上述代码overall_acc 输出不出来

很抱歉，我犯了一个错误。在计算整体准确率时，应该使用 `np.trace()` 函数来计算混淆矩阵对角线上的元素之和。以下是修正后的代码：

with torch.no_grad():
    class_accuracy_list = []
    true_labels = []
    predicted_labels = []
    for data in testloader:
        images, labels = data
        images = images.view(len(images), 1, 121).float()
        inputs, labels = images.to(device), labels.to(device)
        outputs = cnn(inputs)
        _, predicted = torch.max(outputs, 1)
        true_labels.append(labels.cpu().numpy())
        predicted_labels.append(predicted.cpu().numpy())
    true_labels = np.concatenate(true_labels)
    predicted_labels = np.concatenate(predicted_labels)
    cm = confusion_matrix(true_labels, predicted_labels, labels=[0, 1, 2, 3, 4])
    print(cm)
    for i in range(cm.shape[0]):
        acc = cm[i, i] / np.sum(cm[i])
        class_accuracy_list.append(acc)
        print("Class", i, "Accuracy:", acc)
    overall_acc = np.trace(cm) / np.sum(cm)
    print("Overall Accuracy:", overall_acc)

现在，整体准确率应该可以正确输出了。