写一段7分类的深度残差收缩网络pytorch代码

import torch
import torch.nn as nn# 定义残差收缩块
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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)
self.bn2 = nn.BatchNorm2d(out_channels)
self.shortcut = nn.Sequential()
if in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(out_channels)
)
def forward(self, 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.shortcut(x)
return out# 定义深度残差收缩网络
class ResNet(nn.Module):
def __init__(self, num_classes=7):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True)
)
self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1)
self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)
self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2)
self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2)
self.avg_pool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512, num_classes) def make_layer(self, block, channels, num_blocks, stride):
strides = [stride] + [1] * (num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_channels, channels))
self.in_channels = channels
return nn.Sequential(*layers) def forward(self, x):
out = self.conv1(x)
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7分类的深度残差收缩网络
resnet = ResNet(7)