resnet18网络结构代码
时间: 2023-06-01 11:01:33 浏览: 99
以下是ResNet18网络结构的代码实现:
```
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet18(nn.Module):
def __init__(self, num_classes=10):
super(ResNet18, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(64, 2, stride=1)
self.layer2 = self._make_layer(128, 2, stride=2)
self.layer3 = self._make_layer(256, 2, stride=2)
self.layer4 = self._make_layer(512, 2, stride=2)
self.linear = nn.Linear(512*BasicBlock.expansion, num_classes)
def _make_layer(self, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(BasicBlock(self.in_planes, planes, stride))
self.in_planes = planes * BasicBlock.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
```
这里定义了两个类:BasicBlock和ResNet18。
BasicBlock是ResNet18网络中的基本模块,包含两个卷积层和一个残差连接,用于构建ResNet18的各个层。
ResNet18是一个标准的ResNet18网络结构,包含一个卷积层,四个残差块,和一个全连接层。其中,每个残差块由多个BasicBlock组成,用于提高网络的深度和准确率。
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