308 lines
9.6 KiB
Python
308 lines
9.6 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 math
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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__ = [
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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'
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]
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class ShuffleNetV2():
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def __init__(self, scale=1.0):
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self.scale = scale
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def net(self, input, class_dim=1000):
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scale = self.scale
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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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#conv1
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input_channel = stage_out_channels[1]
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conv1 = self.conv_bn_layer(
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input=input,
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filter_size=3,
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num_filters=input_channel,
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padding=1,
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stride=2,
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name='stage1_conv')
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pool1 = fluid.layers.pool2d(
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input=conv1,
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pool_size=3,
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pool_stride=2,
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pool_padding=1,
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pool_type='max')
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conv = pool1
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# bottleneck sequences
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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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conv = self.inverted_residual_unit(
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input=conv,
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num_filters=output_channel,
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stride=2,
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benchmodel=2,
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name=str(idxstage + 2) + '_' + str(i + 1))
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else:
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conv = self.inverted_residual_unit(
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input=conv,
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num_filters=output_channel,
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stride=1,
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benchmodel=1,
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name=str(idxstage + 2) + '_' + str(i + 1))
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conv_last = self.conv_bn_layer(
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input=conv,
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filter_size=1,
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num_filters=stage_out_channels[-1],
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padding=0,
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stride=1,
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name='conv5')
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pool_last = fluid.layers.pool2d(
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input=conv_last,
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pool_size=7,
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pool_stride=1,
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pool_padding=0,
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pool_type='avg')
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output = fluid.layers.fc(input=pool_last,
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size=class_dim,
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param_attr=ParamAttr(
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initializer=MSRA(), name='fc6_weights'),
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bias_attr=ParamAttr(name='fc6_offset'))
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return output
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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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use_cudnn=True,
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if_act=True,
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name=None):
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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(
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initializer=MSRA(), name=name + '_weights'),
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bias_attr=False)
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out = int((input.shape[2] - 1) / float(stride) + 1)
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bn_name = name + '_bn'
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if if_act:
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return fluid.layers.batch_norm(
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input=conv,
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act='relu',
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param_attr=ParamAttr(name=bn_name + "_scale"),
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bias_attr=ParamAttr(name=bn_name + "_offset"),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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else:
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return fluid.layers.batch_norm(
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input=conv,
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param_attr=ParamAttr(name=bn_name + "_scale"),
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bias_attr=ParamAttr(name=bn_name + "_offset"),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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def channel_shuffle(self, x, groups):
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batchsize, num_channels, height, width = x.shape[0], x.shape[
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1], x.shape[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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def inverted_residual_unit(self,
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input,
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num_filters,
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stride,
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benchmodel,
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name=None):
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assert stride in [1, 2], \
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"supported stride are {} but your stride is {}".format([1,2], stride)
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oup_inc = num_filters // 2
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inp = input.shape[1]
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if benchmodel == 1:
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x1, x2 = fluid.layers.split(
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input,
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num_or_sections=[input.shape[1] // 2, input.shape[1] // 2],
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dim=1)
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conv_pw = self.conv_bn_layer(
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input=x2,
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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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name='stage_' + name + '_conv1')
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conv_dw = self.conv_bn_layer(
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input=conv_pw,
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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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use_cudnn=False,
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name='stage_' + name + '_conv2')
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conv_linear = self.conv_bn_layer(
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input=conv_dw,
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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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name='stage_' + name + '_conv3')
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out = fluid.layers.concat([x1, conv_linear], axis=1)
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else:
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#branch1
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conv_dw_1 = self.conv_bn_layer(
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input=input,
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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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use_cudnn=False,
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name='stage_' + name + '_conv4')
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conv_linear_1 = self.conv_bn_layer(
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input=conv_dw_1,
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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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name='stage_' + name + '_conv5')
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#branch2
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conv_pw_2 = self.conv_bn_layer(
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input=input,
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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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name='stage_' + name + '_conv1')
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conv_dw_2 = self.conv_bn_layer(
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input=conv_pw_2,
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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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use_cudnn=False,
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name='stage_' + name + '_conv2')
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conv_linear_2 = self.conv_bn_layer(
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input=conv_dw_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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name='stage_' + name + '_conv3')
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out = fluid.layers.concat([conv_linear_1, conv_linear_2], axis=1)
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return self.channel_shuffle(out, 2)
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def ShuffleNetV2_x0_25():
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model = ShuffleNetV2(scale=0.25)
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return model
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def ShuffleNetV2_x0_33():
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model = ShuffleNetV2(scale=0.33)
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return model
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def ShuffleNetV2_x0_5():
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model = ShuffleNetV2(scale=0.5)
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return model
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def ShuffleNetV2_x1_0():
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model = ShuffleNetV2(scale=1.0)
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return model
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def ShuffleNetV2_x1_5():
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model = ShuffleNetV2(scale=1.5)
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return model
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def ShuffleNetV2_x2_0():
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model = ShuffleNetV2(scale=2.0)
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return model
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