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# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
from paddle import nn
import paddle.nn.functional as F
from paddle import ParamAttr
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
groups=1,
if_act=True,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
self.if_act = if_act
self.act = act
self.conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + '_weights'),
bias_attr=False)
self.bn = nn.BatchNorm(
num_channels=out_channels,
act=act,
param_attr=ParamAttr(name="bn_" + name + "_scale"),
bias_attr=ParamAttr(name="bn_" + name + "_offset"),
moving_mean_name="bn_" + name + "_mean",
moving_variance_name="bn_" + name + "_variance")
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class SAST_Header1(nn.Layer):
def __init__(self, in_channels, **kwargs):
super(SAST_Header1, self).__init__()
out_channels = [64, 64, 128]
self.score_conv = nn.Sequential(
ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_score1'),
ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_score2'),
ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_score3'),
ConvBNLayer(out_channels[2], 1, 3, 1, act=None, name='f_score4')
)
self.border_conv = nn.Sequential(
ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_border1'),
ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_border2'),
ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_border3'),
ConvBNLayer(out_channels[2], 4, 3, 1, act=None, name='f_border4')
)
def forward(self, x):
f_score = self.score_conv(x)
f_score = F.sigmoid(f_score)
f_border = self.border_conv(x)
return f_score, f_border
class SAST_Header2(nn.Layer):
def __init__(self, in_channels, **kwargs):
super(SAST_Header2, self).__init__()
out_channels = [64, 64, 128]
self.tvo_conv = nn.Sequential(
ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_tvo1'),
ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_tvo2'),
ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_tvo3'),
ConvBNLayer(out_channels[2], 8, 3, 1, act=None, name='f_tvo4')
)
self.tco_conv = nn.Sequential(
ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_tco1'),
ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_tco2'),
ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_tco3'),
ConvBNLayer(out_channels[2], 2, 3, 1, act=None, name='f_tco4')
)
def forward(self, x):
f_tvo = self.tvo_conv(x)
f_tco = self.tco_conv(x)
return f_tvo, f_tco
class SASTHead(nn.Layer):
"""
"""
def __init__(self, in_channels, **kwargs):
super(SASTHead, self).__init__()
self.head1 = SAST_Header1(in_channels)
self.head2 = SAST_Header2(in_channels)
def forward(self, x, targets=None):
f_score, f_border = self.head1(x)
f_tvo, f_tco = self.head2(x)
predicts = {}
predicts['f_score'] = f_score
predicts['f_border'] = f_border
predicts['f_tvo'] = f_tvo
predicts['f_tco'] = f_tco
return predicts |