forked from PulseFocusPlatform/PulseFocusPlatform
497 lines
17 KiB
Python
497 lines
17 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 paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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from paddle.regularizer import L2Decay
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from ppdet.core.workspace import register, serializable
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from numbers import Integral
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from ..shape_spec import ShapeSpec
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__all__ = ['MobileNetV3']
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def make_divisible(v, divisor=8, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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in_c,
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out_c,
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filter_size,
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stride,
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padding,
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num_groups=1,
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act=None,
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lr_mult=1.,
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conv_decay=0.,
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norm_type='bn',
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norm_decay=0.,
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freeze_norm=False,
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name=""):
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super(ConvBNLayer, self).__init__()
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self.act = act
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self.conv = nn.Conv2D(
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in_channels=in_c,
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out_channels=out_c,
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kernel_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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weight_attr=ParamAttr(
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learning_rate=lr_mult,
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regularizer=L2Decay(conv_decay),
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name=name + "_weights"),
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bias_attr=False)
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norm_lr = 0. if freeze_norm else lr_mult
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param_attr = ParamAttr(
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learning_rate=norm_lr,
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regularizer=L2Decay(norm_decay),
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name=name + "_bn_scale",
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trainable=False if freeze_norm else True)
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bias_attr = ParamAttr(
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learning_rate=norm_lr,
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regularizer=L2Decay(norm_decay),
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name=name + "_bn_offset",
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trainable=False if freeze_norm else True)
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global_stats = True if freeze_norm else False
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if norm_type == 'sync_bn':
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self.bn = nn.SyncBatchNorm(
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out_c, weight_attr=param_attr, bias_attr=bias_attr)
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else:
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self.bn = nn.BatchNorm(
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out_c,
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act=None,
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param_attr=param_attr,
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bias_attr=bias_attr,
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use_global_stats=global_stats,
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moving_mean_name=name + '_bn_mean',
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moving_variance_name=name + '_bn_variance')
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norm_params = self.bn.parameters()
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if freeze_norm:
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for param in norm_params:
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param.stop_gradient = True
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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if self.act is not None:
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if self.act == "relu":
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x = F.relu(x)
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elif self.act == "relu6":
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x = F.relu6(x)
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elif self.act == "hard_swish":
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x = F.hardswish(x)
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else:
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raise NotImplementedError(
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"The activation function is selected incorrectly.")
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return x
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class ResidualUnit(nn.Layer):
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def __init__(self,
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in_c,
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mid_c,
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out_c,
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filter_size,
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stride,
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use_se,
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lr_mult,
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conv_decay=0.,
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norm_type='bn',
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norm_decay=0.,
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freeze_norm=False,
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act=None,
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return_list=False,
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name=''):
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super(ResidualUnit, self).__init__()
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self.if_shortcut = stride == 1 and in_c == out_c
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self.use_se = use_se
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self.return_list = return_list
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self.expand_conv = ConvBNLayer(
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in_c=in_c,
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out_c=mid_c,
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filter_size=1,
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stride=1,
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padding=0,
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act=act,
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lr_mult=lr_mult,
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conv_decay=conv_decay,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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name=name + "_expand")
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self.bottleneck_conv = ConvBNLayer(
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in_c=mid_c,
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out_c=mid_c,
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filter_size=filter_size,
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stride=stride,
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padding=int((filter_size - 1) // 2),
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num_groups=mid_c,
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act=act,
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lr_mult=lr_mult,
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conv_decay=conv_decay,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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name=name + "_depthwise")
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if self.use_se:
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self.mid_se = SEModule(
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mid_c, lr_mult, conv_decay, name=name + "_se")
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self.linear_conv = ConvBNLayer(
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in_c=mid_c,
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out_c=out_c,
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filter_size=1,
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stride=1,
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padding=0,
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act=None,
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lr_mult=lr_mult,
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conv_decay=conv_decay,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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name=name + "_linear")
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def forward(self, inputs):
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y = self.expand_conv(inputs)
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x = self.bottleneck_conv(y)
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if self.use_se:
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x = self.mid_se(x)
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x = self.linear_conv(x)
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if self.if_shortcut:
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x = paddle.add(inputs, x)
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if self.return_list:
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return [y, x]
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else:
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return x
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class SEModule(nn.Layer):
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def __init__(self, channel, lr_mult, conv_decay, reduction=4, name=""):
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super(SEModule, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2D(1)
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mid_channels = int(channel // reduction)
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self.conv1 = nn.Conv2D(
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in_channels=channel,
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out_channels=mid_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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weight_attr=ParamAttr(
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learning_rate=lr_mult,
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regularizer=L2Decay(conv_decay),
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name=name + "_1_weights"),
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bias_attr=ParamAttr(
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learning_rate=lr_mult,
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regularizer=L2Decay(conv_decay),
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name=name + "_1_offset"))
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self.conv2 = nn.Conv2D(
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in_channels=mid_channels,
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out_channels=channel,
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kernel_size=1,
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stride=1,
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padding=0,
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weight_attr=ParamAttr(
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learning_rate=lr_mult,
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regularizer=L2Decay(conv_decay),
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name=name + "_2_weights"),
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bias_attr=ParamAttr(
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learning_rate=lr_mult,
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regularizer=L2Decay(conv_decay),
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name=name + "_2_offset"))
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def forward(self, inputs):
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outputs = self.avg_pool(inputs)
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outputs = self.conv1(outputs)
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outputs = F.relu(outputs)
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outputs = self.conv2(outputs)
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outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)
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return paddle.multiply(x=inputs, y=outputs)
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class ExtraBlockDW(nn.Layer):
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def __init__(self,
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in_c,
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ch_1,
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ch_2,
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stride,
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lr_mult,
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conv_decay=0.,
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norm_type='bn',
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norm_decay=0.,
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freeze_norm=False,
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name=None):
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super(ExtraBlockDW, self).__init__()
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self.pointwise_conv = ConvBNLayer(
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in_c=in_c,
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out_c=ch_1,
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filter_size=1,
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stride=1,
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padding='SAME',
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act='relu6',
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lr_mult=lr_mult,
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conv_decay=conv_decay,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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name=name + "_extra1")
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self.depthwise_conv = ConvBNLayer(
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in_c=ch_1,
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out_c=ch_2,
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filter_size=3,
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stride=stride,
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padding='SAME',
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num_groups=int(ch_1),
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act='relu6',
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lr_mult=lr_mult,
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conv_decay=conv_decay,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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name=name + "_extra2_dw")
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self.normal_conv = ConvBNLayer(
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in_c=ch_2,
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out_c=ch_2,
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filter_size=1,
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stride=1,
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padding='SAME',
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act='relu6',
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lr_mult=lr_mult,
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conv_decay=conv_decay,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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name=name + "_extra2_sep")
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def forward(self, inputs):
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x = self.pointwise_conv(inputs)
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x = self.depthwise_conv(x)
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x = self.normal_conv(x)
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return x
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@register
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@serializable
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class MobileNetV3(nn.Layer):
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__shared__ = ['norm_type']
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def __init__(
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self,
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scale=1.0,
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model_name="large",
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feature_maps=[6, 12, 15],
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with_extra_blocks=False,
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extra_block_filters=[[256, 512], [128, 256], [128, 256], [64, 128]],
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lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
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conv_decay=0.0,
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multiplier=1.0,
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norm_type='bn',
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norm_decay=0.0,
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freeze_norm=False):
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super(MobileNetV3, self).__init__()
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if isinstance(feature_maps, Integral):
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feature_maps = [feature_maps]
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if norm_type == 'sync_bn' and freeze_norm:
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raise ValueError(
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"The norm_type should not be sync_bn when freeze_norm is True")
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self.feature_maps = feature_maps
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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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inplanes = 16
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if model_name == "large":
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self.cfg = [
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# k, exp, c, se, nl, s,
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[3, 16, 16, False, "relu", 1],
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[3, 64, 24, False, "relu", 2],
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[3, 72, 24, False, "relu", 1],
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[5, 72, 40, True, "relu", 2], # RCNN output
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[5, 120, 40, True, "relu", 1],
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[5, 120, 40, True, "relu", 1], # YOLOv3 output
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[3, 240, 80, False, "hard_swish", 2], # RCNN output
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[3, 200, 80, False, "hard_swish", 1],
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[3, 184, 80, False, "hard_swish", 1],
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[3, 184, 80, False, "hard_swish", 1],
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[3, 480, 112, True, "hard_swish", 1],
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[3, 672, 112, True, "hard_swish", 1], # YOLOv3 output
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[5, 672, 160, True, "hard_swish", 2], # SSD/SSDLite/RCNN output
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[5, 960, 160, True, "hard_swish", 1],
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[5, 960, 160, True, "hard_swish", 1], # YOLOv3 output
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]
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elif model_name == "small":
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self.cfg = [
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# k, exp, c, se, nl, s,
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[3, 16, 16, True, "relu", 2],
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[3, 72, 24, False, "relu", 2], # RCNN output
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[3, 88, 24, False, "relu", 1], # YOLOv3 output
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[5, 96, 40, True, "hard_swish", 2], # RCNN output
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[5, 240, 40, True, "hard_swish", 1],
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[5, 240, 40, True, "hard_swish", 1],
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[5, 120, 48, True, "hard_swish", 1],
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[5, 144, 48, True, "hard_swish", 1], # YOLOv3 output
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[5, 288, 96, True, "hard_swish", 2], # SSD/SSDLite/RCNN output
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[5, 576, 96, True, "hard_swish", 1],
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[5, 576, 96, True, "hard_swish", 1], # YOLOv3 output
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]
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else:
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raise NotImplementedError(
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"mode[{}_model] is not implemented!".format(model_name))
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if multiplier != 1.0:
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self.cfg[-3][2] = int(self.cfg[-3][2] * multiplier)
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self.cfg[-2][1] = int(self.cfg[-2][1] * multiplier)
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self.cfg[-2][2] = int(self.cfg[-2][2] * multiplier)
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self.cfg[-1][1] = int(self.cfg[-1][1] * multiplier)
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self.cfg[-1][2] = int(self.cfg[-1][2] * multiplier)
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self.conv1 = ConvBNLayer(
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in_c=3,
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out_c=make_divisible(inplanes * scale),
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filter_size=3,
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stride=2,
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padding=1,
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num_groups=1,
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act="hard_swish",
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lr_mult=lr_mult_list[0],
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conv_decay=conv_decay,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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name="conv1")
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self._out_channels = []
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self.block_list = []
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i = 0
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inplanes = make_divisible(inplanes * scale)
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for (k, exp, c, se, nl, s) in self.cfg:
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lr_idx = min(i // 3, len(lr_mult_list) - 1)
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lr_mult = lr_mult_list[lr_idx]
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# for SSD/SSDLite, first head input is after ResidualUnit expand_conv
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return_list = self.with_extra_blocks and i + 2 in self.feature_maps
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block = self.add_sublayer(
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"conv" + str(i + 2),
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sublayer=ResidualUnit(
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in_c=inplanes,
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mid_c=make_divisible(scale * exp),
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out_c=make_divisible(scale * c),
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filter_size=k,
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stride=s,
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use_se=se,
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act=nl,
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lr_mult=lr_mult,
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conv_decay=conv_decay,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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return_list=return_list,
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name="conv" + str(i + 2)))
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self.block_list.append(block)
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inplanes = make_divisible(scale * c)
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i += 1
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self._update_out_channels(
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make_divisible(scale * exp)
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if return_list else inplanes, i + 1, feature_maps)
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if self.with_extra_blocks:
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self.extra_block_list = []
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extra_out_c = make_divisible(scale * self.cfg[-1][1])
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lr_idx = min(i // 3, len(lr_mult_list) - 1)
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lr_mult = lr_mult_list[lr_idx]
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conv_extra = self.add_sublayer(
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"conv" + str(i + 2),
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sublayer=ConvBNLayer(
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in_c=inplanes,
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out_c=extra_out_c,
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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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act="hard_swish",
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lr_mult=lr_mult,
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conv_decay=conv_decay,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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name="conv" + str(i + 2)))
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self.extra_block_list.append(conv_extra)
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i += 1
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self._update_out_channels(extra_out_c, i + 1, feature_maps)
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for j, block_filter in enumerate(self.extra_block_filters):
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in_c = extra_out_c if j == 0 else self.extra_block_filters[j -
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1][1]
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conv_extra = self.add_sublayer(
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"conv" + str(i + 2),
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sublayer=ExtraBlockDW(
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in_c,
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block_filter[0],
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block_filter[1],
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stride=2,
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lr_mult=lr_mult,
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conv_decay=conv_decay,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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name='conv' + str(i + 2)))
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self.extra_block_list.append(conv_extra)
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i += 1
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self._update_out_channels(block_filter[1], i + 1, feature_maps)
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def _update_out_channels(self, channel, feature_idx, feature_maps):
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if feature_idx in feature_maps:
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self._out_channels.append(channel)
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def forward(self, inputs):
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x = self.conv1(inputs['image'])
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outs = []
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for idx, block in enumerate(self.block_list):
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x = block(x)
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if idx + 2 in self.feature_maps:
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if isinstance(x, list):
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outs.append(x[0])
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x = x[1]
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else:
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outs.append(x)
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if not self.with_extra_blocks:
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return outs
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for i, block in enumerate(self.extra_block_list):
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idx = i + len(self.block_list)
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x = block(x)
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if idx + 2 in self.feature_maps:
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outs.append(x)
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return outs
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@property
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def out_shape(self):
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return [ShapeSpec(channels=c) for c in self._out_channels]
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