[Fix] param_scheduler can not None when training models (#208)

* [Fix] param_scheduler can not None when training models

* update unit tests

* fix unit tests

* refactor ParamSchedulerHook

* refactor unit tests

* param_schedulers can be an empty list

mmengine/runner/runner.py

@@ -261,7 +261,10 @@ class Runner:
             raise ValueError(
                 'param_scheduler should be None when optimizer is None, '
                 f'but got {param_scheduler}')
-        if not isinstance(param_scheduler, Sequence):
+
+        if param_scheduler is None:
+            self.param_schedulers = []
+        elif not isinstance(param_scheduler, Sequence):
             self.param_schedulers = [param_scheduler]
         else:
             self.param_schedulers = param_scheduler
@@ -1022,8 +1025,9 @@ class Runner:
         #  because the latter depends on the former
         self.optimizer = self.build_optimizer(self.optimizer)
 
-        self.param_schedulers = self.build_param_scheduler(  # type: ignore
-            self.param_schedulers)  # type: ignore
+        if self.param_schedulers:
+            self.param_schedulers = self.build_param_scheduler(  # type: ignore
+                self.param_schedulers)  # type: ignore
 
         return loop  # type: ignore
 

tests/test_runner/test_runner.py

@@ -263,6 +263,7 @@ class TestRunner(TestCase):
         cfg.pop('param_scheduler')
         runner = Runner(**cfg)
         self.assertIsInstance(runner, Runner)
+        self.assertEqual(runner.param_schedulers, [])
 
         # param_scheduler should be None when optimizer is None
         cfg = copy.deepcopy(self.epoch_based_cfg)
@@ -681,6 +682,12 @@ class TestRunner(TestCase):
         # input is a Loop object
         self.assertEqual(id(runner.build_train_loop(loop)), id(loop))
 
+        # param_schedulers can be []
+        cfg = dict(type='EpochBasedTrainLoop', max_epochs=3)
+        runner.param_schedulers = []
+        loop = runner.build_train_loop(cfg)
+        self.assertIsInstance(loop, EpochBasedTrainLoop)
+
         # test custom training loop
         cfg = dict(type='CustomTrainLoop', max_epochs=3)
         loop = runner.build_train_loop(cfg)