def __init__()

in ubteacher/data/dataset_mapper.py [0:0]

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

    def __init__(self, cfg, is_train=True):
        self.augmentation = utils.build_augmentation(cfg, is_train)
        # include crop into self.augmentation
        if cfg.INPUT.CROP.ENABLED and is_train:
            self.augmentation.insert(
                0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)
            )
            logging.getLogger(__name__).info(
                "Cropping used in training: " + str(self.augmentation[0])
            )
            self.compute_tight_boxes = True
        else:
            self.compute_tight_boxes = False
        self.strong_augmentation = build_strong_augmentation(cfg, is_train)

        # fmt: off
        self.img_format = cfg.INPUT.FORMAT
        self.mask_on = cfg.MODEL.MASK_ON
        self.mask_format = cfg.INPUT.MASK_FORMAT
        self.keypoint_on = cfg.MODEL.KEYPOINT_ON
        self.load_proposals = cfg.MODEL.LOAD_PROPOSALS
        # fmt: on
        if self.keypoint_on and is_train:
            self.keypoint_hflip_indices = utils.create_keypoint_hflip_indices(
                cfg.DATASETS.TRAIN
            )
        else:
            self.keypoint_hflip_indices = None

        if self.load_proposals:
            self.proposal_min_box_size = cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE
            self.proposal_topk = (
                cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN
                if is_train
                else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST
            )
        self.is_train = is_train