def __init__()

in train_dense_encoder.py [0:0]

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

    def __init__(self, cfg: DictConfig):
        self.shard_id = cfg.local_rank if cfg.local_rank != -1 else 0
        self.distributed_factor = cfg.distributed_world_size or 1

        logger.info("***** Initializing components for training *****")

        # if model file is specified, encoder parameters from saved state should be used for initialization
        model_file = get_model_file(cfg, cfg.checkpoint_file_name)
        saved_state = None
        if model_file:
            saved_state = load_states_from_checkpoint(model_file)
            set_cfg_params_from_state(saved_state.encoder_params, cfg)

        tensorizer, model, optimizer = init_biencoder_components(cfg.encoder.encoder_model_type, cfg)

        model, optimizer = setup_for_distributed_mode(
            model,
            optimizer,
            cfg.device,
            cfg.n_gpu,
            cfg.local_rank,
            cfg.fp16,
            cfg.fp16_opt_level,
        )
        self.biencoder = model
        self.optimizer = optimizer
        self.tensorizer = tensorizer
        self.start_epoch = 0
        self.start_batch = 0
        self.scheduler_state = None
        self.best_validation_result = None
        self.best_cp_name = None
        self.cfg = cfg
        self.ds_cfg = BiencoderDatasetsCfg(cfg)

        if saved_state:
            self._load_saved_state(saved_state)

        self.dev_iterator = None