sample_info/scripts/compute_influence_functions.py [73:91]:
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    with open(args.config, 'r') as f:
        architecture_args = json.load(f)

    model_class = getattr(methods, args.model_class)

    model = model_class(input_shape=train_data[0][0].shape,
                        architecture_args=architecture_args,
                        l2_reg_coef=args.l2_reg_coef,
                        seed=args.seed,
                        device=args.device)

    # load the final parameters
    saved_file_path = os.path.join(args.output_dir, 'ground-truth', args.exp_name, 'full-data-training.pkl')
    with open(saved_file_path, 'rb') as f:
        saved_data = pickle.load(f)

    params = dict(model.named_parameters())
    for k, v in saved_data['weights'].items():
        params[k].data = v.to(args.device)
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sample_info/scripts/compute_influence_functions_brute_force.py [72:90]:
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    with open(args.config, 'r') as f:
        architecture_args = json.load(f)

    model_class = getattr(methods, args.model_class)

    model = model_class(input_shape=train_data[0][0].shape,
                        architecture_args=architecture_args,
                        l2_reg_coef=args.l2_reg_coef,
                        seed=args.seed,
                        device=args.device)

    # load the final parameters
    saved_file_path = os.path.join(args.output_dir, 'ground-truth', args.exp_name, 'full-data-training.pkl')
    with open(saved_file_path, 'rb') as f:
        saved_data = pickle.load(f)

    params = dict(model.named_parameters())
    for k, v in saved_data['weights'].items():
        params[k].data = v.to(args.device)
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