Some code style changes (#267)

* Some code style changes

* Fix bug in callback list

torchbearer/callbacks/callbacks.py

@@ -181,7 +181,7 @@ def __init__(self, callback_list):
         for i in range(len(callback_list)):
             callback = callback_list[i]
 
-            if type(callback) is CallbackList:
+            if isinstance(callback, CallbackList):
                 self.callback_list = self.callback_list + callback.callback_list
             else:
                 self.callback_list.append(callback)

torchbearer/callbacks/checkpointers.py

@@ -86,9 +86,9 @@ def __init__(self, filepath='model.{epoch:02d}-{val_loss:.2f}.pt', pickle_module
         super().__init__(filepath, pickle_module=pickle_module, pickle_protocol=pickle_protocol)
         self.filepath = filepath
 
-    def on_end_epoch(self, model_state):
-        super().on_end_training(model_state)
-        self.save_checkpoint(model_state, overwrite_most_recent=True)
+    def on_end_epoch(self, state):
+        super().on_end_training(state)
+        self.save_checkpoint(state, overwrite_most_recent=True)
 
 
 class Best(_Checkpointer):

torchbearer/cv_utils.py

@@ -14,7 +14,7 @@ def train_valid_splitter(x, y, split, shuffle=True):
     :type y: torch.Tensor
     :param split: Fraction of dataset to be used for validation
     :type split: float
-    :param shuffle: If True randomize tensor order before splitting else do not randomize 
+    :param shuffle: If True randomize tensor order before splitting else do not randomize
     :type shuffle: bool
     :return: Training and validation tensors (training data, training labels, validation data, validation labels)
     :rtype: tuple
@@ -39,7 +39,7 @@ def get_train_valid_sets(x, y, validation_data, validation_split, shuffle=True):
     :type x: torch.Tensor
     :param y: Label tensor for dataset
     :type y: torch.Tensor
-    :param validation_data: Optional validation data (x_val, y_val) to be used instead of splitting x and y tensors 
+    :param validation_data: Optional validation data (x_val, y_val) to be used instead of splitting x and y tensors
     :type validation_data: (torch.Tensor, torch.Tensor)
     :param validation_split: Fraction of dataset to be used for validation
     :type validation_split: float

torchbearer/torchbearer.py

@@ -72,7 +72,6 @@ def fit(self, x, y, batch_size=None, epochs=1, verbose=1, callbacks=[], validati
         :return: The final state context dictionary
         :rtype: dict[str,any]
         """
-
         trainset, valset = torchbearer.cv_utils.get_train_valid_sets(x, y, validation_data, validation_split, shuffle=shuffle)
         trainloader = DataLoader(trainset, batch_size, shuffle=shuffle, num_workers=workers)
 
@@ -531,7 +530,7 @@ def _update_device_and_dtype_from_args(main_state, *args, **kwargs):
         :return: Updated main state dictionary
         :rtype: dict[str,any]
         """
-        for key, val in kwargs.items():
+        for key, _ in kwargs.items():
             if key == torchbearer.DATA_TYPE:
                 main_state[torchbearer.DATA_TYPE] = kwargs[torchbearer.DATA_TYPE]
             elif torchbearer.DEVICE in kwargs: