forked from PulseFocusPlatform/PulseFocusPlatform
219 lines
7.9 KiB
Python
219 lines
7.9 KiB
Python
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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from paddle import fluid
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from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid.regularizer import L2Decay
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from ppdet.experimental import mixed_precision_global_state
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from ppdet.core.workspace import register
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__all__ = ['MobileNet']
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@register
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class MobileNet(object):
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"""
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MobileNet v1, see https://arxiv.org/abs/1704.04861
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Args:
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norm_type (str): normalization type, 'bn' and 'sync_bn' are supported
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norm_decay (float): weight decay for normalization layer weights
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conv_decay (float): weight decay for convolution layer weights.
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conv_group_scale (int): scaling factor for convolution groups
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with_extra_blocks (bool): if extra blocks should be added
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extra_block_filters (list): number of filter for each extra block
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"""
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__shared__ = ['norm_type', 'weight_prefix_name']
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def __init__(self,
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norm_type='bn',
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norm_decay=0.,
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conv_decay=0.,
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conv_group_scale=1,
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conv_learning_rate=1.0,
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with_extra_blocks=False,
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extra_block_filters=[[256, 512], [128, 256], [128, 256],
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[64, 128]],
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weight_prefix_name=''):
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self.norm_type = norm_type
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self.norm_decay = norm_decay
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self.conv_decay = conv_decay
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self.conv_group_scale = conv_group_scale
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self.conv_learning_rate = conv_learning_rate
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self.with_extra_blocks = with_extra_blocks
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self.extra_block_filters = extra_block_filters
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self.prefix_name = weight_prefix_name
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def _conv_norm(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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act='relu',
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use_cudnn=True,
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name=None):
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parameter_attr = ParamAttr(
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learning_rate=self.conv_learning_rate,
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initializer=fluid.initializer.MSRA(),
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regularizer=L2Decay(self.conv_decay),
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name=name + "_weights")
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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=parameter_attr,
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bias_attr=False)
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bn_name = name + "_bn"
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norm_decay = self.norm_decay
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bn_param_attr = ParamAttr(
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regularizer=L2Decay(norm_decay), name=bn_name + '_scale')
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bn_bias_attr = ParamAttr(
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regularizer=L2Decay(norm_decay), name=bn_name + '_offset')
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return fluid.layers.batch_norm(
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input=conv,
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act=act,
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param_attr=bn_param_attr,
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bias_attr=bn_bias_attr,
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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 depthwise_separable(self,
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input,
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num_filters1,
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num_filters2,
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num_groups,
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stride,
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scale,
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name=None):
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mixed_precision_enabled = mixed_precision_global_state() is not None
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depthwise_conv = self._conv_norm(
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input=input,
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filter_size=3,
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num_filters=int(num_filters1 * scale),
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stride=stride,
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padding=1,
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num_groups=int(num_groups * scale),
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use_cudnn=mixed_precision_enabled,
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name=name + "_dw")
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pointwise_conv = self._conv_norm(
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input=depthwise_conv,
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filter_size=1,
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num_filters=int(num_filters2 * scale),
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stride=1,
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padding=0,
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name=name + "_sep")
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return pointwise_conv
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def _extra_block(self,
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input,
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num_filters1,
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num_filters2,
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num_groups,
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stride,
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name=None):
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pointwise_conv = self._conv_norm(
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input=input,
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filter_size=1,
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num_filters=int(num_filters1),
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stride=1,
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num_groups=int(num_groups),
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padding=0,
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act='relu6',
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name=name + "_extra1")
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normal_conv = self._conv_norm(
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input=pointwise_conv,
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filter_size=3,
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num_filters=int(num_filters2),
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stride=2,
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num_groups=int(num_groups),
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padding=1,
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act='relu6',
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name=name + "_extra2")
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return normal_conv
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def __call__(self, input):
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scale = self.conv_group_scale
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blocks = []
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# input 1/1
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out = self._conv_norm(
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input, 3, int(32 * scale), 2, 1, name=self.prefix_name + "conv1")
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# 1/2
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out = self.depthwise_separable(
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out, 32, 64, 32, 1, scale, name=self.prefix_name + "conv2_1")
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out = self.depthwise_separable(
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out, 64, 128, 64, 2, scale, name=self.prefix_name + "conv2_2")
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# 1/4
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out = self.depthwise_separable(
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out, 128, 128, 128, 1, scale, name=self.prefix_name + "conv3_1")
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out = self.depthwise_separable(
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out, 128, 256, 128, 2, scale, name=self.prefix_name + "conv3_2")
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# 1/8
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blocks.append(out)
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out = self.depthwise_separable(
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out, 256, 256, 256, 1, scale, name=self.prefix_name + "conv4_1")
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out = self.depthwise_separable(
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out, 256, 512, 256, 2, scale, name=self.prefix_name + "conv4_2")
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# 1/16
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blocks.append(out)
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for i in range(5):
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out = self.depthwise_separable(
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out,
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512,
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512,
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512,
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1,
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scale,
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name=self.prefix_name + "conv5_" + str(i + 1))
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module11 = out
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out = self.depthwise_separable(
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out, 512, 1024, 512, 2, scale, name=self.prefix_name + "conv5_6")
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# 1/32
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out = self.depthwise_separable(
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out, 1024, 1024, 1024, 1, scale, name=self.prefix_name + "conv6")
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module13 = out
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blocks.append(out)
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if not self.with_extra_blocks:
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return blocks
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num_filters = self.extra_block_filters
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module14 = self._extra_block(module13, num_filters[0][0],
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num_filters[0][1], 1, 2,
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self.prefix_name + "conv7_1")
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module15 = self._extra_block(module14, num_filters[1][0],
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num_filters[1][1], 1, 2,
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self.prefix_name + "conv7_2")
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module16 = self._extra_block(module15, num_filters[2][0],
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num_filters[2][1], 1, 2,
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self.prefix_name + "conv7_3")
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module17 = self._extra_block(module16, num_filters[3][0],
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num_filters[3][1], 1, 2,
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self.prefix_name + "conv7_4")
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return module11, module13, module14, module15, module16, module17
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