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

        aug_input = T.AugInput(image)
        transforms = self.augmentations(aug_input)
        image = aug_input.image

        image_shape = image.shape[:2]  # 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"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))

        if not self.is_train:
            dataset_dict.pop("annotations", None)
            return dataset_dict

        if "annotations" in dataset_dict:
            # Maps points from the closed interval [0, image_size - 1] on discrete
            # image coordinates to the half-open interval [x1, x2) on continuous image
            # coordinates. We use the continuous-discrete conversion from Heckbert
            # 1990 ("What is the coordinate of a pixel?"): d = floor(c) and c = d + 0.5,
            # where d is a discrete coordinate and c is a continuous coordinate.
            for ann in dataset_dict["annotations"]:
                point_coords_wrt_image = np.array(ann["point_coords"]).astype(np.float)
                point_coords_wrt_image = point_coords_wrt_image + 0.5
                ann["point_coords"] = point_coords_wrt_image

            annos = [
                # also need to transform point coordinates
                transform_instance_annotations(
                    obj,
                    transforms,
                    image_shape,
                )
                for obj in dataset_dict.pop("annotations")
                if obj.get("iscrowd", 0) == 0
            ]
            instances = annotations_to_instances(
                annos,
                image_shape,
                sample_points=self.sample_points,
            )

            dataset_dict["instances"] = utils.filter_empty_instances(instances)
        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
        
EVA-02/det/projects/PointSup/point_sup/dataset_mapper.py