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Merge pull request #179 from littletomatodonkey/add_log_int 

fix hrnet name
pull/180/head
littletomatodonkey 2020-06-24 21:13:33 +08:00 committed by GitHub
parent 27f5ac5a7e 5a37683749
commit 2c0b0f0d18
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1 changed files with 13 additions and 13 deletions
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26
ppcls/modeling/architectures/hrnet.py
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@ -1,16 +1,16 @@
#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),