残差网络pytorch代码

import torch
import torch.nn as nn

# 定义残差块
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
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)

if self.stride != 1 or identity.size(1) != out.size(1):
identity = nn.Conv2d(identity.size(1), out.size(1), kernel_size=1, stride=self.stride, bias=False)(identity)
identity = nn.BatchNorm2d(out.size(1))(identity)

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

return out

# 定义ResNet模型
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 64

self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)

self.layer1 = self._make_layer(block, 64, num_blocks + [1 * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_channels, out_channels, stride))
self.in_channels = out_channels

return nn.Sequential(*layers)

def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)

out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)

out = self.avg_pool(out)
out = out.view(out.size(0), -1)
out = self.fc(out)

return out

# 构建ResNet模型
def ResNet18(num_classes=10):
return ResNet(ResidualBlock, [2, 2, 2, 2], num_classes=num_classes)

# 创建一个ResNet18的实例
model = ResNet18()
print(model)<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [手动实现ResNet残差网络 Pytorch代码](https://blog.csdn.net/bu_fo/article/details/109203360)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}}] [.reference_item style="max-width: 100%"]
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