The import torch. The nn as nn 
The import torch 
From the torch import autograd 

The class DoubleConv (nn Module) : 
Def __init__ (self, in_ch out_ch) : 
Super (DoubleConv, self) __init__ () 
The self. The 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 the self. Conv (input) 


The class Unet (nn Module) : 
Def __init__ (self, in_ch out_ch) : 
Super (Unet, 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) 

Def forward (self, x) : 
C1=self. Conv1 (x) 
P1=self. Pool1 (c1) 
C2=self. Conv2 (p1) 
The 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) 
Merge6=torch. The cat ([up_6, c4], dim=1) 
C6=self. Conv6 (merge6) 
Up_7=self. Up7 (c6) 
Merge7=torch. The cat ([up_7, c3], dim=1) 
C7=self. Conv7 (merge7) 
Up_8=self. Up8 (c7) 
Merge8=torch. The cat ([up_8, c2], dim=1) 
C8=self. Conv8 (merge8) 
Up_9=self. Up9 (c8) 
Merge9=torch. The cat ([up_9, c1], dim=1) 
C9=self. Conv9 (merge9) 
C10=self. Conv10 (c9) 
Out=nn. Sigmoid () (c10) 
Return the out