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- initial_backbone, rpn, head = frcnn_split(frcnn_model)
- compressed_backbone = copy.deepcopy(initial_backbone)
- device = 'cpu'
- train_iters = 10
- val_iters = 10
- for ft_iter in range(1):
- layer_names = get_layer_names(MODEL_NAME, initial_backbone)
- ranks = [None]*len(layer_names)
- # compress backbone
- compressed_backbone = get_compressed_model(compressed_backbone, MODEL_NAME,
- ranks=ranks, layer_names = layer_names,
- decomposition = decomposition,
- vbmf_weaken_factor = 0.7)
- # add new backbone to od model
- compressed_frcnn_model = frcnn_glue(compressed_backbone, rpn, head, device)
- # finetune od model
- fine_tune(compressed_frcnn_model, loaders, device, save_dir,
- train_iters = train_iters, val_iters = val_iters)
- # Load best checkpoint
- compressed_frcnn_model = torch.load('bets_model.pth')
- # get backbone for the next iteration
- compressed_backbone, rpn, head = frcnn_split(compressed_frcnn_model)
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