unet+resnet框架
时间: 2023-05-10 21:03:03 浏览: 226
UNet和ResNet是目前深度学习中非常常用的两个框架。UNet是一个用于图像分割的深度学习框架,可以广泛应用于医学图像分割等领域,而ResNet则是通过解决神经网络的梯度消失问题而变得流行的深度学习框架。
UNet的结构可以分为下采样路径和上采样路径两部分。在下采样路径中,通过卷积和pooling来实现图像大小的不断减小,以提取图像低层次的特征。而在上采样路径中,则通过卷积和上采样来实现低层次特征的提升,以最终得到一个像素级别的分割结果。UNet在图像分割中的应用较为广泛,可以用于医学图像分割、道路检测等任务。
ResNet则是利用残差学习的方法解决神经网络的梯度消失问题。在常规的神经网络中,网络的深度增加后随之而来的训练误差增加和模型过拟合的问题会显著增加。而ResNet则通过如图所示的残差块,将网络从输入到输出的关系更改为输入到输出的差异。这种差异可以端到端地学习,并具有更好的梯度流,从而使得模型在深度增加时仍然能够取得优秀的性能。
在实践中,UNet和ResNet也常常被结合起来使用,以得到更好的性能。通过将UNet与ResNet进行结合,可以不仅提取出图像的低层次特征还能保留图像的高层次特征,从而在图像分割等领域中取得更好的结果。
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SE+aspp+resnet+unet代码
以下是SE-ASPP-ResNet-UNet模型的代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
class ASPP(nn.Module):
def __init__(self, in_channels, out_channels=256):
super(ASPP, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, 1)
self.conv2 = nn.Conv2d(in_channels, out_channels, 3, padding=6, dilation=6)
self.conv3 = nn.Conv2d(in_channels, out_channels, 3, padding=12, dilation=12)
self.conv4 = nn.Conv2d(in_channels, out_channels, 3, padding=18, dilation=18)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv2d(in_channels + 4 * out_channels, out_channels, 1)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
def forward(self, x):
feat1 = self.conv1(x)
feat2 = self.conv2(x)
feat3 = self.conv3(x)
feat4 = self.conv4(x)
feat5 = self.avg_pool(x)
feat5 = F.upsample_bilinear(feat5, size=feat4.size()[2:])
x = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class SEASPPResNet(nn.Module):
def __init__(self):
super(SEASPPResNet, self).__init__()
self.resnet = nn.Sequential(
nn.Conv2d(3, 64, 7, stride=2, padding=3),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(3, stride=2, padding=1),
nn.Conv2d(64, 64, 1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, 64, 3, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, 256, 1),
SELayer(256),
)
self.aspp = ASPP(256, 256)
def forward(self, x):
x = self.resnet(x)
x = self.aspp(x)
return x
class UNet(nn.Module):
def __init__(self, in_channels, out_channels):
super(UNet, self).__init__()
self.down1 = nn.Sequential(
nn.Conv2d(in_channels, 64, 3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, 64, 3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.down2 = nn.Sequential(
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.down3 = nn.Sequential(
nn.MaxPool2d(2),
nn.Conv2d(128, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(256, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.down4 = nn.Sequential(
nn.MaxPool2d(2),
nn.Conv2d(256, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.Conv2d(512, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.up1 = nn.Sequential(
nn.ConvTranspose2d(512, 256, 2, stride=2),
nn.Conv2d(512, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(256, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.up2 = nn.Sequential(
nn.ConvTranspose2d(256, 128, 2, stride=2),
nn.Conv2d(256, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.up3 = nn.Sequential(
nn.ConvTranspose2d(128, 64, 2, stride=2),
nn.Conv2d(128, 64, 3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, 64, 3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.up4 = nn.Sequential(
nn.ConvTranspose2d(64, 32, 2, stride=2),
nn.Conv2d(64, 32, 3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32, 32, 3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
)
self.out = nn.Conv2d(32, out_channels, 1)
def forward(self, x):
x1 = self.down1(x)
x2 = self.down2(x1)
x3 = self.down3(x2)
x4 = self.down4(x3)
x = self.up1(x4)
x = torch.cat([x, x3], dim=1)
x = self.up2(x)
x = torch.cat([x, x2], dim=1)
x = self.up3(x)
x = torch.cat([x, x1], dim=1)
x = self.up4(x)
x = self.out(x)
return x
class SEASPPResNetUNet(nn.Module):
def __init__(self, in_channels, out_channels):
super(SEASPPResNetUNet, self).__init__()
self.se_aspp_resnet = SEASPPResNet()
self.unet = UNet(256, out_channels)
def forward(self, x):
x = self.se_aspp_resnet(x)
x = self.unet(x)
return x
```
这是一个使用PyTorch实现的SE-ASPP-ResNet-UNet模型,其中包含SELayer、ASPP、SEASPPResNet、UNet和SEASPPResNetUNet五个类。模型的输入是一个RGB图像,输出是一个大小为out_channels的向量,表示图像属于每个类别的概率。
unet++有哪些预训练模型
UNet++ 是一种针对语义分割任务的神经网络模型,由于其良好的性能和广泛的应用,已经有很多研究人员在开源社区上分享了预训练的 UNet++ 模型。以下是一些常见的 UNet++ 预训练模型:
1. UNet++ResNet34:使用 ResNet34 作为编码器,具有更好的性能和更高的鲁棒性。
2. UNet++ResNet50:使用 ResNet50 作为编码器,可提高 UNet++ 模型在复杂场景下的效果。
3. UNet++DenseNet121:使用 DenseNet121 作为编码器,能够提高模型的精度和稳定性。
4. UNet++EfficientNet-B0:使用 EfficientNet-B0 作为编码器,具有更快的训练速度和更好的表现。
5. UNet++VGG16:使用 VGG16 作为编码器,适用于需要更轻量级模型的场景。
这些预训练模型可以在 GitHub 等社区上找到并下载使用。