diff --git a/ppocr/modeling/backbones/det_resnet_vd.py b/ppocr/modeling/backbones/det_resnet_vd.py
index 6fa527161cdb6671c5927b6fd2783f3615f2d46e..3bb4a0d50501860d5e9df2971e93fba66c152187 100644
--- a/ppocr/modeling/backbones/det_resnet_vd.py
+++ b/ppocr/modeling/backbones/det_resnet_vd.py
@@ -19,6 +19,7 @@ from __future__ import print_function
 import paddle
 from paddle import ParamAttr
 import paddle.nn as nn
+import paddle.nn.functional as F
 
 __all__ = ["ResNet"]
 
@@ -37,9 +38,9 @@ class ConvBNLayer(nn.Layer):
         super(ConvBNLayer, self).__init__()
 
         self.is_vd_mode = is_vd_mode
-        self._pool2d_avg = nn.AvgPool2d(
+        self._pool2d_avg = nn.AvgPool2D(
             kernel_size=2, stride=2, padding=0, ceil_mode=True)
-        self._conv = nn.Conv2d(
+        self._conv = nn.Conv2D(
             in_channels=in_channels,
             out_channels=out_channels,
             kernel_size=kernel_size,
@@ -118,7 +119,8 @@ class BottleneckBlock(nn.Layer):
             short = inputs
         else:
             short = self.short(inputs)
-        y = paddle.elementwise_add(x=short, y=conv2, act='relu')
+        y = paddle.add(x=short, y=conv2)
+        y = F.relu(y)
         return y
 
 
@@ -165,7 +167,8 @@ class BasicBlock(nn.Layer):
             short = inputs
         else:
             short = self.short(inputs)
-        y = paddle.elementwise_add(x=short, y=conv1, act='relu')
+        y = paddle.add(x=short, y=conv1)
+        y = F.relu(y)
         return y
 
 
@@ -214,7 +217,7 @@ class ResNet(nn.Layer):
             stride=1,
             act='relu',
             name="conv1_3")
-        self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
 
         self.stages = []
         self.out_channels = []
diff --git a/ppocr/modeling/backbones/rec_resnet_vd.py b/ppocr/modeling/backbones/rec_resnet_vd.py
index 20b03c3d48846d2e9d8f93319b9df291cb672059..6837ea0fb2da3347fd8e115f859224e2a61fd578 100644
--- a/ppocr/modeling/backbones/rec_resnet_vd.py
+++ b/ppocr/modeling/backbones/rec_resnet_vd.py
@@ -19,6 +19,7 @@ from __future__ import print_function
 import paddle
 from paddle import ParamAttr
 import paddle.nn as nn
+import paddle.nn.functional as F
 
 __all__ = ["ResNet"]
 
@@ -37,9 +38,9 @@ class ConvBNLayer(nn.Layer):
         super(ConvBNLayer, self).__init__()
 
         self.is_vd_mode = is_vd_mode
-        self._pool2d_avg = nn.AvgPool2d(
+        self._pool2d_avg = nn.AvgPool2D(
             kernel_size=stride, stride=stride, padding=0, ceil_mode=True)
-        self._conv = nn.Conv2d(
+        self._conv = nn.Conv2D(
             in_channels=in_channels,
             out_channels=out_channels,
             kernel_size=kernel_size,
@@ -119,7 +120,8 @@ class BottleneckBlock(nn.Layer):
             short = inputs
         else:
             short = self.short(inputs)
-        y = paddle.elementwise_add(x=short, y=conv2, act='relu')
+        y = paddle.add(x=short, y=conv2)
+        y = F.relu(y)
         return y
 
 
@@ -166,7 +168,8 @@ class BasicBlock(nn.Layer):
             short = inputs
         else:
             short = self.short(inputs)
-        y = paddle.elementwise_add(x=short, y=conv1, act='relu')
+        y = paddle.add(x=short, y=conv1)
+        y = F.relu(y)
         return y
 
 
@@ -215,7 +218,7 @@ class ResNet(nn.Layer):
             stride=1,
             act='relu',
             name="conv1_3")
-        self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
 
         self.block_list = []
         if layers >= 50:
@@ -270,7 +273,7 @@ class ResNet(nn.Layer):
                     shortcut = True
                     self.block_list.append(basic_block)
                 self.out_channels = num_filters[block]
-        self.out_pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+        self.out_pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
 
     def forward(self, inputs):
         y = self.conv1_1(inputs)