fix: fix the pylint check error

Signed-off-by: Marchons <[email protected]>

core/testcasecontroller/algorithm/algorithm.py

@@ -80,6 +80,7 @@ def __init__(self, name, config):
         self._parse_config(config)
         self._load_third_party_packages()
 
+    # pylint: disable=R0911
     def paradigm(self, workspace: str, **kwargs):
         """
         get test process of AI algorithm paradigm.
@@ -98,7 +99,6 @@ def paradigm(self, workspace: str, **kwargs):
         """
 
         config = kwargs
-
         # pylint: disable=C0103
         for k, v in self.__dict__.items():
             config.update({k: v})

core/testcasecontroller/algorithm/paradigm/base.py

@@ -21,6 +21,7 @@
 from core.common.constant import ModuleType, ParadigmType
 from .sedna_federated_learning import FederatedLearning
 
+
 class ParadigmBase:
     """
     Paradigm Base
@@ -96,36 +97,49 @@ def build_paradigm_job(self, paradigm_type):
             return IncrementalLearning(
                 estimator=self.module_instances.get(ModuleType.BASEMODEL.value),
                 hard_example_mining=self.module_instances.get(
-                    ModuleType.HARD_EXAMPLE_MINING.value))
+                    ModuleType.HARD_EXAMPLE_MINING.value
+                ),
+            )
 
         if paradigm_type == ParadigmType.LIFELONG_LEARNING.value:
             return LifelongLearning(
-                estimator=self.module_instances.get(
-                    ModuleType.BASEMODEL.value),
+                estimator=self.module_instances.get(ModuleType.BASEMODEL.value),
                 task_definition=self.module_instances.get(
-                    ModuleType.TASK_DEFINITION.value),
+                    ModuleType.TASK_DEFINITION.value
+                ),
                 task_relationship_discovery=self.module_instances.get(
-                    ModuleType.TASK_RELATIONSHIP_DISCOVERY.value),
+                    ModuleType.TASK_RELATIONSHIP_DISCOVERY.value
+                ),
                 task_allocation=self.module_instances.get(
-                    ModuleType.TASK_ALLOCATION.value),
+                    ModuleType.TASK_ALLOCATION.value
+                ),
                 task_remodeling=self.module_instances.get(
-                    ModuleType.TASK_REMODELING.value),
+                    ModuleType.TASK_REMODELING.value
+                ),
                 inference_integrate=self.module_instances.get(
-                    ModuleType.INFERENCE_INTEGRATE.value),
+                    ModuleType.INFERENCE_INTEGRATE.value
+                ),
                 task_update_decision=self.module_instances.get(
-                    ModuleType.TASK_UPDATE_DECISION.value),
+                    ModuleType.TASK_UPDATE_DECISION.value
+                ),
                 unseen_task_allocation=self.module_instances.get(
-                    ModuleType.UNSEEN_TASK_ALLOCATION.value),
+                    ModuleType.UNSEEN_TASK_ALLOCATION.value
+                ),
                 unseen_sample_recognition=self.module_instances.get(
-                    ModuleType.UNSEEN_SAMPLE_RECOGNITION.value),
+                    ModuleType.UNSEEN_SAMPLE_RECOGNITION.value
+                ),
                 unseen_sample_re_recognition=self.module_instances.get(
-                    ModuleType.UNSEEN_SAMPLE_RE_RECOGNITION.value)
+                    ModuleType.UNSEEN_SAMPLE_RE_RECOGNITION.value
+                ),
             )
         # pylint: disable=E1101
         if paradigm_type == ParadigmType.MULTIEDGE_INFERENCE.value:
             return self.modules_funcs.get(ModuleType.BASEMODEL.value)()
 
-        if paradigm_type == ParadigmType.FEDERATED_LEARNING.value or paradigm_type == ParadigmType.FEDERATED_CLASS_INCREMENTAL_LEARNING.value:
+        if paradigm_type in [
+            ParadigmType.FEDERATED_LEARNING.value,
+            ParadigmType.FEDERATED_CLASS_INCREMENTAL_LEARNING.value,
+        ]:
             return FederatedLearning(
                 estimator=self.module_instances.get(ModuleType.BASEMODEL.value)
             )

core/testcasecontroller/algorithm/paradigm/federated_learning/federated_class_incremental_learning.py

@@ -200,9 +200,6 @@ def _split_test_dataset(self, split_time):
             index += 1
         return test_datasets_files
 
-    def train_data_partition(self, train_dataset_file):
-        return super().train_data_partition(train_dataset_file)
-
     def client_train(self, client_idx, train_datasets, validation_datasets, **kwargs):
         """client train function that will be called by the thread
 
@@ -241,6 +238,7 @@ def helper_function(self, train_infos):
         LOGGER.info("finish helper function")
 
     # pylint: disable=too-many-locals
+    # pylint: disable=consider-using-enumerat
     def evaluation(self, testdataset_files, incremental_round):
         """evaluate the model performance on old classes
 
@@ -254,7 +252,7 @@ def evaluation(self, testdataset_files, incremental_round):
         """
         if self.accuracy_func is None:
             raise ValueError("accuracy function is not defined")
-        LOGGER.info("*" * 20 + "start evaluation" + "*" * 20)
+        LOGGER.info("********start evaluation********")
         if isinstance(testdataset_files, str):
             testdataset_files = [testdataset_files]
         job = self.get_global_model()
@@ -285,7 +283,8 @@ def evaluation(self, testdataset_files, incremental_round):
                 acc_per_round = self.accuracy_per_round[j]
                 if i < len(acc_per_round):
                     LOGGER.info(
-                        f"acc_per_round: {acc_per_round[i]} and diff is {acc_per_round[i] - old_class_acc_list[i]}"
+                        f"acc_per_round: {acc_per_round[i]}"
+                        + f" and diff is {acc_per_round[i] - old_class_acc_list[i]}"
                     )
                     max_acc_diff = max(
                         max_acc_diff, acc_per_round[i] - old_class_acc_list[i]

core/testcasecontroller/algorithm/paradigm/sedna_federated_learning.py

@@ -23,9 +23,10 @@ class FederatedLearning(JobBase):
 
     # pylint: disable=too-many-locals
     def __init__(self, estimator):
-        super(FederatedLearning, self).__init__(estimator)
+        super().__init__()
         self.estimator = estimator
 
+    # pylint: disable=W0221
     def train(self, train_data, vald_data, **kwargs):
         """Local training function
 

core/testenvmanager/dataset/utils.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+""" Dataset utils to read data from file and partition data """
 # pylint: disable=W1203
 import random
 import numpy as np