Imporve according to comments

mmcls/models/backbones/convnext.py

@@ -14,13 +14,25 @@
 
 
 class LayerNorm2d(nn.LayerNorm):
-    """LayerNorm that supports two data formats: channels_last (default) or
-    channels_first.
+    """LayerNorm on channels for 2d images.
 
-    The ordering of the dimensions in the inputs. channels_last corresponds to
-    inputs with shape (batch_size, height, width, channels) while
-    channels_first corresponds to inputs with shape (batch_size, channels,
-    height, width).
+    Args:
+        num_channels (int): The number of channels of the input tensor.
+        dim (int): The dimension of channel in the input tensor. Defaults to 1.
+        eps (float): a value added to the denominator for numerical stability.
+            Defaults to 1e-6.
+        elementwise_affine (bool): a boolean value that when set to ``True``,
+            this module has learnable per-element affine parameters initialized
+            to ones (for weights) and zeros (for biases). Defaults to True.
+
+    Note:
+        Comparing with the original implementation, this implementation uses
+        ``dim`` instead of ``data_format`` to specify the dimension of channel.
+
+        1. For inputs with shape (batch_size, height, width, channels), use
+           ``dim=-1`` or ``dim=3``.
+        2. For inputs with shape (batch_size, channels, height, width), use
+           ``dim=1``.
     """
 
     def __init__(self,
@@ -194,8 +206,15 @@ def __init__(self,
             assert 'depths' in arch and 'channels' in arch, \
                 f'The arch dict must have "depths" and "channels", ' \
                 f'but got {list(arch.keys())}.'
+
         self.depths = arch['depths']
         self.channels = arch['channels']
+        assert (isinstance(self.depths, Sequence)
+                and isinstance(self.channels, Sequence)
+                and len(self.depths) == len(self.channels)), \
+            f'The "depths" ({self.depths}) and "channels" ({self.channels}) ' \
+            'should be both sequence with the same length.'
+
         self.num_stages = len(self.depths)
 
         if isinstance(out_indices, int):

tests/test_models/test_backbones/test_convnext.py

@@ -13,6 +13,10 @@ def test_assertion():
         # ConvNeXt arch dict should include 'embed_dims',
         ConvNeXt(arch=dict(channels=[2, 3, 4, 5]))
 
+    with pytest.raises(AssertionError):
+        # ConvNeXt arch dict should include 'embed_dims',
+        ConvNeXt(arch=dict(depths=[2, 3, 4], channels=[2, 3, 4, 5]))
+
 
 def test_convnext():