ReduceLROnPlateau bug fix (#1126)

* bug fix and test

* update CHANGELOG.md

Co-authored-by: Nicki Skafte <nugginea@gmail.com>

CHANGELOG.md

@@ -28,7 +28,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
--
+- Fixed bug related to type cheking of `ReduceLROnPlateau` lr schedulers([#1114](https://github.com/PyTorchLightning/pytorch-lightning/issues/1114))
 
 ## [0.7.1] - 2020-03-07
 

pytorch_lightning/trainer/trainer.py

@@ -707,8 +707,8 @@ class Trainer(
                 if 'scheduler' not in scheduler:
                     raise ValueError(f'Lr scheduler should have key `scheduler`',
                                      ' with item being a lr scheduler')
-                scheduler['reduce_on_plateau'] = \
-                    isinstance(scheduler, optim.lr_scheduler.ReduceLROnPlateau)
+                scheduler['reduce_on_plateau'] = isinstance(
+                    scheduler['scheduler'], optim.lr_scheduler.ReduceLROnPlateau)
 
                 lr_schedulers.append({**default_config, **scheduler})
 

tests/models/__init__.py

@@ -24,7 +24,8 @@ from .mixins import (
     LightInfTestDataloader,
     LightTestOptimizerWithSchedulingMixin,
     LightTestMultipleOptimizersWithSchedulingMixin,
-    LightTestOptimizersWithMixedSchedulingMixin
+    LightTestOptimizersWithMixedSchedulingMixin,
+    LightTestReduceLROnPlateauMixin
 )
 
 

tests/models/mixins.py

@@ -678,6 +678,16 @@ class LightTestOptimizersWithMixedSchedulingMixin:
             [{'scheduler': lr_scheduler1, 'interval': 'step'}, lr_scheduler2]
 
 
+class LightTestReduceLROnPlateauMixin:
+    def configure_optimizers(self):
+        if self.hparams.optimizer_name == 'lbfgs':
+            optimizer = optim.LBFGS(self.parameters(), lr=self.hparams.learning_rate)
+        else:
+            optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
+        lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer)
+        return [optimizer], [lr_scheduler]
+
+
 def _get_output_metric(output, name):
     if isinstance(output, dict):
         val = output[name]

tests/trainer/test_optimizers.py

@@ -10,9 +10,12 @@ from pytorch_lightning import Trainer
 from tests.models import (
     TestModelBase,
     LightTrainDataloader,
+    LightValidationStepMixin,
+    LightValidationMixin,
     LightTestOptimizerWithSchedulingMixin,
     LightTestMultipleOptimizersWithSchedulingMixin,
-    LightTestOptimizersWithMixedSchedulingMixin
+    LightTestOptimizersWithMixedSchedulingMixin,
+    LightTestReduceLROnPlateauMixin
 )
 
 
@@ -144,3 +147,35 @@ def test_multi_optimizer_with_scheduling_stepping(tmpdir):
     # Called every 3 steps, meaning for 1 epoch of 11 batches, it is called 3 times
     assert init_lr * 0.1 == adjusted_lr2, \
         'lr for optimizer 2 not adjusted correctly'
+
+
+def test_reduce_lr_on_plateau_scheduling(tmpdir):
+    tutils.reset_seed()
+
+    class CurrentTestModel(
+            LightTestReduceLROnPlateauMixin,
+            LightTrainDataloader,
+            LightValidationMixin,
+            LightValidationStepMixin,
+            TestModelBase):
+        pass
+
+    hparams = tutils.get_hparams()
+    model = CurrentTestModel(hparams)
+
+    # logger file to get meta
+    trainer_options = dict(
+        default_save_path=tmpdir,
+        max_epochs=1,
+        val_percent_check=0.1,
+        train_percent_check=0.2
+    )
+
+    # fit model
+    trainer = Trainer(**trainer_options)
+    results = trainer.fit(model)
+
+    assert trainer.lr_schedulers[0] == \
+        dict(scheduler=trainer.lr_schedulers[0]['scheduler'], monitor='val_loss',
+             interval='epoch', frequency=1, reduce_on_plateau=True), \
+        'lr schduler was not correctly converted to dict'