Use completed over processed in reset_on_restart (#9656)

CHANGELOG.md

@@ -27,6 +27,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * Avoid optional `Tracker` attributes ([#9320](https://github.com/PyTorchLightning/pytorch-lightning/pull/9320))
     * 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))
 
 
 - 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/trainer/progress.py

@@ -103,9 +103,8 @@ def reset(self) -> None:
         self.processed = 0
 
     def reset_on_restart(self) -> None:
-        # use `processed` in this case as the reset value
-        self.completed = self.processed
         super().reset_on_restart()
+        self.processed = self.completed
 
 
 @dataclass
@@ -149,13 +148,13 @@ def from_defaults(cls, tracker_cls: Type[ReadyCompletedTracker], **kwargs: int)
         """Utility function to easily create an instance from keyword arguments to both ``Tracker``s."""
         return cls(total=tracker_cls(**kwargs), current=tracker_cls(**kwargs))
 
+    def reset_on_restart(self) -> None:
+        self.current.reset_on_restart()
+
     def load_state_dict(self, state_dict: dict) -> None:
         self.total.load_state_dict(state_dict["total"])
         self.current.load_state_dict(state_dict["current"])
 
-    def reset_on_restart(self) -> None:
-        self.current.reset_on_restart()
-
 
 @dataclass
 class DataLoaderProgress(Progress):
@@ -201,6 +200,10 @@ def reset_on_epoch(self) -> None:
         self.step.current.reset()
         self.zero_grad.current.reset()
 
+    def reset_on_restart(self) -> None:
+        self.step.reset_on_restart()
+        self.zero_grad.reset_on_restart()
+
     def load_state_dict(self, state_dict: dict) -> None:
         self.step.load_state_dict(state_dict["step"])
         self.zero_grad.load_state_dict(state_dict["zero_grad"])
@@ -229,10 +232,9 @@ def optimizer_steps(self) -> int:
     def reset_on_epoch(self) -> None:
         self.optimizer.reset_on_epoch()
 
+    def reset_on_restart(self) -> None:
+        self.optimizer.reset_on_restart()
+
     def load_state_dict(self, state_dict: dict) -> None:
         self.optimizer.load_state_dict(state_dict["optimizer"])
         self.optimizer_position = state_dict["optimizer_position"]
-
-    def reset_on_restart(self) -> None:
-        self.optimizer.step.current.reset_on_restart()
-        self.optimizer.zero_grad.current.reset_on_restart()

tests/loops/test_loops.py

@@ -700,9 +700,11 @@ def test_fit_loop_reset(tmpdir):
 
     assert epoch_loop.restarting
     assert epoch_loop.batch_progress.total.ready == 2
+    assert epoch_loop.batch_progress.total.processed == 2
     assert epoch_loop.batch_progress.total.completed == 1  # the checkpoint was saved on train_batch_end
-    assert epoch_loop.batch_progress.current.ready == 2
-    assert epoch_loop.batch_progress.current.completed == 2
+    assert epoch_loop.batch_progress.current.ready == 1  # currents get set to the completed value
+    assert epoch_loop.batch_progress.current.processed == 1
+    assert epoch_loop.batch_progress.current.completed == 1
 
     assert optimizer_loop.restarting
     assert optimizer_loop.optim_progress.optimizer_position == 1
@@ -730,8 +732,10 @@ def test_fit_loop_reset(tmpdir):
 
     assert epoch_loop.restarting
     assert epoch_loop.batch_progress.total.ready == 4
+    assert epoch_loop.batch_progress.total.processed == 4
     assert epoch_loop.batch_progress.total.completed == 3  # the checkpoint was saved on train_batch_end
-    assert epoch_loop.batch_progress.current.ready == 0
-    assert epoch_loop.batch_progress.current.completed == 0
+    assert epoch_loop.batch_progress.current.ready == 3  # currents get set to the completed value
+    assert epoch_loop.batch_progress.current.processed == 3
+    assert epoch_loop.batch_progress.current.completed == 3
 
     assert optimizer_loop.optim_progress.optimizer_position == 1

tests/trainer/test_progress.py

@@ -38,7 +38,7 @@ def test_tracker_reset_on_restart():
 
     t = ProcessedTracker(ready=4, started=4, processed=3, completed=2)
     t.reset_on_restart()
-    assert t == ProcessedTracker(ready=3, started=3, processed=3, completed=3)
+    assert t == ProcessedTracker(ready=2, started=2, processed=2, completed=2)
 
 
 @pytest.mark.parametrize("attr", ("ready", "started", "processed", "completed"))