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autogl/module/train/graph_classification.py

@@ -1,6 +1,11 @@
 from . import register_trainer, BaseTrainer, Evaluation, EVALUATE_DICT, EarlyStopping
 import torch
-from torch.optim.lr_scheduler import StepLR, MultiStepLR, ExponentialLR, ReduceLROnPlateau
+from torch.optim.lr_scheduler import (
+    StepLR,
+    MultiStepLR,
+    ExponentialLR,
+    ReduceLROnPlateau,
+)
 import torch.nn.functional as F
 from ..model import MODEL_DICT, BaseModel
 from .evaluate import Logloss
@@ -229,14 +234,16 @@ class GraphClassificationTrainer(BaseTrainer):
 
         # scheduler = StepLR(optimizer, step_size=100, gamma=0.1)
         lr_scheduler_type = self.lr_scheduler_type
-        if type(lr_scheduler_type) == str and lr_scheduler_type == 'steplr':
+        if type(lr_scheduler_type) == str and lr_scheduler_type == "steplr":
             scheduler = StepLR(optimizer, step_size=100, gamma=0.1)
-        elif type(lr_scheduler_type) == str and lr_scheduler_type == 'multisteplr':
-            scheduler = MultiStepLR(optimizer, milestones=[30,80], gamma=0.1)
-        elif type(lr_scheduler_type) == str and lr_scheduler_type == 'exponentiallr':
+        elif type(lr_scheduler_type) == str and lr_scheduler_type == "multisteplr":
+            scheduler = MultiStepLR(optimizer, milestones=[30, 80], gamma=0.1)
+        elif type(lr_scheduler_type) == str and lr_scheduler_type == "exponentiallr":
             scheduler = ExponentialLR(optimizer, gamma=0.1)
-        elif type(lr_scheduler_type) == str and lr_scheduler_type == 'reducelronplateau':
-            scheduler = ReduceLROnPlateau(optimizer, 'min')
+        elif (
+            type(lr_scheduler_type) == str and lr_scheduler_type == "reducelronplateau"
+        ):
+            scheduler = ReduceLROnPlateau(optimizer, "min")
         else:
             scheduler = None
 

autogl/module/train/node_classification.py

@@ -1,6 +1,11 @@
 from . import register_trainer, BaseTrainer, Evaluation, EVALUATE_DICT, EarlyStopping
 import torch
-from torch.optim.lr_scheduler import StepLR, MultiStepLR, ExponentialLR, ReduceLROnPlateau
+from torch.optim.lr_scheduler import (
+    StepLR,
+    MultiStepLR,
+    ExponentialLR,
+    ReduceLROnPlateau,
+)
 import torch.nn.functional as F
 from ..model import MODEL_DICT, BaseModel
 from .evaluate import Logloss, Acc, Auc
@@ -86,7 +91,7 @@ class NodeClassificationTrainer(BaseTrainer):
             self.model = MODEL_DICT[model](num_features, num_classes, device, init=init)
         elif isinstance(model, BaseModel):
             self.model = model
-        
+
         self.opt_received = optimizer
         if type(optimizer) == str and optimizer.lower() == "adam":
             self.optimizer = torch.optim.Adam
@@ -207,14 +212,16 @@ class NodeClassificationTrainer(BaseTrainer):
         )
         # scheduler = StepLR(optimizer, step_size=100, gamma=0.1)
         lr_scheduler_type = self.lr_scheduler_type
-        if type(lr_scheduler_type) == str and lr_scheduler_type == 'steplr':
+        if type(lr_scheduler_type) == str and lr_scheduler_type == "steplr":
             scheduler = StepLR(optimizer, step_size=100, gamma=0.1)
-        elif type(lr_scheduler_type) == str and lr_scheduler_type == 'multisteplr':
+        elif type(lr_scheduler_type) == str and lr_scheduler_type == "multisteplr":
             scheduler = MultiStepLR(optimizer, milestones=[30, 80], gamma=0.1)
-        elif type(lr_scheduler_type) == str and lr_scheduler_type == 'exponentiallr':
+        elif type(lr_scheduler_type) == str and lr_scheduler_type == "exponentiallr":
             scheduler = ExponentialLR(optimizer, gamma=0.1)
-        elif type(lr_scheduler_type) == str and lr_scheduler_type == 'reducelronplateau':
-            scheduler = ReduceLROnPlateau(optimizer, 'min')
+        elif (
+            type(lr_scheduler_type) == str and lr_scheduler_type == "reducelronplateau"
+        ):
+            scheduler = ReduceLROnPlateau(optimizer, "min")
         else:
             scheduler = None