new way of passing dataloaders

pytorch_lightning/core/decorators.py

@@ -10,7 +10,7 @@ def data_loader(fn):
     """
     wraps(fn)
     attr_name = '_lazy_' + fn.__name__
-
+    @wraps(fn)
     def _get_data_loader(self):
         try:
             value = getattr(self, attr_name)

pytorch_lightning/core/lightning.py

@@ -868,7 +868,6 @@ def tbptt_split_batch(self, batch, split_size):
         return splits
 
     @data_loader
-    @abstractmethod
     def train_dataloader(self):
         """Implement a PyTorch DataLoader
 
@@ -894,8 +893,8 @@ def train_dataloader(self):
                 )
                 return loader
 
-
         """
+        return None
 
     @data_loader
     def tng_dataloader(self):  # todo: remove in v0.8.0

pytorch_lightning/trainer/trainer.py

@@ -18,7 +18,7 @@
     parse_gpu_ids,
     determine_root_gpu_device
 )
-
+from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.trainer.evaluation_loop import TrainerEvaluationLoopMixin
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
@@ -734,17 +734,53 @@ def tng_tqdm_dic(self):
     # -----------------------------
     # MODEL TRAINING
     # -----------------------------
-    def fit(self, model):
+    def fit(self, model, train_dataloader=None, val_dataloader=None, test_dataloader=None):
         r"""
         Runs the full optimization routine.
 
+        Args:
+            model (LightningModule): Model to fit.
+
+            train_dataloader (:class:`.torch.utils.data.DataLoader`): A Pytorch
+                DataLoader with training samples. If the model has
+                a predefined train_dataloader method this will be skipped.
+
+            val_dataloader (:class:`.torch.utils.data.DataLoader`): Either a single
+                Pytorch Dataloader or a list of them, specifying validation samples. If the model has
+                a predefined val_dataloader method this will be skipped
+
+            test_dataloader (:class:`.torch.utils.data.DataLoader`): Either a single
+                Pytorch Dataloader or a list of them, specifying validation samples. If the model has
+                a predefined val_dataloader method this will be skipped
+
         Example::
 
+            # Option 1,
+            # Basic usecase, dataloaders defined as part of the model
             trainer = Trainer()
             model = LightningModule()
+            trainer.fit(model)
+
+            # Option 2
+            # Dataloaders passed to fit method
+            train, val, test = DataLoader(...), DataLoader(...), DataLoader(...)
+            trainer = Trainer()
+            model = LightningModule()
+            trainer.fit(model, train_dataloader=train,
+                        val_dataloader=val, test_dataloader=test)
+
+            # 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()
 
-            trainer.fit()
         """
+
+        # Update the dataloader attributes of the model with the ones supplied here,
+        # if they are not already defined in model
+        _set_dataloader(model, train_dataloader, 'train_dataloader')
+        _set_dataloader(model, val_dataloader, 'val_dataloader')
+        _set_dataloader(model, test_dataloader, 'test_dataloader')
+
         # when using multi-node or DDP within a node start each module in a separate process
         if self.use_ddp2:
             task = int(os.environ['SLURM_LOCALID'])
@@ -935,3 +971,34 @@ def test(self, model=None):
             self.fit(model)
         else:
             self.run_evaluation(test=True)
+
+
+def _set_dataloader(model, dataloader, attribute):
+    r''' Check dataloaders passed to .fit() method if they are pytorch DataLoader
+         objects and whether or not we should overright the corresponding dataloader
+         in the model '''
+
+    # Check if attribute comes directly from base class or
+    # derived in user subclass
+    if LightningModule.__qualname__ in getattr(model, attribute).__qualname__:
+        # Val and test should be list of dataloaders
+        dataloader = dataloader if attribute == 'train_dataloader' or \
+            (attribute != 'train_dataloader' and isinstance(dataloader, list)) else [dataloader]
+
+        # Check if input is correct
+        if isinstance(dataloader, torch.utils.data.DataLoader) or \
+           (isinstance(dataloader, list) and all(isinstance(d, torch.utils.data.DataLoader) for d in dataloader)):
+
+            # Overwrite abstract methods
+            dl = lambda: dataloader
+            dl.__name__ = attribute
+            setattr(model, attribute, dl)
+
+        elif not dataloader and dataloader != [None]:
+            raise ValueError(f'`{attribute}` needs to be an instance of'
+                             '`torch.utils.data.DataLoader` or a list of, instead got'
+                             f'`{dataloader}`')
+
+    else:
+        warnings.warn(f'Model has predefined `{attribute}`,'
+                      f'will skip `{attribute}` passed to fit method')

tests/models/__init__.py

@@ -2,7 +2,8 @@
 
 import torch
 
-from .base import LightningTestModelBase
+from .base import (LightningTestModelBase,
+                   LightningTestModelBaseWithoutDataloader)
 from .mixins import (
     LightningValidationStepMixin,
     LightningValidationMixin,

tests/models/base.py

@@ -36,7 +36,7 @@ def __init__(self, root, train=True, transform=None, target_transform=None,
         self.targets = self.targets[:num_samples]
 
 
-class LightningTestModelBase(LightningModule):
+class _LightningTestModelBase(LightningModule):
     """
     Base LightningModule for testing. Implements only the required
     interface
@@ -48,7 +48,7 @@ def __init__(self, hparams, force_remove_distributed_sampler=False):
         :param hparams:
         """
         # init superclass
-        super(LightningTestModelBase, self).__init__()
+        super(_LightningTestModelBase, self).__init__()
         self.hparams = hparams
 
         self.batch_size = hparams.batch_size
@@ -178,10 +178,6 @@ def _dataloader(self, train):
 
         return loader
 
-    @data_loader
-    def train_dataloader(self):
-        return self._dataloader(train=True)
-
     @staticmethod
     def add_model_specific_args(parent_parser, root_dir):  # pragma: no cover
         """
@@ -218,3 +214,15 @@ def add_model_specific_args(parent_parser, root_dir):  # pragma: no cover
                         options=[32, 64, 128, 256], tunable=False,
                         help='batch size will be divided over all gpus being used across all nodes')
         return parser
+
+
+class LightningTestModelBase(_LightningTestModelBase):
+    """ with pre-defined train dataloader """
+    @data_loader
+    def train_dataloader(self):
+        return self._dataloader(train=True)
+
+
+class LightningTestModelBaseWithoutDataloader(_LightningTestModelBase):
+    """ without pre-defined train dataloader """
+    pass

tests/test_trainer.py

@@ -11,6 +11,7 @@
 from tests.models import (
     LightningTestModel,
     LightningTestModelBase,
+    LightningTestModelBaseWithoutDataloader,
     LightningValidationStepMixin,
     LightningValidationMultipleDataloadersMixin,
     LightningTestMultipleDataloadersMixin,
@@ -367,7 +368,7 @@ class CurrentTestModel(
 
     # verify there are 2 val loaders
     assert len(trainer.get_val_dataloaders()) == 2, \
-        'Multiple val_dataloaders not initiated properly'
+        'Multiple val_dataloade not initiated properly'
 
     # make sure predictions are good for each val set
     for dataloader in trainer.get_val_dataloaders():
@@ -411,5 +412,103 @@ class CurrentTestModel(
     trainer.test()
 
 
+def test_dataloaders_passed_to_fit(tmpdir):
+    """Verify that dataloaders can be passed to fit"""
+    tutils.reset_seed()
+
+    class CurrentTestModel(
+        LightningTestModelBaseWithoutDataloader
+    ):
+        pass
+
+    hparams = tutils.get_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
+    # only train passed to fit
+    model = CurrentTestModel(hparams)
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(train_dataloader=model._dataloader(train=True))
+    results = trainer.fit(model, **fit_options)
+
+    # train, val passed to fit
+    model = CurrentTestModel(hparams)
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(train_dataloader=model._dataloader(train=True),
+                       val_dataloader=model._dataloader(train=False))
+    results = trainer.fit(model, **fit_options)
+    assert len(trainer.get_val_dataloaders()) == 1, \
+        'val_dataloaders not initiated properly'
+
+    # train, val and test passed to fit
+    model = CurrentTestModel(hparams)
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(train_dataloader=model._dataloader(train=True),
+                       val_dataloader=model._dataloader(train=False),
+                       test_dataloader=model._dataloader(train=False))
+    results = trainer.fit(model, **fit_options)
+    assert len(trainer.get_val_dataloaders()) == 1, \
+        'val_dataloaders not initiated properly'
+    assert len(trainer.get_test_dataloaders()) == 1, \
+        'test_dataloaders not initiated properly'
+
+    # train, multiple val and multiple test passed to fit
+    model = CurrentTestModel(hparams)
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(train_dataloader=model._dataloader(train=True),
+                       val_dataloader=[model._dataloader(train=False),
+                                       model._dataloader(train=False)],
+                       test_dataloader=[model._dataloader(train=False),
+                                        model._dataloader(train=False)])
+    results = trainer.fit(model, **fit_options)
+
+    assert len(trainer.get_val_dataloaders()) == 2, \
+        'Multiple val_dataloaders not initiated properly'
+    assert len(trainer.get_test_dataloaders()) == 2, \
+        'Multiple test_dataloaders not initiated properly'
+
+
+def test_mixing_of_dataloader_options(tmpdir):
+    """Verify that dataloaders can be passed to fit"""
+    tutils.reset_seed()
+
+    class CurrentTestModel(
+        LightningTestModelBase
+    ):
+        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)
+    fit_options = dict(val_dataloader=model._dataloader(train=False))
+    results = trainer.fit(model, **fit_options)
+
+    # fit model
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(val_dataloader=model._dataloader(train=False),
+                       test_dataloader=model._dataloader(train=False))
+    results = trainer.fit(model, **fit_options)
+    assert len(trainer.get_val_dataloaders()) == 1, \
+        'val_dataloaders not initiated properly'
+    assert len(trainer.get_test_dataloaders()) == 1, \
+        'test_dataloaders not initiated properly'
+
 # if __name__ == '__main__':
 #     pytest.main([__file__])