This is the standard pytorch implementation of memory-efficient Densenet-161 I am using.
And as I checked here, https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py 5
There are indeed such functional calls.
Is there are any workaround for this?
Also i think it is important to add - This is an implementation of GradCAM. Therefore it is necessary to access the submodule if its gradients are to be visualized.