Rename reset_on_epoch to reset_on_run

CHANGELOG.md

@@ -29,6 +29,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * Reset `current` progress counters when restarting an epoch loop that had already finished ([#9371](https://github.com/PyTorchLightning/pytorch-lightning/pull/9371))
     * Call `reset_on_restart` in the loop's `reset` hook instead of when loading a checkpoint ([#9561](https://github.com/PyTorchLightning/pytorch-lightning/pull/9561))
     * Use `completed` over `processed` in `reset_on_restart` ([#9656](https://github.com/PyTorchLightning/pytorch-lightning/pull/9656))
+    * Rename `reset_on_epoch` to `reset_on_run` ([#9658](https://github.com/PyTorchLightning/pytorch-lightning/pull/9658))
 
 
 - Added `batch_size` and `rank_zero_only` arguments for `log_dict` to match `log` ([#8628](https://github.com/PyTorchLightning/pytorch-lightning/pull/8628))

pytorch_lightning/loops/dataloader/dataloader_loop.py

@@ -56,9 +56,9 @@ def done(self) -> bool:
     def reset(self) -> None:
         """Resets the internal state."""
         if not self.restarting:
-            self.dataloader_progress.current.reset()
+            self.dataloader_progress.reset_on_run()
         else:
-            self.dataloader_progress.current.reset_on_restart()
+            self.dataloader_progress.reset_on_restart()
 
     def on_advance_start(self, *args: Any, **kwargs: Any) -> None:
         self.dataloader_progress.increment_ready()

pytorch_lightning/loops/epoch/evaluation_epoch_loop.py

@@ -58,9 +58,9 @@ def reset(self) -> None:
         self.outputs = []
 
         if not self.restarting:
-            self.batch_progress.current.reset()
+            self.batch_progress.reset_on_run()
         else:
-            self.batch_progress.current.reset_on_restart()
+            self.batch_progress.reset_on_restart()
 
     def on_run_start(
         self, data_fetcher: AbstractDataFetcher, dataloader_idx: int, dl_max_batches: int, num_dataloaders: int

pytorch_lightning/loops/epoch/prediction_epoch_loop.py

@@ -46,7 +46,7 @@ def reset(self) -> None:
         """Resets the loops internal state."""
         self._all_batch_indices: List[int] = []
         self.predictions: List[Any] = []
-        self.batch_progress.current.reset()
+        self.batch_progress.reset_on_run()
 
     def on_run_start(
         self,

pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -93,16 +93,16 @@ def reset(self) -> None:
         assert self.batch_loop.optimizer_loop is not None
         if self.restarting:
             self.batch_progress.reset_on_restart()
-            self.scheduler_progress.current.reset_on_restart()
+            self.scheduler_progress.reset_on_restart()
             self.batch_loop.optimizer_loop.optim_progress.reset_on_restart()
 
         # track epoch output
         self._epoch_output = [[] for _ in range(self.batch_loop.num_active_optimizers(self.total_batch_idx))]
 
         if not self.restarting or self._num_training_batches_reached():
-            self.batch_progress.reset_on_epoch()
-            self.scheduler_progress.reset_on_epoch()
-            self.batch_loop.optimizer_loop.optim_progress.reset_on_epoch()
+            self.batch_progress.reset_on_run()
+            self.scheduler_progress.reset_on_run()
+            self.batch_loop.optimizer_loop.optim_progress.reset_on_run()
 
     def on_run_start(self, dataloader_iter: Iterator, **kwargs: Any) -> None:
         # hook

pytorch_lightning/loops/fit_loop.py

@@ -175,7 +175,7 @@ def connect(self, epoch_loop: TrainingEpochLoop):
     def reset(self) -> None:
         """Resets the internal state of this loop."""
         if self.restarting:
-            self.epoch_progress.current.reset_on_restart()
+            self.epoch_progress.reset_on_restart()
 
     def on_run_start(self) -> None:
         """Calls the ``on_train_start`` hook."""

pytorch_lightning/trainer/progress.py

@@ -151,6 +151,9 @@ def from_defaults(cls, tracker_cls: Type[ReadyCompletedTracker], **kwargs: int)
     def reset_on_epoch(self) -> None:
         self.current.reset()
 
+    def reset_on_run(self) -> None:
+        self.current.reset()
+
     def reset_on_restart(self) -> None:
         self.current.reset_on_restart()
 
@@ -188,8 +191,8 @@ class BatchProgress(Progress):
 
     is_last_batch: bool = False
 
-    def reset_on_epoch(self) -> None:
-        super().reset_on_epoch()
+    def reset_on_run(self) -> None:
+        super().reset_on_run()
         self.is_last_batch = False
 
 
@@ -220,9 +223,9 @@ class OptimizerProgress(BaseProgress):
     step: Progress = field(default_factory=lambda: Progress.from_defaults(ReadyCompletedTracker))
     zero_grad: Progress = field(default_factory=lambda: Progress.from_defaults(StartedTracker))
 
-    def reset_on_epoch(self) -> None:
-        self.step.reset_on_epoch()
-        self.zero_grad.reset_on_epoch()
+    def reset_on_run(self) -> None:
+        self.step.reset_on_run()
+        self.zero_grad.reset_on_run()
 
     def reset_on_restart(self) -> None:
         self.step.reset_on_restart()
@@ -253,8 +256,8 @@ class OptimizationProgress(BaseProgress):
     def optimizer_steps(self) -> int:
         return self.optimizer.step.total.completed
 
-    def reset_on_epoch(self) -> None:
-        self.optimizer.reset_on_epoch()
+    def reset_on_run(self) -> None:
+        self.optimizer.reset_on_run()
 
     def reset_on_restart(self) -> None:
         self.optimizer.reset_on_restart()