model.py

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

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, downsample=False):
        super().__init__()
        stride = 2 if downsample else 1

        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, 1, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)

        self.skip = nn.Sequential()
        if downsample or in_channels != out_channels:
            self.skip = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 1, stride=stride),
                nn.BatchNorm2d(out_channels),
            )

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

class ArchitectureClassifier(nn.Module):
    def __init__(self, num_classes=5):
        super().__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(3, 32, 3, 1, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2),
        )
        self.layer2 = ResidualBlock(32, 64, downsample=True)
        self.layer3 = ResidualBlock(64, 128, downsample=True)
        self.layer4 = ResidualBlock(128, 128)

        self.global_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Linear(128, num_classes)

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.global_pool(x).squeeze(-1).squeeze(-1)
        return self.fc(x)