dict, train_event)
     else:
         train_event = None
 
     if eval_event_dict is not None:
-        eval_event = summary_pb2.Event()
+        eval_event = summary_pb2.LineageEvent()
         ParseDict(eval_event_dict, eval_event)
     else:
         eval_event = None
 
     if dataset_event_dict is not None:
-        dataset_event = summary_pb2.Event()
+        dataset_event = summary_pb2.LineageEvent()
         ParseDict(dataset_event_dict, dataset_event)
     else:
         dataset_event = None
@@ -97,6 +97,7 @@ def create_filtration_result(summary_dir, train_event_dict,
         "metric": metric_dict,
         "dataset_graph": dataset_dict,
         "dataset_mark": '2',
+        "user_defined": {}
     }
     return filtration_result
 
@@ -208,7 +209,9 @@ LINEAGE_FILTRATION_5 = {
     "model_size": event_data.EVENT_TRAIN_DICT_5['train_lineage']['model']['size'],
     "metric": {},
     "dataset_graph": event_data.DATASET_DICT_0,
-    "dataset_mark": '2'
+    "dataset_mark": '2',
+    "user_defined": {}
+
 }
 LINEAGE_FILTRATION_6 = {
     "summary_dir": '/path/to/summary6',
@@ -228,12 +231,14 @@ LINEAGE_FILTRATION_6 = {
     "model_size": None,
     "metric": event_data.METRIC_5,
     "dataset_graph": event_data.DATASET_DICT_0,
-    "dataset_mark": '2'
+    "dataset_mark": '2',
+    "user_defined": {}
 }
 
 
 class TestQuerier(TestCase):
     """Test the class of `Querier`."""
+    @mock.patch('mindinsight.lineagemgr.querier.querier.LineageSummaryAnalyzer.get_user_defined_info')
     @mock.patch('mindinsight.lineagemgr.querier.querier.LineageSummaryAnalyzer.get_summary_infos')
     def setUp(self, *args):
         """Initialization before test case execution."""
@@ -242,6 +247,7 @@ class TestQuerier(TestCase):
             event_data.EVENT_EVAL_DICT_0,
             event_data.EVENT_DATASET_DICT_0
         )
+        args[1].return_value = []
 
         single_summary_path = '/path/to/summary0/log0'
         self.single_querier = Querier(single_summary_path)
@@ -394,6 +400,7 @@ class TestQuerier(TestCase):
             'sorted_name': 'summary_dir'
         }
         expected_result = {
+            'customized': event_data.CUSTOMIZED_0,
             'object': [
                 LINEAGE_FILTRATION_1,
                 LINEAGE_FILTRATION_2
@@ -418,6 +425,7 @@ class TestQuerier(TestCase):
             'sorted_type': 'descending'
         }
         expected_result = {
+            'customized': event_data.CUSTOMIZED_0,
             'object': [
                 LINEAGE_FILTRATION_2,
                 LINEAGE_FILTRATION_3
@@ -434,6 +442,7 @@ class TestQuerier(TestCase):
             'offset': 1
         }
         expected_result = {
+            'customized': event_data.CUSTOMIZED_0,
             'object': [
                 LINEAGE_FILTRATION_2,
                 LINEAGE_FILTRATION_3
@@ -446,6 +455,7 @@ class TestQuerier(TestCase):
     def test_filter_summary_lineage_success_4(self):
         """Test the success of filter_summary_lineage."""
         expected_result = {
+            'customized': event_data.CUSTOMIZED_0,
             'object': [
                 LINEAGE_FILTRATION_0,
                 LINEAGE_FILTRATION_1,
@@ -468,6 +478,7 @@ class TestQuerier(TestCase):
             }
         }
         expected_result = {
+            'customized': event_data.CUSTOMIZED_0,
             'object': [LINEAGE_FILTRATION_4],
             'count': 1,
         }
@@ -477,10 +488,11 @@ class TestQuerier(TestCase):
     def test_filter_summary_lineage_success_6(self):
         """Test the success of filter_summary_lineage."""
         condition = {
-            'sorted_name': 'metric_accuracy',
+            'sorted_name': 'metric/accuracy',
             'sorted_type': 'ascending'
         }
         expected_result = {
+            'customized': event_data.CUSTOMIZED_0,
             'object': [
                 LINEAGE_FILTRATION_0,
                 LINEAGE_FILTRATION_5,
@@ -498,10 +510,11 @@ class TestQuerier(TestCase):
     def test_filter_summary_lineage_success_7(self):
         """Test the success of filter_summary_lineage."""
         condition = {
-            'sorted_name': 'metric_accuracy',
+            'sorted_name': 'metric/accuracy',
             'sorted_type': 'descending'
         }
         expected_result = {
+            'customized': event_data.CUSTOMIZED_1,
             'object': [
                 LINEAGE_FILTRATION_6,
                 LINEAGE_FILTRATION_4,
@@ -519,12 +532,13 @@ class TestQuerier(TestCase):
     def test_filter_summary_lineage_success_8(self):
         """Test the success of filter_summary_lineage."""
         condition = {
-            'metric_accuracy': {
+            'metric/accuracy': {
                 'lt': 1.0000006,
                 'gt': 1.0000004
             }
         }
         expected_result = {
+            'customized': event_data.CUSTOMIZED_0,
             'object': [LINEAGE_FILTRATION_4],
             'count': 1,
         }
@@ -538,6 +552,7 @@ class TestQuerier(TestCase):
             'offset': 3
         }
         expected_result = {
+            'customized': {},
             'object': [],
             'count': 7,
         }
@@ -594,11 +609,13 @@ class TestQuerier(TestCase):
         with self.assertRaises(LineageSummaryParseException):
             Querier(summary_path)
 
+    @mock.patch('mindinsight.lineagemgr.querier.querier.LineageSummaryAnalyzer.get_user_defined_info')
     @mock.patch('mindinsight.lineagemgr.querier.querier.LineageSummaryAnalyzer.get_summary_infos')
     def test_parse_fail_summary_logs_1(self, *args):
         """Test the function of parsing fail summary logs."""
         lineage_infos = get_lineage_infos()
         args[0].side_effect = lineage_infos
+        args[1].return_value = []
 
         summary_path = ['/path/to/summary0/log0']
         querier = Querier(summary_path)
@@ -611,6 +628,7 @@ class TestQuerier(TestCase):
         self.assertListEqual(expected_result, result)
         self.assertListEqual([], querier._parse_failed_paths)
 
+    @mock.patch('mindinsight.lineagemgr.querier.querier.LineageSummaryAnalyzer.get_user_defined_info')
     @mock.patch('mindinsight.lineagemgr.querier.querier.LineageSummaryAnalyzer.get_summary_infos')
     def test_parse_fail_summary_logs_2(self, *args):
         """Test the function of parsing fail summary logs."""
@@ -619,6 +637,7 @@ class TestQuerier(TestCase):
             event_data.EVENT_EVAL_DICT_0,
             event_data.EVENT_DATASET_DICT_0,
         )
+        args[1].return_value = []
 
         summary_path = ['/path/to/summary0/log0']
         querier = Querier(summary_path)

tests/ut/lineagemgr/summary/test_lineage_summary_analyze.py

@@ -16,7 +16,7 @@
 from unittest import mock, TestCase
 from unittest.mock import MagicMock
 
-from mindinsight.datavisual.proto_files.mindinsight_summary_pb2 import Event
+from mindinsight.datavisual.proto_files.mindinsight_lineage_pb2 import LineageEvent
 from mindinsight.lineagemgr.common.exceptions.exceptions import LineageVerificationException, \
     LineageSummaryAnalyzeException
 from mindinsight.lineagemgr.common.log import logger as log
@@ -57,7 +57,7 @@ class TestSummaryAnalyzer(TestCase):
 
     @mock.patch.object(SummaryAnalyzer, '_read_header')
     @mock.patch.object(SummaryAnalyzer, '_read_body')
-    @mock.patch.object(Event, 'FromString')
+    @mock.patch.object(LineageEvent, 'FromString')
     def test_read_event(self, *args):
         """Test read_event method."""
         args[2].return_value = 10