def __init__(
        self,
        is_train=True,
        min_size_test=None,
        max_size_test=None,
        mean=None,
        std=None,
    ):
        """
        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.min_size_test = min_size_test
        self.max_size_test = max_size_test
        self.pixel_mean = torch.tensor(mean)[:,None,None]
        self.pixel_std = torch.tensor(std)[:,None,None]

        t = []
        t.append(transforms.Resize(self.min_size_test, interpolation=Image.BICUBIC))
        self.transform = transforms.Compose(t)

        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/scannet_dataset_mapper.py