model_loader.py

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

def load_model(model_path):
    # Architektur aufbauen
    model = models.resnet50(pretrained=False)
    model.fc = nn.Linear(2048, 228)

    # State Dict laden
    state_dict = torch.load(model_path, map_location=torch.device("cpu"))

    # Keys ggf. anpassen
    new_state_dict = {}
    for k, v in state_dict.items():
        if k.startswith("predictor."):
            new_k = k.replace("predictor.", "")
        else:
            new_k = k
        new_state_dict[new_k] = v

    model.load_state_dict(new_state_dict)
    model.eval()
    return model

def predict_attributes(model, input_tensor):
    with torch.no_grad():
        output = model(input_tensor)
        prediction = torch.sigmoid(output).squeeze().numpy()
    threshold = 0.5
    predicted_indices = [i for i, p in enumerate(prediction) if p > threshold]
    return predicted_indices