runtime-warn if no schedulers are configured

pytorch_lightning/callbacks/lr_logger.py

@@ -10,6 +10,8 @@
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
+from pytorch_lightning.utilities import rank_zero_warn
+
 
 class LearningRateLogger(Callback):
     r"""
@@ -45,16 +47,17 @@ def on_train_start(self, trainer, pl_module):
             schedulers in the case of multiple of the same type or in
             the case of multiple parameter groups
         """
-        if trainer.lr_schedulers == []:
-            raise MisconfigurationException(
-                'Cannot use LearningRateLogger callback with models that have no'
-                ' learning rate schedulers. Please see documentation for'
-                ' `configure_optimizers` method.')
-
         if not trainer.logger:
             raise MisconfigurationException(
                 'Cannot use LearningRateLogger callback with Trainer that has no logger.')
 
+        if not trainer.lr_schedulers:
+            rank_zero_warn(
+                'You are using LearningRateLogger callback with models that'
+                ' have no learning rate schedulers. Please see documentation'
+                ' for `configure_optimizers` method.', RuntimeWarning
+            )
+
         # Find names for schedulers
         names = self._find_names(trainer.lr_schedulers)
 

tests/callbacks/test_callbacks.py

@@ -3,6 +3,8 @@
 import tests.base.utils as tutils
 from pytorch_lightning import Callback
 from pytorch_lightning import Trainer, LightningModule
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
+
 from pytorch_lightning.callbacks import EarlyStopping, LearningRateLogger, ModelCheckpoint
 from pytorch_lightning.loggers import TensorBoardLogger
 from tests.base import EvalModelTemplate
@@ -308,6 +310,24 @@ def test_lr_logger_single_lr(tmpdir):
         'Names of learning rates not set correctly'
 
 
+def test_lr_logger_no_lr(tmpdir):
+    tutils.reset_seed()
+
+    model = EvalModelTemplate()
+
+    lr_logger = LearningRateLogger()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=5,
+        val_percent_check=0.1,
+        train_percent_check=0.5,
+        callbacks=[lr_logger]
+    )
+
+    with pytest.warns(RuntimeWarning):
+        results = trainer.fit(model)
+
+
 def test_lr_logger_multi_lrs(tmpdir):
     """ Test that learning rates are extracted and logged for multi lr schedulers """
     tutils.reset_seed()