16.Unet

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：Pytorch_learn 
@File    ：unet_parts.py
@IDE     ：PyCharm 
@Author  ：咋
@Date    ：2023/7/14 17:47 
"""
from torch import nn
import torch
import torch.nn.functional as F

class Conv_Block(nn.Module):
    def __init__(self,in_channel,out_channel):
        super(Conv_Block, self).__init__()
        self.layer = nn.Sequential(
            nn.Conv2d(in_channel,out_channel,kernel_size=3,stride=1,padding=1,bias=False),
            nn.BatchNorm2d(out_channel),
            nn.Relu(inplace=True),
            nn.Conv2d(out_channel,out_channel,3,1,1,bias=False),
            nn.BatchNorm2d(out_channel),
            nn.ReLU(inplace=True)
        )

    def forward(self,x):
        return self.layer(x)

class Down(nn.Module):
    def __init__(self,in_channel,out_channel):
        super(Down, self).__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            Conv_Block(in_channel,out_channel)
        )
    def forward(self,x):
        return self.maxpool_conv(x)


class Up(nn.Module):
    def __init__(self,in_channel,out_channel,bilinear=False):
        super(Up, self).__init__()
        self.up = nn.Upsample(scale_factor=2,mode="bilinear",align_corners=True)
        self.conv = Conv_Block(in_channel,out_channel)


    def forward(self,x1,x2):
        x1 = self.up(x1)
        # 下面这里有点不大懂，GPT给的解释是
        # 这样的填充操作是为了保持x1和x2在高度和宽度上的尺寸一致。通过计算尺寸差异并进行填充，可以将x1的尺寸调整为与x2相同，以便进行后续的拼接操作。
        diffY = x2.size()[2]-x1.size()[2]
        diffX = x2.size()[3]-x1.size()[3]
        x1 = F.pad(x1,[diffX//2,diffX-diffX//2,
                       diffY//2,diffY-diffY//2])
        x = torch.cat([x2,x1],dim=1)
        return self.conv(x)

class OutConv(nn.Module):
    def __init__(self,in_channel,out_channel):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_channel,out_channel,1)


    def forward(self,x):
        return self.conv(x)

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：Pytorch_learn 
@File    ：unet.py
@IDE     ：PyCharm 
@Author  ：咋
@Date    ：2023/7/15 15:37 
"""
from unet_parts import *


class Unet(nn.Module):
    def __init__(self,n_channels,n_classes,bilinear=False):
        super(Unet, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear

        self.inc = (Conv_Block(n_channels, 64))
        self.down1 = (Down(64, 128))
        self.down2 = (Down(128, 256))
        self.down3 = (Down(256, 512))
        factor = 2 if bilinear else 1
        self.down4 = (Down(512, 1024 // factor))
        self.up1 = (Up(1024, 512 // factor, bilinear))
        self.up2 = (Up(512, 256 // factor, bilinear))
        self.up3 = (Up(256, 128 // factor, bilinear))
        self.up4 = (Up(128, 64, bilinear))
        self.outc = (OutConv(64, n_classes))


    def forward(self,x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        logits = self.outc(x)
        return logits