attacks/privacy_attacks.py [716:739]:
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    known_masks, hidden_masks = {}, {}
    hidden_masks['public'], hidden_masks['private']={},{}
    known_masks['public'] = torch.load(params.mask_path + "public.pth")
    known_masks['private'] = torch.load( params.mask_path + "private.pth")
    hidden_masks['private']['train']=torch.load( params.mask_path + "hidden/train.pth")
    hidden_masks['private']['heldout'] = torch.load( params.mask_path + "hidden/heldout.pth")
    hidden_masks['public']['train']=torch.load( params.mask_path + "hidden/public_train.pth")
    hidden_masks['public']['heldout'] = torch.load( params.mask_path + "hidden/public_heldout.pth")

    #get the final model parameters
    private_model=build_model(params)
    private_model_path = os.path.join(params.model_path, "checkpoint.pth")
    state_dict_private = torch.load(private_model_path,map_location='cuda:0')
    if params.dataset=='imagenet':
        new_state_dict = OrderedDict()
        for k, v in state_dict_private["model"].items():
            if k[:7]=='module.': # remove `module.`
                new_state_dict[k[7:]] = v
            else:
                new_state_dict[k]=v
        private_model.load_state_dict(new_state_dict)
    else:
        private_model.load_state_dict(state_dict_private['model'])
    private_model=private_model.cuda()
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attacks/privacy_attacks.py [847:870]:
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    known_masks, hidden_masks = {}, {}
    hidden_masks['public'], hidden_masks['private']={},{}
    known_masks['public'] = torch.load(params.mask_path + "public.pth")
    known_masks['private'] = torch.load( params.mask_path + "private.pth")
    hidden_masks['private']['train']=torch.load( params.mask_path + "hidden/train.pth")
    hidden_masks['private']['heldout'] = torch.load( params.mask_path + "hidden/heldout.pth")
    hidden_masks['public']['train']=torch.load( params.mask_path + "hidden/public_train.pth")
    hidden_masks['public']['heldout'] = torch.load( params.mask_path + "hidden/public_heldout.pth")

    #get the final model parameters
    private_model=build_model(params)
    private_model_path = os.path.join(params.model_path, "checkpoint.pth")
    state_dict_private = torch.load(private_model_path,map_location='cuda:0')
    if params.dataset=='imagenet':
        new_state_dict = OrderedDict()
        for k, v in state_dict_private["model"].items():
            if k[:7]=='module.': # remove `module.`
                new_state_dict[k[7:]] = v
            else:
                new_state_dict[k]=v
        private_model.load_state_dict(new_state_dict)
    else:
        private_model.load_state_dict(state_dict_private['model'])
    private_model=private_model.cuda()
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