def __init__(
        self,
        ignore_label,
        thing_ids,
        sigma=8,
        ignore_stuff_in_offset=False,
        small_instance_area=0,
        small_instance_weight=1,
        ignore_crowd_in_semantic=False,
    ):
        """
        Args:
            ignore_label: Integer, the ignore label for semantic segmentation.
            thing_ids: Set, a set of ids from contiguous category ids belonging
                to thing categories.
            sigma: the sigma for Gaussian kernel.
            ignore_stuff_in_offset: Boolean, whether to ignore stuff region when
                training the offset branch.
            small_instance_area: Integer, indicates largest area for small instances.
            small_instance_weight: Integer, indicates semantic loss weights for
                small instances.
            ignore_crowd_in_semantic: Boolean, whether to ignore crowd region in
                semantic segmentation branch, crowd region is ignored in the original
                TensorFlow implementation.
        """
        self.ignore_label = ignore_label
        self.thing_ids = set(thing_ids)
        self.ignore_stuff_in_offset = ignore_stuff_in_offset
        self.small_instance_area = small_instance_area
        self.small_instance_weight = small_instance_weight
        self.ignore_crowd_in_semantic = ignore_crowd_in_semantic

        # Generate the default Gaussian image for each center
        self.sigma = sigma
        size = 6 * sigma + 3
        x = np.arange(0, size, 1, float)
        y = x[:, np.newaxis]
        x0, y0 = 3 * sigma + 1, 3 * sigma + 1
        self.g = np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma**2))

        Args:
            ignore_label: Integer, the ignore label for semantic segmentation.
            thing_ids: Set, a set of ids from contiguous category ids belonging
                to thing categories.
            sigma: the sigma for Gaussian kernel.
            ignore_stuff_in_offset: Boolean, whether to ignore stuff region when
                training the offset branch.
            small_instance_area: Integer, indicates largest area for small instances.
            small_instance_weight: Integer, indicates semantic loss weights for
                small instances.
            ignore_crowd_in_semantic: Boolean, whether to ignore crowd region in
                semantic segmentation branch, crowd region is ignored in the original
                TensorFlow implementation.
        
EVA-02/det/projects/Panoptic-DeepLab/panoptic_deeplab/target_generator.py