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Domain adaptation is an important research topic in the field of computer vision, where the goal is to solve the difference of data distribution between different scenarios of the same task. In recent times, adversarial learning method becomes a mainstream approach to generate complicated images across diverse domains through optimizing deep networks, and it can also improve the recognition accuracy rate of deep networks despite existing domain shift or dataset bias. However, there are few effective efforts of domain adaptation for the disease diagnosis on fundus images. Fundus images are normally captured on different medical devices with different rules. When diagnosing glaucoma, there is a serious homogeneous domain shift, which means feature spaces between target domain and source domain images have a distribution shift although they are very similar. We propose a unified framework to solve this problem. Previous studies have shown that glaucoma can be monitored by analyzing the optic disc/cup and its surroundings. So we exploit a novel reconstruction loss which not only leverages unsupervised data to bring the source and target distributions closer but also keeps original target domain images label unchanged. The experimental results on several public and private datasets demonstrate that our method could increase the classification accuracy of glaucoma diagnosis.