deprecated: epoch indexing from 1 (Lightning-AI#2206)

* epoch indexing from 1

* chlog

* fix tests

* fix tests

* self.min_epochs

Author: Borda

CHANGELOG.md

@@ -48,6 +48,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Raise an error when lightning replaces an existing sampler ([#2020](https://github.com/PyTorchLightning/pytorch-lightning/pull/2020))
 - Enabled prepare_data from correct processes - clarify local vs global rank ([#2166](https://github.com/PyTorchLightning/pytorch-lightning/pull/2166))
 - Remove explicit flush from tensorboard logger ([#2126](https://github.com/PyTorchLightning/pytorch-lightning/pull/2126))
+- Changed epoch/step indexing from 1 instead of 0 ([#2206](https://github.com/PyTorchLightning/pytorch-lightning/pull/2206))
 
 ### Deprecated
 

pytorch_lightning/callbacks/gradient_accumulation_scheduler.py

@@ -42,11 +42,9 @@ def __init__(self, scheduling: dict):
 
         for key in scheduling:
             if not isinstance(key, int) or not isinstance(scheduling[key], int):
-                raise TypeError("All epoches and accumulation factor must be integers")
+                raise TypeError("All epochs and accumulation factor must be integers")
 
         minimal_epoch = min(scheduling.keys())
-        # rank_zero_warn('Epochs indexing of `scheduling` starts from "1" until v0.6.x,'
-        #                ' but will start from "0" in v0.8.0.', DeprecationWarning)
         if minimal_epoch < 1:
             raise IndexError(f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct")
         if minimal_epoch != 1:  # if user didnt define first epoch accumulation factor
@@ -56,9 +54,7 @@ def __init__(self, scheduling: dict):
         self.epochs = sorted(scheduling.keys())
 
     def on_epoch_start(self, trainer, pl_module):
-        # indexing epochs from 1 (until v0.6.x)
-        # In v0.8.0, ` + 1` should be removed.
-        epoch = trainer.current_epoch + 1
+        epoch = trainer.current_epoch
         for i in reversed(range(len(self.epochs))):
             if epoch >= self.epochs[i]:
                 trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i])

pytorch_lightning/callbacks/progress.py

@@ -96,7 +96,7 @@ def total_val_batches(self) -> int:
         if trainer.fast_dev_run and trainer.val_dataloaders is not None:
             total_val_batches = len(trainer.val_dataloaders)
         elif not self.trainer.disable_validation:
-            is_val_epoch = (trainer.current_epoch + 1) % trainer.check_val_every_n_epoch == 0
+            is_val_epoch = trainer.current_epoch % trainer.check_val_every_n_epoch == 0
             total_val_batches = trainer.num_val_batches if is_val_epoch else 0
         return total_val_batches
 
@@ -317,7 +317,7 @@ def on_epoch_start(self, trainer, pl_module):
         total_batches = total_train_batches + total_val_batches
         if not self.main_progress_bar.disable:
             self.main_progress_bar.reset(convert_inf(total_batches))
-        self.main_progress_bar.set_description(f'Epoch {trainer.current_epoch + 1}')
+        self.main_progress_bar.set_description(f'Epoch {trainer.current_epoch}')
 
     def on_batch_end(self, trainer, pl_module):
         super().on_batch_end(trainer, pl_module)

pytorch_lightning/trainer/training_io.py

@@ -323,7 +323,7 @@ def dump_checkpoint(self, weights_only: bool = False) -> dict:
              structured dictionary
         """
         checkpoint = {
-            'epoch': self.current_epoch + 1,
+            'epoch': self.current_epoch,
             'global_step': self.global_step + 1,
             'pytorch-ligthning_version': pytorch_lightning.__version__,
         }

pytorch_lightning/trainer/training_loop.py

@@ -336,8 +336,8 @@ def train(self):
             model.on_train_start()
 
         try:
-            # run all epochs
-            for epoch in range(self.current_epoch, self.max_epochs):
+            # run all epochs from actual + 1 till the maximal
+            for epoch in range(self.current_epoch + 1, self.max_epochs + 1):
                 # reset train dataloader
                 if self.reload_dataloaders_every_epoch:
                     self.reset_train_dataloader(model)
@@ -372,7 +372,7 @@ def train(self):
                 self.update_learning_rates(interval='epoch')
 
                 # early stopping
-                met_min_epochs = epoch >= self.min_epochs - 1
+                met_min_epochs = epoch >= self.min_epochs
                 met_min_steps = self.global_step >= self.min_steps if self.min_steps else True
 
                 # TODO wrap this logic into the callback
@@ -466,7 +466,7 @@ def run_training_epoch(self):
             # RUN VAL STEP
             # ---------------
             is_val_check_batch = (batch_idx + 1) % self.val_check_batch == 0
-            can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0
+            can_check_epoch = self.current_epoch % self.check_val_every_n_epoch == 0
             can_check_val = not self.disable_validation and can_check_epoch
             should_check_val = is_val_check_batch or early_stop_epoch
             should_check_val = should_check_val or (is_last_batch and self.val_check_batch == float('inf'))

tests/callbacks/test_callbacks.py

@@ -248,7 +248,7 @@ def training_step(self, *args, **kwargs):
     result = trainer.fit(model)
 
     assert result == 1, 'training failed to complete'
-    assert trainer.current_epoch < trainer.max_epochs
+    assert trainer.current_epoch <= trainer.max_epochs
 
 
 def test_pickling(tmpdir):

tests/models/test_hooks.py

@@ -68,7 +68,7 @@ def training_epoch_end(self, outputs):
     # a metric shared in both methods gets overwritten by epoch_end
     assert metrics['shared_metric'] == 111
     # metrics are kept after each epoch
-    for i in range(num_epochs):
+    for i in range(1, num_epochs + 1):
         assert metrics[f'epoch_metric_{i}'] == i
 
 

tests/models/test_restore.py

@@ -172,7 +172,7 @@ def test_dp_resume(tmpdir):
     result = trainer.fit(model)
 
     # track epoch before saving. Increment since we finished the current epoch, don't want to rerun
-    real_global_epoch = trainer.current_epoch + 1
+    real_global_epoch = trainer.current_epoch
 
     # correct result and ok accuracy
     assert result == 1, 'amp + dp model failed to complete'

tests/trainer/test_trainer.py

@@ -451,7 +451,7 @@ def test_trainer_max_steps_and_epochs(tmpdir):
 
     # check training stopped at max_epochs
     assert trainer.global_step == num_train_samples * trainer.max_epochs
-    assert trainer.current_epoch == trainer.max_epochs - 1, "Model did not stop at max_epochs"
+    assert trainer.current_epoch == trainer.max_epochs, "Model did not stop at max_epochs"
 
 
 def test_trainer_min_steps_and_epochs(tmpdir):
@@ -619,7 +619,7 @@ def validation_epoch_end(self, *args, **kwargs):
 
     # check that val_percent_check=0 turns off validation
     assert result == 1, 'training failed to complete'
-    assert trainer.current_epoch == 1
+    assert trainer.current_epoch == 2
     assert not model.validation_step_invoked, \
         '`validation_step` should not run when `val_percent_check=0`'
     assert not model.validation_epoch_end_invoked, \
@@ -632,7 +632,7 @@ def validation_epoch_end(self, *args, **kwargs):
     result = trainer.fit(model)
 
     assert result == 1, 'training failed to complete'
-    assert trainer.current_epoch == 0
+    assert trainer.current_epoch == 1
     assert model.validation_step_invoked, \
         'did not run `validation_step` with `fast_dev_run=True`'
     assert model.validation_epoch_end_invoked, \