diff --git a/mmdet/models/backbones/pvt.py b/mmdet/models/backbones/pvt.py
index 243ff603db8..54d49d54ad8 100644
--- a/mmdet/models/backbones/pvt.py
+++ b/mmdet/models/backbones/pvt.py
@@ -9,6 +9,7 @@
                       constant_init, normal_init, trunc_normal_init)
 from mmcv.cnn.bricks.drop import build_dropout
 from mmcv.cnn.bricks.transformer import MultiheadAttention
+from mmcv.cnn.utils.weight_init import trunc_normal_
 from mmcv.runner import (BaseModule, ModuleList, Sequential, _load_checkpoint,
                          load_state_dict)
 from torch.nn.modules.utils import _pair as to_2tuple
@@ -275,7 +276,7 @@ def __init__(self, pos_shape, pos_dim, drop_rate=0., init_cfg=None):
         self.drop = nn.Dropout(p=drop_rate)
 
     def init_weights(self):
-        trunc_normal_init(self.pos_embed, std=0.02)
+        trunc_normal_(self.pos_embed, std=0.02)
 
     def resize_pos_embed(self, pos_embed, input_shape, mode='bilinear'):
         """Resize pos_embed weights.
@@ -486,9 +487,7 @@ def init_weights(self):
                         f'training start from scratch')
             for m in self.modules():
                 if isinstance(m, nn.Linear):
-                    trunc_normal_init(m.weight, std=.02)
-                    if m.bias is not None:
-                        constant_init(m.bias, 0)
+                    trunc_normal_init(m, std=.02, bias=0.)
                 elif isinstance(m, nn.LayerNorm):
                     constant_init(m.bias, 0)
                     constant_init(m.weight, 1.0)
diff --git a/mmdet/models/backbones/swin.py b/mmdet/models/backbones/swin.py
index fce01664ad0..316aec9765f 100644
--- a/mmdet/models/backbones/swin.py
+++ b/mmdet/models/backbones/swin.py
@@ -8,6 +8,7 @@
 import torch.utils.checkpoint as cp
 from mmcv.cnn import build_norm_layer, constant_init, trunc_normal_init
 from mmcv.cnn.bricks.transformer import FFN, build_dropout
+from mmcv.cnn.utils.weight_init import trunc_normal_
 from mmcv.runner import BaseModule, ModuleList, _load_checkpoint
 from mmcv.utils import to_2tuple
 
@@ -74,7 +75,7 @@ def __init__(self,
         self.softmax = nn.Softmax(dim=-1)
 
     def init_weights(self):
-        trunc_normal_init(self.relative_position_bias_table, std=0.02)
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
 
     def forward(self, x, mask=None):
         """
@@ -672,12 +673,10 @@ def init_weights(self):
                         f'{self.__class__.__name__}, '
                         f'training start from scratch')
             if self.use_abs_pos_embed:
-                trunc_normal_init(self.absolute_pos_embed, std=0.02)
+                trunc_normal_(self.absolute_pos_embed, std=0.02)
             for m in self.modules():
                 if isinstance(m, nn.Linear):
-                    trunc_normal_init(m.weight, std=.02)
-                    if m.bias is not None:
-                        constant_init(m.bias, 0)
+                    trunc_normal_init(m, std=.02, bias=0.)
                 elif isinstance(m, nn.LayerNorm):
                     constant_init(m.bias, 0)
                     constant_init(m.weight, 1.0)