forked from PulseFocusPlatform/PulseFocusPlatform
327 lines
11 KiB
Python
327 lines
11 KiB
Python
# 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.experimental import mixed_precision_global_state
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from ppdet.core.workspace import register
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__all__ = ['BlazeNet']
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@register
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class BlazeNet(object):
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"""
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BlazeFace, see https://arxiv.org/abs/1907.05047
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Args:
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blaze_filters (list): number of filter for each blaze block
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double_blaze_filters (list): number of filter for each double_blaze block
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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 blazeface-lite
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use_5x5kernel (bool): whether or not filter size is 5x5 in depth-wise conv
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"""
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def __init__(
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self,
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blaze_filters=[[24, 24], [24, 24], [24, 48, 2], [48, 48], [48, 48]],
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double_blaze_filters=[[48, 24, 96, 2], [96, 24, 96], [96, 24, 96],
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[96, 24, 96, 2], [96, 24, 96], [96, 24, 96]],
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with_extra_blocks=True,
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lite_edition=False,
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use_5x5kernel=True):
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super(BlazeNet, self).__init__()
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self.blaze_filters = blaze_filters
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self.double_blaze_filters = double_blaze_filters
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self.with_extra_blocks = with_extra_blocks
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self.lite_edition = lite_edition
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self.use_5x5kernel = use_5x5kernel
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def __call__(self, input):
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if not self.lite_edition:
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conv1_num_filters = self.blaze_filters[0][0]
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conv = self._conv_norm(
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input=input,
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num_filters=conv1_num_filters,
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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="conv1")
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for k, v in enumerate(self.blaze_filters):
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assert len(v) in [2, 3], \
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"blaze_filters {} not in [2, 3]"
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if len(v) == 2:
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conv = self.BlazeBlock(
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conv,
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v[0],
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v[1],
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use_5x5kernel=self.use_5x5kernel,
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name='blaze_{}'.format(k))
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elif len(v) == 3:
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conv = self.BlazeBlock(
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conv,
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v[0],
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v[1],
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stride=v[2],
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use_5x5kernel=self.use_5x5kernel,
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name='blaze_{}'.format(k))
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layers = []
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for k, v in enumerate(self.double_blaze_filters):
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assert len(v) in [3, 4], \
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"blaze_filters {} not in [3, 4]"
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if len(v) == 3:
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conv = self.BlazeBlock(
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conv,
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v[0],
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v[1],
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double_channels=v[2],
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use_5x5kernel=self.use_5x5kernel,
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name='double_blaze_{}'.format(k))
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elif len(v) == 4:
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layers.append(conv)
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conv = self.BlazeBlock(
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conv,
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v[0],
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v[1],
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double_channels=v[2],
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stride=v[3],
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use_5x5kernel=self.use_5x5kernel,
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name='double_blaze_{}'.format(k))
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layers.append(conv)
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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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else:
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conv1 = self._conv_norm(
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input=input,
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num_filters=24,
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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="conv1")
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conv2 = self.Blaze_lite(conv1, 24, 24, 1, 'conv2')
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conv3 = self.Blaze_lite(conv2, 24, 28, 1, 'conv3')
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conv4 = self.Blaze_lite(conv3, 28, 32, 2, 'conv4')
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conv5 = self.Blaze_lite(conv4, 32, 36, 1, 'conv5')
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conv6 = self.Blaze_lite(conv5, 36, 42, 1, 'conv6')
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conv7 = self.Blaze_lite(conv6, 42, 48, 2, 'conv7')
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in_ch = 48
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for i in range(5):
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conv7 = self.Blaze_lite(conv7, in_ch, in_ch + 8, 1,
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'conv{}'.format(8 + i))
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in_ch += 8
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assert in_ch == 88
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conv13 = self.Blaze_lite(conv7, 88, 96, 2, 'conv13')
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for i in range(4):
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conv13 = self.Blaze_lite(conv13, 96, 96, 1,
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'conv{}'.format(14 + i))
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return conv7, conv13
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def BlazeBlock(self,
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input,
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in_channels,
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out_channels,
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double_channels=None,
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stride=1,
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use_5x5kernel=True,
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name=None):
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assert stride in [1, 2]
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use_pool = not stride == 1
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use_double_block = double_channels is not None
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act = 'relu' if use_double_block else None
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mixed_precision_enabled = mixed_precision_global_state() is not None
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if use_5x5kernel:
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conv_dw = self._conv_norm(
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input=input,
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filter_size=5,
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num_filters=in_channels,
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stride=stride,
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padding=2,
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num_groups=in_channels,
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use_cudnn=mixed_precision_enabled,
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name=name + "1_dw")
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else:
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conv_dw_1 = self._conv_norm(
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input=input,
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filter_size=3,
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num_filters=in_channels,
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stride=1,
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padding=1,
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num_groups=in_channels,
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use_cudnn=mixed_precision_enabled,
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name=name + "1_dw_1")
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conv_dw = self._conv_norm(
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input=conv_dw_1,
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filter_size=3,
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num_filters=in_channels,
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stride=stride,
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padding=1,
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num_groups=in_channels,
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use_cudnn=mixed_precision_enabled,
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name=name + "1_dw_2")
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conv_pw = self._conv_norm(
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input=conv_dw,
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filter_size=1,
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num_filters=out_channels,
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stride=1,
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padding=0,
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act=act,
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name=name + "1_sep")
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if use_double_block:
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if use_5x5kernel:
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conv_dw = self._conv_norm(
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input=conv_pw,
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filter_size=5,
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num_filters=out_channels,
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stride=1,
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padding=2,
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use_cudnn=mixed_precision_enabled,
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name=name + "2_dw")
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else:
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conv_dw_1 = self._conv_norm(
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input=conv_pw,
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filter_size=3,
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num_filters=out_channels,
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stride=1,
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padding=1,
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num_groups=out_channels,
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use_cudnn=mixed_precision_enabled,
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name=name + "2_dw_1")
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conv_dw = self._conv_norm(
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input=conv_dw_1,
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filter_size=3,
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num_filters=out_channels,
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stride=1,
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padding=1,
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num_groups=out_channels,
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use_cudnn=mixed_precision_enabled,
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name=name + "2_dw_2")
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conv_pw = self._conv_norm(
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input=conv_dw,
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filter_size=1,
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num_filters=double_channels,
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stride=1,
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padding=0,
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name=name + "2_sep")
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# shortcut
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if use_pool:
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shortcut_channel = double_channels or out_channels
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shortcut_pool = self._pooling_block(input, stride, stride)
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channel_pad = self._conv_norm(
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input=shortcut_pool,
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filter_size=1,
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num_filters=shortcut_channel,
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stride=1,
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padding=0,
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name="shortcut" + name)
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return fluid.layers.elementwise_add(
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x=channel_pad, y=conv_pw, act='relu')
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return fluid.layers.elementwise_add(x=input, y=conv_pw, act='relu')
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def Blaze_lite(self, input, in_channels, out_channels, stride=1, name=None):
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assert stride in [1, 2]
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use_pool = not stride == 1
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ues_pad = not in_channels == out_channels
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conv_dw = self._conv_norm(
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input=input,
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filter_size=3,
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num_filters=in_channels,
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stride=stride,
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padding=1,
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num_groups=in_channels,
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name=name + "_dw")
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conv_pw = self._conv_norm(
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input=conv_dw,
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filter_size=1,
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num_filters=out_channels,
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stride=1,
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padding=0,
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name=name + "_sep")
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if use_pool:
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shortcut_pool = self._pooling_block(input, stride, stride)
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if ues_pad:
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conv_pad = shortcut_pool if use_pool else input
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channel_pad = self._conv_norm(
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input=conv_pad,
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filter_size=1,
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num_filters=out_channels,
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stride=1,
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padding=0,
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name="shortcut" + name)
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return fluid.layers.elementwise_add(
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x=channel_pad, y=conv_pw, act='relu')
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return fluid.layers.elementwise_add(x=input, y=conv_pw, act='relu')
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def _conv_norm(
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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', # None
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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 _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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