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UREC.py
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UREC.py
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import torch.nn.functional as F
from unet_parts import *
class UNet(nn.Module):
def __init__(self, n_channels, n_classes):
super(UNet, self).__init__()
self.dropout = nn.Dropout(0.4)
#down1
self.conv_1_1 = conv_block(n_channels, 32)
self.conv_1_2 = conv_block(32,32)
self.maxp_1 = MaxP(32,32)
#down2
self.conv_2_1 = conv_block(32, 64)
self.conv_2_2 = conv_block(64,64)
self.maxp_2 = MaxP(64,64)
#down3
self.conv_3_1 = conv_block(64,128)
self.conv_3_2 = conv_block(128,128)
self.conv_3_3 = conv_block(128,128)
self.maxp_3 = MaxP(128,128)
#down4
self.conv_4_1 = conv_block(128,256)
self.conv_4_2 = conv_block(256,256)
self.conv_4_3 = conv_block(256,256)
self.maxp_4 = MaxP(256,256)
#down5
self.conv_5_1 = conv_block(256,512)
self.conv_5_2 = conv_block(512,512)
self.conv_5_3 = conv_block(512,512)
self.maxp_5 = MaxP(512,512)
#up1
self.D_conv_1 = conv_block(512,512)
self.D_up_1 = up(512,512)
#up2
self.D_conv_2_1 = conv_block(512*2, 512)
self.R_conv_2_1 = conv_block(512, 512)
self.D_conv_2_2 = conv_block(512, 512)
self.D_conv_2_3 = conv_block(512, 256)
self.D_conv_2_4 = conv_block(256,256)
self.D_up_2 = up(256,256)
#up3
self.D_conv_3_1 = conv_block(256*2, 256)
self.R_conv_3_1 = conv_block(256,256)
self.D_conv_3_2 = conv_block(256,256)
self.D_conv_3_3 = conv_block(256, 128)
self.D_conv_3_4 = conv_block(128,128)
self.D_up_3 = up(128,128)
#up4
self.D_conv_4_1 = conv_block(128*2,128)
self.R_conv_4_1 = conv_block(128, 128)
self.D_conv_4_2 = conv_block(128, 128)
self.D_conv_4_3 = conv_block(128, 64)
self.D_conv_4_4 = conv_block(64,64)
self.D_up_4 = up(64,64)
#up5
self.D_conv_5_1 = conv_block(64*2,64)
self.R_conv_5_1 = conv_block(64,64)
self.D_conv_5_2 = conv_block(64,32)
self.D_conv_5_3 = conv_block(32,32)
self.D_up_5 = up(32,32)
self.final_block = conv_block(32*2,32)
self.Rfinal_block = conv_block(32,32)
self.outconv = outconv(32,n_classes)
self.Routput = outconv(32, 3)
def encode(self, x):
x = self.conv_1_1(x)
x1 = self.conv_1_2(x)
x = self.maxp_1(x1)
x = self.conv_2_1(x)
x2 = self.conv_2_2(x)
x = self.maxp_2(x2)
x = self.conv_3_1(x)
x = self.conv_3_2(x)
x3 = self.conv_3_3(x)
x = self.maxp_3(x3)
x = self.conv_4_1(x)
x = self.conv_4_2(x)
x4 = self.conv_4_3(x)
x = self.maxp_4(x4)
x = self.conv_5_1(x)
x = self.conv_5_2(x)
x5 = self.conv_5_3(x)
x_mp = self.maxp_5(x5)
return x1,x2,x3,x4,x5,x_mp
def decode(self, x1,x2,x3,x4,x5,x_mp):
m = self.D_conv_1(x_mp)
m = self.D_up_1(m)
m = torch.cat((m,x5), 1)
m = self.D_conv_2_1(m)
m = self.D_conv_2_2(m)
m = self.D_conv_2_3(m)
m = self.D_conv_2_4(m)
m = self.D_up_2(m)
m = torch.cat((m,x4), 1)
m = self.D_conv_3_1(m)
m = self.D_conv_3_2(m)
m = self.D_conv_3_3(m)
m = self.D_conv_3_4(m)
m = self.D_up_3(m)
m = torch.cat((m,x3), 1)
m = self.D_conv_4_1(m)
m = self.D_conv_4_2(m)
m = self.D_conv_4_3(m)
m = self.D_conv_4_4(m)
m = self.D_up_4(m)
m = torch.cat((m,x2), 1)
m = self.D_conv_5_1(m)
m = self.D_conv_5_2(m)
m = self.D_conv_5_3(m)
m = self.D_up_5(m)
m = torch.cat((m,x1), 1)
m = self.final_block(m)
m = self.outconv(m)
return m
def rec(self, x_mp):
m = self.D_conv_1(x_mp)
m = self.D_up_1(m)
m = self.R_conv_2_1(m)
m = self.D_conv_2_2(m)
m = self.D_conv_2_3(m)
m = self.D_conv_2_4(m)
m = self.D_up_2(m)
m = self.R_conv_3_1(m)
m = self.D_conv_3_2(m)
m = self.D_conv_3_3(m)
m = self.D_conv_3_4(m)
m = self.D_up_3(m)
m = self.R_conv_4_1(m)
m = self.D_conv_4_2(m)
m = self.D_conv_4_3(m)
m = self.D_conv_4_4(m)
m = self.D_up_4(m)
m = self.R_conv_5_1(m)
m = self.D_conv_5_2(m)
m = self.D_conv_5_3(m)
m = self.D_up_5(m)
m = self.Rfinal_block(m)
m = self.Routput(m)
return m
def forward(self, x):
x1,x2,x3,x4,x5,x_mp = self.encode(x)
segm = self.decode(x1,x2,x3,x4,x5,x_mp)
rec = self.rec(x_mp)
return segm, rec