ResNet 편집하기 (부분)

==구현 예시 (PyTorch 기준)==
다음은 PyTorch로 ResNet을 구현하는 일반적인 방식의 예시이다. torchvision의 기본 구조를 따르며, 블록 구성과 계층 조립을 유연하게 설정할 수 있도록 한다.<syntaxhighlight lang="python">
import torch
import torch.nn as nn
import torch.nn.functional as F

# 기본 블록 정의 (ResNet-18, 34)
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(BasicBlock, 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.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample

    def forward(self, x):
        identity = x
        if self.downsample:
            identity = self.downsample(x)

        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += identity
        return F.relu(out)

# 병목 블록 정의 (ResNet-50, 101, 152)
class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)

        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
                               stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)

        self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion,
                               kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)

        self.downsample = downsample

    def forward(self, x):
        identity = x
        if self.downsample:
            identity = self.downsample(x)

        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += identity
        return F.relu(out)

# ResNet 본체 정의
class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(ResNet, self).__init__()
        self.in_channels = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2,
                               padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)

        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, blocks, stride=1):
        downsample = None
        if stride != 1 or self.in_channels != out_channels * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, out_channels * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * block.expansion),
            )

        layers = []
        layers.append(block(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels * block.expansion

        for _ in range(1, blocks):
            layers.append(block(self.in_channels, out_channels))

        return nn.Sequential(*layers)

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

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

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x

# 각 버전에 맞춘 팩토리 함수
def ResNet18():
    return ResNet(BasicBlock, [2, 2, 2, 2])

def ResNet34():
    return ResNet(BasicBlock, [3, 4, 6, 3])

def ResNet50():
    return ResNet(Bottleneck, [3, 4, 6, 3])

def ResNet101():
    return ResNet(Bottleneck, [3, 4, 23, 3])

def ResNet152():
    return ResNet(Bottleneck, [3, 8, 36, 3])
</syntaxhighlight>