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DefEDNet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from def_resnet import def_resnet34
from functools import partial
from defconv import DefC
class SeparableConv2d(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,dilation=1,bias=False):
super(SeparableConv2d,self).__init__()
self.conv1 = nn.Conv2d(in_channels,in_channels,kernel_size,stride,padding,dilation,groups=in_channels,bias=bias)
self.pointwise = nn.Conv2d(in_channels,out_channels,1,1,0,1,1,bias=bias)
def forward(self,x):
x = self.conv1(x)
x = self.pointwise(x)
return x
nonlinearity = partial(F.relu, inplace=True)
class Ladder_ASPP(nn.Module):
def __init__(self, channel):
super(Ladder_ASPP,self).__init__()
self.dilate1 = SeparableConv2d(channel,channel,kernel_size=3,dilation=1,padding=1)
self.dilate2 = SeparableConv2d(channel*2,channel,kernel_size=3,dilation=2,padding=2)
self.dilate3 = SeparableConv2d(channel*3,channel,kernel_size=3,dilation=5,padding=5)
self.dilate4 = SeparableConv2d(channel*4,channel,kernel_size=3,dilation=7,padding=7)
self.bn = nn.BatchNorm2d(channel)
self.drop = nn.Dropout2d(0.5)
self.sg = nn.Sigmoid()
self.finalchannel = channel
self.conv1x1_1 = SeparableConv2d(channel*5, channel*3, kernel_size=1, dilation=1, padding=0)
self.conv1x1_2 = SeparableConv2d(channel*3, channel*2, kernel_size=1, dilation=1, padding=0)
# Master branch
self.conv_master = SeparableConv2d(channel, channel, kernel_size=1, bias=False)
self.bn_master = nn.BatchNorm2d(channel)
# Global pooling branch
self.conv_gpb = SeparableConv2d(channel, channel, kernel_size=1, bias=False)
self.bn_gpb = nn.BatchNorm2d(channel)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
x_gpb = self.avg_pool(x)
x_gpb = self.conv_gpb(x_gpb)
x_gpb = self.bn_gpb(x_gpb)
x_gpb = self.sg(x_gpb)
x_se = x_gpb * x
#first block rate1
d1 = self.dilate1(x)
d1 = self.bn(d1)
#second block rate3
d2 = torch.cat([d1,x],1)
d2 = self.dilate2(d2)
d2 = self.bn(d2)
#third block rate5
d3 = torch.cat([d1,d2,x],1)
d3 = self.dilate3(d3)
d3 = self.bn(d3)
#last block rate7
d4 = torch.cat([d1,d2,d3,x],1)
d4 = self.dilate4(d4)
d4 = self.bn(d4)
out = torch.cat([d1,d2,d3,d4,x_se],1)
out = self.drop(out)
out = self.conv1x1_1(out)
out = self.conv1x1_2(out)
return out
class DecoderBlock(nn.Module):
def __init__(self, in_channels, n_filters):
super(DecoderBlock, self).__init__()
self.conv1 = SeparableConv2d(in_channels,in_channels//4,kernel_size=3,stride=1,padding=1)
self.norm1 = nn.BatchNorm2d(in_channels // 4)
self.relu1 = nonlinearity
self.deconv2 = nn.ConvTranspose2d(in_channels // 4, in_channels // 4, 3, stride=2, padding=1, output_padding=1)
self.norm2 = nn.BatchNorm2d(in_channels // 4)
self.relu2 = nonlinearity
self.conv3 = SeparableConv2d(in_channels // 4, n_filters,kernel_size=3,stride=1,padding=1)
self.norm3 = nn.BatchNorm2d(n_filters)
self.relu3 = nonlinearity
def forward(self, x):
x = self.conv1(x)
x = self.norm1(x)
x = self.relu1(x)
x = self.deconv2(x)
x = self.norm2(x)
x = self.relu2(x)
x = self.conv3(x)
x = self.norm3(x)
x = self.relu3(x)
return x
class DefED_Net(nn.Module):
def __init__(self, num_classes=3):
super(DefED_Net, self).__init__()
filters = [64, 128, 256, 512,1024]
def_resnet = def_resnet34()
self.firstconv = DefC(1,64,7,stride=2,padding=3,bias=False)
self.firstbn = nn.BatchNorm2d(64)
self.firstrelu = nn.ReLU(inplace=True)
self.firstmaxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.sconv = DefC(64,64,3,2,1)
self.xe4 = DefC(256,256,3)
self.xe3 = DefC(128,128,3,2,1)
self.xe2 = DefC(64,64,3,1,1)
self.encoder1 = def_resnet.layer1
self.encoder2 = def_resnet.layer2
self.encoder3 = def_resnet.layer3
self.encoder4 = def_resnet.layer4
self.ladder_aspp = Ladder_ASPP(512)
self.decoder4 = DecoderBlock(1024, 512)
self.decoder3 = DecoderBlock(768, 256)
self.decoder2 = DecoderBlock(384, 128)
self.decoder1 = DecoderBlock(192, 64)
self.finaldeconv1 = nn.ConvTranspose2d(filters[0], 32, 4, 2, 1)
self.finalrelu1 = nonlinearity
self.finalconv2 = SeparableConv2d(32, 32, 3, padding=1)
self.finalrelu2 = nonlinearity
self.finalconv3 = SeparableConv2d(32, num_classes, 3, padding=1)
self.drop = nn.Dropout2d(0.5)
def forward(self, x):
# Encoder
x = self.firstconv(x) # 1,64,128,128
x = self.firstbn(x) # 1,64,128,128
x = self.firstrelu(x) # 1,64,128,128
x_p = self.sconv(x) # 1,64,128,128
x_p = self.drop(x_p) # 1,64,128,128
e1 = self.encoder1(x_p) # 1,64,128,128
e1 = self.drop(e1) # 1,64,128,128
xe_2 = self.xe2(e1) # 1,64,128,128
e2 = self.encoder2(e1) # 1,128,64,64
e2 = self.drop(e2)
xe_3 = self.xe3(e2) # 1,128,64,64
e3 = self.encoder3(e2) # 1,256,32,32
e3 = self.drop(e3)
xe_4 = self.xe4(e3) # 1,256,32,32
e4 = self.encoder4(e3) # 1,512,16,16
e4 = self.drop(e4)
# Center
e4 = self.ladder_aspp(e4) # 1,1024,16,16
# Decoder
d4 = self.decoder4(e4) # 1,512,32,32
d4 = self.drop(d4)
d3 = self.decoder3(torch.cat([d4,xe_4],1)) # 512 256
d3 = self.drop(d3) # 1,256,64,64
d2 = self.decoder2(torch.cat([d3,xe_3],1)) # 256 128
d2 = self.drop(d2) # 1,128,128,128
d1 = self.decoder1(torch.cat([d2,xe_2],1)) # 128 64
d1 = self.drop(d1)
out = self.finaldeconv1(d1)
out = self.finalrelu1(out)
out = self.finalconv2(out)
out = self.finalrelu2(out)
out = self.finalconv3(out)
return F.sigmoid(out)