def __call__(self, dataset_dict):
        """
        Args:
            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.

        Returns:
            dict: a format that builtin models in detectron2 accept
        """
        assert self.is_train, "OneFormerUnifiedDatasetMapper should only be used for training!"

        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
        utils.check_image_size(dataset_dict, image)

        # semantic segmentation
        if "sem_seg_file_name" in dataset_dict:
            # PyTorch transformation not implemented for uint16, so converting it to double first
            sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
        else:
            sem_seg_gt = None

        # panoptic segmentation
        if "pan_seg_file_name" in dataset_dict:
            pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
            segments_info = dataset_dict["segments_info"]
        else:
            pan_seg_gt = None
            segments_info = None

        if pan_seg_gt is None:
            raise ValueError(
                "Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.".format(
                    dataset_dict["file_name"]
                )
            )

        aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
        aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
        image = aug_input.image
        if sem_seg_gt is not None:
            sem_seg_gt = aug_input.sem_seg

        # apply the same transformation to panoptic segmentation
        pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)

        from panopticapi.utils import rgb2id

        pan_seg_gt = rgb2id(pan_seg_gt)

        # Pad image and segmentation label here!
        image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
        if sem_seg_gt is not None:
            sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
        pan_seg_gt = torch.as_tensor(pan_seg_gt.astype("long"))

        if self.size_divisibility > 0:
            image_size = (image.shape[-2], image.shape[-1])
            padding_size = [
                0,
                self.size_divisibility - image_size[1],
                0,
                self.size_divisibility - image_size[0],
            ]
            image = F.pad(image, padding_size, value=128).contiguous()
            if sem_seg_gt is not None:
                sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()
            pan_seg_gt = F.pad(
                pan_seg_gt, padding_size, value=0
            ).contiguous()  # 0 is the VOID panoptic label

        image_shape = (image.shape[-2], image.shape[-1])  # h, w

        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
        # Therefore it's important to use torch.Tensor.
        dataset_dict["image"] = image

        if "annotations" in dataset_dict:
            raise ValueError("Pemantic segmentation dataset should not have 'annotations'.")

        prob_task = np.random.uniform(0,1.)

        num_class_obj = {}

        for name in self.class_names:
            num_class_obj[name] = 0

        if prob_task < self.semantic_prob:
            task = "The task is semantic"
            instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
        elif prob_task < self.instance_prob:
            task = "The task is instance"
            instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
        else:
            task = "The task is panoptic"
            instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)

        dataset_dict["sem_seg"] = torch.from_numpy(sem_seg).long()
        dataset_dict["instances"] = instances
        dataset_dict["orig_shape"] = image_shape
        dataset_dict["task"] = task
        dataset_dict["text"] = text
        dataset_dict["thing_ids"] = self.things
        
        return dataset_dict

        Args:
            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.

        Returns:
            dict: a format that builtin models in detectron2 accept
        
ControlNet-v1-1-nightly/annotator/oneformer/oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py