UNet、U2Net 和U2NetP

业界资讯 2023-06-21 14:06:10 阅读次数: 0

UNet：UNet是一种基于全卷积神经网络的语义分割模型，由编码器和解码器两部分组成。编码器通过多层卷积操作对输入图像进行特征提取和降采样，解码器则通过反卷积操作和跳跃连接对编码器输出的特征图进行上采样和重构。UNet具有较好的图像分割效果和较快的推理速度，广泛应用于医疗图像分割等领域。

U2Net：U2Net是一种基于UNet的改进模型，通过增加多个分支和注意力机制来进一步提升分割效果。其中，U2Net包括U2Net和U2NetP两个版本，前者基于纯卷积操作，后者则增加了轻量级的U形网络来进行特征提取。U2Net在各种图像分割任务中都取得了较好的效果，包括自然图像、医学图像和遥感图像等。

U2NetP：U2NetP是U2Net的改进版本，通过添加较少的参数和计算量来进一步提升分割效果和降低推理成本。U2NetP主要通过轻量级的编码器和解码器来实现，在保证分割效果的同时，能够较快地处理大型图像和批量数据

import torch
import torch.nn as nn
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__()
        # 卷积核为3的情况下，padding == dilation，输出HW不变
        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.interpolate(src, size=tar.shape[2:], mode='bilinear') # old version torch
    # tar：NCHW
    src = F.interpolate(src, size=tar.shape[2:], mode='bilinear', align_corners=True)
    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 = 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)
        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 = 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)

        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__()
        # encoder
        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, 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 torch.sigmoid(d0), torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(
            d4), torch.sigmoid(d5), torch.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, 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)
        # 6个特征图 -->1个特征图
        d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))

        return torch.sigmoid(d0), torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(
            d4), torch.sigmoid(d5), torch.sigmoid(d6)


if __name__ == '__main__':
    u2net = U2NET()
    x = torch.randn(1, 3, 224, 224)
    y = u2net(x)
    print(y[0].shape, y[1].shape, y[2].shape, y[3].shape, y[4].shape, y[5].shape, y[6].shape)

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转载自blog.csdn.net/weixin_44659309/article/details/130469172
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