将下列resnet18代码改为resnet101

ResNet是一个深度残差网络系列，ResNet18和ResNet101都是其中的两个版本，它们在结构上有显著的不同。如果你想要将ResNet18的代码修改为ResNet101，你需要做以下几个关键调整：

1. **增加层数**：ResNet101比ResNet18更深，通常有更多的卷积层。你需要查看ResNet101的具体设计，例如它有多少个block（基本模块），每个block包含多少层。

2. **改变block结构**：ResNet101中的BasicBlock或Bottleneck block可能会有所不同。Bottleneck block在ResNet101中更为常见，因为它增加了通道数（即filter的数量），这有助于更深层次的学习。

3. **调整skip连接**：ResNet101中可能有更多的跳跃连接（shortcuts），特别是当增加新层次时，需要确保连接到正确的输出。

4. **处理更大的输入尺寸**：由于网络更深，可能需要稍微调整输入图像的大小，因为ResNet101的设计假设较大的输入。

5. **更新模型配置**：在初始化网络时，需要指定ResNet101而不是ResNet18，比如在PyTorch中可能是`torchvision.models.resnet101()`。

具体的代码改动会涉及更改模型结构定义，这里只是一个概述。如果你有一个现有的ResNet18代码库，建议查阅官方文档（如 torchvision 模块的文档）或者查阅ResNet101的原始论文来找到对应的修改细节。

相关问题

resnet18代码

ResNet-18是一个经典的深度学习模型，它在计算机视觉领域广泛应用。下面是resnet18的代码示例：

from torchvision import models

# 构造resnet18
def resnet18():
    # 实例化网络结构，一共有4个残差结构，每个残差结构由2个残差块组成
    return models.resnet18(pretrained=True)

# 主函数
def main():
    # 构造网络resnet18
    model18 = resnet18()
    # 打印网络结构
    print(model18)

if __name__ == '__main__':
    main()

resnet101代码

以下是使用PyTorch实现的ResNet101代码示例：

import torch
import torch.nn as nn

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


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, 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(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

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

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(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 = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def resnet101(num_classes=1000):
    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes)

其中，Bottleneck类定义了ResNet中的基本块，ResNet类定义了整个模型的结构，resnet101函数则是创建ResNet101模型的工厂函数。可以根据需要修改num_classes参数来适应不同的任务。