Hi.
I have a somewhat related query so thought I’d ask here instead of starting a new thread.
So, I am trying to use VGG as a feature extractor. My model returns a (grayscale) image tensor of size (1, 512, 512) that I want to feed into VGG which takes in 3 channel RGB images. The idea is to compute a loss based on the output of VGG and backprop that into my model.
So, if I repeat the grayscale across the 3 dimensions before feeding into VGG, will .backward() successfully backprop into my model? Some code snippets would be quite helpful.
Thanks.