Merge pull request #162 from Yoda-wu/feat/FCIL

Feat/fcil: Try to remove the print code in rank.py.

core/common/constant.py

@@ -22,6 +22,7 @@ class DatasetFormat(Enum):
     File format of inputting dataset.
     Currently, file formats are as follows: txt, csv.
     """
+
     CSV = "csv"
     TXT = "txt"
     JSON = "json"
@@ -31,16 +32,20 @@ class ParadigmType(Enum):
     """
     Algorithm paradigm type.
     """
+
     SINGLE_TASK_LEARNING = "singletasklearning"
     INCREMENTAL_LEARNING = "incrementallearning"
     MULTIEDGE_INFERENCE = "multiedgeinference"
     LIFELONG_LEARNING = "lifelonglearning"
+    FEDERATED_LEARNING = "federatedlearning"
+    FEDERATED_CLASS_INCREMENTAL_LEARNING = "federatedclassincrementallearning"
 
 
 class ModuleType(Enum):
     """
     Algorithm module type.
     """
+
     BASEMODEL = "basemodel"
 
     # HEM
@@ -63,20 +68,26 @@ class ModuleType(Enum):
     UNSEEN_SAMPLE_RECOGNITION = "unseen_sample_recognition"
     UNSEEN_SAMPLE_RE_RECOGNITION = "unseen_sample_re_recognition"
 
+    # FL_AGG
+    AGGREGATION = "aggregation"
+
 
 class SystemMetricType(Enum):
     """
     System metric type of ianvs.
     """
+
     SAMPLES_TRANSFER_RATIO = "samples_transfer_ratio"
     FWT = "FWT"
     BWT = "BWT"
     TASK_AVG_ACC = "task_avg_acc"
     MATRIX = "MATRIX"
+    FORGET_RATE = "forget_rate"
 
 
 class TestObjectType(Enum):
     """
     Test object type of ianvs.
     """
+
     ALGORITHMS = "algorithms"

core/storymanager/rank/rank.py

@@ -35,15 +35,12 @@ class Rank:
 
     def __init__(self, config):
         self.sort_by: list = []
-        self.visualization: dict = {
-            "mode": "selected_only",
-            "method": "print_table"
-        }
+        self.visualization: dict = {"mode": "selected_only", "method": "print_table"}
         self.selected_dataitem: dict = {
             "paradigms": ["all"],
             "modules": ["all"],
             "hyperparameters": ["all"],
-            "metrics": ["all"]
+            "metrics": ["all"],
         }
         self.save_mode: str = "selected_and_all"
 
@@ -62,15 +59,21 @@ def _parse_config(self, config):
 
     def _check_fields(self):
         if not self.sort_by and not isinstance(self.sort_by, list):
-            raise ValueError(f"rank's sort_by({self.sort_by}) must be provided and be list type.")
+            raise ValueError(
+                f"rank's sort_by({self.sort_by}) must be provided and be list type."
+            )
 
         if not self.visualization and not isinstance(self.visualization, dict):
-            raise ValueError(f"rank's visualization({self.visualization}) "
-                             f"must be provided and be dict type.")
+            raise ValueError(
+                f"rank's visualization({self.visualization}) "
+                f"must be provided and be dict type."
+            )
 
         if not self.selected_dataitem and not isinstance(self.selected_dataitem, dict):
-            raise ValueError(f"rank's selected_dataitem({self.selected_dataitem}) "
-                             f"must be provided and be dict type.")
+            raise ValueError(
+                f"rank's selected_dataitem({self.selected_dataitem}) "
+                f"must be provided and be dict type."
+            )
 
         if not self.selected_dataitem.get("paradigms"):
             raise ValueError("not found paradigms of selected_dataitem in rank.")
@@ -82,8 +85,10 @@ def _check_fields(self):
             raise ValueError("not found metrics of selected_dataitem in rank.")
 
         if not self.save_mode and not isinstance(self.save_mode, list):
-            raise ValueError(f"rank's save_mode({self.save_mode}) "
-                             f"must be provided and be list type.")
+            raise ValueError(
+                f"rank's save_mode({self.save_mode}) "
+                f"must be provided and be list type."
+            )
 
     @classmethod
     def _get_all_metric_names(cls, test_results) -> list:
@@ -133,7 +138,6 @@ def _sort_all_df(self, all_df, all_metric_names):
 
             if metric_name not in all_metric_names:
                 continue
-
             sort_metric_list.append(metric_name)
             is_ascend_list.append(ele.get(metric_name) == "ascend")
 
@@ -198,7 +202,15 @@ def _get_selected(self, test_cases, test_results) -> pd.DataFrame:
         if metric_names == ["all"]:
             metric_names = self._get_all_metric_names(test_results)
 
-        header = ["algorithm", *metric_names, "paradigm", *module_types, *hps_names, "time", "url"]
+        header = [
+            "algorithm",
+            *metric_names,
+            "paradigm",
+            *module_types,
+            *hps_names,
+            "time",
+            "url",
+        ]
 
         all_df = copy.deepcopy(self.all_df)
         selected_df = pd.DataFrame(all_df, columns=header)
@@ -220,8 +232,7 @@ def _draw_pictures(self, test_cases, test_results):
         for test_case in test_cases:
             out_put = test_case.output_dir
             test_result = test_results[test_case.id][0]
-            matrix = test_result.get('Matrix')
-            #print(out_put)
+            matrix = test_result.get("Matrix")
             for key in matrix.keys():
                 draw_heatmap_picture(out_put, key, matrix[key])
 
@@ -285,4 +296,5 @@ def plot(self):
             except Exception as err:
                 raise RuntimeError(
                     f"process visualization(method={method}) of "
-                    f"rank file({self.selected_rank_file}) failed, error: {err}.") from err
+                    f"rank file({self.selected_rank_file}) failed, error: {err}."
+                ) from err

core/testcasecontroller/algorithm/algorithm.py

@@ -24,6 +24,8 @@
     IncrementalLearning,
     MultiedgeInference,
     LifelongLearning,
+    FederatedLearning,
+    FederatedClassIncrementalLearning
 )
 from core.testcasecontroller.generation_assistant import get_full_combinations
 
@@ -64,12 +66,21 @@ def __init__(self, name, config):
             "train_ratio": 0.8,
             "splitting_method": "default"
         }
+        self.fl_data_setting: dict = {
+            "train_ratio": 1.0,
+            "splitting_method": "default",
+            "data_partition": "iid",
+            'non_iid_ratio': 0.6,
+            "label_data_ratio": 1.0
+        }
+
         self.initial_model_url: str = ""
         self.modules: list = []
         self.modules_list = None
         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.
@@ -91,7 +102,6 @@ def paradigm(self, workspace: str, **kwargs):
         # pylint: disable=C0103
         for k, v in self.__dict__.items():
             config.update({k: v})
-
         if self.paradigm_type == ParadigmType.SINGLE_TASK_LEARNING.value:
             return SingleTaskLearning(workspace, **config)
 
@@ -104,6 +114,12 @@ def paradigm(self, workspace: str, **kwargs):
         if self.paradigm_type == ParadigmType.LIFELONG_LEARNING.value:
             return LifelongLearning(workspace, **config)
 
+        if self.paradigm_type == ParadigmType.FEDERATED_LEARNING.value:
+            return FederatedLearning(workspace, **config)
+
+        if self.paradigm_type == ParadigmType.FEDERATED_CLASS_INCREMENTAL_LEARNING.value:
+            return FederatedClassIncrementalLearning(workspace, **config)
+
         return None
 
     def _check_fields(self):

core/testcasecontroller/algorithm/module/module.py

@@ -86,6 +86,7 @@ def get_module_instance(self, module_type):
         function
 
         """
+        print(f'hyperparameters_list: {self.hyperparameters_list}')
         class_factory_type = ClassType.GENERAL
         if module_type in [ModuleType.HARD_EXAMPLE_MINING.value]:
             class_factory_type = ClassType.HEM
@@ -106,14 +107,27 @@ def get_module_instance(self, module_type):
         elif module_type in [ModuleType.UNSEEN_SAMPLE_RECOGNITION.value,
                              ModuleType.UNSEEN_SAMPLE_RE_RECOGNITION.value]:
             class_factory_type = ClassType.UTD
+        elif module_type in [ModuleType.AGGREGATION.value]:
+            class_factory_type = ClassType.FL_AGG
+            agg = None
+            print(self.url)
+            if self.url :
+                try:
+                    utils.load_module(self.url)
+                    agg = ClassFactory.get_cls(
+                        type_name=class_factory_type, t_cls_name=self.name)(**self.hyperparameters)
+                    print(agg)
+                except Exception as err:
+                    raise RuntimeError(f"module(type={module_type} loads class(name={self.name}) "
+                                    f"failed, error: {err}.") from err
+            return self.name, agg
 
         if self.url:
             try:
                 utils.load_module(self.url)
                 # pylint: disable=E1134
                 func = ClassFactory.get_cls(
                     type_name=class_factory_type, t_cls_name=self.name)(**self.hyperparameters)
-
                 return func
             except Exception as err:
                 raise RuntimeError(f"module(type={module_type} loads class(name={self.name}) "

core/testcasecontroller/algorithm/paradigm/__init__.py

@@ -17,3 +17,4 @@
 from .singletask_learning import SingleTaskLearning
 from .multiedge_inference import MultiedgeInference
 from .lifelong_learning import LifelongLearning
+from .federated_learning import FederatedLearning, FederatedClassIncrementalLearning

core/testcasecontroller/algorithm/paradigm/base.py

@@ -18,8 +18,8 @@
 
 from sedna.core.incremental_learning import IncrementalLearning
 from sedna.core.lifelong_learning import LifelongLearning
-
 from core.common.constant import ModuleType, ParadigmType
+from .sedna_federated_learning import FederatedLearning
 
 
 class ParadigmBase:
@@ -97,33 +97,51 @@ 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.module_instances.get(ModuleType.BASEMODEL.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)
+            )
+
         return None

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

@@ -0,0 +1,17 @@
+# Copyright 2022 The KubeEdge Authors.
+#
+# 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.
+
+# pylint: disable=missing-module-docstring
+from .federated_learning import FederatedLearning
+from .federated_class_incremental_learning import FederatedClassIncrementalLearning