🌈 style(text): Rename some variables

Rename test_gap to test_interval

Rename test_flag in client classes to testing

Format some codes by the new black

src/client/fedavg.py

@@ -38,7 +38,8 @@ def __init__(
         except:
             raise FileNotFoundError(f"Please partition {args.dataset} first.")
 
-        self.data_indices: List[List[int]] = partition["data_indices"]
+        # [0: {"train": [...], "val": [...], "test": [...]}, ...]
+        self.data_indices: List[Dict[str, List[int]]] = partition["data_indices"]
 
         # --------- you can define your custom data preprocessing strategy here ------------
         test_data_transform = transforms.Compose(
@@ -311,7 +312,7 @@ def test(
         Returns:
             Dict[str, Dict[str, Metrics]]: the evalutaion metrics stats.
         """
-        self.test_flag = True
+        self.testing = True
         self.client_id = client_id
         self.load_dataset()
         self.set_parameters(new_parameters)
@@ -327,7 +328,8 @@ def test(
             self.finetune()
             results["after"] = self.evaluate()
             self.model.load_state_dict(frz_params_dict)
-        self.test_flag = False
+
+        self.testing = False
         return results
 
     def finetune(self):

src/server/adcol.py

@@ -112,12 +112,12 @@ def train_one_round(self):
 
     def train_and_test_discriminator(self):
         self.generate_client_index()
-        if (self.current_epoch + 1) % self.args.test_gap == 0:
+        if (self.current_epoch + 1) % self.args.test_interval == 0:
             acc_before = self.test_discriminator()
 
         self.train_discriminator()
 
-        if (self.current_epoch + 1) % self.args.test_gap == 0:
+        if (self.current_epoch + 1) % self.args.test_interval == 0:
             acc_after = self.test_discriminator()
             if (self.current_epoch + 1) % self.args.verbose_gap == 0:
                 self.logger.log(

src/server/fedap.py

@@ -139,7 +139,7 @@ def train(self):
             if (E + 1) % self.args.verbose_gap == 0:
                 self.logger.log(" " * 30, f"TRAINING EPOCH: {E + 1}", " " * 30)
 
-            if (E + 1) % self.args.test_gap == 0:
+            if (E + 1) % self.args.test_interval == 0:
                 self.test()
 
             self.selected_clients = self.client_sample_stream[E]

src/server/fedavg.py

@@ -47,7 +47,7 @@ def get_fedavg_argparser() -> ArgumentParser:
     parser.add_argument("-ge", "--global_epoch", type=int, default=100)
     parser.add_argument("-le", "--local_epoch", type=int, default=5)
     parser.add_argument("-fe", "--finetune_epoch", type=int, default=0)
-    parser.add_argument("-tg", "--test_gap", type=int, default=100)
+    parser.add_argument("-ti", "--test_interval", type=int, default=100)
     parser.add_argument("--eval_test", type=int, default=1)
     parser.add_argument("--eval_val", type=int, default=0)
     parser.add_argument("--eval_train", type=int, default=0)
@@ -175,7 +175,7 @@ def __init__(
         self.epoch_test = [
             epoch
             for epoch in range(0, self.args.global_epoch)
-            if (epoch + 1) % self.args.test_gap == 0
+            if (epoch + 1) % self.args.test_interval == 0
         ]
         # For controlling behaviors of some specific methods while testing (not used by all methods)
         self.test_flag = False
@@ -237,7 +237,7 @@ def train(self):
             if (E + 1) % self.args.verbose_gap == 0:
                 self.logger.log("-" * 26, f"TRAINING EPOCH: {E + 1}", "-" * 26)
 
-            if (E + 1) % self.args.test_gap == 0:
+            if (E + 1) % self.args.test_interval == 0:
                 self.test()
 
             self.selected_clients = self.client_sample_stream[E]
@@ -259,15 +259,13 @@ def train_one_round(self):
         weight_cache = []
         for client_id in self.selected_clients:
             client_local_params = self.generate_client_params(client_id)
-            (
-                delta,
-                weight,
-                self.client_metrics[client_id][self.current_epoch],
-            ) = self.trainer.train(
-                client_id=client_id,
-                local_epoch=self.clients_local_epoch[client_id],
-                new_parameters=client_local_params,
-                verbose=((self.current_epoch + 1) % self.args.verbose_gap) == 0,
+            (delta, weight, self.client_metrics[client_id][self.current_epoch]) = (
+                self.trainer.train(
+                    client_id=client_id,
+                    local_epoch=self.clients_local_epoch[client_id],
+                    new_parameters=client_local_params,
+                    verbose=((self.current_epoch + 1) % self.args.verbose_gap) == 0,
+                )
             )
 
             delta_cache.append(delta)

src/server/metafed.py

@@ -64,7 +64,7 @@ def train(self):
             if (E + 1) % self.args.verbose_gap == 0:
                 self.logger.log("-" * 26, f"TRAINING EPOCH: {E + 1}", "-" * 26)
 
-            if (E + 1) % self.args.test_gap == 0:
+            if (E + 1) % self.args.test_interval == 0:
                 self.test()
 
             client_params_cache = []

src/server/pfedsim.py

@@ -66,7 +66,7 @@ def train(self):
             if (E + 1) % self.args.verbose_gap == 0:
                 self.logger.log(" " * 30, f"TRAINING EPOCH: {E + 1}", " " * 30)
 
-            if (E + 1) % self.args.test_gap == 0:
+            if (E + 1) % self.args.test_interval == 0:
                 self.test()
 
             self.selected_clients = self.client_sample_stream[E]