Initialize pruner in YOLOv8_pruning.py make pruning process flaw

[minhhotboy9x]

I see in yolov8_pruning.py prune YOLOv8 that it always has to initialize a pruner and delete it repeatedly. When I try modifying this file, I initialize the pruner outside the iterative loop and I see that after the first normal iterative pruning loop, the model isn't pruned anymore. Can somebody explain this, please?

ignored_layers = []
    unwrapped_parameters = []
    for m in model.model.modules():
        if isinstance(m, (Detect,)):
            ignored_layers.append(m)

    pruner = tp.pruner.MagnitudePruner(
            model.model,
            example_inputs,
            importance = tp.importance.MagnitudeImportance(p=2),  # L2 norm pruning,
            iterative_steps = args.iterative_steps,
            ch_sparsity = args.target_prune_rate,
            ignored_layers = ignored_layers,
            unwrapped_parameters = unwrapped_parameters
        )
    
    for i in range(args.iterative_steps):

        model.model.train()
        
        for name, param in model.model.named_parameters():
            param.requires_grad = True
        
        pruner.step() # remove some weights with lowest importance

        # print(f'-------------{type(pruner.model)}-------------------')
        # print(f'-------------{type(model.device)}-------------------')
        example_inputs = example_inputs.to(model.device)
        

        # pre fine-tuning validation
        pruning_cfg['name'] = os.path.join(prefix_folder, f"step_{i}_pre_val")
        pruning_cfg['batch'] = 1
        validation_model.model = deepcopy(model.model)
        metric = validation_model.val(**pruning_cfg)
        pruned_map = metric.box.map
        pruned_macs, pruned_nparams = tp.utils.count_ops_and_params(model.model, example_inputs)
        current_speed_up = float(macs_list[0]) / pruned_macs
        print(f"After pruning iter {i + 1}: MACs={pruned_macs / 1e9} G, #Params={pruned_nparams / 1e6} M, "
              f"mAP={pruned_map}, speed up={current_speed_up}")

        # fine-tuning
        for name, param in model.model.named_parameters():
            param.requires_grad = True
        pruning_cfg['name'] = os.path.join(prefix_folder, f"step_{i}_finetune")
        pruning_cfg['batch'] = batch_size  # restore batch size
        model.train_v2(pruning=True, **pruning_cfg)

        # post fine-tuning validation
        pruning_cfg['name'] = os.path.join(prefix_folder, f"step_{i}_post_val")
        pruning_cfg['batch'] = 1
        validation_model = YOLO(model.trainer.best)
        metric = validation_model.val(**pruning_cfg)
        current_map = metric.box.map
        print(f"After fine tuning mAP={current_map}")

        macs_list.append(pruned_macs)
        nparams_list.append(pruned_nparams / base_nparams * 100)
        pruned_map_list.append(pruned_map)
        map_list.append(current_map)