Add trainer.predict config validation

CHANGELOG.md

@@ -40,8 +40,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added no return warning to predict ([#6139](https://github.com/PyTorchLightning/pytorch-lightning/pull/6139))
 
 
-- Added `outputs` parameter to callback's `on_validation_epoch_end` & `on_test_epoch_end` hooks ([#6120](https://github.com/PyTorchLightning/pytorch-lightning/pull/6120))
+- Added `Trainer.predict` config validation ([#6543]https://github.com/PyTorchLightning/pytorch-lightning/pull/6543)
+
 
+- Added `outputs` parameter to callback's `on_validation_epoch_end` & `on_test_epoch_end` hooks ([#6120](https://github.com/PyTorchLightning/pytorch-lightning/pull/6120))
 
 
 ### Changed

pytorch_lightning/trainer/configuration_validator.py

@@ -40,7 +40,8 @@ def verify_loop_configurations(self, model: LightningModule) -> None:
  self.__verify_eval_loop_configuration(model, 'val')
  elif self.trainer.state == TrainerState.TESTING:
  self.__verify_eval_loop_configuration(model, 'test')
- # TODO: add predict
+ elif self.trainer.state == TrainerState.PREDICTING:
+ self.__verify_predict_loop_configuration(model)
 
  def __verify_train_loop_configuration(self, model):
  # -----------------------------------
@@ -99,3 +100,9 @@ def __verify_eval_loop_configuration(self, model: LightningModule, stage: str) -
  rank_zero_warn(f'you passed in a {loader_name} but have no {step_name}. Skipping {stage} loop')
  if has_step and not has_loader:
  rank_zero_warn(f'you defined a {step_name} but have no {loader_name}. Skipping {stage} loop')
+
+ def __verify_predict_loop_configuration(self, model: LightningModule) -> None:
+
+ has_predict_dataloader = is_overridden('predict_dataloader', model)
+ if not has_predict_dataloader:
+ raise MisconfigurationException('Dataloader not found for `Trainer.predict`')

tests/trainer/test_config_validator.py

@@ -12,10 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import pytest
+import torch
 
-from pytorch_lightning import Trainer
+from pytorch_lightning import LightningDataModule, LightningModule, Trainer
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from tests.helpers import BoringModel
+from tests.helpers import BoringModel, RandomDataset
 
 
 def test_wrong_train_setting(tmpdir):
@@ -101,3 +102,48 @@ def test_val_loop_config(tmpdir):
  model = BoringModel()
  model.validation_step = None
  trainer.validate(model)
+
+
+@pytest.mark.parametrize("datamodule", [False, True])
+def test_trainer_predict_verify_config(tmpdir, datamodule):
+
+ class TestModel(LightningModule):
+
+ def __init__(self):
+ super().__init__()
+ self.layer = torch.nn.Linear(32, 2)
+
+ def forward(self, x):
+ return self.layer(x)
+
+ class TestLightningDataModule(LightningDataModule):
+
+ def __init__(self, dataloaders):
+ super().__init__()
+ self._dataloaders = dataloaders
+
+ def test_dataloader(self):
+ return self._dataloaders
+
+ def predict_dataloader(self):
+ return self._dataloaders
+
+ dataloaders = [torch.utils.data.DataLoader(RandomDataset(32, 2)), torch.utils.data.DataLoader(RandomDataset(32, 2))]
+
+ model = TestModel()
+
+ trainer = Trainer(default_root_dir=tmpdir)
+
+ if datamodule:
+ datamodule = TestLightningDataModule(dataloaders)
+ results = trainer.predict(model, datamodule=datamodule)
+ else:
+ results = trainer.predict(model, dataloaders=dataloaders)
+
+ assert len(results) == 2
+ assert results[0][0].shape == torch.Size([1, 2])
+
+ model.predict_dataloader = None
+
+ with pytest.raises(MisconfigurationException, match="Dataloader not found for `Trainer.predict`"):
+ trainer.predict(model)