diff --git a/mmcls/__init__.py b/mmcls/__init__.py
index 1c4f7e8fc..638a1549a 100644
--- a/mmcls/__init__.py
+++ b/mmcls/__init__.py
@@ -1,3 +1,3 @@
-from .version import __version__, short_version
+#from .version import __version__, short_version
 
-__all__ = ['__version__', 'short_version']
+#__all__ = ['__version__', 'short_version']
diff --git a/mmcls/models/backbones/shufflenet_v1.py b/mmcls/models/backbones/shufflenet_v1.py
index 67915aa14..3e2ce41cb 100644
--- a/mmcls/models/backbones/shufflenet_v1.py
+++ b/mmcls/models/backbones/shufflenet_v1.py
@@ -111,8 +111,8 @@ class ShuffleUnit(nn.Module):
         if self.combine == 'add':
             self.depthwise_stride = 1
             self._combine_func = self._add
-            assert inplanes == planes, ('inplanes must be equal to '
-                                        'planes when combine is add')
+            assert inplanes == planes, (
+                'inplanes must be equal to planes when combine is add')
         elif self.combine == 'concat':
             self.depthwise_stride = 2
             self._combine_func = self._concat
@@ -273,20 +273,18 @@ class ShuffleNetv1(BaseBackbone):
 
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
 
-        self.layers = []
+        self.layers = nn.ModuleList()
         for i, num_blocks in enumerate(self.stage_blocks):
             first_block = False if i == 0 else True
             layer = self.make_layer(channels[i], num_blocks, first_block)
-            layer_name = f'layer{i + 1}'
-            self.add_module(layer_name, layer)
-            self.layers.append(layer_name)
+            self.layers.append(layer)
 
     def _freeze_stages(self):
         if self.frozen_stages >= 0:
             for param in self.conv1.parameters():
                 param.requires_grad = False
-        for i in range(1, self.frozen_stages + 1):
-            layer = getattr(self, f'layer{i}')
+        for i in range(self.frozen_stages):
+            layer = self.layers[i]
             layer.eval()
             for param in layer.parameters():
                 param.requires_grad = False
@@ -336,8 +334,7 @@ class ShuffleNetv1(BaseBackbone):
         x = self.maxpool(x)
 
         outs = []
-        for i, layer_name in enumerate(self.layers):
-            layer = getattr(self, layer_name)
+        for i, layer in enumerate(self.layers):
             x = layer(x)
             if i in self.out_indices:
                 outs.append(x)
diff --git a/tests/test_backbones/test_shufflenet_v1.py b/tests/test_backbones/test_shufflenet_v1.py
index 0b432e65e..087ce273f 100644
--- a/tests/test_backbones/test_shufflenet_v1.py
+++ b/tests/test_backbones/test_shufflenet_v1.py
@@ -104,11 +104,10 @@ def test_shufflenetv1_backbone():
     model = ShuffleNetv1(frozen_stages=frozen_stages)
     model.init_weights()
     model.train()
-    for layer in [model.conv1]:
-        for param in layer.parameters():
-            assert param.requires_grad is False
-    for i in range(1, frozen_stages + 1):
-        layer = getattr(model, f'layer{i}')
+    for param in model.conv1.parameters():
+        assert param.requires_grad is False
+    for i in range(frozen_stages):
+        layer = model.layers[i]
         for mod in layer.modules():
             if isinstance(mod, _BatchNorm):
                 assert mod.training is False