Merge 3798a62 into a3e5e03

mmengine/runner/runner.py

@@ -1167,7 +1167,7 @@ def build_param_scheduler(
           in runner, ``build_param_scheduler`` will return a dict containing
           the same keys with multiple optimizers and each value is a list of
           parameter schedulers. Note that, if you want different optimizers to
-          use different parameter shedulers to update optimizer's
+          use different parameter schedulers to update optimizer's
           hyper-parameters, the input parameter ``scheduler`` also needs to be
           a dict and its key are consistent with multiple optimizers.
           Otherwise, the same parameter schedulers will be used to update
@@ -1198,7 +1198,7 @@ def build_param_scheduler(
             <mmengine.optim.scheduler.lr_scheduler.StepLR at 0x7f70f6eb6150>]
 
         Above examples only provide the case of one optimizer and one scheduler
-        or multiple shedulers. If you want to know how to set parameter
+        or multiple schedulers. If you want to know how to set parameter
         scheduler when using multiple optimizers, you can find more examples
         `optimizer-docs`_.
 
@@ -2233,7 +2233,7 @@ def _check_scheduler_cfg(
         if is_seq_of(param_scheduler, dict):
             for _param_scheduler in param_scheduler:
                 assert 'type' in _param_scheduler, (
-                    'Each parameter sheduler should contain the key type, '
+                    'Each parameter scheduler should contain the key type, '
                     f'but got {_param_scheduler}')
         elif isinstance(param_scheduler, dict):
             if 'type' not in param_scheduler:

tests/test_registry/test_build_functions.py

@@ -189,7 +189,7 @@ def pseudo_build(cfg):
 
 
 @pytest.mark.skipif(not is_installed('torch'), reason='tests requires torch')
-def test_build_sheduler_from_cfg():
+def test_build_scheduler_from_cfg():
     import torch.nn as nn
     from torch.optim import SGD
     model = nn.Conv2d(1, 1, 1)
@@ -200,9 +200,9 @@ def test_build_sheduler_from_cfg():
         param_name='lr',
         begin=0,
         end=100)
-    sheduler = PARAM_SCHEDULERS.build(cfg)
-    assert sheduler.begin == 0
-    assert sheduler.end == 100
+    scheduler = PARAM_SCHEDULERS.build(cfg)
+    assert scheduler.begin == 0
+    assert scheduler.end == 100
 
     cfg = dict(
         type='LinearParamScheduler',
@@ -213,6 +213,6 @@ def test_build_sheduler_from_cfg():
         end=100,
         epoch_length=10)
 
-    sheduler = PARAM_SCHEDULERS.build(cfg)
-    assert sheduler.begin == 0
-    assert sheduler.end == 1000
+    scheduler = PARAM_SCHEDULERS.build(cfg)
+    assert scheduler.begin == 0
+    assert scheduler.end == 1000

tests/test_runner/test_runner.py

@@ -2303,7 +2303,7 @@ def test_checkpoint(self):
                               MultiStepLR)
         self.assertIsInstance(runner.param_schedulers['linear2'][0], StepLR)
 
-        # 2.7.3 test `resume` 2 optimizers and 0 sheduler list.
+        # 2.7.3 test `resume` 2 optimizers and 0 scheduler list.
         cfg = copy.deepcopy(self.epoch_based_cfg)
         cfg.experiment_name = 'test_checkpoint18'
         cfg.optim_wrapper = optim_cfg