128 lines
4.6 KiB
Python
128 lines
4.6 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 math
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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,
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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(ConvBNLayer, self).__init__()
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self.if_act = if_act
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self.act = act
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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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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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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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return x
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class SAST_Header1(nn.Layer):
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def __init__(self, in_channels, **kwargs):
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super(SAST_Header1, self).__init__()
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out_channels = [64, 64, 128]
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self.score_conv = nn.Sequential(
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ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_score1'),
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ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_score2'),
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ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_score3'),
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ConvBNLayer(out_channels[2], 1, 3, 1, act=None, name='f_score4')
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)
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self.border_conv = nn.Sequential(
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ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_border1'),
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ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_border2'),
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ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_border3'),
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ConvBNLayer(out_channels[2], 4, 3, 1, act=None, name='f_border4')
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)
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def forward(self, x):
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f_score = self.score_conv(x)
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f_score = F.sigmoid(f_score)
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f_border = self.border_conv(x)
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return f_score, f_border
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class SAST_Header2(nn.Layer):
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def __init__(self, in_channels, **kwargs):
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super(SAST_Header2, self).__init__()
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out_channels = [64, 64, 128]
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self.tvo_conv = nn.Sequential(
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ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_tvo1'),
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ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_tvo2'),
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ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_tvo3'),
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ConvBNLayer(out_channels[2], 8, 3, 1, act=None, name='f_tvo4')
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)
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self.tco_conv = nn.Sequential(
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ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_tco1'),
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ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_tco2'),
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ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_tco3'),
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ConvBNLayer(out_channels[2], 2, 3, 1, act=None, name='f_tco4')
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)
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def forward(self, x):
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f_tvo = self.tvo_conv(x)
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f_tco = self.tco_conv(x)
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return f_tvo, f_tco
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class SASTHead(nn.Layer):
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"""
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"""
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def __init__(self, in_channels, **kwargs):
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super(SASTHead, self).__init__()
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self.head1 = SAST_Header1(in_channels)
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self.head2 = SAST_Header2(in_channels)
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def forward(self, x):
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f_score, f_border = self.head1(x)
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f_tvo, f_tco = self.head2(x)
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predicts = {}
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predicts['f_score'] = f_score
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predicts['f_border'] = f_border
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predicts['f_tvo'] = f_tvo
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predicts['f_tco'] = f_tco
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return predicts |