forked from PulseFocusPlatform/PulseFocusPlatform
228 lines
7.1 KiB
Python
228 lines
7.1 KiB
Python
# Copyright (c) 2021 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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import paddle
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import paddle.nn.functional as F
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from paddle import ParamAttr
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import paddle.nn as nn
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from paddle.nn.initializer import KaimingNormal
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from ppdet.core.workspace import register, serializable
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from ..shape_spec import ShapeSpec
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__all__ = ['BlazeNeck']
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def hard_swish(x):
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return x * F.relu6(x + 3) / 6.
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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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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conv_lr=0.1,
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conv_decay=0.,
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norm_decay=0.,
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norm_type='bn',
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name=None):
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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,
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out_channels,
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kernel_size=kernel_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=conv_lr,
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initializer=KaimingNormal(),
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name=name + "_weights"),
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bias_attr=False)
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param_attr = ParamAttr(name=name + "_bn_scale")
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bias_attr = ParamAttr(name=name + "_bn_offset")
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if norm_type == 'sync_bn':
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self._batch_norm = nn.SyncBatchNorm(
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out_channels, weight_attr=param_attr, bias_attr=bias_attr)
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else:
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self._batch_norm = nn.BatchNorm(
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out_channels,
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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=False,
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moving_mean_name=name + '_bn_mean',
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moving_variance_name=name + '_bn_variance')
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def forward(self, x):
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x = self._conv(x)
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x = self._batch_norm(x)
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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 == 'leaky':
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x = F.leaky_relu(x)
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elif self.act == 'hard_swish':
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x = hard_swish(x)
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return x
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class FPN(nn.Layer):
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def __init__(self, in_channels, out_channels, name=None):
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super(FPN, self).__init__()
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self.conv1_fpn = ConvBNLayer(
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in_channels,
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out_channels // 2,
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kernel_size=1,
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padding=0,
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stride=1,
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act='leaky',
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name=name + '_output1')
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self.conv2_fpn = ConvBNLayer(
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in_channels,
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out_channels // 2,
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kernel_size=1,
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padding=0,
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stride=1,
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act='leaky',
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name=name + '_output2')
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self.conv3_fpn = ConvBNLayer(
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out_channels // 2,
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out_channels // 2,
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kernel_size=3,
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padding=1,
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stride=1,
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act='leaky',
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name=name + '_merge')
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def forward(self, input):
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output1 = self.conv1_fpn(input[0])
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output2 = self.conv2_fpn(input[1])
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up2 = F.upsample(
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output2, size=paddle.shape(output1)[-2:], mode='nearest')
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output1 = paddle.add(output1, up2)
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output1 = self.conv3_fpn(output1)
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return output1, output2
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class SSH(nn.Layer):
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def __init__(self, in_channels, out_channels, name=None):
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super(SSH, self).__init__()
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assert out_channels % 4 == 0
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self.conv0_ssh = ConvBNLayer(
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in_channels,
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out_channels // 2,
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kernel_size=3,
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padding=1,
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stride=1,
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act=None,
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name=name + 'ssh_conv3')
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self.conv1_ssh = ConvBNLayer(
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out_channels // 2,
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out_channels // 4,
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kernel_size=3,
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padding=1,
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stride=1,
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act='leaky',
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name=name + 'ssh_conv5_1')
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self.conv2_ssh = ConvBNLayer(
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out_channels // 4,
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out_channels // 4,
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kernel_size=3,
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padding=1,
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stride=1,
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act=None,
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name=name + 'ssh_conv5_2')
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self.conv3_ssh = ConvBNLayer(
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out_channels // 4,
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out_channels // 4,
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kernel_size=3,
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padding=1,
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stride=1,
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act='leaky',
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name=name + 'ssh_conv7_1')
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self.conv4_ssh = ConvBNLayer(
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out_channels // 4,
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out_channels // 4,
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kernel_size=3,
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padding=1,
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stride=1,
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act=None,
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name=name + 'ssh_conv7_2')
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def forward(self, x):
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conv0 = self.conv0_ssh(x)
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conv1 = self.conv1_ssh(conv0)
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conv2 = self.conv2_ssh(conv1)
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conv3 = self.conv3_ssh(conv2)
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conv4 = self.conv4_ssh(conv3)
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concat = paddle.concat([conv0, conv2, conv4], axis=1)
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return F.relu(concat)
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@register
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@serializable
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class BlazeNeck(nn.Layer):
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def __init__(self, in_channel, neck_type="None", data_format='NCHW'):
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super(BlazeNeck, self).__init__()
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self.neck_type = neck_type
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self.reture_input = False
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self._out_channels = in_channel
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if self.neck_type == 'None':
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self.reture_input = True
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if "fpn" in self.neck_type:
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self.fpn = FPN(self._out_channels[0],
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self._out_channels[1],
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name='fpn')
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self._out_channels = [
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self._out_channels[0] // 2, self._out_channels[1] // 2
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]
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if "ssh" in self.neck_type:
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self.ssh1 = SSH(self._out_channels[0],
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self._out_channels[0],
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name='ssh1')
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self.ssh2 = SSH(self._out_channels[1],
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self._out_channels[1],
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name='ssh2')
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self._out_channels = [self._out_channels[0], self._out_channels[1]]
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def forward(self, inputs):
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if self.reture_input:
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return inputs
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output1, output2 = None, None
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if "fpn" in self.neck_type:
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backout_4, backout_1 = inputs
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output1, output2 = self.fpn([backout_4, backout_1])
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if self.neck_type == "only_fpn":
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return [output1, output2]
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if self.neck_type == "only_ssh":
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output1, output2 = inputs
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feature1 = self.ssh1(output1)
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feature2 = self.ssh2(output2)
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return [feature1, feature2]
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@property
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def out_shape(self):
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return [
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ShapeSpec(channels=c)
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for c in [self._out_channels[0], self._out_channels[1]]
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]
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