We know that pytorch pruning does not actually speed up inference.
I plan to do the following:
- train a faster rcnn resnet18 on my dataset
- apply group pruning (from the pruning module) on the backbone, in order to select kernel filters to be pruned
- iterate on layers, and create a new backbone containing only non-pruned filters, and copy weights
- retrain on the smaller model
In this way, I hope that I can speed up inference.
Is my approach reasonnable ?
Thank you for your help