278 lines
8.8 KiB
Python
278 lines
8.8 KiB
Python
# copyright (c) 2019 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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from paddle import nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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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=1,
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groups=1,
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is_vd_mode=False,
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act=None,
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name=None):
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super(ConvBNLayer, self).__init__()
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self.is_vd_mode = is_vd_mode
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self._pool2d_avg = nn.AvgPool2D(
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kernel_size=2, stride=2, padding=0, ceil_mode=True)
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self._conv = nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=(kernel_size - 1) // 2,
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groups=groups,
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weight_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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if name == "conv1":
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bn_name = "bn_" + name
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else:
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bn_name = "bn" + name[3:]
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self._batch_norm = nn.BatchNorm(
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out_channels,
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act=act,
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param_attr=ParamAttr(name=bn_name + '_scale'),
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bias_attr=ParamAttr(bn_name + '_offset'),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance',
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use_global_stats=False)
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def forward(self, inputs):
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# if self.is_vd_mode:
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# inputs = self._pool2d_avg(inputs)
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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class DeConvBNLayer(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=4,
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stride=2,
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padding=1,
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groups=1,
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if_act=True,
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act=None,
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name=None):
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super(DeConvBNLayer, self).__init__()
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self.if_act = if_act
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self.act = act
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self.deconv = nn.Conv2DTranspose(
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in_channels=in_channels,
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out_channels=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=groups,
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weight_attr=ParamAttr(name=name + '_weights'),
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bias_attr=False)
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self.bn = nn.BatchNorm(
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num_channels=out_channels,
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act=act,
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param_attr=ParamAttr(name="bn_" + name + "_scale"),
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bias_attr=ParamAttr(name="bn_" + name + "_offset"),
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moving_mean_name="bn_" + name + "_mean",
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moving_variance_name="bn_" + name + "_variance",
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use_global_stats=False)
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def forward(self, x):
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x = self.deconv(x)
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x = self.bn(x)
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return x
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class FPN_Up_Fusion(nn.Layer):
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def __init__(self, in_channels):
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super(FPN_Up_Fusion, self).__init__()
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in_channels = in_channels[::-1]
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out_channels = [256, 256, 192, 192, 128]
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self.h0_conv = ConvBNLayer(
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in_channels[0], out_channels[0], 1, 1, act=None, name='conv_h0')
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self.h1_conv = ConvBNLayer(
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in_channels[1], out_channels[1], 1, 1, act=None, name='conv_h1')
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self.h2_conv = ConvBNLayer(
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in_channels[2], out_channels[2], 1, 1, act=None, name='conv_h2')
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self.h3_conv = ConvBNLayer(
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in_channels[3], out_channels[3], 1, 1, act=None, name='conv_h3')
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self.h4_conv = ConvBNLayer(
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in_channels[4], out_channels[4], 1, 1, act=None, name='conv_h4')
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self.dconv0 = DeConvBNLayer(
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in_channels=out_channels[0],
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out_channels=out_channels[1],
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name="dconv_{}".format(0))
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self.dconv1 = DeConvBNLayer(
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in_channels=out_channels[1],
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out_channels=out_channels[2],
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act=None,
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name="dconv_{}".format(1))
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self.dconv2 = DeConvBNLayer(
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in_channels=out_channels[2],
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out_channels=out_channels[3],
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act=None,
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name="dconv_{}".format(2))
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self.dconv3 = DeConvBNLayer(
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in_channels=out_channels[3],
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out_channels=out_channels[4],
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act=None,
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name="dconv_{}".format(3))
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self.conv_g1 = ConvBNLayer(
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in_channels=out_channels[1],
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out_channels=out_channels[1],
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kernel_size=3,
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stride=1,
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act='relu',
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name="conv_g{}".format(1))
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self.conv_g2 = ConvBNLayer(
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in_channels=out_channels[2],
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out_channels=out_channels[2],
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kernel_size=3,
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stride=1,
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act='relu',
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name="conv_g{}".format(2))
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self.conv_g3 = ConvBNLayer(
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in_channels=out_channels[3],
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out_channels=out_channels[3],
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kernel_size=3,
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stride=1,
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act='relu',
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name="conv_g{}".format(3))
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self.conv_g4 = ConvBNLayer(
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in_channels=out_channels[4],
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out_channels=out_channels[4],
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kernel_size=3,
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stride=1,
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act='relu',
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name="conv_g{}".format(4))
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self.convf = ConvBNLayer(
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in_channels=out_channels[4],
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out_channels=out_channels[4],
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kernel_size=1,
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stride=1,
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act=None,
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name="conv_f{}".format(4))
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def _add_relu(self, x1, x2):
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x = paddle.add(x=x1, y=x2)
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x = F.relu(x)
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return x
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def forward(self, x):
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f = x[2:][::-1]
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h0 = self.h0_conv(f[0])
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h1 = self.h1_conv(f[1])
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h2 = self.h2_conv(f[2])
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h3 = self.h3_conv(f[3])
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h4 = self.h4_conv(f[4])
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g0 = self.dconv0(h0)
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g1 = self.dconv2(self.conv_g2(self._add_relu(g0, h1)))
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g2 = self.dconv2(self.conv_g2(self._add_relu(g1, h2)))
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g3 = self.dconv3(self.conv_g2(self._add_relu(g2, h3)))
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g4 = self.dconv4(self.conv_g2(self._add_relu(g3, h4)))
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return g4
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class FPN_Down_Fusion(nn.Layer):
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def __init__(self, in_channels):
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super(FPN_Down_Fusion, self).__init__()
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out_channels = [32, 64, 128]
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self.h0_conv = ConvBNLayer(
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in_channels[0], out_channels[0], 3, 1, act=None, name='FPN_d1')
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self.h1_conv = ConvBNLayer(
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in_channels[1], out_channels[1], 3, 1, act=None, name='FPN_d2')
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self.h2_conv = ConvBNLayer(
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in_channels[2], out_channels[2], 3, 1, act=None, name='FPN_d3')
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self.g0_conv = ConvBNLayer(
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out_channels[0], out_channels[1], 3, 2, act=None, name='FPN_d4')
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self.g1_conv = nn.Sequential(
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ConvBNLayer(
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out_channels[1],
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out_channels[1],
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3,
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1,
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act='relu',
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name='FPN_d5'),
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ConvBNLayer(
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out_channels[1], out_channels[2], 3, 2, act=None,
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name='FPN_d6'))
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self.g2_conv = nn.Sequential(
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ConvBNLayer(
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out_channels[2],
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out_channels[2],
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3,
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1,
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act='relu',
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name='FPN_d7'),
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ConvBNLayer(
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out_channels[2], out_channels[2], 1, 1, act=None,
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name='FPN_d8'))
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def forward(self, x):
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f = x[:3]
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h0 = self.h0_conv(f[0])
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h1 = self.h1_conv(f[1])
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h2 = self.h2_conv(f[2])
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g0 = self.g0_conv(h0)
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g1 = paddle.add(x=g0, y=h1)
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g1 = F.relu(g1)
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g1 = self.g1_conv(g1)
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g2 = paddle.add(x=g1, y=h2)
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g2 = F.relu(g2)
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g2 = self.g2_conv(g2)
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return g2
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class PGFPN(nn.Layer):
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def __init__(self, in_channels, with_cab=False, **kwargs):
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super(PGFPN, self).__init__()
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self.in_channels = in_channels
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self.with_cab = with_cab
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self.FPN_Down_Fusion = FPN_Down_Fusion(self.in_channels)
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self.FPN_Up_Fusion = FPN_Up_Fusion(self.in_channels)
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self.out_channels = 128
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def forward(self, x):
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# down fpn
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f_down = self.FPN_Down_Fusion(x)
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# up fpn
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f_up = self.FPN_Up_Fusion(x)
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# fusion
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f_common = paddle.add(x=f_down, y=f_up)
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f_common = F.relu(f_common)
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return f_common
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