model.py

import torch.nn as nn
from torchvision import models


class ResNet18Classifier(nn.Module):
    def __init__(
        self,
        num_classes: int,
        dropout: float = 0.4,
        fc_dim: int = 256,
        fine_tune_mode: str = "layer4",
    ):
        super().__init__()

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

        in_features = self.backbone.fc.in_features

        # Freeze everything first
        for param in self.backbone.parameters():
            param.requires_grad = False

        # Fine-tuning strategy
        if fine_tune_mode == "frozen":
            pass

        elif fine_tune_mode == "layer4":
            for param in self.backbone.layer4.parameters():
                param.requires_grad = True

        elif fine_tune_mode == "full":
            for param in self.backbone.parameters():
                param.requires_grad = True

        else:
            raise ValueError(f"Unsupported fine_tune_mode: {fine_tune_mode}")

        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),
        )

        # Always train classifier head
        for param in self.backbone.fc.parameters():
            param.requires_grad = True

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