test.py

from datasets.dataset_loader import MimlDataset, DataLoader
from utils.config import batch_size
from evaluation.eval import Eval
from utils.transform import transform_vgg, transform_alexnet, transform_resnet
import torch

test_model = 'alexnet'
test_data = {
    'vgg': {
        'transform': transform_vgg,
        'model_name' : 'vgg.pth'
    },
    'resnet': {
        'transform': transform_resnet,
        'model_name' : 'resnet.pth'
    },
    'alexnet': {
        'transform': transform_alexnet,
        'model_name' : 'alexnet.pth'
    }
}

# load và transform data
test_path = "datasets/miml_dataset/miml_test.csv"
test_dataset = MimlDataset(test_path, transform=test_data[test_model]['transform'])
test_loader = DataLoader(test_dataset, batch_size=batch_size[test_model], shuffle=False)

# load model
model = torch.load(f"pretrained_models/{test_data[test_model]['model_name']}")

# evaluation
total_loss, total_acc = Eval.evaluation_model(model, test_loader)
print(f'Test Loss: {total_loss:.4f}, Binary Accuracy: {total_acc*100:.4f}%')