写一段7分类的深度残差网络pytorch代码
时间: 2023-03-12 18:12:00 浏览: 68
代码如下:import torch
import torch.nn as nn class ResNet(nn.Module):
def __init__(self, num_class):
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 3, stride=2, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = nn.Conv2d(64, 128, 3, stride=2, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.conv3 = nn.Conv2d(128, 256, 3, stride=2, padding=1)
self.bn3 = nn.BatchNorm2d(256)
self.conv4 = nn.Conv2d(256, 512, 3, stride=2, padding=1)
self.bn4 = nn.BatchNorm2d(512)
self.conv5 = nn.Conv2d(512, 1024, 3, stride=2, padding=1)
self.bn5 = nn.BatchNorm2d(1024)
self.fc = nn.Linear(1024, num_class)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
x = F.relu(self.bn4(self.conv4(x)))
x = F.relu(self.bn5(self.conv5(x)))
x = F.adaptive_avg_pool2d(x, (1, 1))
x = x.view(x.size(0), -1)
x = self.fc(x)
return x