355 lines
12 KiB
Python
355 lines
12 KiB
Python
# 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 numpy as np
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
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from paddle.fluid.initializer import MSRA
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import math
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__all__ = [
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"ShuffleNetV2_x0_25", "ShuffleNetV2_x0_33", "ShuffleNetV2_x0_5",
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"ShuffleNetV2_x1_0", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0",
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"ShuffleNetV2_swish"
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]
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def channel_shuffle(x, groups):
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batchsize, num_channels, height, width = x.shape[0], x.shape[1], x.shape[
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2], x.shape[3]
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channels_per_group = num_channels // groups
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# reshape
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x = fluid.layers.reshape(
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x=x, shape=[batchsize, groups, channels_per_group, height, width])
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x = fluid.layers.transpose(x=x, perm=[0, 2, 1, 3, 4])
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# flatten
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x = fluid.layers.reshape(
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x=x, shape=[batchsize, num_channels, height, width])
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return x
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class ConvBNLayer(fluid.dygraph.Layer):
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def __init__(self,
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num_channels,
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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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channels=None,
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num_groups=1,
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if_act=True,
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act='relu',
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name=None,
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use_cudnn=True):
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super(ConvBNLayer, self).__init__()
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self._if_act = if_act
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assert act in ['relu', 'swish'], \
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"supported act are {} but your act is {}".format(
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['relu', 'swish'], act)
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self._act = act
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self._conv = Conv2D(
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num_channels=num_channels,
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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(
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initializer=MSRA(), name=name + "_weights"),
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bias_attr=False)
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self._batch_norm = BatchNorm(
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num_filters,
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param_attr=ParamAttr(name=name + "_bn_scale"),
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bias_attr=ParamAttr(name=name + "_bn_offset"),
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moving_mean_name=name + "_bn_mean",
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moving_variance_name=name + "_bn_variance")
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def forward(self, inputs, if_act=True):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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if self._if_act:
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y = fluid.layers.relu(
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y) if self._act == 'relu' else fluid.layers.swish(y)
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return y
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class InvertedResidualUnit(fluid.dygraph.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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stride,
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benchmodel,
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act='relu',
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name=None):
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super(InvertedResidualUnit, self).__init__()
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assert stride in [1, 2], \
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"supported stride are {} but your stride is {}".format([
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1, 2], stride)
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self.benchmodel = benchmodel
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oup_inc = num_filters // 2
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inp = num_channels
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if benchmodel == 1:
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self._conv_pw = ConvBNLayer(
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num_channels=num_channels // 2,
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num_filters=oup_inc,
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filter_size=1,
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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=act,
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name='stage_' + name + '_conv1')
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self._conv_dw = ConvBNLayer(
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num_channels=oup_inc,
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num_filters=oup_inc,
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filter_size=3,
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stride=stride,
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padding=1,
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num_groups=oup_inc,
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if_act=False,
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act=act,
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use_cudnn=False,
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name='stage_' + name + '_conv2')
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self._conv_linear = ConvBNLayer(
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num_channels=oup_inc,
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num_filters=oup_inc,
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filter_size=1,
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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=act,
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name='stage_' + name + '_conv3')
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else:
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# branch1
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self._conv_dw_1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=inp,
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filter_size=3,
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stride=stride,
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padding=1,
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num_groups=inp,
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if_act=False,
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act=act,
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use_cudnn=False,
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name='stage_' + name + '_conv4')
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self._conv_linear_1 = ConvBNLayer(
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num_channels=inp,
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num_filters=oup_inc,
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filter_size=1,
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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=act,
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name='stage_' + name + '_conv5')
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# branch2
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self._conv_pw_2 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=oup_inc,
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filter_size=1,
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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=act,
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name='stage_' + name + '_conv1')
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self._conv_dw_2 = ConvBNLayer(
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num_channels=oup_inc,
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num_filters=oup_inc,
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filter_size=3,
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stride=stride,
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padding=1,
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num_groups=oup_inc,
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if_act=False,
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act=act,
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use_cudnn=False,
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name='stage_' + name + '_conv2')
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self._conv_linear_2 = ConvBNLayer(
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num_channels=oup_inc,
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num_filters=oup_inc,
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filter_size=1,
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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=act,
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name='stage_' + name + '_conv3')
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def forward(self, inputs):
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if self.benchmodel == 1:
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x1, x2 = fluid.layers.split(
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inputs,
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num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2],
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dim=1)
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x2 = self._conv_pw(x2)
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x2 = self._conv_dw(x2)
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x2 = self._conv_linear(x2)
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out = fluid.layers.concat([x1, x2], axis=1)
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else:
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x1 = self._conv_dw_1(inputs)
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x1 = self._conv_linear_1(x1)
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x2 = self._conv_pw_2(inputs)
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x2 = self._conv_dw_2(x2)
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x2 = self._conv_linear_2(x2)
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out = fluid.layers.concat([x1, x2], axis=1)
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return channel_shuffle(out, 2)
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class ShuffleNet(fluid.dygraph.Layer):
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def __init__(self, class_dim=1000, scale=1.0, act='relu'):
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super(ShuffleNet, self).__init__()
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self.scale = scale
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self.class_dim = class_dim
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stage_repeats = [4, 8, 4]
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if scale == 0.25:
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stage_out_channels = [-1, 24, 24, 48, 96, 512]
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elif scale == 0.33:
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stage_out_channels = [-1, 24, 32, 64, 128, 512]
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elif scale == 0.5:
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stage_out_channels = [-1, 24, 48, 96, 192, 1024]
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elif scale == 1.0:
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stage_out_channels = [-1, 24, 116, 232, 464, 1024]
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elif scale == 1.5:
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stage_out_channels = [-1, 24, 176, 352, 704, 1024]
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elif scale == 2.0:
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stage_out_channels = [-1, 24, 224, 488, 976, 2048]
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else:
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raise NotImplementedError("This scale size:[" + str(scale) +
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"] is not implemented!")
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# 1. conv1
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self._conv1 = ConvBNLayer(
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num_channels=3,
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num_filters=stage_out_channels[1],
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filter_size=3,
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stride=2,
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padding=1,
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if_act=True,
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act=act,
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name='stage1_conv')
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self._max_pool = Pool2D(
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pool_type='max', pool_size=3, pool_stride=2, pool_padding=1)
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# 2. bottleneck sequences
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self._block_list = []
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i = 1
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in_c = int(32 * scale)
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for idxstage in range(len(stage_repeats)):
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numrepeat = stage_repeats[idxstage]
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output_channel = stage_out_channels[idxstage + 2]
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for i in range(numrepeat):
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if i == 0:
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block = self.add_sublayer(
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str(idxstage + 2) + '_' + str(i + 1),
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InvertedResidualUnit(
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num_channels=stage_out_channels[idxstage + 1],
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num_filters=output_channel,
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stride=2,
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benchmodel=2,
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act=act,
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name=str(idxstage + 2) + '_' + str(i + 1)))
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self._block_list.append(block)
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else:
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block = self.add_sublayer(
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str(idxstage + 2) + '_' + str(i + 1),
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InvertedResidualUnit(
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num_channels=output_channel,
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num_filters=output_channel,
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stride=1,
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benchmodel=1,
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act=act,
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name=str(idxstage + 2) + '_' + str(i + 1)))
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self._block_list.append(block)
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# 3. last_conv
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self._last_conv = ConvBNLayer(
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num_channels=stage_out_channels[-2],
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num_filters=stage_out_channels[-1],
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filter_size=1,
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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='conv5')
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# 4. pool
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self._pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
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self._out_c = stage_out_channels[-1]
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# 5. fc
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self._fc = Linear(
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stage_out_channels[-1],
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class_dim,
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param_attr=ParamAttr(name='fc6_weights'),
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bias_attr=ParamAttr(name='fc6_offset'))
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def forward(self, inputs):
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y = self._conv1(inputs)
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y = self._max_pool(y)
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for inv in self._block_list:
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y = inv(y)
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y = self._last_conv(y)
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y = self._pool2d_avg(y)
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y = fluid.layers.reshape(y, shape=[-1, self._out_c])
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y = self._fc(y)
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return y
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def ShuffleNetV2_x0_25(**args):
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model = ShuffleNetV2(scale=0.25, **args)
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return model
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def ShuffleNetV2_x0_33(**args):
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model = ShuffleNet(scale=0.33, **args)
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return model
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def ShuffleNetV2_x0_5(**args):
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model = ShuffleNet(scale=0.5, **args)
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return model
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def ShuffleNetV2(**args):
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model = ShuffleNet(scale=1.0, **args)
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return model
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def ShuffleNetV2_x1_5(**args):
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model = ShuffleNet(scale=1.5, **args)
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return model
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def ShuffleNetV2_x2_0(**args):
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model = ShuffleNet(scale=2.0, **args)
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return model
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def ShuffleNetV2_swish(**args):
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model = ShuffleNet(scale=1.0, act='swish', **args)
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return model
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