Pytorch documentation provides a concise way to apply MiDaS monocular depth estimation network for depth extraction. But how should I modify their code to extract network representation at some intermediate layer? I know that I could download the model from github and modify forward function to return what I want, but I am interested in the simplest solution, leaving outer code as is.
I’m aware of subclassing the model class and writing my own forward function, like here, but I don’t know how to access the class in the code. The model instance is created straight away with midas = torch.hub.load("intel-isl/MiDaS", model_type). Maybe an example of using a forward hook will be easier.