diff --git a/ppcls/modeling/architectures/res2net_vd.py b/ppcls/modeling/architectures/res2net_vd.py
index 21c4397d98c26bc2100b6e07605df2184120c6f8..694edd877b7f352ed277c158d3daf89d7ac53ea0 100644
--- a/ppcls/modeling/architectures/res2net_vd.py
+++ b/ppcls/modeling/architectures/res2net_vd.py
@@ -18,9 +18,12 @@ from __future__ import print_function
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-from paddle.fluid.param_attr import ParamAttr
-from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
+from paddle import ParamAttr
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn.initializer import Uniform
 
 import math
 
@@ -31,7 +34,7 @@ __all__ = [
 ]
 
 
-class ConvBNLayer(fluid.dygraph.Layer):
+class ConvBNLayer(nn.Layer):
     def __init__(
             self,
             num_channels,
@@ -45,21 +48,17 @@ class ConvBNLayer(fluid.dygraph.Layer):
         super(ConvBNLayer, self).__init__()
 
         self.is_vd_mode = is_vd_mode
-        self._pool2d_avg = Pool2D(
-            pool_size=2,
-            pool_stride=2,
-            pool_padding=0,
-            pool_type='avg',
-            ceil_mode=True)
-        self._conv = Conv2D(
-            num_channels=num_channels,
-            num_filters=num_filters,
-            filter_size=filter_size,
+        self._pool2d_avg = AvgPool2d(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self._conv = Conv2d(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
             stride=stride,
             padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
-            param_attr=ParamAttr(name=name + "_weights"),
+            weight_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False)
         if name == "conv1":
             bn_name = "bn_" + name
@@ -81,7 +80,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
         return y
 
 
-class BottleneckBlock(fluid.dygraph.Layer):
+class BottleneckBlock(nn.Layer):
     def __init__(self,
                  num_channels1,
                  num_channels2,
@@ -112,8 +111,8 @@ class BottleneckBlock(fluid.dygraph.Layer):
                     act='relu',
                     name=name + '_branch2b_' + str(s + 1)))
             self.conv1_list.append(conv1)
-        self.pool2d_avg = Pool2D(
-            pool_size=3, pool_stride=stride, pool_padding=1, pool_type='avg')
+        self.pool2d_avg = AvgPool2d(
+            kernel_size=3, stride=stride, padding=1, ceil_mode=True)
 
         self.conv2 = ConvBNLayer(
             num_channels=num_filters,
@@ -135,7 +134,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
 
     def forward(self, inputs):
         y = self.conv0(inputs)
-        xs = fluid.layers.split(y, self.scales, 1)
+        xs = paddle.split(y, self.scales, 1)
         ys = []
         for s, conv1 in enumerate(self.conv1_list):
             if s == 0 or self.stride == 2:
@@ -146,18 +145,18 @@ class BottleneckBlock(fluid.dygraph.Layer):
             ys.append(xs[-1])
         else:
             ys.append(self.pool2d_avg(xs[-1]))
-        conv1 = fluid.layers.concat(ys, axis=1)
+        conv1 = paddle.concat(ys, axis=1)
         conv2 = self.conv2(conv1)
 
         if self.shortcut:
             short = inputs
         else:
             short = self.short(inputs)
-        y = fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
+        y = paddle.elementwise_add(x=short, y=conv2, act='relu')
         return y
 
 
-class Res2Net_vd(fluid.dygraph.Layer):
+class Res2Net_vd(nn.Layer):
     def __init__(self, layers=50, scales=4, width=26, class_dim=1000):
         super(Res2Net_vd, self).__init__()
 
@@ -203,8 +202,8 @@ class Res2Net_vd(fluid.dygraph.Layer):
             stride=1,
             act='relu',
             name="conv1_3")
-        self.pool2d_max = Pool2D(
-            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+        self.pool2d_max = MaxPool2d(
+            kernel_size=3, stride=2, padding=1, ceil_mode=True)
 
         self.block_list = []
         for block in range(len(depth)):
@@ -232,8 +231,7 @@ class Res2Net_vd(fluid.dygraph.Layer):
                 self.block_list.append(bottleneck_block)
                 shortcut = True
 
-        self.pool2d_avg = Pool2D(
-            pool_size=7, pool_type='avg', global_pooling=True)
+        self.pool2d_avg = AdaptiveAvgPool2d(1)
 
         self.pool2d_avg_channels = num_channels[-1] * 2
 
@@ -242,9 +240,8 @@ class Res2Net_vd(fluid.dygraph.Layer):
         self.out = Linear(
             self.pool2d_avg_channels,
             class_dim,
-            param_attr=ParamAttr(
-                initializer=fluid.initializer.Uniform(-stdv, stdv),
-                name="fc_weights"),
+            weight_attr=ParamAttr(
+                initializer=Uniform(-stdv, stdv), name="fc_weights"),
             bias_attr=ParamAttr(name="fc_offset"))
 
     def forward(self, inputs):
@@ -255,7 +252,7 @@ class Res2Net_vd(fluid.dygraph.Layer):
         for block in self.block_list:
             y = block(y)
         y = self.pool2d_avg(y)
-        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
+        y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
         y = self.out(y)
         return y