diff --git a/pdseg/models/backbone/resnet.py b/pdseg/models/backbone/resnet.py
index b1d7893dc75ec03aeca79973e46129dd654dac36..6eb9f12bc33c97f42664327ada3712c8283943b1 100644
--- a/pdseg/models/backbone/resnet.py
+++ b/pdseg/models/backbone/resnet.py
@@ -139,14 +139,15 @@ class ResNet():
                         else:
                             conv_name = "res" + str(block + 2) + "b" + str(i)
                     else:
-                        conv_name = "conv" + str(block + 2) + '_' + str(1 + i)
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
                     dilation_rate = get_dilated_rate(dilation_dict, block)
+                    
                     conv = self.bottleneck_block(
                         input=conv,
                         num_filters=int(num_filters[block] * self.scale),
                         stride=2
                         if i == 0 and block != 0 and dilation_rate == 1 else 1,
-                        name=conv_name, 
+                        name=conv_name,
                         dilation=dilation_rate)
                     layer_count += 3
 
@@ -216,7 +217,7 @@ class ResNet():
                       name=None):
    
         if self.stem == 'pspnet':
-            bias_attr=ParamAttr(name=name + "/biases")
+            bias_attr=ParamAttr(name=name + "_biases")
         else:
             bias_attr=False
 
@@ -229,20 +230,21 @@ class ResNet():
             dilation=dilation,
             groups=groups,
             act=None,
-            param_attr=ParamAttr(name=name + "/weights"),
+            param_attr=ParamAttr(name=name + "_weights"),
             bias_attr=bias_attr,
             name=name + '.conv2d.output.1')
 
-        bn_name = name + '/BatchNorm/'
-        return fluid.layers.batch_norm(
-            input=conv,
-            act=act,
-            name=bn_name + '.output.1',
-            param_attr=ParamAttr(name=bn_name + 'gamma'),
-            bias_attr=ParamAttr(bn_name + 'beta'),
-            moving_mean_name=bn_name + 'moving_mean',
-            moving_variance_name=bn_name + 'moving_variance',
-        )
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        return fluid.layers.batch_norm(input=conv,
+                                       act=act,
+                                       name=bn_name + '.output.1',
+                                       param_attr=ParamAttr(name=bn_name + '_scale'),
+                                       bias_attr=ParamAttr(bn_name + '_offset'),
+                                       moving_mean_name=bn_name + '_mean',
+                                       moving_variance_name=bn_name + '_variance', )
 
     def shortcut(self, input, ch_out, stride, is_first, name):
         ch_in = input.shape[1]
diff --git a/pdseg/models/modeling/pspnet.py b/pdseg/models/modeling/pspnet.py
index ad0c5abe1f2ed78e3824151db9f6396850c80f6f..5792f46d35a1d81fef71f29dbca20dc6bac3da8d 100644
--- a/pdseg/models/modeling/pspnet.py
+++ b/pdseg/models/modeling/pspnet.py
@@ -1,13 +1,26 @@
-# -*- coding: utf-8 -*- 
+# coding: utf8
+# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-import math
-import paddle
+
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 from models.libs.model_libs import scope, name_scope
-from models.libs.model_libs import avg_pool , conv, bn
+from models.libs.model_libs import avg_pool, conv, bn
 from models.backbone.resnet import ResNet as resnet_backbone
 from utils.config import cfg