def __init__()

in codes/rnn_models.py [0:0]

• 48 lines of code
• 7 McCabe index (conditional complexity)

    def __init__(self, args):
        super().__init__(args)
        ## Model specific init
        params = get_model_args()
        params.encoder_type = args.encoder_type
        nlipath = "mnli" if "nlipath" not in args else args.nlipath
        params.nlipath = "ocnli" if "ocnli" in args.model_name else nlipath
        if "outputdir" in args:
            print(f"Changing otuputdir to {args.outputdir}")
            params.outputdir = args.outputdir
        print(f"NLIPATH : {params.nlipath}")
        self.params = params
        exp_folder = os.path.join(
            params.outputdir,
            params.nlipath,
            params.encoder_type,
            "exp_seed_{}".format(params.seed),
        )
        if not os.path.exists(exp_folder):
            os.makedirs(exp_folder)

        # set proper name
        save_folder_name = os.path.join(exp_folder, "model")

        params.outputmodelname = os.path.join(
            save_folder_name, "{}_model.pkl".format(params.encoder_type)
        )

        if params.nlipath != "ocnli":
            word_vec = getEmbeddingWeights(
                [
                    _.split("\n")[0]
                    for _ in open(
                        "rnn_models/vocab/glove_" + params.nlipath + "_vocab.txt"
                    ).readlines()
                ],
                params.nlipath,
            )
        else:
            word_vec = None
        model_config = get_model_config(params)
        print(
            params.nlipath,
            params.outputmodelname,
            word_vec is None,
            "-----------!!------------",
        )
        self.model = NLINet(model_config, weights=word_vec)
        self.model.load_state_dict(
            torch.load(params.outputmodelname, map_location=torch.device("cpu"))
        )
        self.model.eval()
        # nothing to do here, as its handled internally
        self.label_fn = {"c": "c", "n": "n", "e": "e"}