from torch import nn
from torchvision import models


class CustomResNetModel(nn.Module):
    """Custom model based on ResNet50."""

    def __init__(self, num_classes=24):
        super(CustomResNetModel, self).__init__()
        self.model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)
        for param in self.model.parameters():
            param.requires_grad = False
        self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)

    def forward(self, x):
        return self.model(x)