model.py

"""
ELIAS — Eyelid Lesion Intelligent Analysis System
model.py

Frozen ResNet-18 classifier for epiblepharon detection.
Compatible with Hugging Face model loading.
"""

import torch
import torch.nn as nn
from torchvision import models


def build_elias_model(num_classes: int = 2, freeze_backbone: bool = True) -> nn.Module:
    """
    Build ELIAS classifier.

    Args:
        num_classes:      2 for binary (CrossEntropyLoss)
        freeze_backbone:  Freeze all layers except the final FC head.

    Returns:
        ResNet-18 model with task-specific classification head.
    """
    model = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)

    if freeze_backbone:
        for param in model.parameters():
            param.requires_grad = False

    # Replace final FC with task-specific head
    in_features = model.fc.in_features  # 512
    model.fc = nn.Sequential(
        nn.Dropout(p=0.3),
        nn.Linear(in_features, num_classes),
    )

    return model


def load_elias_model(checkpoint_path: str, device: str = "cpu") -> nn.Module:
    """
    Load a trained ELIAS model from checkpoint.

    Usage:
        model = load_elias_model("pytorch_model.pt")
    """
    model = build_elias_model()
    state_dict = torch.load(checkpoint_path, map_location=device)
    model.load_state_dict(state_dict)
    model.eval()
    return model


if __name__ == "__main__":
    model = build_elias_model()
    trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
    total = sum(p.numel() for p in model.parameters())
    print(f"Trainable parameters: {trainable:,} / {total:,}")

    # Sanity check
    x = torch.randn(2, 3, 224, 224)
    with torch.no_grad():
        out = model(x)
    print(f"Output shape: {out.shape}")  # (2, 2)