Simplify callbacks (#4289)

utils/callbacks.py

         else:
             return self._callbacks
 
-    @staticmethod
-    def run_callbacks(register, *args, **kwargs):
+    def run_callbacks(self, hook, *args, **kwargs):
         """
         Loop through the registered actions and fire all callbacks
         """
-        for logger in register:
+        for logger in self._callbacks[hook]:
             # print(f"Running callbacks.{logger['callback'].__name__}()")
             logger['callback'](*args, **kwargs)
 
         """
         Fires all registered callbacks at the start of each pretraining routine
         """
-        self.run_callbacks(self._callbacks['on_pretrain_routine_start'], *args, **kwargs)
+        self.run_callbacks('on_pretrain_routine_start', *args, **kwargs)
 
     def on_pretrain_routine_end(self, *args, **kwargs):
         """
         Fires all registered callbacks at the end of each pretraining routine
         """
-        self.run_callbacks(self._callbacks['on_pretrain_routine_end'], *args, **kwargs)
+        self.run_callbacks('on_pretrain_routine_end', *args, **kwargs)
 
     def on_train_start(self, *args, **kwargs):
         """
         Fires all registered callbacks at the start of each training
         """
-        self.run_callbacks(self._callbacks['on_train_start'], *args, **kwargs)
+        self.run_callbacks('on_train_start', *args, **kwargs)
 
     def on_train_epoch_start(self, *args, **kwargs):
         """
         Fires all registered callbacks at the start of each training epoch
         """
-        self.run_callbacks(self._callbacks['on_train_epoch_start'], *args, **kwargs)
+        self.run_callbacks('on_train_epoch_start', *args, **kwargs)
 
     def on_train_batch_start(self, *args, **kwargs):
         """
         Fires all registered callbacks at the start of each training batch
         """
-        self.run_callbacks(self._callbacks['on_train_batch_start'], *args, **kwargs)
+        self.run_callbacks('on_train_batch_start', *args, **kwargs)
 
     def optimizer_step(self, *args, **kwargs):
         """
         Fires all registered callbacks on each optimizer step
         """
-        self.run_callbacks(self._callbacks['optimizer_step'], *args, **kwargs)
+        self.run_callbacks('optimizer_step', *args, **kwargs)
 
     def on_before_zero_grad(self, *args, **kwargs):
         """
         Fires all registered callbacks before zero grad
         """
-        self.run_callbacks(self._callbacks['on_before_zero_grad'], *args, **kwargs)
+        self.run_callbacks('on_before_zero_grad', *args, **kwargs)
 
     def on_train_batch_end(self, *args, **kwargs):
         """
         Fires all registered callbacks at the end of each training batch
         """
-        self.run_callbacks(self._callbacks['on_train_batch_end'], *args, **kwargs)
+        self.run_callbacks('on_train_batch_end', *args, **kwargs)
 
     def on_train_epoch_end(self, *args, **kwargs):
         """
         Fires all registered callbacks at the end of each training epoch
         """
-        self.run_callbacks(self._callbacks['on_train_epoch_end'], *args, **kwargs)
+        self.run_callbacks('on_train_epoch_end', *args, **kwargs)
 
     def on_val_start(self, *args, **kwargs):
         """
         Fires all registered callbacks at the start of the validation
         """
-        self.run_callbacks(self._callbacks['on_val_start'], *args, **kwargs)
+        self.run_callbacks('on_val_start', *args, **kwargs)
 
     def on_val_batch_start(self, *args, **kwargs):
         """
         Fires all registered callbacks at the start of each validation batch
         """
-        self.run_callbacks(self._callbacks['on_val_batch_start'], *args, **kwargs)
+        self.run_callbacks('on_val_batch_start', *args, **kwargs)
 
     def on_val_image_end(self, *args, **kwargs):
         """
         Fires all registered callbacks at the end of each val image
         """
-        self.run_callbacks(self._callbacks['on_val_image_end'], *args, **kwargs)
+        self.run_callbacks('on_val_image_end', *args, **kwargs)
 
     def on_val_batch_end(self, *args, **kwargs):
         """
         Fires all registered callbacks at the end of each validation batch
         """
-        self.run_callbacks(self._callbacks['on_val_batch_end'], *args, **kwargs)
+        self.run_callbacks('on_val_batch_end', *args, **kwargs)
 
     def on_val_end(self, *args, **kwargs):
         """
         Fires all registered callbacks at the end of the validation
         """
-        self.run_callbacks(self._callbacks['on_val_end'], *args, **kwargs)
+        self.run_callbacks('on_val_end', *args, **kwargs)
 
     def on_fit_epoch_end(self, *args, **kwargs):
         """
         Fires all registered callbacks at the end of each fit (train+val) epoch
         """
-        self.run_callbacks(self._callbacks['on_fit_epoch_end'], *args, **kwargs)
+        self.run_callbacks('on_fit_epoch_end', *args, **kwargs)
 
     def on_model_save(self, *args, **kwargs):
         """
         Fires all registered callbacks after each model save
         """
-        self.run_callbacks(self._callbacks['on_model_save'], *args, **kwargs)
+        self.run_callbacks('on_model_save', *args, **kwargs)
 
     def on_train_end(self, *args, **kwargs):
         """
         Fires all registered callbacks at the end of training
         """
-        self.run_callbacks(self._callbacks['on_train_end'], *args, **kwargs)
+        self.run_callbacks('on_train_end', *args, **kwargs)
 
     def teardown(self, *args, **kwargs):
         """
         Fires all registered callbacks before teardown
         """
-        self.run_callbacks(self._callbacks['teardown'], *args, **kwargs)
+        self.run_callbacks('teardown', *args, **kwargs)