diff --git a/ppcls/arch/backbone/legendary_models/resnet.py b/ppcls/arch/backbone/legendary_models/resnet.py
index e045fb62ec2c8cdfb25ed2d942b6baf8d443836e..b82df7947aa7ccb1a51e32b6dc18b61fa415d5a1 100644
--- a/ppcls/arch/backbone/legendary_models/resnet.py
+++ b/ppcls/arch/backbone/legendary_models/resnet.py
@@ -82,7 +82,7 @@ class ConvBNLayer(TheseusLayer):
         super().__init__()
         self.is_vd_mode = is_vd_mode
         self.act = act
-        self.avgpool = AvgPool2D(
+        self.avg_pool = AvgPool2D(
             kernel_size=2, stride=2, padding=0, ceil_mode=True)
         self.conv = Conv2D(
             in_channels=num_channels,
@@ -101,7 +101,7 @@ class ConvBNLayer(TheseusLayer):
 
     def forward(self, x):
         if self.is_vd_mode:
-            x = self.avgpool(x)
+            x = self.avg_pool(x)
         x = self.conv(x)
         x = self.bn(x)
         if self.act:
@@ -273,7 +273,7 @@ class ResNet(TheseusLayer):
             for in_c, out_c, k, s in self.stem_cfg[version]
         ])
         
-        self.maxpool = MaxPool2D(kernel_size=3, stride=2, padding=1)
+        self.max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
         block_list = []
         for block_idx in range(len(self.block_depth)):
             shortcut = False
@@ -290,22 +290,22 @@ class ResNet(TheseusLayer):
                 shortcut = True    
         self.blocks = nn.Sequential(*block_list)
 
-        self.avgpool = AdaptiveAvgPool2D(1)
-        self.avgpool_channels = self.num_channels[-1] * 2
+        self.avg_pool = AdaptiveAvgPool2D(1)
+        self.avg_pool_channels = self.num_channels[-1] * 2
 
-        stdv = 1.0 / math.sqrt(self.avgpool_channels * 1.0)
+        stdv = 1.0 / math.sqrt(self.avg_pool_channels * 1.0)
         self.out = Linear(
-            self.avgpool_channels,
+            self.avg_pool_channels,
             self.class_num,
             weight_attr=ParamAttr(
                 initializer=Uniform(-stdv, stdv)))
 
     def forward(self, x):
         x = self.stem(x)
-        x = self.maxpool(x)
+        x = self.max_pool(x)
         x = self.blocks(x)
-        x = self.avgpool(x)
-        x = paddle.reshape(x, shape=[-1, self.avgpool_channels])
+        x = self.avg_pool(x)
+        x = paddle.reshape(x, shape=[-1, self.avg_pool_channels])
         x = self.out(x)
         return x