fix(federated_class_incremental_learning) support multiple client ini…

…t and deepcopy estimator

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

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

@@ -101,9 +101,8 @@ def get_task_size(self, train_datasets):
         Returns:
             int: task size for each task
         """
-        return np.unique(
-            [train_datasets[i][1] for i in range(len(train_datasets))]
-        ).shape[0]
+        LOGGER.info(f"train_datasets: {len(train_datasets[0])}")
+        return np.unique(train_datasets[0][1]).shape[0]
 
     def split_label_unlabel_data(self, train_datasets):
         """split train dataset into label and unlabel data for semi-supervised learning
@@ -140,9 +139,6 @@ def init_client(self):
             )
             for _ in range(self.clients_number)
         ]
-        LOGGER.info(
-            f"init client {self.clients[0].estimator == self.clients[1].estimator}"
-        )
 
     def run(self):
         """run the Federated Class-Incremental Learning paradigm

examples/cifar100/fci_ssl/fed_ci_match/algorithm/algorithm.yaml

@@ -3,7 +3,7 @@ algorithm:
   fl_data_setting:
     train_ratio: 1.0
     splitting_method: "default"
-    label_data_ratio: 0.3
+    label_data_ratio: 1.0
     data_partition: "iid"
     non_iid_ratio: "0.6"
   initial_model_url: "/home/wyd/ianvs/project/init_model/cnn.pb"
@@ -21,7 +21,7 @@ algorithm:
               - 0.001
         - epochs:
             values:
-              - 16
+              - 1
     - type: "aggregation"
       name: "FedAvg"
       url: "./examples/cifar100/fci_ssl/fed_ci_match/algorithm/aggregation.py"

examples/cifar100/fci_ssl/fed_ci_match/testenv/testenv.yaml

@@ -33,6 +33,6 @@ testenv:
     - name: "forget_rate"
     - name: "task_avg_acc"
   # incremental rounds setting of incremental learning; int type; default value is 2;
-  incremental_rounds: 10
+  incremental_rounds: 2
   round: 1
-  client_number: 2
+  client_number: 5

examples/cifar100/fci_ssl/glfc/algorithm/GLFC.py

@@ -132,13 +132,16 @@ def _initialize_classifier(self):
     def before_train(self, task_id, train_data, class_learned, old_model):
         logging.info(f"------before train task_id: {task_id}------")
         # print(f'train data len is :{len(train_data[1])}')
+
         self.need_update = task_id != self.old_task_id
         if self.need_update:
             self.old_task_id = task_id
             self.num_classes = self.task_size * (task_id + 1)
             if self.current_classes is not None:
                 self.last_class = self.current_classes
-            logging.info(f"self.last_class is , {self.last_class}, {self.num_classes}")
+            logging.info(
+                f"self.last_class is , {self.last_class}, {self.num_classes} tasksize is {self.task_size}, task_id is {task_id}"
+            )
             self._initialize_classifier()
             self.current_classes = np.unique(train_data["label_y"]).tolist()
             self.update_new_set(self.need_update)
@@ -265,7 +268,7 @@ def _compute_loss(self, imgs, labels):
         correct = tf.cast(tf.equal(pred, y), dtype=tf.int32)
         correct = tf.reduce_sum(correct)
         logging.info(
-            f"current class numbers is {self.num_classes} correct is {correct} and acc is {correct/imgs.shape[0]}"
+            f"current class numbers is {self.num_classes} correct is {correct} and acc is {correct/imgs.shape[0]} tasksize is {self.task_size} self.old_task_id {self.old_task_id}"
         )
         # print(f"total_correct: {total_correct}, total_num: {total_num}")
         if self.old_model == None:

examples/cifar100/fci_ssl/glfc/algorithm/aggregation.py

@@ -35,7 +35,7 @@ def __init__(self):
             learning_rate=0.01, num_classes=10, test_data=None
         )
         self.task_id = -1
-        self.num_classes = 50
+        self.num_classes = 10
 
     def aggregate(self, clients):
         """

examples/cifar100/fci_ssl/glfc/algorithm/basemodel.py

@@ -34,7 +34,7 @@ def __init__(self, **kwargs) -> None:
         self.learning_rate = kwargs.get("learning_rate", 0.001)
         self.epochs = kwargs.get("epochs", 1)
         self.batch_size = kwargs.get("batch_size", 32)
-        self.task_size = kwargs.get("task_size", 50)
+        self.task_size = kwargs.get("task_size", 10)
         self.memory_size = kwargs.get("memory_size", 2000)
         self.encode_model = lenet5(32, 100)
         self.encode_model.call(keras.Input(shape=(32, 32, 3)))