loading.py

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List

import mmcv
import numpy as np
from mmcv.transforms import LoadImageFromFile
from mmcv.transforms.base import BaseTransform

from mmdet3d.core.points import BasePoints, get_points_type
from mmdet3d.registry import TRANSFORMS
from mmdet.datasets.pipelines import LoadAnnotations


@TRANSFORMS.register_module()
class LoadMultiViewImageFromFiles(object):
    """Load multi channel images from a list of separate channel files.

    Expects results['img_filename'] to be a list of filenames.

    Args:
        to_float32 (bool, optional): Whether to convert the img to float32.
            Defaults to False.
        color_type (str, optional): Color type of the file.
            Defaults to 'unchanged'.
    """

    def __init__(self, to_float32=False, color_type='unchanged'):
        self.to_float32 = to_float32
        self.color_type = color_type

    def __call__(self, results):
        """Call function to load multi-view image from files.

        Args:
            results (dict): Result dict containing multi-view image filenames.

        Returns:
            dict: The result dict containing the multi-view image data.
                Added keys and values are described below.

                - filename (str): Multi-view image filenames.
                - img (np.ndarray): Multi-view image arrays.
                - img_shape (tuple[int]): Shape of multi-view image arrays.
                - ori_shape (tuple[int]): Shape of original image arrays.
                - pad_shape (tuple[int]): Shape of padded image arrays.
                - scale_factor (float): Scale factor.
                - img_norm_cfg (dict): Normalization configuration of images.
        """
        filename = results['img_filename']
        # img is of shape (h, w, c, num_views)
        img = np.stack(
            [mmcv.imread(name, self.color_type) for name in filename], axis=-1)
        if self.to_float32:
            img = img.astype(np.float32)
        results['filename'] = filename
        # unravel to list, see `DefaultFormatBundle` in formatting.py
        # which will transpose each image separately and then stack into array
        results['img'] = [img[..., i] for i in range(img.shape[-1])]
        results['img_shape'] = img.shape
        results['ori_shape'] = img.shape
        # Set initial values for default meta_keys
        results['pad_shape'] = img.shape
        results['scale_factor'] = 1.0
        num_channels = 1 if len(img.shape) < 3 else img.shape[2]
        results['img_norm_cfg'] = dict(
            mean=np.zeros(num_channels, dtype=np.float32),
            std=np.ones(num_channels, dtype=np.float32),
            to_rgb=False)
        return results

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(to_float32={self.to_float32}, '
        repr_str += f"color_type='{self.color_type}')"
        return repr_str


@TRANSFORMS.register_module()
class LoadImageFromFileMono3D(LoadImageFromFile):
    """Load an image from file in monocular 3D object detection. Compared to 2D
    detection, additional camera parameters need to be loaded.

    Args:
        kwargs (dict): Arguments are the same as those in
            :class:`LoadImageFromFile`.
    """

    def __call__(self, results):
        """Call functions to load image and get image meta information.

        Args:
            results (dict): Result dict from :obj:`mmdet.CustomDataset`.

        Returns:
            dict: The dict contains loaded image and meta information.
        """
        super().__call__(results)
        results['cam2img'] = results['img_info']['cam_intrinsic']
        return results


@TRANSFORMS.register_module()
class LoadPointsFromMultiSweeps(BaseTransform):
    """Load points from multiple sweeps.

    This is usually used for nuScenes dataset to utilize previous sweeps.

    Args:
        sweeps_num (int, optional): Number of sweeps. Defaults to 10.
        load_dim (int, optional): Dimension number of the loaded points.
            Defaults to 5.
        use_dim (list[int], optional): Which dimension to use.
            Defaults to [0, 1, 2, 4].
        file_client_args (dict, optional): Config dict of file clients,
            refer to
            https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
            for more details. Defaults to dict(backend='disk').
        pad_empty_sweeps (bool, optional): Whether to repeat keyframe when
            sweeps is empty. Defaults to False.
        remove_close (bool, optional): Whether to remove close points.
            Defaults to False.
        test_mode (bool, optional): If `test_mode=True`, it will not
            randomly sample sweeps but select the nearest N frames.
            Defaults to False.
    """

    def __init__(self,
                 sweeps_num=10,
                 load_dim=5,
                 use_dim=[0, 1, 2, 4],
                 file_client_args=dict(backend='disk'),
                 pad_empty_sweeps=False,
                 remove_close=False,
                 test_mode=False):
        self.load_dim = load_dim
        self.sweeps_num = sweeps_num
        self.use_dim = use_dim
        self.file_client_args = file_client_args.copy()
        self.file_client = None
        self.pad_empty_sweeps = pad_empty_sweeps
        self.remove_close = remove_close
        self.test_mode = test_mode

    def _load_points(self, pts_filename):
        """Private function to load point clouds data.

        Args:
            pts_filename (str): Filename of point clouds data.

        Returns:
            np.ndarray: An array containing point clouds data.
        """
        if self.file_client is None:
            self.file_client = mmcv.FileClient(**self.file_client_args)
        try:
            pts_bytes = self.file_client.get(pts_filename)
            points = np.frombuffer(pts_bytes, dtype=np.float32)
        except ConnectionError:
            mmcv.check_file_exist(pts_filename)
            if pts_filename.endswith('.npy'):
                points = np.load(pts_filename)
            else:
                points = np.fromfile(pts_filename, dtype=np.float32)
        return points

    def _remove_close(self, points, radius=1.0):
        """Removes point too close within a certain radius from origin.

        Args:
            points (np.ndarray | :obj:`BasePoints`): Sweep points.
            radius (float, optional): Radius below which points are removed.
                Defaults to 1.0.

        Returns:
            np.ndarray: Points after removing.
        """
        if isinstance(points, np.ndarray):
            points_numpy = points
        elif isinstance(points, BasePoints):
            points_numpy = points.tensor.numpy()
        else:
            raise NotImplementedError
        x_filt = np.abs(points_numpy[:, 0]) < radius
        y_filt = np.abs(points_numpy[:, 1]) < radius
        not_close = np.logical_not(np.logical_and(x_filt, y_filt))
        return points[not_close]

    def transform(self, results):
        """Call function to load multi-sweep point clouds from files.

        Args:
            results (dict): Result dict containing multi-sweep point cloud
                filenames.

        Returns:
            dict: The result dict containing the multi-sweep points data.
                Added key and value are described below.

                - points (np.ndarray | :obj:`BasePoints`): Multi-sweep point
                    cloud arrays.
        """
        points = results['points']
        points.tensor[:, 4] = 0
        sweep_points_list = [points]
        ts = results['timestamp']
        if 'lidar_sweeps' not in results:
            if self.pad_empty_sweeps:
                for i in range(self.sweeps_num):
                    if self.remove_close:
                        sweep_points_list.append(self._remove_close(points))
                    else:
                        sweep_points_list.append(points)
        else:
            if len(results['lidar_sweeps']) <= self.sweeps_num:
                choices = np.arange(len(results['lidar_sweeps']))
            elif self.test_mode:
                choices = np.arange(self.sweeps_num)
            else:
                choices = np.random.choice(
                    len(results['lidar_sweeps']),
                    self.sweeps_num,
                    replace=False)
            for idx in choices:
                sweep = results['lidar_sweeps'][idx]
                points_sweep = self._load_points(
                    sweep['lidar_points']['lidar_path'])
                points_sweep = np.copy(points_sweep).reshape(-1, self.load_dim)
                if self.remove_close:
                    points_sweep = self._remove_close(points_sweep)
                # bc-breaking: Timestamp has divided 1e6 in pkl infos.
                sweep_ts = sweep['timestamp']
                lidar2cam = np.array(sweep['lidar_points']['lidar2sensor'])
                points_sweep[:, :3] = points_sweep[:, :3] @ lidar2cam[:3, :3]
                points_sweep[:, :3] -= lidar2cam[:3, 3]
                points_sweep[:, 4] = ts - sweep_ts
                points_sweep = points.new_point(points_sweep)
                sweep_points_list.append(points_sweep)

        points = points.cat(sweep_points_list)
        points = points[:, self.use_dim]
        results['points'] = points
        return results

    def __repr__(self):
        """str: Return a string that describes the module."""
        return f'{self.__class__.__name__}(sweeps_num={self.sweeps_num})'


@TRANSFORMS.register_module()
class PointSegClassMapping(BaseTransform):
    """Map original semantic class to valid category ids.

    Required Keys:

    - lidar_points (dict)

        - lidar_path (str)

    Added Keys:

    - points (np.float32)

    Map valid classes as 0~len(valid_cat_ids)-1 and
    others as len(valid_cat_ids).

    Args:
        valid_cat_ids (tuple[int]): A tuple of valid category.
        max_cat_id (int, optional): The max possible cat_id in input
            segmentation mask. Defaults to 40.
    """

    def transform(self, results: dict) -> None:
        """Call function to map original semantic class to valid category ids.

        Args:
            results (dict): Result dict containing point semantic masks.

        Returns:
            dict: The result dict containing the mapped category ids.
                Updated key and value are described below.

                - pts_semantic_mask (np.ndarray): Mapped semantic masks.
        """
        assert 'pts_semantic_mask' in results
        pts_semantic_mask = results['pts_semantic_mask']

        assert 'label_mapping' in results
        label_mapping = results['label_mapping']
        converted_pts_sem_mask = \
            np.array([label_mapping[mask] for mask in pts_semantic_mask])

        results['pts_semantic_mask'] = converted_pts_sem_mask

        # 'eval_ann_info' will be passed to evaluator
        if 'eval_ann_info' in results:
            assert 'pts_semantic_mask' in results['eval_ann_info']
            results['eval_ann_info']['pts_semantic_mask'] = \
                converted_pts_sem_mask

        return results

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(valid_cat_ids={self.valid_cat_ids}, '
        repr_str += f'max_cat_id={self.max_cat_id})'
        return repr_str


@TRANSFORMS.register_module()
class NormalizePointsColor(BaseTransform):
    """Normalize color of points.

    Args:
        color_mean (list[float]): Mean color of the point cloud.
    """

    def __init__(self, color_mean: List[float]) -> None:
        self.color_mean = color_mean

    def transform(self, input_dict: dict) -> dict:
        """Call function to normalize color of points.

        Args:
            results (dict): Result dict containing point clouds data.

        Returns:
            dict: The result dict containing the normalized points.
                Updated key and value are described below.

                - points (:obj:`BasePoints`): Points after color normalization.
        """
        points = input_dict['points']
        assert points.attribute_dims is not None and \
               'color' in points.attribute_dims.keys(), \
               'Expect points have color attribute'
        if self.color_mean is not None:
            points.color = points.color - \
                           points.color.new_tensor(self.color_mean)
        points.color = points.color / 255.0
        input_dict['points'] = points
        return input_dict

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(color_mean={self.color_mean})'
        return repr_str


@TRANSFORMS.register_module()
class LoadPointsFromFile(BaseTransform):
    """Load Points From File.

    Required Keys:

    - lidar_points (dict)

        - lidar_path (str)

    Added Keys:

    - points (np.float32)

    Args:
        coord_type (str): The type of coordinates of points cloud.
            Available options includes:
            - 'LIDAR': Points in LiDAR coordinates.
            - 'DEPTH': Points in depth coordinates, usually for indoor dataset.
            - 'CAMERA': Points in camera coordinates.
        load_dim (int, optional): The dimension of the loaded points.
            Defaults to 6.
        use_dim (list[int], optional): Which dimensions of the points to use.
            Defaults to [0, 1, 2]. For KITTI dataset, set use_dim=4
            or use_dim=[0, 1, 2, 3] to use the intensity dimension.
        shift_height (bool, optional): Whether to use shifted height.
            Defaults to False.
        use_color (bool, optional): Whether to use color features.
            Defaults to False.
        file_client_args (dict, optional): Config dict of file clients,
            refer to
            https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
            for more details. Defaults to dict(backend='disk').
    """

    def __init__(
        self,
        coord_type: str,
        load_dim: int = 6,
        use_dim: list = [0, 1, 2],
        shift_height: bool = False,
        use_color: bool = False,
        file_client_args: dict = dict(backend='disk')
    ) -> None:
        self.shift_height = shift_height
        self.use_color = use_color
        if isinstance(use_dim, int):
            use_dim = list(range(use_dim))
        assert max(use_dim) < load_dim, \
            f'Expect all used dimensions < {load_dim}, got {use_dim}'
        assert coord_type in ['CAMERA', 'LIDAR', 'DEPTH']

        self.coord_type = coord_type
        self.load_dim = load_dim
        self.use_dim = use_dim
        self.file_client_args = file_client_args.copy()
        self.file_client = None

    def _load_points(self, pts_filename: str) -> np.ndarray:
        """Private function to load point clouds data.

        Args:
            pts_filename (str): Filename of point clouds data.

        Returns:
            np.ndarray: An array containing point clouds data.
        """
        if self.file_client is None:
            self.file_client = mmcv.FileClient(**self.file_client_args)
        try:
            pts_bytes = self.file_client.get(pts_filename)
            points = np.frombuffer(pts_bytes, dtype=np.float32)
        except ConnectionError:
            mmcv.check_file_exist(pts_filename)
            if pts_filename.endswith('.npy'):
                points = np.load(pts_filename)
            else:
                points = np.fromfile(pts_filename, dtype=np.float32)

        return points

    def transform(self, results: dict) -> dict:
        """Method to load points data from file.

        Args:
            results (dict): Result dict containing point clouds data.

        Returns:
            dict: The result dict containing the point clouds data.
                Added key and value are described below.

                - points (:obj:`BasePoints`): Point clouds data.
        """
        pts_file_path = results['lidar_points']['lidar_path']
        points = self._load_points(pts_file_path)
        points = points.reshape(-1, self.load_dim)
        points = points[:, self.use_dim]
        attribute_dims = None

        if self.shift_height:
            floor_height = np.percentile(points[:, 2], 0.99)
            height = points[:, 2] - floor_height
            points = np.concatenate(
                [points[:, :3],
                 np.expand_dims(height, 1), points[:, 3:]], 1)
            attribute_dims = dict(height=3)

        if self.use_color:
            assert len(self.use_dim) >= 6
            if attribute_dims is None:
                attribute_dims = dict()
            attribute_dims.update(
                dict(color=[
                    points.shape[1] - 3,
                    points.shape[1] - 2,
                    points.shape[1] - 1,
                ]))

        points_class = get_points_type(self.coord_type)
        points = points_class(
            points, points_dim=points.shape[-1], attribute_dims=attribute_dims)
        results['points'] = points

        return results

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__ + '('
        repr_str += f'shift_height={self.shift_height}, '
        repr_str += f'use_color={self.use_color}, '
        repr_str += f'file_client_args={self.file_client_args}, '
        repr_str += f'load_dim={self.load_dim}, '
        repr_str += f'use_dim={self.use_dim})'
        return repr_str


@TRANSFORMS.register_module()
class LoadPointsFromDict(LoadPointsFromFile):
    """Load Points From Dict."""

    def __call__(self, results):
        assert 'points' in results
        return results


@TRANSFORMS.register_module()
class LoadAnnotations3D(LoadAnnotations):
    """Load Annotations3D.

    Load instance mask and semantic mask of points and
    encapsulate the items into related fields.

    Required Keys:

    - ann_info (dict)
        - gt_bboxes_3d (:obj:`LiDARInstance3DBoxes` |
          :obj:`DepthInstance3DBoxes` | :obj:`CameraInstance3DBoxes`):
          3D ground truth bboxes. Only when `with_bbox_3d` is True
        - gt_labels_3d (np.int64): Labels of ground truths.
          Only when `with_label_3d` is True.
        - gt_bboxes (np.float32): 2D ground truth bboxes.
          Only when `with_bbox` is True.
        - gt_labels (np.ndarray): Labels of ground truths.
          Only when `with_label` is True.
        - depths (np.ndarray): Only when
          `with_bbox_depth` is True.
        - centers_2d (np.ndarray): Only when
          `with_bbox_depth` is True.
        - attr_labels (np.ndarray): Attribute labels of instances.
          Only when `with_attr_label` is True.

    - pts_instance_mask_path (str): Path of instance mask file.
      Only when `with_mask_3d` is True.
    - pts_semantic_mask_path (str): Path of semantic mask file.
      Only when

    Added Keys:

    - gt_bboxes_3d (:obj:`LiDARInstance3DBoxes` |
      :obj:`DepthInstance3DBoxes` | :obj:`CameraInstance3DBoxes`):
      3D ground truth bboxes. Only when `with_bbox_3d` is True
    - gt_labels_3d (np.int64): Labels of ground truths.
      Only when `with_label_3d` is True.
    - gt_bboxes (np.float32): 2D ground truth bboxes.
      Only when `with_bbox` is True.
    - gt_labels (np.int64): Labels of ground truths.
      Only when `with_label` is True.
    - depths (np.float32): Only when
      `with_bbox_depth` is True.
    - centers_2d (np.ndarray): Only when
      `with_bbox_depth` is True.
    - attr_labels (np.int64): Attribute labels of instances.
      Only when `with_attr_label` is True.
    - pts_instance_mask (np.int64): Instance mask of each point.
      Only when `with_mask_3d` is True.
    - pts_semantic_mask (np.int64): Semantic mask of each point.
      Only when `with_seg_3d` is True.

    Args:
        with_bbox_3d (bool, optional): Whether to load 3D boxes.
            Defaults to True.
        with_label_3d (bool, optional): Whether to load 3D labels.
            Defaults to True.
        with_attr_label (bool, optional): Whether to load attribute label.
            Defaults to False.
        with_mask_3d (bool, optional): Whether to load 3D instance masks.
            for points. Defaults to False.
        with_seg_3d (bool, optional): Whether to load 3D semantic masks.
            for points. Defaults to False.
        with_bbox (bool, optional): Whether to load 2D boxes.
            Defaults to False.
        with_label (bool, optional): Whether to load 2D labels.
            Defaults to False.
        with_mask (bool, optional): Whether to load 2D instance masks.
            Defaults to False.
        with_seg (bool, optional): Whether to load 2D semantic masks.
            Defaults to False.
        with_bbox_depth (bool, optional): Whether to load 2.5D boxes.
            Defaults to False.
        poly2mask (bool, optional): Whether to convert polygon annotations
            to bitmasks. Defaults to True.
        seg_3d_dtype (dtype, optional): Dtype of 3D semantic masks.
            Defaults to int64.
        file_client_args (dict): Config dict of file clients, refer to
            https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
            for more details.
    """

    def __init__(
        self,
        with_bbox_3d: bool = True,
        with_label_3d: bool = True,
        with_attr_label: bool = False,
        with_mask_3d: bool = False,
        with_seg_3d: bool = False,
        with_bbox: bool = False,
        with_label: bool = False,
        with_mask: bool = False,
        with_seg: bool = False,
        with_bbox_depth: bool = False,
        poly2mask: bool = True,
        seg_3d_dtype: np.dtype = np.int64,
        file_client_args: dict = dict(backend='disk')
    ) -> None:
        super().__init__(
            with_bbox=with_bbox,
            with_label=with_label,
            with_mask=with_mask,
            with_seg=with_seg,
            poly2mask=poly2mask,
            file_client_args=file_client_args)
        self.with_bbox_3d = with_bbox_3d
        self.with_bbox_depth = with_bbox_depth
        self.with_label_3d = with_label_3d
        self.with_attr_label = with_attr_label
        self.with_mask_3d = with_mask_3d
        self.with_seg_3d = with_seg_3d
        self.seg_3d_dtype = seg_3d_dtype

    def _load_bboxes_3d(self, results: dict) -> dict:
        """Private function to move the 3D bounding box annotation from
        `ann_info` field to the root of `results`.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded 3D bounding box annotations.
        """

        results['gt_bboxes_3d'] = results['ann_info']['gt_bboxes_3d']
        return results

    def _load_bboxes_depth(self, results: dict) -> dict:
        """Private function to load 2.5D bounding box annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded 2.5D bounding box annotations.
        """

        results['depths'] = results['ann_info']['depths']
        results['centers_2d'] = results['ann_info']['centers_2d']
        return results

    def _load_labels_3d(self, results: dict) -> dict:
        """Private function to load label annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded label annotations.
        """

        results['gt_labels_3d'] = results['ann_info']['gt_labels_3d']
        return results

    def _load_attr_labels(self, results: dict) -> dict:
        """Private function to load label annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded label annotations.
        """
        results['attr_labels'] = results['ann_info']['attr_labels']
        return results

    def _load_masks_3d(self, results: dict) -> dict:
        """Private function to load 3D mask annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded 3D mask annotations.
        """
        pts_instance_mask_path = results['pts_instance_mask_path']

        if self.file_client is None:
            self.file_client = mmcv.FileClient(**self.file_client_args)
        try:
            mask_bytes = self.file_client.get(pts_instance_mask_path)
            pts_instance_mask = np.frombuffer(mask_bytes, dtype=np.int64)
        except ConnectionError:
            mmcv.check_file_exist(pts_instance_mask_path)
            pts_instance_mask = np.fromfile(
                pts_instance_mask_path, dtype=np.int64)

        results['pts_instance_mask'] = pts_instance_mask
        # 'eval_ann_info' will be passed to evaluator
        if 'eval_ann_info' in results:
            results['eval_ann_info']['pts_instance_mask'] = pts_instance_mask
        return results

    def _load_semantic_seg_3d(self, results: dict) -> dict:
        """Private function to load 3D semantic segmentation annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing the semantic segmentation annotations.
        """
        pts_semantic_mask_path = results['pts_semantic_mask_path']

        if self.file_client is None:
            self.file_client = mmcv.FileClient(**self.file_client_args)
        try:
            mask_bytes = self.file_client.get(pts_semantic_mask_path)
            # add .copy() to fix read-only bug
            pts_semantic_mask = np.frombuffer(
                mask_bytes, dtype=self.seg_3d_dtype).copy()
        except ConnectionError:
            mmcv.check_file_exist(pts_semantic_mask_path)
            pts_semantic_mask = np.fromfile(
                pts_semantic_mask_path, dtype=np.int64)

        results['pts_semantic_mask'] = pts_semantic_mask
        # 'eval_ann_info' will be passed to evaluator
        if 'eval_ann_info' in results:
            results['eval_ann_info']['pts_semantic_mask'] = pts_semantic_mask
        return results

    def transform(self, results: dict) -> dict:
        """Function to load multiple types annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded 3D bounding box, label, mask and
            semantic segmentation annotations.
        """
        results = super().transform(results)
        if self.with_bbox_3d:
            results = self._load_bboxes_3d(results)
        if self.with_bbox_depth:
            results = self._load_bboxes_depth(results)
        if self.with_label_3d:
            results = self._load_labels_3d(results)
        if self.with_attr_label:
            results = self._load_attr_labels(results)
        if self.with_mask_3d:
            results = self._load_masks_3d(results)
        if self.with_seg_3d:
            results = self._load_semantic_seg_3d(results)

        return results

    def __repr__(self):
        """str: Return a string that describes the module."""
        indent_str = '    '
        repr_str = self.__class__.__name__ + '(\n'
        repr_str += f'{indent_str}with_bbox_3d={self.with_bbox_3d}, '
        repr_str += f'{indent_str}with_label_3d={self.with_label_3d}, '
        repr_str += f'{indent_str}with_attr_label={self.with_attr_label}, '
        repr_str += f'{indent_str}with_mask_3d={self.with_mask_3d}, '
        repr_str += f'{indent_str}with_seg_3d={self.with_seg_3d}, '
        repr_str += f'{indent_str}with_bbox={self.with_bbox}, '
        repr_str += f'{indent_str}with_label={self.with_label}, '
        repr_str += f'{indent_str}with_mask={self.with_mask}, '
        repr_str += f'{indent_str}with_seg={self.with_seg}, '
        repr_str += f'{indent_str}with_bbox_depth={self.with_bbox_depth}, '
        repr_str += f'{indent_str}poly2mask={self.poly2mask})'
        return repr_str