forked from PulseFocusPlatform/PulseFocusPlatform
360 lines
9.6 KiB
Python
360 lines
9.6 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 ppdet.core.workspace import register
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__all__ = ['FaceBoxNet']
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@register
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class FaceBoxNet(object):
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"""
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FaceBoxes, see https://https://arxiv.org/abs/1708.05234
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Args:
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with_extra_blocks (bool): whether or not extra blocks should be added
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lite_edition (bool): whether or not is FaceBoxes-lite
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"""
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def __init__(self, with_extra_blocks=True, lite_edition=False):
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super(FaceBoxNet, self).__init__()
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self.with_extra_blocks = with_extra_blocks
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self.lite_edition = lite_edition
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def __call__(self, input):
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if self.lite_edition:
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return self._simplified_edition(input)
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else:
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return self._original_edition(input)
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def _simplified_edition(self, input):
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conv_1_1 = self._conv_norm_crelu(
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input=input,
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num_filters=8,
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filter_size=3,
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stride=2,
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padding=1,
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act='relu',
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name="conv_1_1")
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conv_1_2 = self._conv_norm_crelu(
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input=conv_1_1,
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num_filters=24,
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filter_size=3,
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stride=2,
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padding=1,
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act='relu',
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name="conv_1_2")
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pool1 = fluid.layers.pool2d(
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input=conv_1_2,
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pool_size=3,
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pool_padding=1,
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pool_type='avg',
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name="pool_1")
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conv_2_1 = self._conv_norm(
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input=pool1,
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num_filters=48,
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filter_size=3,
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stride=2,
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padding=1,
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act='relu',
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name="conv_2_1")
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conv_2_2 = self._conv_norm(
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input=conv_2_1,
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num_filters=64,
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filter_size=1,
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stride=1,
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padding=0,
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act='relu',
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name="conv_2_2")
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conv_inception = conv_2_2
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for i in range(3):
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conv_inception = self._inceptionA(conv_inception, i)
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layers = []
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layers.append(conv_inception)
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conv_3_1 = self._conv_norm(
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input=conv_inception,
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num_filters=128,
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filter_size=1,
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stride=1,
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padding=0,
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act='relu',
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name="conv_3_1")
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conv_3_2 = self._conv_norm(
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input=conv_3_1,
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num_filters=256,
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filter_size=3,
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stride=2,
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padding=1,
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act='relu',
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name="conv_3_2")
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layers.append(conv_3_2)
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if not self.with_extra_blocks:
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return layers[-1]
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return layers[-2], layers[-1]
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def _original_edition(self, input):
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conv_1 = self._conv_norm_crelu(
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input=input,
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num_filters=24,
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filter_size=7,
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stride=4,
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padding=3,
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act='relu',
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name="conv_1")
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pool_1 = fluid.layers.pool2d(
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input=conv_1,
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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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name="pool_1")
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conv_2 = self._conv_norm_crelu(
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input=pool_1,
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num_filters=64,
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filter_size=5,
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stride=2,
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padding=2,
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act='relu',
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name="conv_2")
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pool_2 = fluid.layers.pool2d(
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input=conv_1,
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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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name="pool_2")
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conv_inception = pool_2
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for i in range(3):
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conv_inception = self._inceptionA(conv_inception, i)
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layers = []
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layers.append(conv_inception)
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conv_3_1 = self._conv_norm(
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input=conv_inception,
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num_filters=128,
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filter_size=1,
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stride=1,
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padding=0,
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act='relu',
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name="conv_3_1")
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conv_3_2 = self._conv_norm(
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input=conv_3_1,
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num_filters=256,
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filter_size=3,
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stride=2,
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padding=1,
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act='relu',
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name="conv_3_2")
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layers.append(conv_3_2)
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conv_4_1 = self._conv_norm(
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input=conv_3_2,
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num_filters=128,
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filter_size=1,
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stride=1,
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padding=0,
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act='relu',
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name="conv_4_1")
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conv_4_2 = self._conv_norm(
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input=conv_4_1,
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num_filters=256,
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filter_size=3,
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stride=2,
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padding=1,
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act='relu',
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name="conv_4_2")
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layers.append(conv_4_2)
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if not self.with_extra_blocks:
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return layers[-1]
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return layers[-3], layers[-2], layers[-1]
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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=0.1,
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initializer=fluid.initializer.MSRA(),
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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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return fluid.layers.batch_norm(input=conv, act=act)
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def _conv_norm_crelu(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=0.1,
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initializer=fluid.initializer.MSRA(),
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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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conv_a = fluid.layers.batch_norm(input=conv, act=act)
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conv_b = fluid.layers.scale(conv_a, -1)
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concat = fluid.layers.concat([conv_a, conv_b], axis=1)
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return concat
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def _pooling_block(self,
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conv,
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pool_size,
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pool_stride,
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pool_padding=0,
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ceil_mode=True):
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pool = fluid.layers.pool2d(
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input=conv,
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pool_size=pool_size,
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pool_type='max',
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pool_stride=pool_stride,
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pool_padding=pool_padding,
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ceil_mode=ceil_mode)
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return pool
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def _inceptionA(self, data, idx):
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idx = str(idx)
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pool1 = fluid.layers.pool2d(
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input=data,
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pool_size=3,
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pool_padding=1,
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pool_type='avg',
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name='inceptionA_' + idx + '_pool1')
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conv1 = self._conv_norm(
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input=pool1,
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filter_size=1,
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num_filters=32,
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stride=1,
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padding=0,
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act='relu',
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name='inceptionA_' + idx + '_conv1')
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conv2 = self._conv_norm(
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input=data,
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filter_size=1,
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num_filters=32,
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stride=1,
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padding=0,
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act='relu',
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name='inceptionA_' + idx + '_conv2')
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conv3 = self._conv_norm(
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input=data,
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filter_size=1,
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num_filters=24,
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stride=1,
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padding=0,
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act='relu',
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name='inceptionA_' + idx + '_conv3_1')
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conv3 = self._conv_norm(
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input=conv3,
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filter_size=3,
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num_filters=32,
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stride=1,
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padding=1,
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act='relu',
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name='inceptionA_' + idx + '_conv3_2')
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conv4 = self._conv_norm(
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input=data,
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filter_size=1,
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num_filters=24,
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stride=1,
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padding=0,
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act='relu',
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name='inceptionA_' + idx + '_conv4_1')
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conv4 = self._conv_norm(
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input=conv4,
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filter_size=3,
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num_filters=32,
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stride=1,
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padding=1,
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act='relu',
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name='inceptionA_' + idx + '_conv4_2')
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conv4 = self._conv_norm(
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input=conv4,
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filter_size=3,
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num_filters=32,
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stride=1,
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padding=1,
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act='relu',
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name='inceptionA_' + idx + '_conv4_3')
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concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
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return concat
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