Add test_dataloaders to test method

CHANGELOG.md

@@ -9,6 +9,11 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Added
 
 - Added `rank_zero_warn` for warning only in rank 0 ([#1428](https://github.com/PyTorchLightning/pytorch-lightning/pull/1428))
+- Added `test_dataloaders` parameter to `Trainer.test()` ([#1393](https://github.com/PyTorchLightning/pytorch-lightning/issues/1393))
+
+### Changed
+
+- Removed `test_dataloaders` parameter from `Trainer.fit()` ([#1393](https://github.com/PyTorchLightning/pytorch-lightning/issues/1393))
 
 ### Fixed
 

pytorch_lightning/trainer/trainer.py

@@ -612,8 +612,7 @@ def fit(
             self,
             model: LightningModule,
             train_dataloader: Optional[DataLoader] = None,
-            val_dataloaders: Optional[DataLoader] = None,
-            test_dataloaders: Optional[DataLoader] = None
+            val_dataloaders: Optional[DataLoader] = None
     ):
         r"""
         Runs the full optimization routine.
@@ -629,14 +628,10 @@ def fit(
                 Pytorch Dataloader or a list of them, specifying validation samples.
                 If the model has a predefined val_dataloaders method this will be skipped
 
-            test_dataloaders: Either a single
-                Pytorch Dataloader or a list of them, specifying validation samples.
-                If the model has a predefined test_dataloaders method this will be skipped
-
         Example::
 
             # Option 1,
-            # Define the train_dataloader(), test_dataloader() and val_dataloader() fxs
+            # Define the train_dataloader() and val_dataloader() fxs
             # in the lightningModule
             # RECOMMENDED FOR MOST RESEARCH AND APPLICATIONS TO MAINTAIN READABILITY
             trainer = Trainer()
@@ -646,23 +641,21 @@ def fit(
             # Option 2
             # in production cases we might want to pass different datasets to the same model
             # Recommended for PRODUCTION SYSTEMS
-            train, val, test = DataLoader(...), DataLoader(...), DataLoader(...)
+            train, val = DataLoader(...), DataLoader(...)
             trainer = Trainer()
             model = LightningModule()
-            trainer.fit(model, train_dataloader=train,
-                        val_dataloader=val, test_dataloader=test)
+            trainer.fit(model, train_dataloader=train, val_dataloader=val)
 
             # Option 1 & 2 can be mixed, for example the training set can be
-            # defined as part of the model, and validation/test can then be
-            # feed to .fit()
+            # defined as part of the model, and validation can then be feed to .fit()
 
         """
         # bind logger and other properties
         model.logger = self.logger
         self.copy_trainer_model_properties(model)
 
         # set up the passed in dataloaders (if needed)
-        self.__attach_dataloaders(model, train_dataloader, val_dataloaders, test_dataloaders)
+        self.__attach_dataloaders(model, train_dataloader, val_dataloaders)
 
         # check that model is configured correctly
         self.check_model_configuration(model)
@@ -747,7 +740,7 @@ def __set_random_port(self):
             default_port = random.randint(10000, 19000)
             os.environ['MASTER_PORT'] = str(default_port)
 
-    def __attach_dataloaders(self, model, train_dataloader, val_dataloaders, test_dataloaders):
+    def __attach_dataloaders(self, model, train_dataloader=None, val_dataloaders=None, test_dataloaders=None):
         # when dataloader is passed via fit, patch the train_dataloader
         # functions to overwrite with these implementations
         if train_dataloader is not None:
@@ -863,32 +856,44 @@ def run_pretrain_routine(self, model: LightningModule):
         # CORE TRAINING LOOP
         self.train()
 
-    def test(self, model: Optional[LightningModule] = None):
+    def test(self, model: Optional[LightningModule] = None, test_dataloaders: Optional[DataLoader] = None):
         r"""
 
         Separates from fit to make sure you never run on your test set until you want to.
 
         Args:
             model: The model to test.
 
+            test_dataloaders: Either a single
+                Pytorch Dataloader or a list of them, specifying validation samples.
+
         Example::
 
             # Option 1
             # run test after fitting
+            test = DataLoader(...)
             trainer = Trainer()
             model = LightningModule()
 
-            trainer.fit()
-            trainer.test()
+            trainer.fit(model)
+            trainer.test(test_dataloaders=test)
 
             # Option 2
             # run test from a loaded model
+            test = DataLoader(...)
             model = LightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')
             trainer = Trainer()
-            trainer.test(model)
+            trainer.test(model, test_dataloaders=test)
         """
 
         self.testing = True
+
+        if test_dataloaders is not None:
+            if model is not None:
+                self.__attach_dataloaders(model, test_dataloaders=test_dataloaders)
+            else:
+                self.__attach_dataloaders(self.model, test_dataloaders=test_dataloaders)
+
         if model is not None:
             self.model = model
             self.fit(model)

tests/trainer/test_dataloaders.py

@@ -260,12 +260,12 @@ class CurrentTestModel(
     model = CurrentTestModel(hparams)
     trainer = Trainer(**trainer_options)
     fit_options = dict(train_dataloader=model._dataloader(train=True),
-                       val_dataloaders=model._dataloader(train=False),
-                       test_dataloaders=model._dataloader(train=False))
+                       val_dataloaders=model._dataloader(train=False))
+    test_options = dict(test_dataloaders=model._dataloader(train=False))
 
     result = trainer.fit(model, **fit_options)
 
-    trainer.test()
+    trainer.test(**test_options)
 
     assert result == 1
     assert len(trainer.val_dataloaders) == 1, \
@@ -300,11 +300,12 @@ class CurrentTestModel(
     trainer = Trainer(**trainer_options)
     fit_options = dict(train_dataloader=model._dataloader(train=True),
                        val_dataloaders=[model._dataloader(train=False),
-                                        model._dataloader(train=False)],
-                       test_dataloaders=[model._dataloader(train=False),
-                                         model._dataloader(train=False)])
+                                        model._dataloader(train=False)])
+    test_options = dict(test_dataloaders=[model._dataloader(train=False),
+                                          model._dataloader(train=False)])
+
     results = trainer.fit(model, **fit_options)
-    trainer.test()
+    trainer.test(**test_options)
 
     assert len(trainer.val_dataloaders) == 2, \
         f'Multiple `val_dataloaders` not initiated properly, got {trainer.val_dataloaders}'
@@ -342,10 +343,11 @@ class CurrentTestModel(
 
     # fit model
     trainer = Trainer(**trainer_options)
-    fit_options = dict(val_dataloaders=model._dataloader(train=False),
-                       test_dataloaders=model._dataloader(train=False))
+    fit_options = dict(val_dataloaders=model._dataloader(train=False))
+    test_options = dict(test_dataloaders=model._dataloader(train=False))
+
     _ = trainer.fit(model, **fit_options)
-    trainer.test()
+    trainer.test(**test_options)
 
     assert len(trainer.val_dataloaders) == 1, \
         f'`val_dataloaders` not initiated properly, got {trainer.val_dataloaders}'
@@ -511,8 +513,8 @@ class CurrentTestModel(
     )
 
     fit_options = dict(train_dataloader=model._dataloader(train=True),
-                       val_dataloaders=model._dataloader(train=False),
-                       test_dataloaders=model._dataloader(train=False))
+                       val_dataloaders=model._dataloader(train=False))
+    test_options = dict(test_dataloaders=model._dataloader(train=False))
 
     trainer = Trainer(**trainer_options)
 
@@ -524,7 +526,7 @@ class CurrentTestModel(
         trainer.fit(model, **fit_options)
 
     with pytest.warns(UserWarning, match='test'):
-        trainer.test()
+        trainer.test(**test_options)
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason='Test requires multiple GPUs')