def _setup()

in source/train.py [0:0]

• 23 lines of code
• 4 McCabe index (conditional complexity)

def _setup(sm_args):
    """
    Create D2 configs and perform basic setups.  
    """

    # Choose whether to use config file from D2 model zoo or 
    # user supplied config file ("local_config_file")
    if sm_args.local_config_file is not None:
        config_file_path = f"/opt/ml/code/{sm_args.local_config_file}"
        config_file = sm_args.local_config_file
    else:
        config_file_path = f"/opt/ml/code/detectron2/configs/{sm_args.config_file}"
        config_file = sm_args.config_file

    # Register custom dataset
    dataset_train_name = "cb_train"
    _register_dataset(dataset_train_name, "train.json", "train")
    
    dataset_val_name = "cb_val"
    _register_dataset(dataset_val_name, "val.json", "val")
    
    # Build config file
    cfg = get_cfg() # retrieve baseline config: https://github.com/facebookresearch/detectron2/blob/master/detectron2/config/defaults.py
    cfg.merge_from_file(config_file_path) # merge defaults with provided config file
    list_opts = _opts_to_list(sm_args.opts)
    cfg.merge_from_list(list_opts) # override parameters with user defined opts
    cfg.DATASETS.TRAIN = (dataset_train_name,) # define dataset used for training
    cfg.DATASETS.TEST = (dataset_val_name,)  # no test dataset available
    cfg.OUTPUT_DIR = os.environ['SM_OUTPUT_DATA_DIR']
    cfg.TEST.EVAL_PERIOD = 500
    cfg.freeze()
    
    # D2 expects ArgParser.NameSpace object to ammend Cfg node.
    d2_args = _custom_argument_parser(config_file_path, sm_args.opts, sm_args.resume)
    # Perform training setup before training job starts
    default_setup(cfg, d2_args)
    
    return cfg