将下列resnet18代码改为resnet101
时间: 2024-08-06 14:01:32 浏览: 29
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:
```python
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:
```python
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。