Merge branch 'master' into flexible_checkpoint_filenames

Author: maxfrei750

CHANGELOG.md

@@ -23,6 +23,9 @@ 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
 
 

azure-pipelines.yml

@@ -23,7 +23,7 @@ jobs:
     # how much time to give 'run always even if cancelled tasks' before stopping them
     cancelTimeoutInMinutes: 2
 
-    pool: dsvm-spot-pool
+    pool: gridai-spot-pool
 
     #strategy:
     #  matrix:
@@ -58,7 +58,7 @@ jobs:
         export GIT_TERMINAL_PROMPT=1
         #sudo apt-get install -y cmake
         # python -m pip install "pip==20.1"
-        pip install --requirement requirements.txt --find-links https://download.pytorch.org/whl/cpu/torch_stable.html
+        pip install --requirement requirements.txt
         python -c "fname = 'requirements/extra.txt' ; lines = [line for line in open(fname).readlines() if 'fairscale' not in line] ; open(fname, 'w').writelines(lines)"
         python -c "fname = 'requirements/extra.txt' ; lines = [line for line in open(fname).readlines() if 'horovod' not in line] ; open(fname, 'w').writelines(lines)"
         pip install --requirement ./requirements/devel.txt --upgrade-strategy only-if-needed

docs/source/common/lightning_module.rst

@@ -946,7 +946,7 @@ When set to ``False``, Lightning does not automate the optimization process. Thi
         opt = self.optimizers(use_pl_optimizer=True)
 
         loss = ...
-        self.manual_backward(loss, opt)
+        self.manual_backward(loss)
         opt.step()
         opt.zero_grad()
 
@@ -961,16 +961,16 @@ In the multi-optimizer case, ignore the ``optimizer_idx`` argument and use the o
 
     def training_step(self, batch, batch_idx, optimizer_idx):
         # access your optimizers with use_pl_optimizer=False. Default is True
-        (opt_a, opt_b) = self.optimizers(use_pl_optimizer=True)
+        opt_a, opt_b = self.optimizers(use_pl_optimizer=True)
 
         gen_loss = ...
         opt_a.zero_grad()
-        self.manual_backward(gen_loss, opt_a)
+        self.manual_backward(gen_loss)
         opt_a.step()
 
         disc_loss = ...
         opt_b.zero_grad()
-        self.manual_backward(disc_loss, opt_b)
+        self.manual_backward(disc_loss)
         opt_b.step()
 
 --------------

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 renamed to 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/core/lightning.py

@@ -1211,10 +1211,10 @@ def manual_backward(self, loss: Tensor, optimizer: Optional[Optimizer] = None, *
         Example::
 
             def training_step(...):
-                (opt_a, opt_b) = self.optimizers()
+                opt_a, opt_b = self.optimizers()
                 loss = ...
                 # automatically applies scaling, etc...
-                self.manual_backward(loss, opt_a)
+                self.manual_backward(loss)
                 opt_a.step()
         """
         if optimizer is not None:

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