Fixed val interval (#405)

* added fixed frequency val batch check

* added fixed frequency val batch check

* Finished IterableDataset support

* flake8

* flake8

* flake8

docs/Trainer/Validation loop.md

@@ -43,13 +43,20 @@ trainer = Trainer(test_percent_check=0.1)
 
 ---
 ####  Set validation check frequency within 1 training epoch
-For large datasets it's often desirable to check validation multiple times within a training loop
+For large datasets it's often desirable to check validation multiple times within a training loop.
+Pass in a float to check that often within 1 training epoch.
+Pass in an int k to check every k training batches. Must use an int if using 
+an IterableDataset.
+
 ``` {.python}
 # DEFAULT
 trainer = Trainer(val_check_interval=0.95)
 
 # check every .25 of an epoch 
 trainer = Trainer(val_check_interval=0.25)
+
+# check every 100 train batches (ie: for IterableDatasets or fixed frequency)
+trainer = Trainer(val_check_interval=100)
 ```
 
 ---

pytorch_lightning/trainer/data_loading_mixin.py

@@ -2,6 +2,9 @@
 
 from torch.utils.data.distributed import DistributedSampler
 import torch.distributed as dist
+from torch.utils.data import IterableDataset
+
+from pytorch_lightning.utilities.debugging import MisconfigurationException
 
 try:
     from apex import amp
@@ -15,8 +18,11 @@ class TrainerDataLoadingMixin(object):
     def layout_bookeeping(self):
 
         # determine number of training batches
-        self.nb_training_batches = len(self.get_train_dataloader())
-        self.nb_training_batches = int(self.nb_training_batches * self.train_percent_check)
+        if isinstance(self.get_train_dataloader(), IterableDataset):
+            self.nb_training_batches = float('inf')
+        else:
+            self.nb_training_batches = len(self.get_train_dataloader())
+            self.nb_training_batches = int(self.nb_training_batches * self.train_percent_check)
 
         # determine number of validation batches
         # val datasets could be none, 1 or 2+
@@ -34,8 +40,13 @@ def layout_bookeeping(self):
             self.nb_test_batches = max(1, self.nb_test_batches)
 
         # determine when to check validation
-        self.val_check_batch = int(self.nb_training_batches * self.val_check_interval)
-        self.val_check_batch = max(1, self.val_check_batch)
+        # if int passed in, val checks that often
+        # otherwise, it checks in [0, 1.0] % range of a training epoch
+        if isinstance(self.val_check_interval, int):
+            self.val_check_batch = self.val_check_interval
+        else:
+            self.val_check_batch = int(self.nb_training_batches * self.val_check_interval)
+            self.val_check_batch = max(1, self.val_check_batch)
 
     def get_dataloaders(self, model):
         """
@@ -127,6 +138,16 @@ def get_dataloaders(self, model):
             self.get_test_dataloaders()
             self.get_val_dataloaders()
 
+        # support IterableDataset for train data
+        self.is_iterable_train_dataloader = isinstance(self.get_train_dataloader(), IterableDataset)
+        if self.is_iterable_train_dataloader and not isinstance(self.val_check_interval, int):
+            m = '''
+            When using an iterableDataset for train_dataloader,
+            Trainer(val_check_interval) must be an int.
+            An int k specifies checking validation every k training batches
+            '''
+            raise MisconfigurationException('when using ')
+
     def determine_data_use_amount(self, train_percent_check, val_percent_check,
                                   test_percent_check, overfit_pct):
         """

pytorch_lightning/trainer/train_loop_mixin.py

@@ -31,7 +31,12 @@ def train(self):
 
             # init progress_bar when requested
             if self.show_progress_bar:
-                self.progress_bar.reset(self.total_batches)
+                nb_iterations = self.total_batches
+
+                #  for iterable train loader, the progress bar never ends
+                if self.is_iterable_train_dataloader:
+                    nb_iterations = float('inf')
+                self.progress_bar.reset(nb_iterations)
 
             # changing gradient according accumulation_scheduler
             self.accumulation_scheduler.on_epoch_begin(epoch_nb, self)

pytorch_lightning/trainer/trainer.py

@@ -101,7 +101,7 @@ def __init__(self,
         :param train_percent_check: int. How much of train set to check
         :param val_percent_check: int. How much of val set to check
         :param test_percent_check: int. How much of test set to check
-        :param val_check_interval: int. Check val this frequently within a train epoch
+        :param val_check_interval: float/int. If float, % of tng epoch. If int, check every n batch
         :param log_save_interval: int. Writes logs to disk this often
         :param row_log_interval: int. How often to add logging rows
         :param add_row_log_interval: int. How often to add logging rows. Deprecated.
@@ -160,6 +160,7 @@ def __init__(self,
         self.get_train_dataloader = None
         self.get_test_dataloaders = None
         self.get_val_dataloaders = None
+        self.is_iterable_train_dataloader = False
 
         # training state
         self.model = None