luckmatter/recon_multilayer.py [699:709]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        checkpoint = torch.load(args.load_teacher)
        teacher.load_state_dict(checkpoint['net'])

        if "inactive_nodes" in checkpoint: 
            inactive_nodes = checkpoint["inactive_nodes"]
            masks = checkpoint["masks"]
            ratios = checkpoint["ratios"]
            inactive_nodes2, masks2 = prune(teacher, ratios)

            for m, m2 in zip(masks, masks2):
                if (m - m2).norm() > 1e-3:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



student_specialization/recon_multilayer.py [237:247]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        checkpoint = torch.load(args.load_teacher)
        teacher.load_state_dict(checkpoint['net'])

        if "inactive_nodes" in checkpoint: 
            inactive_nodes = checkpoint["inactive_nodes"]
            masks = checkpoint["masks"]
            ratios = checkpoint["ratios"]
            inactive_nodes2, masks2 = prune(teacher, ratios)

            for m, m2 in zip(masks, masks2):
                if (m - m2).norm() > 1e-3:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -