make-private

make attributes private to the class

pytorch_lightning/callbacks/model_checkpoint.py

@@ -205,7 +205,7 @@ def on_train_batch_end(self, trainer, pl_module, *args, **kwargs) -> None:
         if self._should_skip_saving_checkpoint(trainer):
             return
         step = trainer.global_step
-        skip_batch = self.every_n_train_steps < 1 or ((step + 1) % self.every_n_train_steps != 0)
+        skip_batch = self._every_n_train_steps < 1 or ((step + 1) % self._every_n_train_steps != 0)
         if skip_batch:
             return
         self.save_checkpoint(trainer, pl_module)
@@ -215,8 +215,8 @@ def on_validation_end(self, trainer, pl_module):
         checkpoints can be saved at the end of the val loop
         """
         skip = (
-            self._should_skip_saving_checkpoint(trainer) or self.every_n_val_epochs is None
-            or self.every_n_val_epochs < 1 or (trainer.current_epoch + 1) % self.every_n_val_epochs != 0
+            self._should_skip_saving_checkpoint(trainer) or self._every_n_val_epochs < 1
+            or (trainer.current_epoch + 1) % self._every_n_val_epochs != 0
         )
         if skip:
             return
@@ -279,18 +279,18 @@ def _should_skip_saving_checkpoint(self, trainer) -> bool:
     def __validate_init_configuration(self):
         if self.save_top_k is not None and self.save_top_k < -1:
             raise MisconfigurationException(f'Invalid value for save_top_k={self.save_top_k}. Must be None or >= -1')
-        if self.every_n_train_steps < 0:
+        if self._every_n_train_steps < 0:
             raise MisconfigurationException(
-                f'Invalid value for every_n_train_steps={self.every_n_train_steps}. Must be >= 0'
+                f'Invalid value for every_n_train_steps={self._every_n_train_steps}. Must be >= 0'
             )
-        if self.every_n_val_epochs < 0:
+        if self._every_n_val_epochs < 0:
             raise MisconfigurationException(
-                f'Invalid value for every_n_val_epochs={self.every_n_val_epochs}. Must be >= 0'
+                f'Invalid value for every_n_val_epochs={self._every_n_val_epochs}. Must be >= 0'
             )
-        if self.every_n_train_steps > 0 and self.every_n_val_epochs > 0:
+        if self._every_n_train_steps > 0 and self._every_n_val_epochs > 0:
             raise MisconfigurationException(
-                f'Invalid values for every_n_train_steps={self.every_n_train_steps}'
-                ' and every_n_val_epochs={self.every_n_val_epochs}.'
+                f'Invalid values for every_n_train_steps={self._every_n_train_steps}'
+                ' and every_n_val_epochs={self._every_n_val_epochs}.'
                 'Both cannot be enabled at the same time.'
             )
         if self.monitor is None:
@@ -346,22 +346,22 @@ def __init_triggers(
         # Default to running once after each validation epoch if neither
         # every_n_train_steps nor every_n_val_epochs is set
         if every_n_train_steps is None and every_n_val_epochs is None:
-            self.every_n_val_epochs = 1
-            self.every_n_train_steps = 0
+            self._every_n_val_epochs = 1
+            self._every_n_train_steps = 0
             log.debug("Both every_n_train_steps and every_n_val_epochs are not set. Setting every_n_val_epochs=1")
         else:
-            self.every_n_val_epochs = every_n_val_epochs or 0
-            self.every_n_train_steps = every_n_train_steps or 0
+            self._every_n_val_epochs = every_n_val_epochs or 0
+            self._every_n_train_steps = every_n_train_steps or 0
 
         # period takes precedence over every_n_val_epochs for backwards compatibility
         if period is not None:
             rank_zero_warn(
                 'Argument `period` in `ModelCheckpoint` is deprecated in v1.3 and will be removed in v1.5.'
                 ' Please use `every_n_val_epochs` instead.', DeprecationWarning
             )
-            self.every_n_val_epochs = period
+            self._every_n_val_epochs = period
 
-        self._period = self.every_n_val_epochs
+        self._period = self._every_n_val_epochs
 
     @property
     def period(self) -> Optional[int]:

tests/checkpointing/test_model_checkpoint.py

@@ -550,8 +550,8 @@ def test_invalid_every_n_train_steps_val_epochs_combination(tmpdir):
 def test_none_every_n_train_steps_val_epochs(tmpdir):
     checkpoint_callback = ModelCheckpoint(dirpath=tmpdir)
     assert checkpoint_callback.period == 1
-    assert checkpoint_callback.every_n_val_epochs == 1
-    assert checkpoint_callback.every_n_train_steps == 0
+    assert checkpoint_callback._every_n_val_epochs == 1
+    assert checkpoint_callback._every_n_train_steps == 0
 
 
 def test_model_checkpoint_save_last_none_monitor(tmpdir, caplog):