请说说如何改进U-Net模型和DeepLabv3+模型用于提取雷达遥感水体信息的神经网络,并给出他们的模型代码
时间: 2023-06-12 20:07:44 浏览: 130
U-Net和DeepLabv3+是常用的用于图像分割的神经网络模型,可以用于提取雷达遥感水体信息。以下是我对这两种模型的改进建议:
1. U-Net模型改进:
- 使用更深的网络结构,例如加入更多的卷积层和池化层,以提高模型的表现力和泛化能力。
- 使用不同的损失函数,例如Focal Loss,Dice Loss等,以提高模型对边缘信息的准确度。
- 数据增强处理,例如旋转、翻转、缩放等,以增加数据的多样性,提高模型的鲁棒性。
以下是U-Net模型的代码:
```python
import torch.nn as nn
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(DoubleConv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.conv(x)
class UNet(nn.Module):
def __init__(self, in_channels=1, out_channels=1, features=[64, 128, 256, 512]):
super(UNet, self).__init__()
self.ups = nn.ModuleList()
self.downs = nn.ModuleList()
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
# Down part of U-Net
for feature in features:
self.downs.append(DoubleConv(in_channels, feature))
in_channels = feature
# Up part of U-Net
for feature in reversed(features):
self.ups.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride=2))
self.ups.append(DoubleConv(feature*2, feature))
self.bottleneck = DoubleConv(features[-1], features[-1]*2)
self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)
def forward(self, x):
skip_connections = []
for down in self.downs:
x = down(x)
skip_connections.append(x)
x = self.pool(x)
x = self.bottleneck(x)
skip_connections = skip_connections[::-1]
for idx in range(0, len(self.ups), 2):
x = self.ups[idx](x)
skip_connection = skip_connections[idx//2]
if x.shape != skip_connection.shape:
x = TF.resize(x, size=skip_connection.shape[2:])
concat_skip = torch.cat((skip_connection, x), dim=1)
x = self.ups[idx+1](concat_skip)
return self.final_conv(x)
```
2. DeepLabv3+模型改进:
- 加入空间金字塔池化模块,提高模型对不同尺度信息的捕获能力。
- 采用可变形卷积层,增加模型的感受野,提高模型对目标形状的适应性。
- 采用多尺度训练和测试,提高模型对不同尺度目标的检测能力。
以下是DeepLabv3+模型的代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils import model_zoo
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
}
class ASPP(nn.Module):
def __init__(self, in_channels, out_channels=256, rates=[6, 12, 18]):
super(ASPP, self).__init__()
self.conv1x1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.atrous_conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[0], dilation=rates[0])
self.atrous_conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[1], dilation=rates[1])
self.atrous_conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[2], dilation=rates[2])
self.pool = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, out_channels, kernel_size=1)
)
self.conv = nn.Conv2d(out_channels*5, out_channels, kernel_size=1)
def forward(self, x):
feature_map = self.conv1x1(x)
atrous_1 = self.atrous_conv1(x)
atrous_2 = self.atrous_conv2(x)
atrous_3 = self.atrous_conv3(x)
pool = F.interpolate(self.pool(x), size=feature_map.shape[2:], mode='bilinear', align_corners=True)
x = torch.cat((feature_map, atrous_1, atrous_2, atrous_3, pool), dim=1)
return self.conv(x)
class DeepLabv3Plus(nn.Module):
def __init__(self, in_channels=3, out_channels=21, backbone='resnet50', pretrained=True):
super(DeepLabv3Plus, self).__init__()
if backbone == 'resnet50':
resnet = models.resnet50(pretrained=pretrained)
channels = 2048
elif backbone == 'resnet101':
resnet = models.resnet101(pretrained=pretrained)
channels = 2048
self.conv1 = resnet.conv1
self.bn1 = resnet.bn1
self.relu = resnet.relu
self.maxpool = resnet.maxpool
self.layer1 = resnet.layer1
self.layer2 = resnet.layer2
self.layer3 = resnet.layer3
self.layer4 = resnet.layer4
self.aspp = ASPP(channels)
self.up_conv1 = nn.ConvTranspose2d(channels//2, channels//4, kernel_size=4, stride=2, padding=1)
self.up_conv2 = nn.ConvTranspose2d(channels//4, channels//8, kernel_size=4, stride=2, padding=1)
self.up_conv3 = nn.ConvTranspose2d(channels//8, channels//16, kernel_size=4, stride=2, padding=1)
self.final_conv = nn.Conv2d(channels//16, out_channels, kernel_size=1)
def forward(self, x):
x_size = x.size()
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.aspp(x)
x = self.up_conv1(x)
x = F.interpolate(x, size=self.layer3(x).size()[2:], mode='bilinear', align_corners=True)
x = torch.cat((x, self.layer3(x)), dim=1)
x = self.up_conv2(x)
x = F.interpolate(x, size=self.layer2(x).size()[2:], mode='bilinear', align_corners=True)
x = torch.cat((x, self.layer2(x)), dim=1)
x = self.up_conv3(x)
x = F.interpolate(x, size=self.layer1(x).size()[2:], mode='bilinear', align_corners=True)
x = torch.cat((x, self.layer1(x)), dim=1)
x = self.final_conv(x)
x = F.interpolate(x, size=x_size[2:], mode='bilinear', align_corners=True)
return x
```
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