test_model.py

import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from model import MiniResNet

def test(model, batch_size=1000):
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])
    
    test_dataset = datasets.MNIST('./data', train=False, transform=transform)
    test_loader = DataLoader(test_dataset, batch_size=batch_size)
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.to(device)
    model.eval()
    
    correct = 0
    total = 0
    
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            _, predicted = output.max(1)
            total += target.size(0)
            correct += predicted.eq(target).sum().item()
    
    accuracy = 100. * correct / total
    print(f'Test Accuracy: {accuracy:.2f}%')
    return accuracy

if __name__ == '__main__':
    model = MiniResNet()
    model.load_state_dict(torch.load('model.pth'))
    test(model)