fix hrnet name

ppcls/modeling/architectures/hrnet.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 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
+# 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.
+# 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
@@ -74,7 +74,7 @@ class HRNet():
         tr3 = self.transition_layer(st3, channels_3, channels_4, name='tr3')
         st4 = self.stage(tr3, num_modules_4, channels_4, name='st4')
 
-        #classification
+        # classification
         last_cls = self.last_cls_out(x=st4, name='cls_head')
         y = last_cls[0]
         last_num_filters = [256, 512, 1024]
@@ -273,7 +273,7 @@ class HRNet():
                 input=conv,
                 num_channels=num_filters,
                 reduction_ratio=16,
-                name=name + '_fc')
+                name="fc" + name)
         return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
 
     def bottleneck_block(self,
@@ -312,7 +312,7 @@ class HRNet():
                 input=conv,
                 num_channels=num_filters * 4,
                 reduction_ratio=16,
-                name=name + '_fc')
+                name="fc" + name)
         return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
 
     def squeeze_excitation(self,
@@ -325,7 +325,7 @@ class HRNet():
         stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
         squeeze = fluid.layers.fc(
             input=pool,
-            size=num_channels / reduction_ratio,
+            size=int(num_channels / reduction_ratio),
             act='relu',
             param_attr=fluid.param_attr.ParamAttr(
                 initializer=fluid.initializer.Uniform(-stdv, stdv),