mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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315 lines
9.6 KiB
Python
315 lines
9.6 KiB
Python
# copyright (c) 2021 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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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 PGFPN(nn.Layer):
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def __init__(self, in_channels, **kwargs):
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super(PGFPN, self).__init__()
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num_inputs = [2048, 2048, 1024, 512, 256]
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num_outputs = [256, 256, 192, 192, 128]
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self.out_channels = 128
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self.conv_bn_layer_1 = ConvBNLayer(
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in_channels=3,
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out_channels=32,
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kernel_size=3,
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stride=1,
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act=None,
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name='FPN_d1')
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self.conv_bn_layer_2 = ConvBNLayer(
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in_channels=64,
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out_channels=64,
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kernel_size=3,
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stride=1,
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act=None,
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name='FPN_d2')
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self.conv_bn_layer_3 = ConvBNLayer(
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in_channels=256,
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out_channels=128,
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kernel_size=3,
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stride=1,
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act=None,
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name='FPN_d3')
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self.conv_bn_layer_4 = ConvBNLayer(
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in_channels=32,
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out_channels=64,
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kernel_size=3,
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stride=2,
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act=None,
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name='FPN_d4')
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self.conv_bn_layer_5 = ConvBNLayer(
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in_channels=64,
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out_channels=64,
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kernel_size=3,
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stride=1,
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act='relu',
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name='FPN_d5')
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self.conv_bn_layer_6 = ConvBNLayer(
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in_channels=64,
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out_channels=128,
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kernel_size=3,
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stride=2,
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act=None,
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name='FPN_d6')
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self.conv_bn_layer_7 = ConvBNLayer(
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in_channels=128,
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out_channels=128,
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kernel_size=3,
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stride=1,
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act='relu',
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name='FPN_d7')
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self.conv_bn_layer_8 = ConvBNLayer(
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in_channels=128,
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out_channels=128,
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kernel_size=1,
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stride=1,
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act=None,
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name='FPN_d8')
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self.conv_h0 = ConvBNLayer(
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in_channels=num_inputs[0],
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out_channels=num_outputs[0],
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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_h{}".format(0))
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self.conv_h1 = ConvBNLayer(
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in_channels=num_inputs[1],
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out_channels=num_outputs[1],
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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_h{}".format(1))
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self.conv_h2 = ConvBNLayer(
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in_channels=num_inputs[2],
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out_channels=num_outputs[2],
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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_h{}".format(2))
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self.conv_h3 = ConvBNLayer(
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in_channels=num_inputs[3],
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out_channels=num_outputs[3],
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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_h{}".format(3))
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self.conv_h4 = ConvBNLayer(
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in_channels=num_inputs[4],
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out_channels=num_outputs[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_h{}".format(4))
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self.dconv0 = DeConvBNLayer(
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in_channels=num_outputs[0],
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out_channels=num_outputs[0 + 1],
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name="dconv_{}".format(0))
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self.dconv1 = DeConvBNLayer(
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in_channels=num_outputs[1],
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out_channels=num_outputs[1 + 1],
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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=num_outputs[2],
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out_channels=num_outputs[2 + 1],
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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=num_outputs[3],
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out_channels=num_outputs[3 + 1],
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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=num_outputs[1],
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out_channels=num_outputs[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=num_outputs[2],
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out_channels=num_outputs[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=num_outputs[3],
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out_channels=num_outputs[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=num_outputs[4],
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out_channels=num_outputs[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=num_outputs[4],
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out_channels=num_outputs[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 forward(self, x):
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c0, c1, c2, c3, c4, c5, c6 = x
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# FPN_Down_Fusion
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f = [c0, c1, c2]
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g = [None, None, None]
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h = [None, None, None]
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h[0] = self.conv_bn_layer_1(f[0])
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h[1] = self.conv_bn_layer_2(f[1])
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h[2] = self.conv_bn_layer_3(f[2])
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g[0] = self.conv_bn_layer_4(h[0])
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g[1] = paddle.add(g[0], h[1])
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g[1] = F.relu(g[1])
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g[1] = self.conv_bn_layer_5(g[1])
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g[1] = self.conv_bn_layer_6(g[1])
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g[2] = paddle.add(g[1], h[2])
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g[2] = F.relu(g[2])
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g[2] = self.conv_bn_layer_7(g[2])
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f_down = self.conv_bn_layer_8(g[2])
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# FPN UP Fusion
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f1 = [c6, c5, c4, c3, c2]
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g = [None, None, None, None, None]
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h = [None, None, None, None, None]
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h[0] = self.conv_h0(f1[0])
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h[1] = self.conv_h1(f1[1])
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h[2] = self.conv_h2(f1[2])
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h[3] = self.conv_h3(f1[3])
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h[4] = self.conv_h4(f1[4])
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g[0] = self.dconv0(h[0])
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g[1] = paddle.add(g[0], h[1])
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g[1] = F.relu(g[1])
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g[1] = self.conv_g1(g[1])
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g[1] = self.dconv1(g[1])
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g[2] = paddle.add(g[1], h[2])
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g[2] = F.relu(g[2])
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g[2] = self.conv_g2(g[2])
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g[2] = self.dconv2(g[2])
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g[3] = paddle.add(g[2], h[3])
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g[3] = F.relu(g[3])
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g[3] = self.conv_g3(g[3])
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g[3] = self.dconv3(g[3])
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g[4] = paddle.add(x=g[3], y=h[4])
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g[4] = F.relu(g[4])
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g[4] = self.conv_g4(g[4])
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f_up = self.convf(g[4])
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f_common = paddle.add(f_down, f_up)
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f_common = F.relu(f_common)
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return f_common
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