We consider the problem of adapting semantic segmentation models to new target domains, only from the trained source model, without the source data. Not only is this setting much harder than if one had access to the source data, this is necessary in many practical situations where source data is not available due to privacy and storage reasons. Our algorithm has two parts - first, we update that normalization statistics which helps to compensate for the distribution shift and second, we transfer knowledge from the source models adhering to certain equivariant and invariant transforms. The transforms helps to efficiently extract the knowledge beyond vanilla self-training. Through extensive experiments on multiple semantic segmentation tasks, we show how such a simple framework can be effective in extracting knowledge from the source model, for a variety of problem settings, and performs much better or at par with current state-of-the-art methods which are specifically tuned for the respective settings.