diff --git a/oneflow/core/functional/functional_api.yaml b/oneflow/core/functional/functional_api.yaml
index f3081365bb2..6e7973f1fb9 100644
--- a/oneflow/core/functional/functional_api.yaml
+++ b/oneflow/core/functional/functional_api.yaml
@@ -603,21 +603,21 @@
              Int32List strides, Bool ceil_mode) => PoolNdGrad"
   bind_python: False
 
-- name: "maxpool_1d"
+- name: "max_pool1d"
   signature:
     "TensorTuple (Tensor x, String data_format=\"channels_first\", Int32List padding,
                   Int32List kernel_size, Int32List stride, Int32List dilation, 
                   Bool return_indices=True, Bool ceil_mode=False) => Maxpool1D"
   bind_python: True
 
-- name: "maxpool_2d"
+- name: "max_pool2d"
   signature:
     "TensorTuple (Tensor x, String data_format=\"channels_first\", Int32List padding,
                   Int32List kernel_size, Int32List stride, Int32List dilation, 
                   Bool return_indices=True, Bool ceil_mode=False) => Maxpool2D"
   bind_python: True
 
-- name: "maxpool_3d"
+- name: "max_pool3d"
   signature:
     "TensorTuple (Tensor x, String data_format=\"channels_first\", Int32List padding,
                   Int32List kernel_size, Int32List stride, Int32List dilation, 
@@ -966,21 +966,21 @@
   signature: "Void (Tensor x, TensorIndex index, Tensor value) => TensorSetItem"
   bind_python: True
 
-- name: "avgpool_1d"
+- name: "avg_pool1d"
   signature:
     "Tensor (Tensor x, String data_format=\"channels_first\", Int32List padding,
              Int32List kernel_size, Int32List stride, Bool ceil_mode=False, Bool count_include_pad=True, 
              Int64 divisor_override=0) => Avgpool1D"
   bind_python: True 
 
-- name: "avgpool_2d"
+- name: "avg_pool2d"
   signature:
     "Tensor (Tensor x, String data_format=\"channels_first\", Int32List padding,
              Int32List kernel_size, Int32List stride, Bool ceil_mode=False, Bool count_include_pad=True, 
              Int64 divisor_override=0) => Avgpool2D"
   bind_python: True 
 
-- name: "avgpool_3d"
+- name: "avg_pool3d"
   signature:
     "Tensor (Tensor x, String data_format=\"channels_first\", Int32List padding,
              Int32List kernel_size, Int32List stride, Bool ceil_mode=False, Bool count_include_pad=True, 
diff --git a/python/oneflow/nn/functional/__init__.py b/python/oneflow/nn/functional/__init__.py
index ea5959b879b..de4107c32a9 100644
--- a/python/oneflow/nn/functional/__init__.py
+++ b/python/oneflow/nn/functional/__init__.py
@@ -20,12 +20,12 @@
 from oneflow._C import conv1d
 from oneflow._C import conv2d
 from oneflow._C import conv3d
-from oneflow._C import avgpool_1d
-from oneflow._C import avgpool_2d
-from oneflow._C import avgpool_3d
-from oneflow._C import maxpool_1d
-from oneflow._C import maxpool_2d
-from oneflow._C import maxpool_3d
+from oneflow._C import avg_pool1d
+from oneflow._C import avg_pool2d
+from oneflow._C import avg_pool3d
+from oneflow._C import max_pool1d
+from oneflow._C import max_pool2d
+from oneflow._C import max_pool3d
 from oneflow._C import adaptive_avg_pool1d
 from oneflow._C import adaptive_avg_pool2d
 from oneflow._C import adaptive_avg_pool3d
diff --git a/python/oneflow/nn/modules/pooling.py b/python/oneflow/nn/modules/pooling.py
index 48f713cfe1b..6ba7f1513d7 100644
--- a/python/oneflow/nn/modules/pooling.py
+++ b/python/oneflow/nn/modules/pooling.py
@@ -101,7 +101,7 @@ def __init__(
         self.ceil_mode = ceil_mode
 
     def forward(self, x):
-        y, indice = flow._C.maxpool_1d(
+        y, indice = flow._C.max_pool1d(
             x,
             data_format=self.channel_pos,
             padding=self.padding,
@@ -208,7 +208,7 @@ def __init__(
         self.padding = _pair(padding)
 
     def forward(self, x):
-        y, indice = flow._C.maxpool_2d(
+        y, indice = flow._C.max_pool2d(
             x,
             data_format=self.channel_pos,
             padding=self.padding,
@@ -322,7 +322,7 @@ def __init__(
         self.ceil_mode = ceil_mode
 
     def forward(self, x):
-        y, indice = flow._C.maxpool_3d(
+        y, indice = flow._C.max_pool3d(
             x,
             data_format=self.channel_pos,
             padding=self.padding,
@@ -375,9 +375,9 @@ class AvgPool1d(Module):
         import oneflow as flow 
         import numpy as np
 
-        of_avgpool1d = flow.nn.AvgPool1d(kernel_size=3, padding=1, stride=1)
-        x = flow.Tensor(np.random.randn(1, 4, 4))
-        y = of_avgpool1d(x)
+        m = flow.nn.AvgPool1d(kernel_size=3, padding=1, stride=1)
+        x = flow.tensor(np.random.randn(1, 4, 4))
+        y = m(x)
         y.shape 
         flow.Size([1, 4, 4])
 
@@ -403,7 +403,7 @@ def __init__(
         self.padding = _single(padding)
 
     def forward(self, x):
-        return flow._C.avgpool_1d(
+        return flow._C.avg_pool1d(
             x,
             kernel_size=self.kernel_size,
             stride=self.stride,
@@ -446,9 +446,9 @@ class AvgPool2d(Module):
         import oneflow as flow 
         import numpy as np 
 
-        of_avgpool2d = flow.nn.AvgPool2d(kernel_size=3, padding=1, stride=1)
-        x = flow.Tensor(np.random.randn(1, 4, 4, 4))
-        y = of_avgpool2d(x)   
+        m = flow.nn.AvgPool2d(kernel_size=3, padding=1, stride=1)
+        x = flow.tensor(np.random.randn(1, 4, 4, 4))
+        y = m(x)   
         y.shape
         flow.Size([1, 4, 4, 4])
 
@@ -476,7 +476,7 @@ def __init__(
         self.padding = _pair(padding)
 
     def forward(self, x):
-        return flow._C.avgpool_2d(
+        return flow._C.avg_pool2d(
             x,
             kernel_size=self.kernel_size,
             stride=self.stride,
@@ -540,7 +540,7 @@ class AvgPool3d(Module):
         import numpy as np
         
         m = flow.nn.AvgPool3d(kernel_size=(2,2,2),padding=(0,0,0),stride=(1,1,1))
-        x = flow.Tensor(np.random.randn(9, 7, 11, 32, 20))
+        x = flow.tensor(np.random.randn(9, 7, 11, 32, 20))
         y = m(x)
         y.shape
         flow.Size([9, 7, 10, 31, 19])
@@ -569,7 +569,7 @@ def __init__(
         self.padding = _triple(padding)
 
     def forward(self, x):
-        return flow._C.avgpool_3d(
+        return flow._C.avg_pool3d(
             x,
             kernel_size=self.kernel_size,
             stride=self.stride,