def test()

in source/monai_skin_cancer.py [0:0]

• 15 lines of code
• 2 McCabe index (conditional complexity)

def test(model, test_loader, device):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, size_average=False).item()  # sum up batch loss
            pred = output.max(1, keepdim=True)[1]  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    logger.info("Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))