remove args in super(...).__init__(...) for python3 style

Author: HydrogenSulfate

ppsci/data/dataset/trphysx_dataset.py

@@ -63,7 +63,7 @@ def __init__(
         weight_dict: Optional[Dict[str, float]] = None,
         embedding_model: Optional[base.Arch] = None,
     ):
-        super(LorenzDataset, self).__init__()
+        super().__init__()
         self.file_path = file_path
         self.input_keys = input_keys
         self.label_keys = label_keys
@@ -160,7 +160,7 @@ def __init__(
         weight_dict: Optional[Dict[str, float]] = None,
         embedding_model: Optional[base.Arch] = None,
     ):
-        super(RosslerDataset, self).__init__(
+        super().__init__(
             file_path,
             input_keys,
             label_keys,
@@ -209,7 +209,7 @@ def __init__(
         embedding_model: Optional[base.Arch] = None,
         embedding_batch_size: int = 64,
     ):
-        super(CylinderDataset, self).__init__()
+        super().__init__()
         self.file_path = file_path
         self.input_keys = input_keys
         self.label_keys = label_keys

ppsci/optimizer/lr_scheduler.py

@@ -58,7 +58,7 @@ def __init__(
         verbose: bool = False,
     ) -> None:
         """Initialize and record the necessary parameters"""
-        super(LRBase, self).__init__()
+        super().__init__()
         if warmup_epoch >= epochs:
             msg = (
                 "When using warm up, the value of 'Global.epochs' should be greater "
@@ -124,7 +124,7 @@ class Constant(lr.LRScheduler):
     def __init__(self, learning_rate: float, last_epoch: int = -1):
         self.learning_rate = learning_rate
         self.last_epoch = last_epoch
-        super(Constant, self).__init__()
+        super().__init__()
 
     def get_lr(self) -> float:
         """always return the same learning rate"""
@@ -163,7 +163,7 @@ def __init__(
         last_epoch: int = -1,
         by_epoch: bool = False,
     ):
-        super(Linear, self).__init__(
+        super().__init__(
             epochs,
             iters_per_epoch,
             learning_rate,
@@ -230,7 +230,7 @@ def __init__(
         last_epoch: int = -1,
         by_epoch: bool = False,
     ):
-        super(ExponentialDecay, self).__init__(
+        super().__init__(
             epochs,
             iters_per_epoch,
             learning_rate,
@@ -291,7 +291,7 @@ def __init__(
         last_epoch: int = -1,
         by_epoch: bool = False,
     ):
-        super(Cosine, self).__init__(
+        super().__init__(
             epochs,
             iters_per_epoch,
             learning_rate,
@@ -357,7 +357,7 @@ def __init__(
         last_epoch: int = -1,
         by_epoch: bool = False,
     ):
-        super(Step, self).__init__(
+        super().__init__(
             epochs,
             iters_per_epoch,
             learning_rate,
@@ -418,7 +418,7 @@ def __init__(
         last_epoch: int = -1,
         by_epoch: bool = False,
     ):
-        super(Piecewise, self).__init__(
+        super().__init__(
             epochs,
             iters_per_epoch,
             values[0],
@@ -479,7 +479,7 @@ def __init__(
         last_epoch: int = -1,
         by_epoch: bool = False,
     ):
-        super(MultiStepDecay, self).__init__(
+        super().__init__(
             epochs,
             iters_per_epoch,
             learning_rate,
@@ -538,9 +538,7 @@ def __init__(
         self.T_mult = T_mult
         self.eta_min = eta_min
         self.T_cur = last_epoch
-        super(CosineAnnealingWarmRestarts, self).__init__(
-            learning_rate, last_epoch, verbose
-        )
+        super().__init__(learning_rate, last_epoch, verbose)
 
     def get_lr(self):
         return (
@@ -616,7 +614,7 @@ def __init__(
         last_epoch: int = -1,
         by_epoch: bool = False,
     ):
-        super(CosineWarmRestarts, self).__init__(
+        super().__init__(
             epochs,
             iters_per_epoch,
             learning_rate,