Fix/replay validation (#520)

* Fix none steps validation bug

* Update changelog

CHANGELOG.md

@@ -3,6 +3,14 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
+## [Unreleased]
+### Added
+### Changed
+### Deprecated
+### Removed
+### Fixed
+- Fixed bug where replay errored when train or val steps were None
+
 ## [0.3.0] - 2019-02-28
 ### Added
 - Added torchbearer.variational, a sub-package for implementations of state of the art variational auto-encoders

tests/test_trial.py

@@ -1780,6 +1780,28 @@ def test_replay_callback_calls(self):
         self.assertTrue(callback.on_sample.call_count == 100)
         self.assertTrue(callback.on_sample_validation.call_count == 50)
 
+    def test_replay_none_train_steps(self):
+        t = Trial(MagicMock())
+        callback = MagicMock()
+        history = [((None, 5), {'test': i, 'val_test2': i+1}) for i in range(10)]
+
+        t.state[tb.HISTORY] = history
+        t.replay(callbacks=[callback], verbose=0)
+        self.assertEqual(callback.on_start.call_count, 1)
+        self.assertTrue(callback.on_sample.call_count == 0)
+        self.assertTrue(callback.on_sample_validation.call_count == 50)
+
+    def test_replay_none_validation_steps(self):
+        t = Trial(MagicMock())
+        callback = MagicMock()
+        history = [((10, None), {'test': i}) for i in range(10)]
+
+        t.state[tb.HISTORY] = history
+        t.replay(callbacks=[callback], verbose=0)
+        self.assertEqual(callback.on_start.call_count, 1)
+        self.assertTrue(callback.on_sample.call_count == 100)
+        self.assertTrue(callback.on_sample_validation.call_count == 0)
+
     def test_replay_one_batch_true(self):
         t = Trial(MagicMock())
         callback = MagicMock()

torchbearer/trial.py

@@ -932,7 +932,7 @@ def _replay_pass(self, state, callback_list):
         all_metrics = state[torchbearer.METRICS]
 
         # Training pass
-        state[torchbearer.STEPS] = state[torchbearer.TRAIN_STEPS]
+        state[torchbearer.STEPS] = state[torchbearer.TRAIN_STEPS] if state[torchbearer.TRAIN_STEPS] is not None else 0
         state[torchbearer.METRICS] = {key: all_metrics[key] for key in all_metrics.keys() if "val_" not in key}
         callback_list.on_start_training(state)
         for state[torchbearer.BATCH] in range(state[torchbearer.STEPS]):
@@ -947,7 +947,7 @@ def _replay_pass(self, state, callback_list):
 
         # Validation pass
         if not state[torchbearer.STOP_TRAINING]:
-            state[torchbearer.STEPS] = state[torchbearer.VALIDATION_STEPS]
+            state[torchbearer.STEPS] = state[torchbearer.VALIDATION_STEPS] if state[torchbearer.VALIDATION_STEPS] is not None else 0
             state[torchbearer.METRICS] = {key: all_metrics[key] for key in all_metrics.keys() if "val_" in key}
             callback_list.on_start_validation(state)
             for state[torchbearer.BATCH] in range(state[torchbearer.STEPS]):