resnet.py

import torch
import torch.nn.functional as F
import torch.nn as nn

# 定义残差块
class ResBlk(nn.Module):
    def __init__(self, ch_in, ch_out, stride):
        super(ResBlk, self).__init__()
        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(ch_out)
        self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(ch_out)

        self.extra = nn.Sequential(
            nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=stride),
            nn.BatchNorm2d(ch_out)
        )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out = self.extra(x) + out
        out = F.relu(out)
        return out

# 定义ResNet18网络结构
class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=3, padding=0),
            nn.BatchNorm2d(64)
        )
        self.blk1 = ResBlk(64, 64, stride=2)
        self.blk2 = ResBlk(64, 128, stride=2)
        self.blk3 = ResBlk(128, 256, stride=2)
        self.blk4 = ResBlk(256, 512, stride=2)

        self.outlayer = nn.Linear(512*1*1, 10)

    def forward(self, x):

        x = F.relu(self.conv1(x))

        x = self.blk1(x)
        x = self.blk2(x)
        x = self.blk3(x)
        x = self.blk4(x)

        x = F.adaptive_avg_pool2d(x, [1, 1])
        x = x.view(x.size(0), -1)
        x = self.outlayer(x)

        return x