cleaning SWA

CHANGELOG.md

@@ -20,6 +20,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed the order of `backward`, `step`, `zero_grad` to `zero_grad`, `backward`, `step` ([#6147](https://github.com/PyTorchLightning/pytorch-lightning/pull/6147))
 
 
+- Renamed `pytorch_lightning.callbacks.swa` to `pytorch_lightning.callbacks.stochastic_weight_avg` ([#6259](https://github.com/PyTorchLightning/pytorch-lightning/pull/6259))
+
+
 ### Deprecated
 
 

pytorch_lightning/callbacks/__init__.py

@@ -22,7 +22,7 @@
 from pytorch_lightning.callbacks.progress import ProgressBar, ProgressBarBase
 from pytorch_lightning.callbacks.pruning import ModelPruning
 from pytorch_lightning.callbacks.quantization import QuantizationAwareTraining
-from pytorch_lightning.callbacks.swa import StochasticWeightAveraging
+from pytorch_lightning.callbacks.stochastic_weight_avg import StochasticWeightAveraging
 
 __all__ = [
  'BackboneFinetuning',

pytorch_lightning/callbacks/swa.py → pytorch_lightning/callbacks/stochastic_weight_avg.py

@@ -102,12 +102,10 @@ def __init__(
  if isinstance(swa_epoch_start, float) and not (0 <= swa_epoch_start <= 1):
  raise MisconfigurationException(err_msg)
 
- if (
- swa_lrs is not None and (
- not isinstance(swa_lrs, (float, list)) or isinstance(swa_lrs, float) and swa_lrs <= 0
- or isinstance(swa_lrs, list) and not all(lr > 0 and isinstance(lr, float) for lr in swa_lrs)
- )
- ):
+ _wrong_type = not isinstance(swa_lrs, (float, list))
+ _wrong_float = isinstance(swa_lrs, float) and swa_lrs <= 0
+ _wrong_list = isinstance(swa_lrs, list) and not all(lr > 0 and isinstance(lr, float) for lr in swa_lrs)
+ if (swa_lrs is not None and (_wrong_type or _wrong_float or _wrong_list)):
  raise MisconfigurationException("The `swa_lrs` should be a positive float or a list of positive float.")
 
  if avg_fn is not None and not isinstance(avg_fn, Callable):

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -76,7 +76,7 @@ def _configure_swa_callbacks(self):
  if not self.trainer._stochastic_weight_avg:
  return
 
- from pytorch_lightning.callbacks.swa import StochasticWeightAveraging
+ from pytorch_lightning.callbacks.stochastic_weight_avg import StochasticWeightAveraging
  existing_swa = [cb for cb in self.trainer.callbacks if isinstance(cb, StochasticWeightAveraging)]
  if not existing_swa:
  self.trainer.callbacks = [StochasticWeightAveraging()] + self.trainer.callbacks