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unet_cascade_residual.py
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import torch
from torch import nn
class DoubleConv(nn.Module):
def __init__(self, in_ch, out_ch):
super(DoubleConv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
def forward(self, input):
return self.conv(input)
class Unet_cascade_residual(nn.Module):
def __init__(self,in_ch,out_ch):
super(Unet_cascade_residual, self).__init__()
self.conv1 = DoubleConv(in_ch, 64)
self.pool1 = nn.MaxPool2d(2)
self.conv2 = DoubleConv(64, 128)
self.pool2 = nn.MaxPool2d(2)
self.conv3 = DoubleConv(128, 256)
self.pool3 = nn.MaxPool2d(2)
self.conv4 = DoubleConv(256, 512)
self.pool4 = nn.MaxPool2d(2)
self.conv5 = DoubleConv(512, 1024)
self.up6 = nn.ConvTranspose2d(1024, 512, 2, stride=2)
self.conv6 = DoubleConv(1024, 512)
self.up7 = nn.ConvTranspose2d(512, 256, 2, stride=2)
self.conv7 = DoubleConv(512, 256)
self.up8 = nn.ConvTranspose2d(256, 128, 2, stride=2)
self.conv8 = DoubleConv(256, 128)
self.up9 = nn.ConvTranspose2d(128, 64, 2, stride=2)
self.conv9 = DoubleConv(128, 64)
self.conv10 = nn.Conv2d(64,out_ch, 1)
self.conv11 = DoubleConv(in_ch, 64)
self.pool11 = nn.MaxPool2d(2)
self.conv12 = DoubleConv(64, 128)
self.pool12 = nn.MaxPool2d(2)
self.conv13 = DoubleConv(128, 256)
self.pool13 = nn.MaxPool2d(2)
self.conv14 = DoubleConv(256, 512)
self.pool14 = nn.MaxPool2d(2)
self.conv15 = DoubleConv(512, 1024)
self.up16 = nn.ConvTranspose2d(1024, 512, 2, stride=2)
self.conv16 = DoubleConv(1024, 512)
self.up17 = nn.ConvTranspose2d(512, 256, 2, stride=2)
self.conv17 = DoubleConv(512, 256)
self.up18 = nn.ConvTranspose2d(256, 128, 2, stride=2)
self.conv18 = DoubleConv(256, 128)
self.up19 = nn.ConvTranspose2d(128, 64, 2, stride=2)
self.conv19 = DoubleConv(128, 64)
self.conv20 = nn.Conv2d(64,out_ch, 1)
def forward(self, x):
c1 = self.conv1(x)
p1 = self.pool1(c1)
c2 = self.conv2(p1)
p2 = self.pool2(c2)
c3 = self.conv3(p2)
p3 = self.pool3(c3)
c4 = self.conv4(p3)
p4 = self.pool4(c4)
c5 = self.conv5(p4)
up_6 = self.up6(c5)
# print('every_size',c5.shape,up_6.shape, c4.shape,p4.shape)
merge6 = torch.cat([up_6, c4], dim=1)
c6 = self.conv6(merge6)
up_7 = self.up7(c6)
merge7 = torch.cat([up_7, c3], dim=1)
c7 = self.conv7(merge7)
up_8 = self.up8(c7)
merge8 = torch.cat([up_8, c2], dim=1)
c8 = self.conv8(merge8)
up_9 = self.up9(c8)
merge9 = torch.cat([up_9, c1], dim=1)
c9 = self.conv9(merge9)
c10 = self.conv10(c9)
# out = nn.Sigmoid()(c10)
c10=c10+x
c11 = self.conv11(c10)
p11 = self.pool11(c11)
c12 = self.conv12(p11)
p12 = self.pool12(c12)
c13 = self.conv13(p12)
p13 = self.pool13(c13)
c14 = self.conv14(p13)
p14 = self.pool14(c14)
c15 = self.conv15(p14)
up_16 = self.up16(c15)
# print('every_size',c15.shape,up_16.shape, c14.shape,p14.shape)
merge16 = torch.cat([up_16, c14], dim=1)
c16 = self.conv16(merge16)
up_17 = self.up17(c16)
merge17 = torch.cat([up_17, c13], dim=1)
c17 = self.conv17(merge17)
up_18 = self.up18(c17)
merge18 = torch.cat([up_18, c12], dim=1)
c18 = self.conv18(merge18)
up_19 = self.up19(c18)
merge19 = torch.cat([up_19, c11], dim=1)
c19 = self.conv19(merge19)
c20 = self.conv20(c19)
res=c20+x
res=res.permute(0, 2, 3, 1)
res=res.contiguous().view(-1, 2)
out = torch.softmax(res,dim=1)
return out