Modify Workflow to Allow IterableDataset Inputs (#8263)

### Description

This modifies the behaviour of `Workflow` to permit `IterableDataset` to
be used correctly. A check against the `epoch_length` value is removed,
to allow that value to be `None`, and a test is added to verify this.
The length of a data loader is not defined when using iterable datasets,
so try/raise is added to allow that to be queried safely. This is
related to my work on the streaming support, in my [prototype
gist](https://gist.github.com/ericspod/1904713716b45631260784ac3fcd6fb3)
I had to provide a bogus epoch length value in the then change it to
`None` later once the evaluator object was created. This PR will remove
the need for this hack.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [x] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Eric Kerfoot <[email protected]>
Signed-off-by: Eric Kerfoot <eric.kerfoot@gmail>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Eric Kerfoot <eric.kerfoot@gmail>

monai/engines/workflow.py

@@ -12,7 +12,7 @@
 from __future__ import annotations
 
 import warnings
-from collections.abc import Callable, Iterable, Sequence
+from collections.abc import Callable, Iterable, Sequence, Sized
 from typing import TYPE_CHECKING, Any
 
 import torch
@@ -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 epoch_length is None:
+        # if the epoch_length isn't given, attempt to get it from the length of the data loader
+        if epoch_length is None and isinstance(data_loader, Sized):
+            try:
                 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.")
+            except TypeError:  # raised when data_loader has an iterable dataset with no length, or is some other type
+                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()