src/evaluate.py

import torch

def evaluate_model(model, testloader):
    """

    Evaluates the model on the test set.

    """
    correct = 0
    total = 0
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval()
    
    with torch.no_grad():
        for data in testloader:
            images, labels = data[0].to(device), data[1].to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    accuracy = correct / total
    return accuracy