disparity label setting after random shift augmentation

[chaowentao]

we select the 7×7 views and disparity image of its center view to train our network.

The paper mentioned the disparity label should respond to its center view after random shift augmentation. But in the current code, load_LFdata function introduces the code:
traindata_label=np.zeros((len(dir_LFimages), 512, 512),np.float32), it doesn't include all view disparity.
Although the generate_traindata_for_train function assumes the situation, the disparity image of its center view can't be produced. So I want to clarify the detail of whether to use the center disparity label during the training process.