252 lines
8.9 KiB
Python
252 lines
8.9 KiB
Python
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#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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#Licensed under the Apache License, Version 2.0 (the "License");
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#you may not use this file except in compliance with the License.
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#You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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#Unless required by applicable law or agreed to in writing, software
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#distributed under the License is distributed on an "AS IS" BASIS,
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#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#See the License for the specific language governing permissions and
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#limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle.fluid as fluid
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from paddle.fluid.initializer import MSRA
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from paddle.fluid.param_attr import ParamAttr
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__all__ = ['MobileNetV3']
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class MobileNetV3():
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def __init__(self, params):
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"""
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the MobilenetV3 backbone network for detection module.
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Args:
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params(dict): the super parameters for build network
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"""
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self.scale = params['scale']
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model_name = params['model_name']
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self.inplanes = 16
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if model_name == "large":
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self.cfg = [
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# k, exp, c, se, nl, s,
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[3, 16, 16, False, 'relu', 1],
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[3, 64, 24, False, 'relu', 2],
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[3, 72, 24, False, 'relu', 1],
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[5, 72, 40, True, 'relu', 2],
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[5, 120, 40, True, 'relu', 1],
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[5, 120, 40, True, 'relu', 1],
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[3, 240, 80, False, 'hard_swish', 2],
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[3, 200, 80, False, 'hard_swish', 1],
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[3, 184, 80, False, 'hard_swish', 1],
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[3, 184, 80, False, 'hard_swish', 1],
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[3, 480, 112, True, 'hard_swish', 1],
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[3, 672, 112, True, 'hard_swish', 1],
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[5, 672, 160, True, 'hard_swish', 2],
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[5, 960, 160, True, 'hard_swish', 1],
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[5, 960, 160, True, 'hard_swish', 1],
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]
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self.cls_ch_squeeze = 960
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self.cls_ch_expand = 1280
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elif model_name == "small":
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self.cfg = [
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# k, exp, c, se, nl, s,
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[3, 16, 16, True, 'relu', 2],
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[3, 72, 24, False, 'relu', 2],
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[3, 88, 24, False, 'relu', 1],
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[5, 96, 40, True, 'hard_swish', 2],
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[5, 240, 40, True, 'hard_swish', 1],
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[5, 240, 40, True, 'hard_swish', 1],
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[5, 120, 48, True, 'hard_swish', 1],
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[5, 144, 48, True, 'hard_swish', 1],
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[5, 288, 96, True, 'hard_swish', 2],
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[5, 576, 96, True, 'hard_swish', 1],
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[5, 576, 96, True, 'hard_swish', 1],
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]
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self.cls_ch_squeeze = 576
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self.cls_ch_expand = 1280
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else:
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raise NotImplementedError("mode[" + model_name +
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"_model] is not implemented!")
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supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
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assert self.scale in supported_scale, \
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"supported scale are {} but input scale is {}".format(supported_scale, self.scale)
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def __call__(self, input):
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scale = self.scale
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inplanes = self.inplanes
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cfg = self.cfg
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cls_ch_squeeze = self.cls_ch_squeeze
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cls_ch_expand = self.cls_ch_expand
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#conv1
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conv = self.conv_bn_layer(
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input,
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filter_size=3,
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num_filters=self.make_divisible(inplanes * scale),
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stride=2,
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padding=1,
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num_groups=1,
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if_act=True,
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act='hard_swish',
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name='conv1')
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i = 0
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inplanes = self.make_divisible(inplanes * scale)
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outs = []
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for layer_cfg in cfg:
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if layer_cfg[5] == 2 and i > 2:
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outs.append(conv)
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conv = self.residual_unit(
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input=conv,
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num_in_filter=inplanes,
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num_mid_filter=self.make_divisible(scale * layer_cfg[1]),
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num_out_filter=self.make_divisible(scale * layer_cfg[2]),
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act=layer_cfg[4],
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stride=layer_cfg[5],
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filter_size=layer_cfg[0],
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use_se=layer_cfg[3],
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name='conv' + str(i + 2))
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inplanes = self.make_divisible(scale * layer_cfg[2])
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i += 1
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conv = self.conv_bn_layer(
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input=conv,
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filter_size=1,
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num_filters=self.make_divisible(scale * cls_ch_squeeze),
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stride=1,
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padding=0,
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num_groups=1,
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if_act=True,
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act='hard_swish',
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name='conv_last')
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outs.append(conv)
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return outs
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def conv_bn_layer(self,
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input,
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filter_size,
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num_filters,
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stride,
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padding,
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num_groups=1,
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if_act=True,
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act=None,
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name=None,
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use_cudnn=True,
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res_last_bn_init=False):
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conv = fluid.layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=padding,
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groups=num_groups,
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act=None,
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use_cudnn=use_cudnn,
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param_attr=ParamAttr(name=name + '_weights'),
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bias_attr=False)
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bn_name = name + '_bn'
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bn = fluid.layers.batch_norm(
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input=conv,
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param_attr=ParamAttr(
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name=bn_name + "_scale",
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=0.0)),
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bias_attr=ParamAttr(
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name=bn_name + "_offset",
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=0.0)),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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if if_act:
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if act == 'relu':
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bn = fluid.layers.relu(bn)
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elif act == 'hard_swish':
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bn = fluid.layers.hard_swish(bn)
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return bn
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def make_divisible(self, v, divisor=8, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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def se_block(self, input, num_out_filter, ratio=4, name=None):
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num_mid_filter = num_out_filter // ratio
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pool = fluid.layers.pool2d(
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input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
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conv1 = fluid.layers.conv2d(
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input=pool,
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filter_size=1,
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num_filters=num_mid_filter,
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act='relu',
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param_attr=ParamAttr(name=name + '_1_weights'),
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bias_attr=ParamAttr(name=name + '_1_offset'))
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conv2 = fluid.layers.conv2d(
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input=conv1,
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filter_size=1,
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num_filters=num_out_filter,
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act='hard_sigmoid',
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param_attr=ParamAttr(name=name + '_2_weights'),
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bias_attr=ParamAttr(name=name + '_2_offset'))
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scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0)
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return scale
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def residual_unit(self,
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input,
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num_in_filter,
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num_mid_filter,
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num_out_filter,
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stride,
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filter_size,
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act=None,
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use_se=False,
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name=None):
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conv0 = self.conv_bn_layer(
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input=input,
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filter_size=1,
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num_filters=num_mid_filter,
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stride=1,
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padding=0,
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if_act=True,
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act=act,
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name=name + '_expand')
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conv1 = self.conv_bn_layer(
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input=conv0,
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filter_size=filter_size,
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num_filters=num_mid_filter,
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stride=stride,
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padding=int((filter_size - 1) // 2),
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if_act=True,
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act=act,
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num_groups=num_mid_filter,
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use_cudnn=False,
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name=name + '_depthwise')
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if use_se:
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conv1 = self.se_block(
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input=conv1, num_out_filter=num_mid_filter, name=name + '_se')
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conv2 = self.conv_bn_layer(
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input=conv1,
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filter_size=1,
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num_filters=num_out_filter,
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stride=1,
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padding=0,
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if_act=False,
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name=name + '_linear',
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res_last_bn_init=True)
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if num_in_filter != num_out_filter or stride != 1:
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return conv2
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else:
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return fluid.layers.elementwise_add(x=input, y=conv2, act=None)
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