def __init__(
        self,
        is_train=True,
        dataset_name='',
        min_size_test=None,
        max_size_test=None,
        shape_sampler=None,
        grounding=False,
    ):
        """
        NOTE: this interface is experimental.
        Args:
            is_train: for training or inference
            augmentations: a list of augmentations or deterministic transforms to apply
            tfm_gens: data augmentation
            image_format: an image format supported by :func:`detection_utils.read_image`.
        """
        self.is_train = is_train
        self.dataset_name = dataset_name
        self.min_size_test = min_size_test
        self.max_size_test = max_size_test

        t = []
        t.append(transforms.Resize(self.min_size_test, interpolation=Image.BICUBIC, max_size=max_size_test))
        self.transform = transforms.Compose(t)
        self.shape_sampler = shape_sampler
        self.ignore_id = 220

        if grounding:
            def _setattr(self, name, value):
                object.__setattr__(self, name, value)
            Metadata.__setattr__ = _setattr
            MetadataCatalog.get(dataset_name).evaluator_type = "interactive_grounding"

        NOTE: this interface is experimental.
        Args:
            is_train: for training or inference
            augmentations: a list of augmentations or deterministic transforms to apply
            tfm_gens: data augmentation
            image_format: an image format supported by :func:`detection_utils.read_image`.
        
datasets/dataset_mappers/pascalvoc_dataset_mapper_ix.py