UNet与U2Net

业界资讯 2023-06-21 14:06:25 阅读次数: 0

UNet和U2Net都是语义分割领域中的深度学习模型

UNet是一种用于图像分割的深度学习模型，由Olaf Ronneberger等人在2015年提出
具有一种特殊的U形结构，可以将输入图像逐步缩小，然后再将其逐步放大，以产生分割结果。
UNet在医学图像分割、自然图像分割等领域具有广泛的应用

U2Net是由Ningning Wang等人在2020年提出的一种语义分割网络，可以高效地提取图像中的边缘和细节信息。
相对于UNet，U2Net采用了更深的网络结构，并引入了一个新的Attention U-Net模块，以更好地保留图像中的细节信息。

UNet

import torch
import torch.nn as nn
import torch.nn.functional as F


class CNN_Block(nn.Module):
    def __init__(self, c_in, c_out):
        super().__init__()
        self.layer = nn.Sequential(
            # 不改变特征的HW，改变特征的C
            # 完成了CHW层面的像素融合
            nn.Conv2d(in_channels=c_in, out_channels=c_out, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(c_out),
            nn.LeakyReLU(),
            # nn.Dropout(0.2)
            nn.Conv2d(in_channels=c_out, out_channels=c_out, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(c_out),
            nn.LeakyReLU(),
            # nn.Dropout(0.2)
        )

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


# 下采样，/2，C不变。
class DownSample_Block(nn.Module):
    def __init__(self, c):
        super().__init__()
        self.layer = nn.Sequential(
            nn.MaxPool2d(2),
            CNN_Block(c, c)
        )

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


# 上采样，*2，C减半
class UpSample_Block(nn.Module):
    def __init__(self, c):
        super().__init__()
        self.layer = nn.Sequential(
            nn.Conv2d(in_channels=c, out_channels=c // 2, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(c // 2),
            nn.LeakyReLU(),
            # nn.Dropout(0.2)
        )

    # x:o5  r:o4
    # x:o6  r:o3
    # x:o7  r:o2
    # x:o8  r:o1
    def forward(self, x, r):
        # 先把HW * 2，再把C / 2
        data = F.interpolate(x, scale_factor=2, mode="nearest")
        data = self.layer(data)
        # 信息补全
        return torch.cat((data, r), dim=1)


class UNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = CNN_Block(3, 64)
        self.down1 = DownSample_Block(64)
        self.conv2 = CNN_Block(64, 128)
        self.down2 = DownSample_Block(128)
        self.conv3 = CNN_Block(128, 256)
        self.down3 = DownSample_Block(256)
        self.conv4 = CNN_Block(256, 512)
        self.down4 = DownSample_Block(512)
        self.conv5 = CNN_Block(512, 1024)
        self.up1 = UpSample_Block(1024)
        #
        self.conv6 = CNN_Block(1024, 512)
        self.up2 = UpSample_Block(512)
        self.conv7 = CNN_Block(512, 256)
        self.up3 = UpSample_Block(256)
        self.conv8 = CNN_Block(256, 128)
        self.up4 = UpSample_Block(128)
        self.conv9 = CNN_Block(128, 64)
        # 输出层
        self.out_layer = nn.Conv2d(64, 1, 1, 1)

    def forward(self, x):
        o1 = self.conv1(x)
        o2 = self.conv2(self.down1(o1))
        o3 = self.conv3(self.down2(o2))
        o4 = self.conv4(self.down3(o3))
        o5 = self.conv5(self.down4(o4))
        # 信息补全
        # x:o5  r:o4
        # x:o6  r:o3
        # x:o7  r:o2
        # x:o8  r:o1
        o6 = self.conv6(self.up1(o5, o4))
        o7 = self.conv7(self.up2(o6, o3))
        o8 = self.conv8(self.up3(o7, o2))
        o9 = self.conv9(self.up4(o8, o1))
        return self.out_layer(o9)


# if __name__ == '__main__':
#     data = torch.randn(1, 3, 256, 256)
#     unet = UNet()
#     data = unet(data)
#     print(data.shape)

U2Net

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)

猜你喜欢

转载自blog.csdn.net/weixin_44659309/article/details/130452445
今日推荐
美国拟限制 AI 大模型出口中国和俄罗斯
苹果将与 OpenAI 达成协议，将 ChatGPT 应用于 iPhone
openKylin 社区生态委员会第六次会议圆满召开
阿里云正式发布通义千问 2.5
Python 3.13 发布首个 Beta：实验性自由线程模式和 JIT、改进交互式解释器
Stack Overflow 拿我的代码去训练 AI 大模型，还封了我的账号
Pop!_OS 的 COSMIC 桌面完成 App Store 上架工作
报告：Django 仍然是 74% 开发者的首选
《2024 年一季度互联网投融资运行情况》研究报告
15 年前上了“FFmpeg 耻辱柱”，今天他还得谢谢咱——腾讯QQPlayer一雪前耻？
TIOBE 5 月榜单：Fortran “复活”进入 Top 10
GCC 14.1 发布
周排行
curl的POST请求，封装方法
8.1.1. Integer Types
Java基础 Day05(个人复习整理)
Python - Django - 中间件 process_exception
小L的试卷
【Shell编程】 （函数）判断用户是否存在
python(css样式)
spring ant path 匹配原则 - 【笔记】
《JavaScript与JScript从入门到精通》(美)James.Jaworski.中译本.扫描版.pdf
Eclipse运行带参数的java程序
每日归档
更多
2024-05-12(0)
2024-05-11(38)
2024-05-10(38)
2024-05-09(35)
2024-05-08(42)
2024-05-07(14)
2024-05-06(40)
2024-05-05(0)
2024-05-04(7)
2024-05-03(19)

代码天地 © 2018 codetd.com

境外服务器   最新电视剧2022