Add useful errors when model is not configured correctly

CHANGELOG.md

@@ -19,6 +19,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Updated references to self.forward() to instead use the `__call__` interface. ([#1211](https://github.com/PyTorchLightning/pytorch-lightning/pull/1211))
 - Added support for `IterableDataset` when `val_check_interval=1.0` (default), this will trigger validation at the end of each epoch. ([#1283](https://github.com/PyTorchLightning/pytorch-lightning/pull/1283))
 - Added informative errors if user defined dataloader has zero length ([#1280](https://github.com/PyTorchLightning/pytorch-lightning/pull/1280))
+- Added model configuration checking ([#1199](https://github.com/PyTorchLightning/pytorch-lightning/pull/1199))
 
 ### Changed
 

pl_examples/basic_examples/lightning_module_template.py

@@ -241,6 +241,37 @@ def test_dataloader(self):
         log.info('Test data loader called.')
         return self.__dataloader(train=False)
 
+    def test_step(self, batch, batch_idx):
+        """
+        Lightning calls this during testing, similar to val_step
+        :param batch:
+        :return:val
+        """
+        output = self.validation_step(batch, batch_idx)
+        # Rename output keys
+        output['test_loss'] = output.pop('val_loss')
+        output['test_acc'] = output.pop('val_acc')
+
+        return output
+
+    def test_epoch_end(self, outputs):
+        """
+        Called at the end of test to aggregate outputs, similar to validation_epoch_end
+        :param outputs: list of individual outputs of each validation step
+        :return:
+        """
+        results = self.validation_step_end(outputs)
+
+        # rename some keys
+        results['progress_bar'].update({
+            'test_loss': results['progress_bar'].pop('val_loss'),
+            'test_acc': results['progress_bar'].pop('val_acc'),
+        })
+        results['log'] = results['progress_bar']
+        results['test_loss'] = results.pop('val_loss')
+
+        return results
+
     @staticmethod
     def add_model_specific_args(parent_parser, root_dir):  # pragma: no-cover
         """

pytorch_lightning/trainer/trainer.py

@@ -651,6 +651,9 @@ def fit(
         # set up the passed in dataloaders (if needed)
         self.__attach_dataloaders(model, train_dataloader, val_dataloaders, test_dataloaders)
 
+        # check that model is configured correctly
+        self.check_model_configuration(model)
+
         # download the data and do whatever transforms we need
         # do before any spawn calls so that the model can assign properties
         # only on proc 0 because no spawn has happened yet
@@ -735,24 +738,12 @@ def __attach_dataloaders(self, model, train_dataloader, val_dataloaders, test_da
         # when dataloader is passed via fit, patch the train_dataloader
         # functions to overwrite with these implementations
         if train_dataloader is not None:
-            if not self.is_overriden('training_step', model):
-                raise MisconfigurationException(
-                    'You called `.fit()` with a `train_dataloader` but did not define `training_step()`')
-
             model.train_dataloader = _PatchDataLoader(train_dataloader)
 
         if val_dataloaders is not None:
-            if not self.is_overriden('validation_step', model):
-                raise MisconfigurationException(
-                    'You called `.fit()` with a `val_dataloaders` but did not define `validation_step()`')
-
             model.val_dataloader = _PatchDataLoader(val_dataloaders)
 
         if test_dataloaders is not None:
-            if not self.is_overriden('test_step', model):
-                raise MisconfigurationException(
-                    'You called `.fit()` with a `test_dataloaders` but did not define `test_step()`')
-
             model.test_dataloader = _PatchDataLoader(test_dataloaders)
 
     def init_optimizers(
@@ -957,6 +948,62 @@ def test(self, model: Optional[LightningModule] = None):
 
         self.testing = False
 
+    def check_model_configuration(self, model: LightningModule):
+        r"""
+        Checks that the model is configured correctly before training is started.
+
+        Args:
+            model: The model to test.
+
+        """
+        # Check training_step, train_dataloader, configure_optimizer methods
+        if not self.is_overriden('training_step', model):
+            raise MisconfigurationException(
+                'No `training_step()` method defined. Lightning expects as minimum a `training_step()` and'
+                ' `training_dataloader()` to be defined.')
+
+        if not self.is_overriden('train_dataloader', model):
+            raise MisconfigurationException(
+                'No `train_dataloader()` defined. Lightning expects as minimum a `training_step()` and'
+                ' `training_dataloader()` to be defined.')
+
+        if not self.is_overriden('configure_optimizers', model):
+            warnings.warn('`configure_optimizers()` method not defined by user,'
+                          ' will default to Adam optimizer with learning rate set to 0.0001.',
+                          RuntimeWarning)
+
+        # Check val_dataloader, validation_step and validation_epoch_end
+        if self.is_overriden('val_dataloader', model):
+            if not self.is_overriden('validation_step', model):
+                raise MisconfigurationException('You have passed in a `val_dataloader()`'
+                                                ' but have not defined `validation_step()`.')
+            else:
+                if not self.is_overriden('validation_epoch_end', model):
+                    warnings.warn('You have defined a `val_dataloader()` and have'
+                                  ' defined a `validation_step()`, you may also want to'
+                                  ' define `validation_epoch_end()` for accumulating stats.',
+                                  RuntimeWarning)
+        else:
+            if self.is_overriden('validation_step', model):
+                raise MisconfigurationException('You have defined `validation_step()`,'
+                                                ' but have not passed in a val_dataloader().')
+
+        # Check test_dataloader, test_step and test_epoch_end
+        if self.is_overriden('test_dataloader', model):
+            if not self.is_overriden('test_step', model):
+                raise MisconfigurationException('You have passed in a `test_dataloader()`'
+                                                ' but have not defined `test_step()`.')
+            else:
+                if not self.is_overriden('test_epoch_end', model):
+                    warnings.warn('You have defined a `test_dataloader()` and'
+                                  ' have defined a `test_step()`, you may also want to'
+                                  ' define `test_epoch_end()` for accumulating stats.',
+                                  RuntimeWarning)
+        else:
+            if self.is_overriden('test_step', model):
+                raise MisconfigurationException('You have defined `test_step()`,'
+                                                ' but have not passed in a `test_dataloader()`.')
+
 
 class _PatchDataLoader(object):
     r"""
@@ -970,5 +1017,8 @@ class _PatchDataLoader(object):
     def __init__(self, dataloader: Union[List[DataLoader], DataLoader]):
         self.dataloader = dataloader
 
+        # Assign __code__, needed for checking if method has been overriden
+        self.__code__ = self.__call__.__code__
+
     def __call__(self) -> Union[List[DataLoader], DataLoader]:
         return self.dataloader

tests/base/__init__.py

@@ -18,6 +18,7 @@
     LightValStepFitSingleDataloaderMixin,
     LightValStepFitMultipleDataloadersMixin,
     LightTrainDataloader,
+    LightValidationDataloader,
     LightTestDataloader,
     LightInfTrainDataloader,
     LightInfValDataloader,

tests/base/mixins.py

@@ -203,6 +203,13 @@ def train_dataloader(self):
         return self._dataloader(train=True)
 
 
+class LightValidationDataloader:
+    """Simple validation dataloader."""
+
+    def val_dataloader(self):
+        return self._dataloader(train=False)
+
+
 class LightTestDataloader:
     """Simple test dataloader."""
 
@@ -412,6 +419,9 @@ def test_step(self, batch, batch_idx, dataloader_idx, **kwargs):
 class LightTestFitSingleTestDataloadersMixin:
     """Test fit single test dataloaders mixin."""
 
+    def test_dataloader(self):
+        return self._dataloader(train=False)
+
     def test_step(self, batch, batch_idx, *args, **kwargs):
         """
         Lightning calls this inside the validation loop

tests/test_deprecated.py

@@ -64,9 +64,15 @@ class ModelVer0_6(LightTrainDataloader, LightEmptyTestStep, TestModelBase):
     def val_dataloader(self):
         return self._dataloader(train=False)
 
+    def validation_step(self, batch, batch_idx, *args, **kwargs):
+        return {'val_loss': 0.6}
+
     def validation_end(self, outputs):
         return {'val_loss': 0.6}
 
+    def test_dataloader(self):
+        return self._dataloader(train=False)
+
     def test_end(self, outputs):
         return {'test_loss': 0.6}
 
@@ -77,9 +83,15 @@ class ModelVer0_7(LightTrainDataloader, LightEmptyTestStep, TestModelBase):
     def val_dataloader(self):
         return self._dataloader(train=False)
 
+    def validation_step(self, batch, batch_idx, *args, **kwargs):
+        return {'val_loss': 0.7}
+
     def validation_end(self, outputs):
         return {'val_loss': 0.7}
 
+    def test_dataloader(self):
+        return self._dataloader(train=False)
+
     def test_end(self, outputs):
         return {'test_loss': 0.7}
 

tests/trainer/test_checks.py

@@ -0,0 +1,135 @@
+import pytest
+
+import tests.base.utils as tutils
+from pytorch_lightning import Trainer, LightningModule
+from pytorch_lightning.utilities.debugging import MisconfigurationException
+from tests.base import (
+    TestModelBase,
+    LightValidationDataloader,
+    LightTestDataloader,
+    LightValidationStepMixin,
+    LightValStepFitSingleDataloaderMixin,
+    LightTrainDataloader,
+    LightTestStepMixin,
+    LightTestFitMultipleTestDataloadersMixin,
+)
+
+
+def test_error_on_no_train_step(tmpdir):
+    """ Test that an error is thrown when no `training_step()` is defined """
+    tutils.reset_seed()
+
+    class CurrentTestModel(LightningModule):
+        def forward(self, x):
+            pass
+
+    trainer_options = dict(default_save_path=tmpdir, max_epochs=1)
+    trainer = Trainer(**trainer_options)
+
+    with pytest.raises(MisconfigurationException):
+        model = CurrentTestModel()
+        trainer.fit(model)
+
+
+def test_error_on_no_train_dataloader(tmpdir):
+    """ Test that an error is thrown when no `training_dataloader()` is defined """
+    tutils.reset_seed()
+    hparams = tutils.get_default_hparams()
+
+    class CurrentTestModel(TestModelBase):
+        pass
+
+    trainer_options = dict(default_save_path=tmpdir, max_epochs=1)
+    trainer = Trainer(**trainer_options)
+
+    with pytest.raises(MisconfigurationException):
+        model = CurrentTestModel(hparams)
+        trainer.fit(model)
+
+
+def test_warning_on_wrong_validation_settings(tmpdir):
+    """ Test the following cases related to validation configuration of model:
+        * error if `val_dataloader()` is overriden but `validation_step()` is not
+        * if both `val_dataloader()` and `validation_step()` is overriden,
+            throw warning if `val_epoch_end()` is not defined
+        * error if `validation_step()` is overriden but `val_dataloader()` is not
+    """
+    tutils.reset_seed()
+    hparams = tutils.get_default_hparams()
+
+    trainer_options = dict(default_save_path=tmpdir, max_epochs=1)
+    trainer = Trainer(**trainer_options)
+
+    class CurrentTestModel(LightTrainDataloader,
+                           LightValidationDataloader,
+                           TestModelBase):
+        pass
+
+    # check val_dataloader -> val_step
+    with pytest.raises(MisconfigurationException):
+        model = CurrentTestModel(hparams)
+        trainer.fit(model)
+
+    class CurrentTestModel(LightTrainDataloader,
+                           LightValidationStepMixin,
+                           TestModelBase):
+        pass
+
+    # check val_dataloader + val_step -> val_epoch_end
+    with pytest.warns(RuntimeWarning):
+        model = CurrentTestModel(hparams)
+        trainer.fit(model)
+
+    class CurrentTestModel(LightTrainDataloader,
+                           LightValStepFitSingleDataloaderMixin,
+                           TestModelBase):
+        pass
+
+    # check val_step -> val_dataloader
+    with pytest.raises(MisconfigurationException):
+        model = CurrentTestModel(hparams)
+        trainer.fit(model)
+
+
+def test_warning_on_wrong_test_settigs(tmpdir):
+    """ Test the following cases related to test configuration of model:
+        * error if `test_dataloader()` is overriden but `test_step()` is not
+        * if both `test_dataloader()` and `test_step()` is overriden,
+            throw warning if `test_epoch_end()` is not defined
+        * error if `test_step()` is overriden but `test_dataloader()` is not
+    """
+    tutils.reset_seed()
+    hparams = tutils.get_default_hparams()
+
+    trainer_options = dict(default_save_path=tmpdir, max_epochs=1)
+    trainer = Trainer(**trainer_options)
+
+    class CurrentTestModel(LightTrainDataloader,
+                           LightTestDataloader,
+                           TestModelBase):
+        pass
+
+    # check test_dataloader -> test_step
+    with pytest.raises(MisconfigurationException):
+        model = CurrentTestModel(hparams)
+        trainer.fit(model)
+
+    class CurrentTestModel(LightTrainDataloader,
+                           LightTestStepMixin,
+                           TestModelBase):
+        pass
+
+    # check test_dataloader + test_step -> test_epoch_end
+    with pytest.warns(RuntimeWarning):
+        model = CurrentTestModel(hparams)
+        trainer.fit(model)
+
+    class CurrentTestModel(LightTrainDataloader,
+                           LightTestFitMultipleTestDataloadersMixin,
+                           TestModelBase):
+        pass
+
+    # check test_step -> test_dataloader
+    with pytest.raises(MisconfigurationException):
+        model = CurrentTestModel(hparams)
+        trainer.fit(model)

tests/trainer/test_trainer.py

@@ -23,7 +23,7 @@
     LightValidationStepMixin,
     LightValidationMultipleDataloadersMixin,
     LightTrainDataloader,
-    LightTestDataloader,
+    LightTestStepMixin,
 )
 
 
@@ -495,7 +495,7 @@ def test_testpass_overrides(tmpdir):
     class LocalModel(LightTrainDataloader, TestModelBase):
         pass
 
-    class LocalModelNoEnd(LightTrainDataloader, LightTestDataloader, LightEmptyTestStep, TestModelBase):
+    class LocalModelNoEnd(LightTrainDataloader, LightTestStepMixin, LightEmptyTestStep, TestModelBase):
         pass
 
     class LocalModelNoStep(LightTrainDataloader, TestModelBase):