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我正在使用 Swin Transformer 解决多类多标签分类的分层问题。我想在我的输入图像上可视化自我注意图，试图从模型中提取它们，不幸的是我没有成功完成这项任务。你能给我一个提示吗？我与您分享我尝试执行此任务的代码部分。

attention_maps = []
for module in model.modules():
    #print(module)
    if hasattr(module,'attention_patches'):  #controlla se la variabile ha l' attributo
        print(module.attention_patches.shape)
        if module.attention_patches.numel() == 224*224:
            attention_maps.append(module.attention_patches)
for attention_map in attention_maps:
    attention_map = attention_map.reshape(224, 224, 1)
    plt.imshow(sample['image'].permute(1, 2, 0), interpolation='nearest')
    plt.imshow(attention_map, alpha=0.7, cmap=plt.cm.Greys)
    plt.show()
``

In addition if you know about some explainability techniques, like Grad-CAM, which could be used with a hierarchical Swin Transformer, feel free to attach a link, it would be very helpful for me.
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