resnet101 语义分割代码

以下是基于PyTorch框架的ResNet101语义分割代码：

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

# 定义ResNet101网络结构
class ResNet101(nn.Module):
    def __init__(self, num_classes):
        super(ResNet101, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 3)
        self.layer2 = self._make_layer(128, 4, stride=2)
        self.layer3 = self._make_layer(256, 23, stride=2)
        self.layer4 = self._make_layer(512, 3, stride=1)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * 4, num_classes)

    def _make_layer(self, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * 4:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * 4, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * 4),
            )

        layers = []
        layers.append(Bottleneck(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * 4
        for i in range(1, blocks):
            layers.append(Bottleneck(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        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.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

# 定义ResNet101中的Bottleneck结构
class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

# 定义语义分割网络
class SegmentationNet(nn.Module):
    def __init__(self, num_classes):
        super(SegmentationNet, self).__init__()
        self.resnet101 = ResNet101(num_classes)
        self.conv1 = nn.Conv2d(2048, 512, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(512)
        self.conv2 = nn.Conv2d(512, num_classes, kernel_size=1, stride=1, bias=False)

    def forward(self, x):
        x = self.resnet101(x)
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.interpolate(x, scale_factor=8, mode='bilinear', align_corners=True)

        return x

以上代码实现了ResNet101的网络结构和语义分割网络结构，其中`ResNet101`类实现了ResNet101的基本结构，`Bottleneck`类实现了ResNet101中的Bottleneck结构，`SegmentationNet`类实现了语义分割网络。在`SegmentationNet`中，首先使用ResNet101提取图像特征，然后通过一个卷积层和一个反卷积层将特征图恢复到原图大小，并输出分割结果。