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fix lr mult

pull/168/head
littletomatodonkey 2020-06-16 09:02:44 +00:00
parent 91ac110f55
commit f51b63c2e5
1 changed files with 20 additions and 13 deletions
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33
ppcls/modeling/architectures/resnet_vd.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
@ -49,7 +49,8 @@ class ResNet():
layers = self.layers
supported_layers = [18, 34, 50, 101, 152, 200]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers, layers)
"supported layers are {} but input layer is {}".format(
supported_layers, layers)
if layers == 18:
depth = [2, 2, 2, 2]
@ -159,7 +160,9 @@ class ResNet():
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights" + self.postfix_name),
param_attr=ParamAttr(
name=name + "_weights" + self.postfix_name,
learning_rate=lr_mult),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
@ -168,8 +171,12 @@ class ResNet():
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale' + self.postfix_name),
bias_attr=ParamAttr(bn_name + '_offset' + self.postfix_name),
param_attr=ParamAttr(
name=bn_name + '_scale' + self.postfix_name,
learning_rate=lr_mult),
bias_attr=ParamAttr(
bn_name + '_offset' + self.postfix_name,
learning_rate=lr_mult),
moving_mean_name=bn_name + '_mean' + self.postfix_name,
moving_variance_name=bn_name + '_variance' + self.postfix_name)