基于Pytorch框架的深度学习U2Net网络天空语义精细分割系统源码

第一步：准备数据

头发分割数据，总共有10276张图片，里面的像素值为0和1，所以看起来全部是黑的，不影响使用

第二步：搭建模型

级联模式

通常多个类似U-Net按顺序堆叠，以建立级联模型，并可归纳为(Uxn-Net)，n是重复U-Net模块的数目，带来的问题是计算和内存开销被n放大了。如DocUNet, CU-Net网络等，如下图所示，为DocUNet网络的构成：

U型嵌套模式

作者提出一种不同的U型结构叠加模型。我们的指数表示法是指嵌套的U型结构，而不是级联叠加。理论上，可以将指数n设为任意正整数，实现单级或多级嵌套U型结构。但是，嵌套层太多的体系结构过于复杂，无法在实际中实现和应用。

我们将n设为2来构建U2-Net，是一个两层嵌套的U型结构，如图5所示。它的顶层是一个由11 stages（图5中的立方体）组成的大U型结构，每一stage由一个配置良好的RSU填充。因此，嵌套的U结构可以更有效的提取stage内的多尺度特征和聚集阶段的多层次特征。

第三步：代码

1）损失函数为：交叉熵损失函数

2）网络代码：

import torch
import torch.nn as nn
from torchvision import models
import torch.nn.functional as Fclass REBNCONV(nn.Module):def __init__(self,in_ch=3,out_ch=3,dirate=1):super(REBNCONV,self).__init__()self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate)self.bn_s1 = nn.BatchNorm2d(out_ch)self.relu_s1 = nn.ReLU(inplace=True)def forward(self,x):hx = xxout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))return xout## upsample tensor 'src' to have the same spatial size with tensor 'tar'
def _upsample_like(src,tar):src = F.upsample(src,size=tar.shape[2:],mode='bilinear')return src### RSU-7 ###
class RSU7(nn.Module):#UNet07DRES(nn.Module):def __init__(self, in_ch=3, mid_ch=12, out_ch=3):super(RSU7,self).__init__()self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)def forward(self,x):hx = xhxin = self.rebnconvin(hx)hx1 = self.rebnconv1(hxin)hx = self.pool1(hx1)hx2 = self.rebnconv2(hx)hx = self.pool2(hx2)hx3 = self.rebnconv3(hx)hx = self.pool3(hx3)hx4 = self.rebnconv4(hx)hx = self.pool4(hx4)hx5 = self.rebnconv5(hx)hx = self.pool5(hx5)hx6 = self.rebnconv6(hx)hx7 = self.rebnconv7(hx6)hx6d =  self.rebnconv6d(torch.cat((hx7,hx6),1))hx6dup = _upsample_like(hx6d,hx5)hx5d =  self.rebnconv5d(torch.cat((hx6dup,hx5),1))hx5dup = _upsample_like(hx5d,hx4)hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))hx4dup = _upsample_like(hx4d,hx3)hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))hx3dup = _upsample_like(hx3d,hx2)hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))hx2dup = _upsample_like(hx2d,hx1)hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))return hx1d + hxin### RSU-6 ###
class RSU6(nn.Module):#UNet06DRES(nn.Module):def __init__(self, in_ch=3, mid_ch=12, out_ch=3):super(RSU6,self).__init__()self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)def forward(self,x):hx = xhxin = self.rebnconvin(hx)hx1 = self.rebnconv1(hxin)hx = self.pool1(hx1)hx2 = self.rebnconv2(hx)hx = self.pool2(hx2)hx3 = self.rebnconv3(hx)hx = self.pool3(hx3)hx4 = self.rebnconv4(hx)hx = self.pool4(hx4)hx5 = self.rebnconv5(hx)hx6 = self.rebnconv6(hx5)hx5d =  self.rebnconv5d(torch.cat((hx6,hx5),1))hx5dup = _upsample_like(hx5d,hx4)hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))hx4dup = _upsample_like(hx4d,hx3)hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))hx3dup = _upsample_like(hx3d,hx2)hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))hx2dup = _upsample_like(hx2d,hx1)hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))return hx1d + hxin### RSU-5 ###
class RSU5(nn.Module):#UNet05DRES(nn.Module):def __init__(self, in_ch=3, mid_ch=12, out_ch=3):super(RSU5,self).__init__()self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)def forward(self,x):hx = xhxin = self.rebnconvin(hx)hx1 = self.rebnconv1(hxin)hx = self.pool1(hx1)hx2 = self.rebnconv2(hx)hx = self.pool2(hx2)hx3 = self.rebnconv3(hx)hx = self.pool3(hx3)hx4 = self.rebnconv4(hx)hx5 = self.rebnconv5(hx4)hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))hx4dup = _upsample_like(hx4d,hx3)hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))hx3dup = _upsample_like(hx3d,hx2)hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))hx2dup = _upsample_like(hx2d,hx1)hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))return hx1d + hxin### RSU-4 ###
class RSU4(nn.Module):#UNet04DRES(nn.Module):def __init__(self, in_ch=3, mid_ch=12, out_ch=3):super(RSU4,self).__init__()self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)def forward(self,x):hx = xhxin = self.rebnconvin(hx)hx1 = self.rebnconv1(hxin)hx = self.pool1(hx1)hx2 = self.rebnconv2(hx)hx = self.pool2(hx2)hx3 = self.rebnconv3(hx)hx4 = self.rebnconv4(hx3)hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))hx3dup = _upsample_like(hx3d,hx2)hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))hx2dup = _upsample_like(hx2d,hx1)hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))return hx1d + hxin### RSU-4F ###
class RSU4F(nn.Module):#UNet04FRES(nn.Module):def __init__(self, in_ch=3, mid_ch=12, out_ch=3):super(RSU4F,self).__init__()self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)def forward(self,x):hx = xhxin = self.rebnconvin(hx)hx1 = self.rebnconv1(hxin)hx2 = self.rebnconv2(hx1)hx3 = self.rebnconv3(hx2)hx4 = self.rebnconv4(hx3)hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))return hx1d + hxin##### U^2-Net ####
class U2NET(nn.Module):def __init__(self,in_ch=3,out_ch=1):super(U2NET,self).__init__()self.stage1 = RSU7(in_ch,32,64)self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage2 = RSU6(64,32,128)self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage3 = RSU5(128,64,256)self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage4 = RSU4(256,128,512)self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage5 = RSU4F(512,256,512)self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage6 = RSU4F(512,256,512)# decoderself.stage5d = RSU4F(1024,256,512)self.stage4d = RSU4(1024,128,256)self.stage3d = RSU5(512,64,128)self.stage2d = RSU6(256,32,64)self.stage1d = RSU7(128,16,64)self.side1 = nn.Conv2d(64,out_ch,3,padding=1)self.side2 = nn.Conv2d(64,out_ch,3,padding=1)self.side3 = nn.Conv2d(128,out_ch,3,padding=1)self.side4 = nn.Conv2d(256,out_ch,3,padding=1)self.side5 = nn.Conv2d(512,out_ch,3,padding=1)self.side6 = nn.Conv2d(512,out_ch,3,padding=1)self.outconv = nn.Conv2d(6,out_ch,1)def forward(self,x):hx = x#stage 1hx1 = self.stage1(hx)hx = self.pool12(hx1)#stage 2hx2 = self.stage2(hx)hx = self.pool23(hx2)#stage 3hx3 = self.stage3(hx)hx = self.pool34(hx3)#stage 4hx4 = self.stage4(hx)hx = self.pool45(hx4)#stage 5hx5 = self.stage5(hx)hx = self.pool56(hx5)#stage 6hx6 = self.stage6(hx)hx6up = _upsample_like(hx6,hx5)#-------------------- decoder --------------------hx5d = self.stage5d(torch.cat((hx6up,hx5),1))hx5dup = _upsample_like(hx5d,hx4)hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))hx4dup = _upsample_like(hx4d,hx3)hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))hx3dup = _upsample_like(hx3d,hx2)hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))hx2dup = _upsample_like(hx2d,hx1)hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))#side outputd1 = self.side1(hx1d)d2 = self.side2(hx2d)d2 = _upsample_like(d2,d1)d3 = self.side3(hx3d)d3 = _upsample_like(d3,d1)d4 = self.side4(hx4d)d4 = _upsample_like(d4,d1)d5 = self.side5(hx5d)d5 = _upsample_like(d5,d1)d6 = self.side6(hx6)d6 = _upsample_like(d6,d1)d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))#return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)return d0, d1, d2, d3, d4, d5, d6class U2NET_half(nn.Module):def __init__(self,in_ch=3,out_ch=1):super(U2NET_half,self).__init__()self.stage1 = RSU7(in_ch,16,32)self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage2 = RSU6(32,16,64)self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage3 = RSU5(64,32,128)self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage4 = RSU4(128,64,256)self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage5 = RSU4F(256,128,256)self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage6 = RSU4F(256,128,256)# decoderself.stage5d = RSU4F(512,128,256)self.stage4d = RSU4(512,64,128)self.stage3d = RSU5(256,32,64)self.stage2d = RSU6(128,16,32)self.stage1d = RSU7(64,8,32)self.side1 = nn.Conv2d(32,out_ch,3,padding=1)self.side2 = nn.Conv2d(32,out_ch,3,padding=1)self.side3 = nn.Conv2d(64,out_ch,3,padding=1)self.side4 = nn.Conv2d(128,out_ch,3,padding=1)self.side5 = nn.Conv2d(256,out_ch,3,padding=1)self.side6 = nn.Conv2d(256,out_ch,3,padding=1)self.outconv = nn.Conv2d(6,out_ch,1)def forward(self,x):hx = x#stage 1hx1 = self.stage1(hx)hx = self.pool12(hx1)#stage 2hx2 = self.stage2(hx)hx = self.pool23(hx2)#stage 3hx3 = self.stage3(hx)hx = self.pool34(hx3)#stage 4hx4 = self.stage4(hx)hx = self.pool45(hx4)#stage 5hx5 = self.stage5(hx)hx = self.pool56(hx5)#stage 6hx6 = self.stage6(hx)hx6up = _upsample_like(hx6,hx5)#-------------------- decoder --------------------hx5d = self.stage5d(torch.cat((hx6up,hx5),1))hx5dup = _upsample_like(hx5d,hx4)hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))hx4dup = _upsample_like(hx4d,hx3)hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))hx3dup = _upsample_like(hx3d,hx2)hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))hx2dup = _upsample_like(hx2d,hx1)hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))#side outputd1 = self.side1(hx1d)d2 = self.side2(hx2d)d2 = _upsample_like(d2,d1)d3 = self.side3(hx3d)d3 = _upsample_like(d3,d1)d4 = self.side4(hx4d)d4 = _upsample_like(d4,d1)d5 = self.side5(hx5d)d5 = _upsample_like(d5,d1)d6 = self.side6(hx6)d6 = _upsample_like(d6,d1)d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))#return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)return d0, d1, d2, d3, d4, d5, d6### U^2-Net small ###
class U2NETP(nn.Module):def __init__(self,in_ch=3,out_ch=1):super(U2NETP,self).__init__()self.stage1 = RSU7(in_ch,16,64)self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage2 = RSU6(64,16,64)self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage3 = RSU5(64,16,64)self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage4 = RSU4(64,16,64)self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage5 = RSU4F(64,16,64)self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)self.stage6 = RSU4F(64,16,64)# decoderself.stage5d = RSU4F(128,16,64)self.stage4d = RSU4(128,16,64)self.stage3d = RSU5(128,16,64)self.stage2d = RSU6(128,16,64)self.stage1d = RSU7(128,16,64)self.side1 = nn.Conv2d(64,out_ch,3,padding=1)self.side2 = nn.Conv2d(64,out_ch,3,padding=1)self.side3 = nn.Conv2d(64,out_ch,3,padding=1)self.side4 = nn.Conv2d(64,out_ch,3,padding=1)self.side5 = nn.Conv2d(64,out_ch,3,padding=1)self.side6 = nn.Conv2d(64,out_ch,3,padding=1)self.outconv = nn.Conv2d(6,out_ch,1)def forward(self,x):hx = x#stage 1hx1 = self.stage1(hx)hx = self.pool12(hx1)#stage 2hx2 = self.stage2(hx)hx = self.pool23(hx2)#stage 3hx3 = self.stage3(hx)hx = self.pool34(hx3)#stage 4hx4 = self.stage4(hx)hx = self.pool45(hx4)#stage 5hx5 = self.stage5(hx)hx = self.pool56(hx5)#stage 6hx6 = self.stage6(hx)hx6up = _upsample_like(hx6,hx5)#decoderhx5d = self.stage5d(torch.cat((hx6up,hx5),1))hx5dup = _upsample_like(hx5d,hx4)hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))hx4dup = _upsample_like(hx4d,hx3)hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))hx3dup = _upsample_like(hx3d,hx2)hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))hx2dup = _upsample_like(hx2d,hx1)hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))#side outputd1 = self.side1(hx1d)d2 = self.side2(hx2d)d2 = _upsample_like(d2,d1)d3 = self.side3(hx3d)d3 = _upsample_like(d3,d1)d4 = self.side4(hx4d)d4 = _upsample_like(d4,d1)d5 = self.side5(hx5d)d5 = _upsample_like(d5,d1)d6 = self.side6(hx6)d6 = _upsample_like(d6,d1)d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))# return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)return d0, d1, d2, d3, d4, d5, d6

第四步：搭建GUI界面

第五步：整个工程的内容

有训练代码和训练好的模型以及训练过程，提供数据，提供GUI界面代码

代码见：基于Pytorch框架的深度学习U2Net网络天空语义精细分割系统源码

有问题可以私信或者留言，有问必答