Modify Workflow to Allow IterableDataset Inputs

monai/engines/workflow.py

@@ -121,24 +121,24 @@ def __init__(
         to_kwargs: dict | None = None,
         amp_kwargs: dict | None = None,
     ) -> None:
-        if iteration_update is not None:
-            super().__init__(iteration_update)
-        else:
-            super().__init__(self._iteration)
+        super().__init__(self._iteration if iteration_update is None else iteration_update)
 
         if isinstance(data_loader, DataLoader):
-            sampler = data_loader.__dict__["sampler"]
+            sampler = getattr(data_loader, "sampler", None)
+
+            # set the epoch value for DistributedSampler objects when an epoch starts
             if isinstance(sampler, DistributedSampler):
 
                 @self.on(Events.EPOCH_STARTED)
                 def set_sampler_epoch(engine: Engine) -> None:
                     sampler.set_epoch(engine.state.epoch)
 
+            # if the epoch_length isn't given, attempt to get it from the length of the data loader
             if epoch_length is None:
-                epoch_length = len(data_loader)
-        else:
-            if epoch_length is None:
-                raise ValueError("If data_loader is not PyTorch DataLoader, must specify the epoch_length.")
+                try:
+                    epoch_length = len(data_loader)
+                except TypeError:  # raised when data_loader is given an iterable dataset which has no length
+                    pass  # deliberately leave epoch_length as None
 
         # set all sharable data for the workflow based on Ignite engine.state
         self.state: Any = State(
@@ -147,7 +147,7 @@ def set_sampler_epoch(engine: Engine) -> None:
             iteration=0,
             epoch=0,
             max_epochs=max_epochs,
-            epoch_length=epoch_length,
+            epoch_length=epoch_length,  # None when the dataset is iterable and so has no length
             output=None,
             batch=None,
             metrics={},

tests/test_iterable_dataset.py

@@ -18,8 +18,10 @@
 
 import nibabel as nib
 import numpy as np
+import torch.nn as nn
 
 from monai.data import DataLoader, Dataset, IterableDataset
+from monai.engines import SupervisedEvaluator
 from monai.transforms import Compose, LoadImaged, SimulateDelayd
 
 
@@ -59,6 +61,17 @@ def test_shape(self):
             for d in dataloader:
                 self.assertTupleEqual(d["image"].shape[1:], expected_shape)
 
+    def test_supervisedevaluator(self):
+        """
+        Test that a SupervisedEvaluator is compatible with IterableDataset in conjunction with DataLoader.
+        """
+        data = list(range(10))
+        dl = DataLoader(IterableDataset(data))
+        evaluator = SupervisedEvaluator(device="cpu", val_data_loader=dl, network=nn.Identity())
+        evaluator.run()  # fails if the epoch length or other internal setup is not done correctly
+
+        self.assertEqual(evaluator.state.iteration, len(data))
+
 
 if __name__ == "__main__":
     unittest.main()