unet系列

TianFeng 深度学习 YOLO180阅读模式

前言概念

图像分割

分割任务就是在原始图像中逐像素的找到你需要的家伙!

unet系列

语义分割

就是把每个像素都打上标签(这个像素点是人,树,背景等)

(语义分割只区分类别,不区分类别中具体单位)unet系列

实例分割

实例分割不光要区别类别,还要区分类别中每一个个体

unet系列

损失函数:

逐像素的交叉熵:还经常需要考虑样本均衡问题,交叉熵损失函数公式如下:

unet系列

Focal loss:样本也由难易之分,就跟玩游戏一样,难度越高的BOSS奖励越高

unet系列
Gamma通常设置为2,例如预测正样本概率0.95,如果预测正样本概率0.4, (相当于样本的难易权值)

unet系列
(再结合样本数量的权值就是Focal Loss)

IOU计算

多分类任务时:iou_dog = 801 / true_dog + predict_dog - 801

unet系列

MIOU指标:
MIOU就是计算所有类别的平均值,一般当作分割任务评估指标

Unet

整体结构:概述就是编码解码过程;简单但是很实用,应用广;起初是做医学方向,现在也是

unet系列

 

Unet++

整体网络结构:特征融合,拼接更全面;其实跟densenet思想一致;把能拼能凑的特征全用上

Deep Supervision :多输出损失;由多个位置计算,再更新

容易剪枝:可以根据速度要求来快速完成剪枝;训练的时候同样会用到L4,效果还不错

unet系列

U²net

代码  论文

听名字知道就是把Unet中每个stage再变成一个Unet,这样就嵌套了一个Unet变成U²net;

输出为解码器各个阶段输出再拼接,经过一次卷积输出

unet系列

现有卷积块和我们提出的残差U形块RSU的说明:(a)普通卷积块PLN,(b)残差类块RES,(c)密集类块DSE,(d)启始类块INC和(e)我们的残差U型块RSU

unet系列

残差块与我们的RSU的比较

unet系列

就作者展示的效果而言,出奇的不错,有兴趣去代码界面看看,使用也很简单,下面展示一些

unet系列

unet系列

unet系列

代码结构放最后;有兴趣看看

#U²net结构;387行forward开始
import torch
import torch.nn as nn
from torchvision import models
import torch.nn.functional as F

class 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 = x
        xout = 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):
        print(x.shape)
        hx = x
        hxin = self.rebnconvin(hx)
        print(hxin.shape)
        hx1 = self.rebnconv1(hxin)
        print(hx1.shape)
        hx = self.pool1(hx1)
        print(hx.shape)

        hx2 = self.rebnconv2(hx)
        print(hx2.shape)
        hx = self.pool2(hx2)
        print(hx.shape)

        hx3 = self.rebnconv3(hx)
        print(hx3.shape)
        hx = self.pool3(hx3)
        print(hx.shape)

        hx4 = self.rebnconv4(hx)
        print(hx4.shape)
        hx = self.pool4(hx4)
        print(hx.shape)

        hx5 = self.rebnconv5(hx)
        print(hx5.shape)
        hx = self.pool5(hx5)
        print(hx.shape)

        hx6 = self.rebnconv6(hx)
        print(hx6.shape)

        hx7 = self.rebnconv7(hx6)
        print(hx7.shape)

        hx6d =  self.rebnconv6d(torch.cat((hx7,hx6),1))
        print(hx6d.shape)
        hx6dup = _upsample_like(hx6d,hx5)
        print(hx6dup.shape)

        hx5d =  self.rebnconv5d(torch.cat((hx6dup,hx5),1))
        print(hx5d.shape)
        hx5dup = _upsample_like(hx5d,hx4)
        print(hx5dup.shape)

        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
        print(hx4d.shape)
        hx4dup = _upsample_like(hx4d,hx3)
        print(hx4dup.shape)

        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
        print(hx3d.shape)
        hx3dup = _upsample_like(hx3d,hx2)
        print(hx3dup.shape)

        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
        print(hx2d.shape)
        hx2dup = _upsample_like(hx2d,hx1)
        print(hx2dup.shape)

        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
        print(hx1d.shape)

        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 = x

        hxin = 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 = x

        hxin = 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 = x

        hxin = 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 = x

        hxin = self.rebnconvin(hx)
        print(hxin.shape)
        hx1 = self.rebnconv1(hxin)
        print(hx1.shape)
        hx2 = self.rebnconv2(hx1)
        print(hx2.shape)
        hx3 = self.rebnconv3(hx2)
        print(hx3.shape)

        hx4 = self.rebnconv4(hx3)
        print(hx4.shape)
        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
        print(hx3d.shape)
        hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
        print(hx2d.shape)
        hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
        print(hx1d.shape)

        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)

        # decoder
        self.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):
        print(x.shape)
        hx = x

        #stage 1
        hx1 = self.stage1(hx)
        print(hx1.shape)
        hx = self.pool12(hx1)
        print(hx.shape)
        #stage 2
        hx2 = self.stage2(hx)
        print(hx2.shape)
        hx = self.pool23(hx2)
        print(hx.shape)

        #stage 3
        hx3 = self.stage3(hx)
        print(hx3.shape)
        hx = self.pool34(hx3)
        print(hx.shape)

        #stage 4
        hx4 = self.stage4(hx)
        print(hx4.shape)
        hx = self.pool45(hx4)
        print(hx.shape)

        #stage 5
        hx5 = self.stage5(hx)
        print(hx5.shape)
        hx = self.pool56(hx5)
        print(hx.shape)

        #stage 6
        hx6 = self.stage6(hx)
        print(hx6.shape)
        hx6up = _upsample_like(hx6,hx5)
        print(hx6up.shape)

        #-------------------- decoder --------------------
        hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
        print(hx5d.shape)
        hx5dup = _upsample_like(hx5d,hx4)
        print(hx5dup.shape)
        hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
        print(hx4d.shape)
        hx4dup = _upsample_like(hx4d,hx3)
        print(hx4dup.shape)

        hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
        print(hx3d.shape)
        hx3dup = _upsample_like(hx3d,hx2)
        print(hx3dup.shape)

        hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
        print(hx2d.shape)
        hx2dup = _upsample_like(hx2d,hx1)
        print(hx2dup.shape)

        hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
        print(hx1d.shape)


        #side output
        d1 = self.side1(hx1d)
        print(d1.shape)

        d2 = self.side2(hx2d)
        print(d2.shape)
        d2 = _upsample_like(d2,d1)
        print(d2.shape)

        d3 = self.side3(hx3d)
        print(d3.shape)
        d3 = _upsample_like(d3,d1)
        print(d3.shape)

        d4 = self.side4(hx4d)
        print(d4.shape)
        d4 = _upsample_like(d4,d1)
        print(d4.shape)

        d5 = self.side5(hx5d)
        print(d5.shape)
        d5 = _upsample_like(d5,d1)
        print(d5.shape)

        d6 = self.side6(hx6)
        print(d6.shape)
        d6 = _upsample_like(d6,d1)
        print(d6.shape)

        d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
        print(d0.shape)

        return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(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)

        # decoder
        self.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 1
        hx1 = self.stage1(hx)
        hx = self.pool12(hx1)

        #stage 2
        hx2 = self.stage2(hx)
        hx = self.pool23(hx2)

        #stage 3
        hx3 = self.stage3(hx)
        hx = self.pool34(hx3)

        #stage 4
        hx4 = self.stage4(hx)
        hx = self.pool45(hx4)

        #stage 5
        hx5 = self.stage5(hx)
        hx = self.pool56(hx5)

        #stage 6
        hx6 = 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 output
        d1 = 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)

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  • 本文由 发表于 2023年 10月 5日 15:44:09
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