Set warnings : Unify epoch numbers to be zero-based : #675 (#786)

* [update] : #675 : set warnings

* [fix] : #675 : remove white space

pytorch_lightning/callbacks/pt_callbacks.py

@@ -174,6 +174,8 @@ class EarlyStopping(Callback):
 
     def on_train_end(self, logs=None):
         if self.stopped_epoch > 0 and self.verbose > 0:
+            warnings.warn('Displayed epoch numbers by `EarlyStopping` start from "1" until v0.6.x,'
+                          ' but will start from "0" in v0.8.0.', DeprecationWarning)
             log.info(f'Epoch {self.stopped_epoch + 1:05d}: early stopping')
 
 
@@ -374,6 +376,7 @@ class GradientAccumulationScheduler(Callback):
 
     Args:
         scheduling (dict): scheduling in format {epoch: accumulation_factor}
+        warning:: Epochs indexing starts from "1" until v0.6.x, but will start from "0" in v0.8.0.
 
     Example::
 
@@ -394,6 +397,8 @@ class GradientAccumulationScheduler(Callback):
                 raise TypeError("All epoches and accumulation factor must be integers")
 
         minimal_epoch = min(scheduling.keys())
+        warnings.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:
             msg = f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct"
             raise IndexError(msg)
@@ -404,7 +409,9 @@ class GradientAccumulationScheduler(Callback):
         self.epochs = sorted(scheduling.keys())
 
     def on_epoch_begin(self, epoch, trainer):
-        epoch += 1  # indexing epochs from 1
+        # indexing epochs from 1 (until v0.6.x)
+        # In v0.8.0, `epoch += 1` should be removed.
+        epoch += 1
         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/trainer/training_loop.py

@@ -281,6 +281,8 @@ class TrainerTrainLoopMixin(ABC):
         pass
 
     def train(self):
+        warnings.warn('Displayed epoch numbers in the progress bar start from "1" until v0.6.x,'
+                      ' but will start from "0" in v0.8.0.', DeprecationWarning)
         model = self.get_model()
         # run all epochs
         for epoch in range(self.current_epoch, self.max_epochs):