Add support for IterableDatasets everywhere

CHANGELOG.md

@@ -8,7 +8,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
-- 
+- Added support for IterableDataset in validation and testing ([#1104](https://github.com/PyTorchLightning/pytorch-lightning/pull/1104))
 
 ### Changed
 

pytorch_lightning/trainer/data_loading.py

@@ -1,9 +1,11 @@
 from abc import ABC, abstractmethod
+from typing import Union, List, Tuple, Callable
 
 import torch.distributed as dist
 from torch.utils.data import SequentialSampler, DataLoader
 from torch.utils.data.distributed import DistributedSampler
 
+from pytorch_lightning.core import LightningModule
 from pytorch_lightning.utilities.debugging import MisconfigurationException
 
 try:
@@ -23,6 +25,15 @@
     XLA_AVAILABLE = True
 
 
+def _has_len(dataloader: DataLoader) -> bool:
+    try:
+        # try getting the length
+        _ = len(dataloader)
+        return True
+    except TypeError:
+        return False
+
+
 class TrainerDataLoadingMixin(ABC):
 
     # this is just a summary on variables used in this abstract class,
@@ -35,27 +46,30 @@ class TrainerDataLoadingMixin(ABC):
     use_tpu: bool
     tpu_local_core_rank: int
     train_dataloader: DataLoader
-    num_training_batches: int
+    num_training_batches: Union[int, float]
     val_check_batch: ...
-    val_dataloaders: DataLoader
-    num_val_batches: int
-    test_dataloaders: DataLoader
-    num_test_batches: int
+    val_dataloaders: List[DataLoader]
+    num_val_batches: Union[int, float]
+    test_dataloaders: List[DataLoader]
+    num_test_batches: Union[int, float]
+    train_percent_check: float
+    val_percent_check: float
+    test_percent_check: float
 
     @abstractmethod
     def is_overriden(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
 
-    def _percent_range_check(self, name):
+    def _percent_range_check(self, name: str) -> None:
         value = getattr(self, name)
-        msg = f"`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}."
-        if name == "val_check_interval":
-            msg += " If you want to disable validation set `val_percent_check` to 0.0 instead."
+        msg = f'`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}.'
+        if name == 'val_check_interval':
+            msg += ' If you want to disable validation set `val_percent_check` to 0.0 instead.'
 
         if not 0. <= value <= 1.:
             raise ValueError(msg)
 
-    def auto_add_sampler(self, dataloader, train):
+    def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
         if self.use_ddp or self.use_ddp2 or self.use_tpu:
             dl_args = {
                 'dataset': dataloader.dataset,
@@ -88,22 +102,22 @@ def auto_add_sampler(self, dataloader, train):
             dataloader = DataLoader(**dl_args)
         return dataloader
 
-    def reset_train_dataloader(self, model):
-        """
-        Dataloaders are provided by the model
-        :param model:
-        :return:
-        """
+    def reset_train_dataloader(self, model: LightningModule) -> None:
+        """Resets the train dataloader and initialises required variables
+        (number of batches, when to validate, etc.).
 
-        self.train_dataloader = self.request_data_loader(model.train_dataloader)
+        Args:
+            model: The current `LightningModule`
+        """
+        self.train_dataloader = self.request_dataloader(model.train_dataloader)
         self.num_training_batches = 0
 
         # automatically add samplers
         self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)
 
         self._percent_range_check('train_percent_check')
 
-        if self.is_infinite_dataloader(self.train_dataloader):
+        if not _has_len(self.train_dataloader):
             self.num_training_batches = float('inf')
         else:
             # try getting the length
@@ -117,122 +131,119 @@ def reset_train_dataloader(self, model):
             self.val_check_batch = self.val_check_interval
             if self.val_check_batch > self.num_training_batches:
                 raise ValueError(
-                    f"`val_check_interval` ({self.val_check_interval}) must be less than or equal "
-                    f"to the number of the training batches ({self.num_training_batches}). "
-                    f"If you want to disable validation set `val_percent_check` to 0.0 instead.")
+                    f'`val_check_interval` ({self.val_check_interval}) must be less than or equal '
+                    f'to the number of the training batches ({self.num_training_batches}). '
+                    'If you want to disable validation set `val_percent_check` to 0.0 instead.')
         else:
-            if self.is_infinite_dataloader(self.train_dataloader):
-                m = '''
-                When using an infinite DataLoader (e.g. with an IterableDataset or when DataLoader
-                does not implement `__len__`) for `train_dataloader`, `Trainer(val_check_interval)`
-                must be an int. An int k specifies checking validation every k training batches.
-                '''
-                raise MisconfigurationException(m)
+            if not _has_len(self.train_dataloader):
+                raise MisconfigurationException(
+                    'When using an infinite DataLoader (e.g. with an IterableDataset or when '
+                    'DataLoader does not implement `__len__`) for `train_dataloader`, '
+                    '`Trainer(val_check_interval)` must be an int. An int k specifies checking '
+                    'validation every k training batches.')
 
             self._percent_range_check('val_check_interval')
 
             self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
             self.val_check_batch = max(1, self.val_check_batch)
 
-    def is_infinite_dataloader(self, dataloader):
-        try:
-            # try getting the length
-            _ = len(dataloader)
-            return False
-        except TypeError as e:
-            return True
+    def _reset_eval_dataloader(self, model: LightningModule,
+                               mode: str) -> Tuple[int, List[DataLoader]]:
+        """Generic method to reset a dataloader for evaluation.
 
-    def reset_val_dataloader(self, model):
-        """
-        Dataloaders are provided by the model
-        :param model:
-        :return:
+        Args:
+            model: The current `LightningModule`
+            mode: Either `'val'` or `'test'`
+
+        Returns:
+            Tuple (num_batches, dataloaders)
         """
-        if not self.is_overriden('validation_step'):
-            return
+        dataloaders = self.request_dataloader(getattr(model, f'{mode}_dataloader'))
 
-        self.val_dataloaders = self.request_data_loader(model.val_dataloader)
-        if not isinstance(self.val_dataloaders, list):
-            self.val_dataloaders = [self.val_dataloaders]
-        self.num_val_batches = 0
+        if not isinstance(dataloaders, list):
+            dataloaders = [dataloaders]
 
         # add samplers
-        self.val_dataloaders = [self.auto_add_sampler(dl, train=False)
-                                for dl in self.val_dataloaders if dl]
+        dataloaders = [self.auto_add_sampler(dl, train=False) for dl in dataloaders if dl]
 
-        # determine number of validation batches
-        # val datasets could be none, 1 or 2+
-        if self.val_dataloaders is not None:
-            self._percent_range_check('val_percent_check')
+        num_batches = 0
 
-            self.num_val_batches = sum(len(dataloader) for dataloader in self.val_dataloaders)
-            self.num_val_batches = int(self.num_val_batches * self.val_percent_check)
+        # determine number of batches
+        # datasets could be none, 1 or 2+
+        if len(dataloaders) != 0:
+            for dataloader in dataloaders:
+                if not _has_len(dataloader):
+                    num_batches = float('inf')
+                    break
 
-    def reset_test_dataloader(self, model):
-        """Dataloaders are provided by the model.
+            percent_check = getattr(self, f'{mode}_percent_check')
 
-        :param model:
-        """
-        if not self.is_overriden('test_step'):
-            return
+            if num_batches != float('inf'):
+                self._percent_range_check(f'{mode}_percent_check')
 
-        # get actual loader
-        self.test_dataloaders = self.request_data_loader(model.test_dataloader)
-        if not isinstance(self.test_dataloaders, list):
-            self.test_dataloaders = [self.test_dataloaders]
-        self.num_test_batches = 0
+                num_batches = sum(len(dataloader) for dataloader in dataloaders)
+                num_batches = int(num_batches * percent_check)
+            elif percent_check not in (0.0, 1.0):
+                raise MisconfigurationException(
+                    'When using an infinite DataLoader (e.g. with an IterableDataset or when '
+                    f'DataLoader does not implement `__len__`) for `{mode}_dataloader`, '
+                    f'`Trainer({mode}_percent_check)` must be `0.0` or `1.0`.')
+        return num_batches, dataloaders
 
-        # add samplers
-        self.test_dataloaders = [self.auto_add_sampler(dl, train=False)
-                                 for dl in self.test_dataloaders if dl]
+    def reset_val_dataloader(self, model: LightningModule) -> None:
+        """Resets the validation dataloader and determines the number of batches.
 
-        # determine number of test batches
-        if self.test_dataloaders is not None:
-            self._percent_range_check('test_percent_check')
+        Args:
+            model: The current `LightningModule`
+        """
+        if self.is_overriden('validation_step'):
+            self.num_val_batches, self.val_dataloaders =\
+                self._reset_eval_dataloader(model, 'val')
 
-            len_sum = sum(len(dataloader) for dataloader in self.test_dataloaders)
-            self.num_test_batches = len_sum
-            self.num_test_batches = int(self.num_test_batches * self.test_percent_check)
+    def reset_test_dataloader(self, model) -> None:
+        """Resets the validation dataloader and determines the number of batches.
 
-    def request_data_loader(self, data_loader_fx):
+        Args:
+            model: The current `LightningModule`
         """
-        Handles downloading data in the GPU or TPU case.
+        if self.is_overriden('test_step'):
+            self.num_test_batches, self.test_dataloaders =\
+                self._reset_eval_dataloader(model, 'test')
 
-        :param data_loader_fx:
-        :return:
+    def request_dataloader(self, dataloader_fx: Callable) -> DataLoader:
+        """Handles downloading data in the GPU or TPU case.
+
+        Args:
+            dataloader_fx: The bound dataloader getter
+
+        Returns:
+            The dataloader
         """
+        dataloader = dataloader_fx()
+
         # get the function we'll use to get data
         if self.use_ddp or self.use_ddp2:
-            data_loader = data_loader_fx()
-
             # all processes wait until data download has happened
             dist.barrier()
 
         # data download/load on TPU
         elif self.use_tpu and XLA_AVAILABLE:
-            data_loader = data_loader_fx()
-
             # all processes wait until data download has happened
-            torch_xla.core.xla_model.rendezvous("pl.TrainerDataLoadingMixin.get_dataloaders")
+            torch_xla.core.xla_model.rendezvous('pl.TrainerDataLoadingMixin.get_dataloaders')
 
-        # regular start
-        else:
-            data_loader = data_loader_fx()
-
-        return data_loader
+        return dataloader
 
-    def determine_data_use_amount(self, train_percent_check, val_percent_check,
-                                  test_percent_check, overfit_pct):
-        """
-        Use less data for debugging purposes
+    def determine_data_use_amount(self, train_percent_check: float, val_percent_check: float,
+                                  test_percent_check: float, overfit_pct: float) -> None:
+        """Use less data for debugging purposes
         """
         self.train_percent_check = train_percent_check
         self.val_percent_check = val_percent_check
         self.test_percent_check = test_percent_check
         if overfit_pct > 0:
             if overfit_pct > 1:
-                raise ValueError(f"`overfit_pct` must be not greater than 1.0, but got "
-                                 f"{overfit_pct:.3f}.")
+                raise ValueError(
+                    f'`overfit_pct` must be not greater than 1.0, but got {overfit_pct:.3f}.')
 
             self.train_percent_check = overfit_pct
             self.val_percent_check = overfit_pct

pytorch_lightning/trainer/evaluation_loop.py

@@ -359,9 +359,9 @@ def run_evaluation(self, test_mode: bool = False):
         # main progress bar will already be closed when testing so initial position is free
         position = 2 * self.process_position + (not test_mode)
         desc = 'Testing' if test_mode else 'Validating'
-        pbar = tqdm(desc=desc, total=max_batches, leave=test_mode, position=position,
-                    disable=not self.show_progress_bar, dynamic_ncols=True,
-                    file=sys.stdout)
+        total = max_batches if max_batches != float('inf') else None
+        pbar = tqdm(desc=desc, total=total, leave=test_mode, position=position,
+                    disable=not self.show_progress_bar, dynamic_ncols=True, file=sys.stdout)
         setattr(self, f'{"test" if test_mode else "val"}_progress_bar', pbar)
 
         # run evaluation

pytorch_lightning/trainer/training_loop.py

@@ -223,10 +223,6 @@ def get_model(self):
     def is_function_implemented(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
 
-    @abstractmethod
-    def is_infinite_dataloader(self, *args):
-        """Warning: this is just empty shell for code implemented in other class."""
-
     @abstractmethod
     def run_evaluation(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
@@ -310,7 +306,7 @@ def train(self):
 
                 total_val_batches = 0
                 is_val_epoch = False
-                if not self.disable_validation:
+                if not self.disable_validation and self.num_training_batches != float('inf'):
                     # val can be checked multiple times in epoch
                     is_val_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0
                     val_checks_per_epoch = self.num_training_batches // self.val_check_batch
@@ -324,8 +320,8 @@ def train(self):
                 if self.fast_dev_run:
                     # limit the number of batches to 2 (1 train and 1 val) in fast_dev_run
                     num_iterations = 2
-                elif self.is_infinite_dataloader(self.train_dataloader):
-                    # for infinite train loader, the progress bar never ends
+                elif self.total_batches == float('inf'):
+                    # for infinite train or val loader, the progress bar never ends
                     num_iterations = None
                 else:
                     num_iterations = self.total_batches
@@ -334,7 +330,7 @@ def train(self):
                 # .reset() doesn't work on disabled progress bar so we should check
                 if not self.main_progress_bar.disable:
                     self.main_progress_bar.reset(num_iterations)
-                desc = f'Epoch {epoch + 1}' if not self.is_infinite_dataloader(self.train_dataloader) else ''
+                desc = f'Epoch {epoch + 1}'
                 self.main_progress_bar.set_description(desc)
 
                 # -----------------

tests/models/__init__.py

@@ -19,6 +19,9 @@
     LightValStepFitMultipleDataloadersMixin,
     LightTrainDataloader,
     LightTestDataloader,
+    LightInfTrainDataloader,
+    LightInfValDataloader,
+    LightInfTestDataloader,
     LightTestOptimizerWithSchedulingMixin,
     LightTestMultipleOptimizersWithSchedulingMixin,
     LightTestOptimizersWithMixedSchedulingMixin