diff --git a/mmrazor/models/pruners/ratio_pruning.py b/mmrazor/models/pruners/ratio_pruning.py
index 151677d99..e4a54de0d 100644
--- a/mmrazor/models/pruners/ratio_pruning.py
+++ b/mmrazor/models/pruners/ratio_pruning.py
@@ -32,20 +32,23 @@ def __init__(self, ratios, **kwargs):
         self.ratios = ratios
         self.min_ratio = ratios[0]
 
-    def _check_pruner(self, supernet):
+    def _check_pruner_ratios(self, supernet):
+        """Check whether the ``ratios`` is correct."""
         for module in supernet.model.modules():
             if isinstance(module, GroupNorm):
                 num_channels = module.num_channels
                 num_groups = module.num_groups
                 for ratio in self.ratios:
                     new_channels = int(round(num_channels * ratio))
-                    assert (num_channels * ratio) % num_groups == 0, \
+                    assert new_channels % num_groups == 0, \
                         f'Expected number of channels in input of GroupNorm ' \
                         f'to be divisible by num_groups, but number of ' \
                         f'channels may be {new_channels} according to ' \
                         f'ratio {ratio} and num_groups={num_groups}'
 
     def prepare_from_supernet(self, supernet):
+        """Prepare for pruning."""
+        self._check_pruner_ratios(supernet)
         super(RatioPruner, self).prepare_from_supernet(supernet)
 
     def get_channel_mask(self, out_mask):
diff --git a/tests/test_models/test_pruner.py b/tests/test_models/test_pruner.py
index 12323022a..ff4b59dd9 100644
--- a/tests/test_models/test_pruner.py
+++ b/tests/test_models/test_pruner.py
@@ -212,9 +212,7 @@ def test_ratio_pruner():
             frozen_stages=1,
             norm_cfg=dict(type='BN', requires_grad=True),
             norm_eval=True,
-            style='pytorch',
-            init_cfg=dict(
-                type='Pretrained', checkpoint='torchvision://resnet50')),
+            style='pytorch'),
         neck=dict(
             type='FPN',
             in_channels=[256, 512, 1024, 2048],
@@ -280,6 +278,15 @@ def test_ratio_pruner():
         pruner.deploy_subnet(architecture, subnet_dict)
         architecture.forward_dummy(imgs)
 
+    # test invalid ``ratios``
+    # Expected number of channels in input of GroupNorm to be divisible
+    # by ``num_groups``
+    pruner_cfg = dict(type='RatioPruner', ratios=[1 / 10])
+    architecture = ARCHITECTURES.build(architecture_cfg)
+    pruner = PRUNERS.build(pruner_cfg)
+    with pytest.raises(AssertionError):
+        pruner.prepare_from_supernet(architecture)
+
 
 def _test_reset_bn_running_stats(architecture_cfg, pruner_cfg, should_fail):
     import os