import torch.nn as nn
from torchvision import models


class SimpleCNN(nn.Module):
    def __init__(
        self,
        num_classes: int,
        conv1_channels: int = 16,
        conv2_channels: int = 32,
        kernel_size: int = 3,
        dropout: float = 0.2,
        fc_dim: int = 128,
    ):
        super().__init__()

        weights = models.ResNet18_Weights.DEFAULT
        self.backbone = models.resnet18(weights=weights)

        in_features = self.backbone.fc.in_features
        self.backbone.fc = nn.Sequential(
            nn.Dropout(dropout),
            nn.Linear(in_features, fc_dim),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(fc_dim, num_classes),
        )

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