Fix resuming the tqdm progress bar (#13962)

src/pytorch_lightning/CHANGELOG.md

@@ -408,6 +408,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed Python 3.10 compatibility for truncated back-propagation through time (TBPTT) ([#13973](https://github.com/Lightning-AI/lightning/pull/13973))
 
 
+- Fixed `TQDMProgressBar` reset and update to show correct time estimation (2/2) ([#13962](https://github.com/Lightning-AI/lightning/pull/13962))
+
+
 
 ## [1.6.5] - 2022-07-13
 
@@ -463,7 +466,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `fuse_modules` to be qat-aware for `torch>=1.11` ([#12891](https://github.com/Lightning-AI/lightning/pull/12891))
 - Enforced eval shuffle warning only for default samplers in DataLoader ([#12653](https://github.com/Lightning-AI/lightning/pull/12653))
 - Enable mixed precision in `DDPFullyShardedStrategy` when `precision=16` ([#12965](https://github.com/Lightning-AI/lightning/pull/12965))
-- Fixed `TQDMProgressBar` reset and update to show correct time estimation ([#12889](https://github.com/Lightning-AI/lightning/pull/12889))
+- Fixed `TQDMProgressBar` reset and update to show correct time estimation (1/2) ([#12889](https://github.com/Lightning-AI/lightning/pull/12889))
 - Fixed fit loop restart logic to enable resume using the checkpoint ([#12821](https://github.com/Lightning-AI/lightning/pull/12821))
 
 

src/pytorch_lightning/callbacks/progress/tqdm_progress.py

@@ -254,12 +254,13 @@ def on_train_start(self, *_: Any) -> None:
     def on_train_epoch_start(self, trainer: "pl.Trainer", *_: Any) -> None:
         total_batches = self.total_batches_current_epoch
         self.main_progress_bar.reset(convert_inf(total_batches))
+        self.main_progress_bar.initial = 0
         self.main_progress_bar.set_description(f"Epoch {trainer.current_epoch}")
 
     def on_train_batch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", *_: Any) -> None:
         current = self.train_batch_idx + self._val_processed
         if self._should_update(current, self.main_progress_bar.total):
-            _update_n(self.main_progress_bar, current, self.refresh_rate)
+            _update_n(self.main_progress_bar, current)
             self.main_progress_bar.set_postfix(self.get_metrics(trainer, pl_module))
 
     def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
@@ -280,16 +281,17 @@ def on_validation_batch_start(
             return
 
         self.val_progress_bar.reset(convert_inf(self.total_val_batches_current_dataloader))
+        self.val_progress_bar.initial = 0
         desc = self.sanity_check_description if trainer.sanity_checking else self.validation_description
         self.val_progress_bar.set_description(f"{desc} DataLoader {dataloader_idx}")
 
     def on_validation_batch_end(self, trainer: "pl.Trainer", *_: Any) -> None:
         if self._should_update(self.val_batch_idx, self.val_progress_bar.total):
-            _update_n(self.val_progress_bar, self.val_batch_idx, self.refresh_rate)
+            _update_n(self.val_progress_bar, self.val_batch_idx)
 
         current = self.train_batch_idx + self._val_processed
         if trainer.state.fn == "fit" and self._should_update(current, self.main_progress_bar.total):
-            _update_n(self.main_progress_bar, current, self.refresh_rate)
+            _update_n(self.main_progress_bar, current)
 
     def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         if self._main_progress_bar is not None and trainer.state.fn == "fit":
@@ -307,11 +309,12 @@ def on_test_batch_start(
             return
 
         self.test_progress_bar.reset(convert_inf(self.total_test_batches_current_dataloader))
+        self.test_progress_bar.initial = 0
         self.test_progress_bar.set_description(f"{self.test_description} DataLoader {dataloader_idx}")
 
     def on_test_batch_end(self, *_: Any) -> None:
         if self._should_update(self.test_batch_idx, self.test_progress_bar.total):
-            _update_n(self.test_progress_bar, self.test_batch_idx, self.refresh_rate)
+            _update_n(self.test_progress_bar, self.test_batch_idx)
 
     def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         self.test_progress_bar.close()
@@ -327,11 +330,12 @@ def on_predict_batch_start(
             return
 
         self.predict_progress_bar.reset(convert_inf(self.total_predict_batches_current_dataloader))
+        self.predict_progress_bar.initial = 0
         self.predict_progress_bar.set_description(f"{self.predict_description} DataLoader {dataloader_idx}")
 
     def on_predict_batch_end(self, *_: Any) -> None:
         if self._should_update(self.predict_batch_idx, self.predict_progress_bar.total):
-            _update_n(self.predict_progress_bar, self.predict_batch_idx, self.refresh_rate)
+            _update_n(self.predict_progress_bar, self.predict_batch_idx)
 
     def on_predict_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         self.predict_progress_bar.close()
@@ -375,9 +379,7 @@ def convert_inf(x: Optional[Union[int, float]]) -> Optional[Union[int, float]]:
     return x
 
 
-def _update_n(bar: _tqdm, current: int, refresh_rate: int) -> None:
+def _update_n(bar: _tqdm, value: int) -> None:
     if not bar.disable:
-        total = bar.total
-        leftover = current % refresh_rate
-        advance = leftover if (current == total and leftover != 0) else refresh_rate
-        bar.update(advance)
+        bar.n = value
+        bar.refresh()