Unsupervised domain adaptation is essential for generalization on unlabeled target domains. Generative domain adaptation methods achieve domain adaptation by synthesizing intermediate source-to-target images. The inspection of such images can assist in identifying successful sets of hyperparameters and methods, however, this is both time-consuming and frequently challenging. In practical applications, selecting an appropriate method and tuning its parameters is difficult when target labels are entirely absent. We develop a metric for automatically assessing unsupervised generative domain adaptation methods based on the generated source-to-target images. We show that this metric correlates well with the performance of the downstream machine learning task, which is, in this case, semantic segmentation.