transforms_3d.py

# Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings

import cv2
import numpy as np
from mmcv import is_tuple_of
from mmcv.utils import build_from_cfg

from mmdet3d.core import VoxelGenerator
from mmdet3d.core.bbox import (CameraInstance3DBoxes, DepthInstance3DBoxes,
                               LiDARInstance3DBoxes, box_np_ops)
from mmdet.datasets.builder import PIPELINES
from mmdet.datasets.pipelines import RandomFlip
from ..builder import OBJECTSAMPLERS
from .data_augment_utils import noise_per_object_v3_


@PIPELINES.register_module()
class RandomDropPointsColor(object):
    r"""Randomly set the color of points to all zeros.

    Once this transform is executed, all the points' color will be dropped.
    Refer to `PAConv <https://github.com/CVMI-Lab/PAConv/blob/main/scene_seg/
    util/transform.py#L223>`_ for more details.

    Args:
        drop_ratio (float, optional): The probability of dropping point colors.
            Defaults to 0.2.
    """

    def __init__(self, drop_ratio=0.2):
        assert isinstance(drop_ratio, (int, float)) and 0 <= drop_ratio <= 1, \
            f'invalid drop_ratio value {drop_ratio}'
        self.drop_ratio = drop_ratio

    def __call__(self, input_dict):
        """Call function to drop point colors.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after color dropping,
                'points' key is updated in the result dict.
        """
        points = input_dict['points']
        assert points.attribute_dims is not None and \
            'color' in points.attribute_dims, \
            'Expect points have color attribute'

        # this if-expression is a bit strange
        # `RandomDropPointsColor` is used in training 3D segmentor PAConv
        # we discovered in our experiments that, using
        # `if np.random.rand() > 1.0 - self.drop_ratio` consistently leads to
        # better results than using `if np.random.rand() < self.drop_ratio`
        # so we keep this hack in our codebase
        if np.random.rand() > 1.0 - self.drop_ratio:
            points.color = points.color * 0.0
        return input_dict

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


@PIPELINES.register_module()
class RandomFlip3D(RandomFlip):
    """Flip the points & bbox.

    If the input dict contains the key "flip", then the flag will be used,
    otherwise it will be randomly decided by a ratio specified in the init
    method.

    Args:
        sync_2d (bool, optional): Whether to apply flip according to the 2D
            images. If True, it will apply the same flip as that to 2D images.
            If False, it will decide whether to flip randomly and independently
            to that of 2D images. Defaults to True.
        flip_ratio_bev_horizontal (float, optional): The flipping probability
            in horizontal direction. Defaults to 0.0.
        flip_ratio_bev_vertical (float, optional): The flipping probability
            in vertical direction. Defaults to 0.0.
    """

    def __init__(self,
                 sync_2d=True,
                 flip_ratio_bev_horizontal=0.0,
                 flip_ratio_bev_vertical=0.0,
                 **kwargs):
        super(RandomFlip3D, self).__init__(
            flip_ratio=flip_ratio_bev_horizontal, **kwargs)
        self.sync_2d = sync_2d
        self.flip_ratio_bev_vertical = flip_ratio_bev_vertical
        if flip_ratio_bev_horizontal is not None:
            assert isinstance(
                flip_ratio_bev_horizontal,
                (int, float)) and 0 <= flip_ratio_bev_horizontal <= 1
        if flip_ratio_bev_vertical is not None:
            assert isinstance(
                flip_ratio_bev_vertical,
                (int, float)) and 0 <= flip_ratio_bev_vertical <= 1

    def random_flip_data_3d(self, input_dict, direction='horizontal'):
        """Flip 3D data randomly.

        Args:
            input_dict (dict): Result dict from loading pipeline.
            direction (str, optional): Flip direction.
                Default: 'horizontal'.

        Returns:
            dict: Flipped results, 'points', 'bbox3d_fields' keys are
                updated in the result dict.
        """
        assert direction in ['horizontal', 'vertical']
        # for semantic segmentation task, only points will be flipped.
        if 'bbox3d_fields' not in input_dict:
            input_dict['points'].flip(direction)
            return
        if len(input_dict['bbox3d_fields']) == 0:  # test mode
            input_dict['bbox3d_fields'].append('empty_box3d')
            input_dict['empty_box3d'] = input_dict['box_type_3d'](
                np.array([], dtype=np.float32))
        assert len(input_dict['bbox3d_fields']) == 1
        for key in input_dict['bbox3d_fields']:
            if 'points' in input_dict:
                input_dict['points'] = input_dict[key].flip(
                    direction, points=input_dict['points'])
            else:
                input_dict[key].flip(direction)
        if 'centers2d' in input_dict:
            assert self.sync_2d is True and direction == 'horizontal', \
                'Only support sync_2d=True and horizontal flip with images'
            w = input_dict['ori_shape'][1]
            input_dict['centers2d'][..., 0] = \
                w - input_dict['centers2d'][..., 0]
            # need to modify the horizontal position of camera center
            # along u-axis in the image (flip like centers2d)
            # ['cam2img'][0][2] = c_u
            # see more details and examples at
            # https://github.com/open-mmlab/mmdetection3d/pull/744
            input_dict['cam2img'][0][2] = w - input_dict['cam2img'][0][2]

    def __call__(self, input_dict):
        """Call function to flip points, values in the ``bbox3d_fields`` and
        also flip 2D image and its annotations.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Flipped results, 'flip', 'flip_direction',
                'pcd_horizontal_flip' and 'pcd_vertical_flip' keys are added
                into result dict.
        """
        # flip 2D image and its annotations
        super(RandomFlip3D, self).__call__(input_dict)

        if self.sync_2d:
            input_dict['pcd_horizontal_flip'] = input_dict['flip']
            input_dict['pcd_vertical_flip'] = False
        else:
            if 'pcd_horizontal_flip' not in input_dict:
                flip_horizontal = True if np.random.rand(
                ) < self.flip_ratio else False
                input_dict['pcd_horizontal_flip'] = flip_horizontal
            if 'pcd_vertical_flip' not in input_dict:
                flip_vertical = True if np.random.rand(
                ) < self.flip_ratio_bev_vertical else False
                input_dict['pcd_vertical_flip'] = flip_vertical

        if 'transformation_3d_flow' not in input_dict:
            input_dict['transformation_3d_flow'] = []

        if input_dict['pcd_horizontal_flip']:
            self.random_flip_data_3d(input_dict, 'horizontal')
            input_dict['transformation_3d_flow'].extend(['HF'])
        if input_dict['pcd_vertical_flip']:
            self.random_flip_data_3d(input_dict, 'vertical')
            input_dict['transformation_3d_flow'].extend(['VF'])
        return input_dict

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(sync_2d={self.sync_2d},'
        repr_str += f' flip_ratio_bev_vertical={self.flip_ratio_bev_vertical})'
        return repr_str


@PIPELINES.register_module()
class RandomJitterPoints(object):
    """Randomly jitter point coordinates.

    Different from the global translation in ``GlobalRotScaleTrans``, here we
        apply different noises to each point in a scene.

    Args:
        jitter_std (list[float]): The standard deviation of jittering noise.
            This applies random noise to all points in a 3D scene, which is
            sampled from a gaussian distribution whose standard deviation is
            set by ``jitter_std``. Defaults to [0.01, 0.01, 0.01]
        clip_range (list[float]): Clip the randomly generated jitter
            noise into this range. If None is given, don't perform clipping.
            Defaults to [-0.05, 0.05]

    Note:
        This transform should only be used in point cloud segmentation tasks
            because we don't transform ground-truth bboxes accordingly.
        For similar transform in detection task, please refer to `ObjectNoise`.
    """

    def __init__(self,
                 jitter_std=[0.01, 0.01, 0.01],
                 clip_range=[-0.05, 0.05]):
        seq_types = (list, tuple, np.ndarray)
        if not isinstance(jitter_std, seq_types):
            assert isinstance(jitter_std, (int, float)), \
                f'unsupported jitter_std type {type(jitter_std)}'
            jitter_std = [jitter_std, jitter_std, jitter_std]
        self.jitter_std = jitter_std

        if clip_range is not None:
            if not isinstance(clip_range, seq_types):
                assert isinstance(clip_range, (int, float)), \
                    f'unsupported clip_range type {type(clip_range)}'
                clip_range = [-clip_range, clip_range]
        self.clip_range = clip_range

    def __call__(self, input_dict):
        """Call function to jitter all the points in the scene.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after adding noise to each point,
                'points' key is updated in the result dict.
        """
        points = input_dict['points']
        jitter_std = np.array(self.jitter_std, dtype=np.float32)
        jitter_noise = \
            np.random.randn(points.shape[0], 3) * jitter_std[None, :]
        if self.clip_range is not None:
            jitter_noise = np.clip(jitter_noise, self.clip_range[0],
                                   self.clip_range[1])

        points.translate(jitter_noise)
        return input_dict

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


@PIPELINES.register_module()
class ObjectSample(object):
    """Sample GT objects to the data.

    Args:
        db_sampler (dict): Config dict of the database sampler.
        sample_2d (bool): Whether to also paste 2D image patch to the images
            This should be true when applying multi-modality cut-and-paste.
            Defaults to False.
    """

    def __init__(self, db_sampler, sample_2d=False):
        self.sampler_cfg = db_sampler
        self.sample_2d = sample_2d
        if 'type' not in db_sampler.keys():
            db_sampler['type'] = 'DataBaseSampler'
        self.db_sampler = build_from_cfg(db_sampler, OBJECTSAMPLERS)

    @staticmethod
    def remove_points_in_boxes(points, boxes):
        """Remove the points in the sampled bounding boxes.

        Args:
            points (:obj:`BasePoints`): Input point cloud array.
            boxes (np.ndarray): Sampled ground truth boxes.

        Returns:
            np.ndarray: Points with those in the boxes removed.
        """
        masks = box_np_ops.points_in_rbbox(points.coord.numpy(), boxes)
        points = points[np.logical_not(masks.any(-1))]
        return points

    def __call__(self, input_dict):
        """Call function to sample ground truth objects to the data.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after object sampling augmentation,
                'points', 'gt_bboxes_3d', 'gt_labels_3d' keys are updated
                in the result dict.
        """
        gt_bboxes_3d = input_dict['gt_bboxes_3d']
        gt_labels_3d = input_dict['gt_labels_3d']

        # change to float for blending operation
        points = input_dict['points']
        if self.sample_2d:
            img = input_dict['img']
            gt_bboxes_2d = input_dict['gt_bboxes']
            # Assume for now 3D & 2D bboxes are the same
            sampled_dict = self.db_sampler.sample_all(
                gt_bboxes_3d.tensor.numpy(),
                gt_labels_3d,
                gt_bboxes_2d=gt_bboxes_2d,
                img=img)
        else:
            sampled_dict = self.db_sampler.sample_all(
                gt_bboxes_3d.tensor.numpy(), gt_labels_3d, img=None)

        if sampled_dict is not None:
            sampled_gt_bboxes_3d = sampled_dict['gt_bboxes_3d']
            sampled_points = sampled_dict['points']
            sampled_gt_labels = sampled_dict['gt_labels_3d']

            gt_labels_3d = np.concatenate([gt_labels_3d, sampled_gt_labels],
                                          axis=0)
            gt_bboxes_3d = gt_bboxes_3d.new_box(
                np.concatenate(
                    [gt_bboxes_3d.tensor.numpy(), sampled_gt_bboxes_3d]))

            points = self.remove_points_in_boxes(points, sampled_gt_bboxes_3d)
            # check the points dimension
            points = points.cat([sampled_points, points])

            if self.sample_2d:
                sampled_gt_bboxes_2d = sampled_dict['gt_bboxes_2d']
                gt_bboxes_2d = np.concatenate(
                    [gt_bboxes_2d, sampled_gt_bboxes_2d]).astype(np.float32)

                input_dict['gt_bboxes'] = gt_bboxes_2d
                input_dict['img'] = sampled_dict['img']

        input_dict['gt_bboxes_3d'] = gt_bboxes_3d
        input_dict['gt_labels_3d'] = gt_labels_3d.astype(np.long)
        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' sample_2d={self.sample_2d},'
        repr_str += f' data_root={self.sampler_cfg.data_root},'
        repr_str += f' info_path={self.sampler_cfg.info_path},'
        repr_str += f' rate={self.sampler_cfg.rate},'
        repr_str += f' prepare={self.sampler_cfg.prepare},'
        repr_str += f' classes={self.sampler_cfg.classes},'
        repr_str += f' sample_groups={self.sampler_cfg.sample_groups}'
        return repr_str


@PIPELINES.register_module()
class ObjectNoise(object):
    """Apply noise to each GT objects in the scene.

    Args:
        translation_std (list[float], optional): Standard deviation of the
            distribution where translation noise are sampled from.
            Defaults to [0.25, 0.25, 0.25].
        global_rot_range (list[float], optional): Global rotation to the scene.
            Defaults to [0.0, 0.0].
        rot_range (list[float], optional): Object rotation range.
            Defaults to [-0.15707963267, 0.15707963267].
        num_try (int, optional): Number of times to try if the noise applied is
            invalid. Defaults to 100.
    """

    def __init__(self,
                 translation_std=[0.25, 0.25, 0.25],
                 global_rot_range=[0.0, 0.0],
                 rot_range=[-0.15707963267, 0.15707963267],
                 num_try=100):
        self.translation_std = translation_std
        self.global_rot_range = global_rot_range
        self.rot_range = rot_range
        self.num_try = num_try

    def __call__(self, input_dict):
        """Call function to apply noise to each ground truth in the scene.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after adding noise to each object,
                'points', 'gt_bboxes_3d' keys are updated in the result dict.
        """
        gt_bboxes_3d = input_dict['gt_bboxes_3d']
        points = input_dict['points']

        # TODO: this is inplace operation
        numpy_box = gt_bboxes_3d.tensor.numpy()
        numpy_points = points.tensor.numpy()

        noise_per_object_v3_(
            numpy_box,
            numpy_points,
            rotation_perturb=self.rot_range,
            center_noise_std=self.translation_std,
            global_random_rot_range=self.global_rot_range,
            num_try=self.num_try)

        input_dict['gt_bboxes_3d'] = gt_bboxes_3d.new_box(numpy_box)
        input_dict['points'] = points.new_point(numpy_points)
        return input_dict

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


@PIPELINES.register_module()
class GlobalAlignment(object):
    """Apply global alignment to 3D scene points by rotation and translation.

    Args:
        rotation_axis (int): Rotation axis for points and bboxes rotation.

    Note:
        We do not record the applied rotation and translation as in
            GlobalRotScaleTrans. Because usually, we do not need to reverse
            the alignment step.
        For example, ScanNet 3D detection task uses aligned ground-truth
            bounding boxes for evaluation.
    """

    def __init__(self, rotation_axis):
        self.rotation_axis = rotation_axis

    def _trans_points(self, input_dict, trans_factor):
        """Private function to translate points.

        Args:
            input_dict (dict): Result dict from loading pipeline.
            trans_factor (np.ndarray): Translation vector to be applied.

        Returns:
            dict: Results after translation, 'points' is updated in the dict.
        """
        input_dict['points'].translate(trans_factor)

    def _rot_points(self, input_dict, rot_mat):
        """Private function to rotate bounding boxes and points.

        Args:
            input_dict (dict): Result dict from loading pipeline.
            rot_mat (np.ndarray): Rotation matrix to be applied.

        Returns:
            dict: Results after rotation, 'points' is updated in the dict.
        """
        # input should be rot_mat_T so I transpose it here
        input_dict['points'].rotate(rot_mat.T)

    def _check_rot_mat(self, rot_mat):
        """Check if rotation matrix is valid for self.rotation_axis.

        Args:
            rot_mat (np.ndarray): Rotation matrix to be applied.
        """
        is_valid = np.allclose(np.linalg.det(rot_mat), 1.0)
        valid_array = np.zeros(3)
        valid_array[self.rotation_axis] = 1.0
        is_valid &= (rot_mat[self.rotation_axis, :] == valid_array).all()
        is_valid &= (rot_mat[:, self.rotation_axis] == valid_array).all()
        assert is_valid, f'invalid rotation matrix {rot_mat}'

    def __call__(self, input_dict):
        """Call function to shuffle points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after global alignment, 'points' and keys in
                input_dict['bbox3d_fields'] are updated in the result dict.
        """
        assert 'axis_align_matrix' in input_dict['ann_info'].keys(), \
            'axis_align_matrix is not provided in GlobalAlignment'

        axis_align_matrix = input_dict['ann_info']['axis_align_matrix']
        assert axis_align_matrix.shape == (4, 4), \
            f'invalid shape {axis_align_matrix.shape} for axis_align_matrix'
        rot_mat = axis_align_matrix[:3, :3]
        trans_vec = axis_align_matrix[:3, -1]

        self._check_rot_mat(rot_mat)
        self._rot_points(input_dict, rot_mat)
        self._trans_points(input_dict, trans_vec)

        return input_dict

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(rotation_axis={self.rotation_axis})'
        return repr_str


@PIPELINES.register_module()
class GlobalRotScaleTrans(object):
    """Apply global rotation, scaling and translation to a 3D scene.

    Args:
        rot_range (list[float], optional): Range of rotation angle.
            Defaults to [-0.78539816, 0.78539816] (close to [-pi/4, pi/4]).
        scale_ratio_range (list[float], optional): Range of scale ratio.
            Defaults to [0.95, 1.05].
        translation_std (list[float], optional): The standard deviation of
            translation noise applied to a scene, which
            is sampled from a gaussian distribution whose standard deviation
            is set by ``translation_std``. Defaults to [0, 0, 0]
        shift_height (bool, optional): Whether to shift height.
            (the fourth dimension of indoor points) when scaling.
            Defaults to False.
    """

    def __init__(self,
                 rot_range=[-0.78539816, 0.78539816],
                 scale_ratio_range=[0.95, 1.05],
                 translation_std=[0, 0, 0],
                 shift_height=False):
        seq_types = (list, tuple, np.ndarray)
        if not isinstance(rot_range, seq_types):
            assert isinstance(rot_range, (int, float)), \
                f'unsupported rot_range type {type(rot_range)}'
            rot_range = [-rot_range, rot_range]
        self.rot_range = rot_range

        assert isinstance(scale_ratio_range, seq_types), \
            f'unsupported scale_ratio_range type {type(scale_ratio_range)}'
        self.scale_ratio_range = scale_ratio_range

        if not isinstance(translation_std, seq_types):
            assert isinstance(translation_std, (int, float)), \
                f'unsupported translation_std type {type(translation_std)}'
            translation_std = [
                translation_std, translation_std, translation_std
            ]
        assert all([std >= 0 for std in translation_std]), \
            'translation_std should be positive'
        self.translation_std = translation_std
        self.shift_height = shift_height

    def _trans_bbox_points(self, input_dict):
        """Private function to translate bounding boxes and points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after translation, 'points', 'pcd_trans'
                and keys in input_dict['bbox3d_fields'] are updated
                in the result dict.
        """
        translation_std = np.array(self.translation_std, dtype=np.float32)
        trans_factor = np.random.normal(scale=translation_std, size=3).T

        input_dict['points'].translate(trans_factor)
        input_dict['pcd_trans'] = trans_factor
        for key in input_dict['bbox3d_fields']:
            input_dict[key].translate(trans_factor)

    def _rot_bbox_points(self, input_dict):
        """Private function to rotate bounding boxes and points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after rotation, 'points', 'pcd_rotation'
                and keys in input_dict['bbox3d_fields'] are updated
                in the result dict.
        """
        rotation = self.rot_range
        noise_rotation = np.random.uniform(rotation[0], rotation[1])

        # if no bbox in input_dict, only rotate points
        if len(input_dict['bbox3d_fields']) == 0:
            rot_mat_T = input_dict['points'].rotate(noise_rotation)
            input_dict['pcd_rotation'] = rot_mat_T
            input_dict['pcd_rotation_angle'] = noise_rotation
            return

        # rotate points with bboxes
        for key in input_dict['bbox3d_fields']:
            if len(input_dict[key].tensor) != 0:
                points, rot_mat_T = input_dict[key].rotate(
                    noise_rotation, input_dict['points'])
                input_dict['points'] = points
                input_dict['pcd_rotation'] = rot_mat_T
                input_dict['pcd_rotation_angle'] = noise_rotation

    def _scale_bbox_points(self, input_dict):
        """Private function to scale bounding boxes and points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after scaling, 'points'and keys in
                input_dict['bbox3d_fields'] are updated in the result dict.
        """
        scale = input_dict['pcd_scale_factor']
        points = input_dict['points']
        points.scale(scale)
        if self.shift_height:
            assert 'height' in points.attribute_dims.keys(), \
                'setting shift_height=True but points have no height attribute'
            points.tensor[:, points.attribute_dims['height']] *= scale
        input_dict['points'] = points

        for key in input_dict['bbox3d_fields']:
            input_dict[key].scale(scale)

    def _random_scale(self, input_dict):
        """Private function to randomly set the scale factor.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after scaling, 'pcd_scale_factor' are updated
                in the result dict.
        """
        scale_factor = np.random.uniform(self.scale_ratio_range[0],
                                         self.scale_ratio_range[1])
        input_dict['pcd_scale_factor'] = scale_factor

    def __call__(self, input_dict):
        """Private function to rotate, scale and translate bounding boxes and
        points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after scaling, 'points', 'pcd_rotation',
                'pcd_scale_factor', 'pcd_trans' and keys in
                input_dict['bbox3d_fields'] are updated in the result dict.
        """
        if 'transformation_3d_flow' not in input_dict:
            input_dict['transformation_3d_flow'] = []

        self._rot_bbox_points(input_dict)

        if 'pcd_scale_factor' not in input_dict:
            self._random_scale(input_dict)
        self._scale_bbox_points(input_dict)

        self._trans_bbox_points(input_dict)

        input_dict['transformation_3d_flow'].extend(['R', 'S', 'T'])
        return input_dict

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


@PIPELINES.register_module()
class PointShuffle(object):
    """Shuffle input points."""

    def __call__(self, input_dict):
        """Call function to shuffle points.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after filtering, 'points', 'pts_instance_mask'
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        idx = input_dict['points'].shuffle()
        idx = idx.numpy()

        pts_instance_mask = input_dict.get('pts_instance_mask', None)
        pts_semantic_mask = input_dict.get('pts_semantic_mask', None)

        if pts_instance_mask is not None:
            input_dict['pts_instance_mask'] = pts_instance_mask[idx]

        if pts_semantic_mask is not None:
            input_dict['pts_semantic_mask'] = pts_semantic_mask[idx]

        return input_dict

    def __repr__(self):
        return self.__class__.__name__


@PIPELINES.register_module()
class ObjectRangeFilter(object):
    """Filter objects by the range.

    Args:
        point_cloud_range (list[float]): Point cloud range.
    """

    def __init__(self, point_cloud_range):
        self.pcd_range = np.array(point_cloud_range, dtype=np.float32)

    def __call__(self, input_dict):
        """Call function to filter objects by the range.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d'
                keys are updated in the result dict.
        """
        # Check points instance type and initialise bev_range
        if isinstance(input_dict['gt_bboxes_3d'],
                      (LiDARInstance3DBoxes, DepthInstance3DBoxes)):
            bev_range = self.pcd_range[[0, 1, 3, 4]]
        elif isinstance(input_dict['gt_bboxes_3d'], CameraInstance3DBoxes):
            bev_range = self.pcd_range[[0, 2, 3, 5]]

        gt_bboxes_3d = input_dict['gt_bboxes_3d']
        gt_labels_3d = input_dict['gt_labels_3d']
        mask = gt_bboxes_3d.in_range_bev(bev_range)
        gt_bboxes_3d = gt_bboxes_3d[mask]
        # mask is a torch tensor but gt_labels_3d is still numpy array
        # using mask to index gt_labels_3d will cause bug when
        # len(gt_labels_3d) == 1, where mask=1 will be interpreted
        # as gt_labels_3d[1] and cause out of index error
        gt_labels_3d = gt_labels_3d[mask.numpy().astype(np.bool)]

        # limit rad to [-pi, pi]
        gt_bboxes_3d.limit_yaw(offset=0.5, period=2 * np.pi)
        input_dict['gt_bboxes_3d'] = gt_bboxes_3d
        input_dict['gt_labels_3d'] = gt_labels_3d

        return input_dict

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


@PIPELINES.register_module()
class PointsRangeFilter(object):
    """Filter points by the range.

    Args:
        point_cloud_range (list[float]): Point cloud range.
    """

    def __init__(self, point_cloud_range):
        self.pcd_range = np.array(point_cloud_range, dtype=np.float32)

    def __call__(self, input_dict):
        """Call function to filter points by the range.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after filtering, 'points', 'pts_instance_mask'
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        points = input_dict['points']
        points_mask = points.in_range_3d(self.pcd_range)
        clean_points = points[points_mask]
        input_dict['points'] = clean_points
        points_mask = points_mask.numpy()

        pts_instance_mask = input_dict.get('pts_instance_mask', None)
        pts_semantic_mask = input_dict.get('pts_semantic_mask', None)

        if pts_instance_mask is not None:
            input_dict['pts_instance_mask'] = pts_instance_mask[points_mask]

        if pts_semantic_mask is not None:
            input_dict['pts_semantic_mask'] = pts_semantic_mask[points_mask]

        return input_dict

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


@PIPELINES.register_module()
class ObjectNameFilter(object):
    """Filter GT objects by their names.

    Args:
        classes (list[str]): List of class names to be kept for training.
    """

    def __init__(self, classes):
        self.classes = classes
        self.labels = list(range(len(self.classes)))

    def __call__(self, input_dict):
        """Call function to filter objects by their names.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d'
                keys are updated in the result dict.
        """
        gt_labels_3d = input_dict['gt_labels_3d']
        gt_bboxes_mask = np.array([n in self.labels for n in gt_labels_3d],
                                  dtype=np.bool_)
        input_dict['gt_bboxes_3d'] = input_dict['gt_bboxes_3d'][gt_bboxes_mask]
        input_dict['gt_labels_3d'] = input_dict['gt_labels_3d'][gt_bboxes_mask]

        return input_dict

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


@PIPELINES.register_module()
class PointSample(object):
    """Point sample.

    Sampling data to a certain number.

    Args:
        num_points (int): Number of points to be sampled.
        sample_range (float, optional): The range where to sample points.
            If not None, the points with depth larger than `sample_range` are
            prior to be sampled. Defaults to None.
        replace (bool, optional): Whether the sampling is with or without
            replacement. Defaults to False.
    """

    def __init__(self, num_points, sample_range=None, replace=False):
        self.num_points = num_points
        self.sample_range = sample_range
        self.replace = replace

    def _points_random_sampling(self,
                                points,
                                num_samples,
                                sample_range=None,
                                replace=False,
                                return_choices=False):
        """Points random sampling.

        Sample points to a certain number.

        Args:
            points (np.ndarray | :obj:`BasePoints`): 3D Points.
            num_samples (int): Number of samples to be sampled.
            sample_range (float, optional): Indicating the range where the
                points will be sampled. Defaults to None.
            replace (bool, optional): Sampling with or without replacement.
                Defaults to None.
            return_choices (bool, optional): Whether return choice.
                Defaults to False.
        Returns:
            tuple[np.ndarray] | np.ndarray:
                - points (np.ndarray | :obj:`BasePoints`): 3D Points.
                - choices (np.ndarray, optional): The generated random samples.
        """
        if not replace:
            replace = (points.shape[0] < num_samples)
        point_range = range(len(points))
        if sample_range is not None and not replace:
            # Only sampling the near points when len(points) >= num_samples
            depth = np.linalg.norm(points.tensor, axis=1)
            far_inds = np.where(depth >= sample_range)[0]
            near_inds = np.where(depth < sample_range)[0]
            # in case there are too many far points
            if len(far_inds) > num_samples:
                far_inds = np.random.choice(
                    far_inds, num_samples, replace=False)
            point_range = near_inds
            num_samples -= len(far_inds)
        choices = np.random.choice(point_range, num_samples, replace=replace)
        if sample_range is not None and not replace:
            choices = np.concatenate((far_inds, choices))
            # Shuffle points after sampling
            np.random.shuffle(choices)
        if return_choices:
            return points[choices], choices
        else:
            return points[choices]

    def __call__(self, results):
        """Call function to sample points to in indoor scenes.

        Args:
            input_dict (dict): Result dict from loading pipeline.
        Returns:
            dict: Results after sampling, 'points', 'pts_instance_mask'
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        points = results['points']
        # Points in Camera coord can provide the depth information.
        # TODO: Need to support distance-based sampling for other coord system.
        if self.sample_range is not None:
            from mmdet3d.core.points import CameraPoints
            assert isinstance(points, CameraPoints), \
                'Sampling based on distance is only applicable for CAM coord'
        points, choices = self._points_random_sampling(
            points,
            self.num_points,
            self.sample_range,
            self.replace,
            return_choices=True)
        results['points'] = points

        pts_instance_mask = results.get('pts_instance_mask', None)
        pts_semantic_mask = results.get('pts_semantic_mask', None)

        if pts_instance_mask is not None:
            pts_instance_mask = pts_instance_mask[choices]
            results['pts_instance_mask'] = pts_instance_mask

        if pts_semantic_mask is not None:
            pts_semantic_mask = pts_semantic_mask[choices]
            results['pts_semantic_mask'] = pts_semantic_mask

        return results

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

        return repr_str


@PIPELINES.register_module()
class IndoorPointSample(PointSample):
    """Indoor point sample.

    Sampling data to a certain number.
    NOTE: IndoorPointSample is deprecated in favor of PointSample

    Args:
        num_points (int): Number of points to be sampled.
    """

    def __init__(self, *args, **kwargs):
        warnings.warn(
            'IndoorPointSample is deprecated in favor of PointSample')
        super(IndoorPointSample, self).__init__(*args, **kwargs)


@PIPELINES.register_module()
class IndoorPatchPointSample(object):
    r"""Indoor point sample within a patch. Modified from `PointNet++ <https://
    github.com/charlesq34/pointnet2/blob/master/scannet/scannet_dataset.py>`_.

    Sampling data to a certain number for semantic segmentation.

    Args:
        num_points (int): Number of points to be sampled.
        block_size (float, optional): Size of a block to sample points from.
            Defaults to 1.5.
        sample_rate (float, optional): Stride used in sliding patch generation.
            This parameter is unused in `IndoorPatchPointSample` and thus has
            been deprecated. We plan to remove it in the future.
            Defaults to None.
        ignore_index (int, optional): Label index that won't be used for the
            segmentation task. This is set in PointSegClassMapping as neg_cls.
            If not None, will be used as a patch selection criterion.
            Defaults to None.
        use_normalized_coord (bool, optional): Whether to use normalized xyz as
            additional features. Defaults to False.
        num_try (int, optional): Number of times to try if the patch selected
            is invalid. Defaults to 10.
        enlarge_size (float, optional): Enlarge the sampled patch to
            [-block_size / 2 - enlarge_size, block_size / 2 + enlarge_size] as
            an augmentation. If None, set it as 0. Defaults to 0.2.
        min_unique_num (int, optional): Minimum number of unique points
            the sampled patch should contain. If None, use PointNet++'s method
            to judge uniqueness. Defaults to None.
        eps (float, optional): A value added to patch boundary to guarantee
            points coverage. Defaults to 1e-2.

    Note:
        This transform should only be used in the training process of point
            cloud segmentation tasks. For the sliding patch generation and
            inference process in testing, please refer to the `slide_inference`
            function of `EncoderDecoder3D` class.
    """

    def __init__(self,
                 num_points,
                 block_size=1.5,
                 sample_rate=None,
                 ignore_index=None,
                 use_normalized_coord=False,
                 num_try=10,
                 enlarge_size=0.2,
                 min_unique_num=None,
                 eps=1e-2):
        self.num_points = num_points
        self.block_size = block_size
        self.ignore_index = ignore_index
        self.use_normalized_coord = use_normalized_coord
        self.num_try = num_try
        self.enlarge_size = enlarge_size if enlarge_size is not None else 0.0
        self.min_unique_num = min_unique_num
        self.eps = eps

        if sample_rate is not None:
            warnings.warn(
                "'sample_rate' has been deprecated and will be removed in "
                'the future. Please remove them from your code.')

    def _input_generation(self, coords, patch_center, coord_max, attributes,
                          attribute_dims, point_type):
        """Generating model input.

        Generate input by subtracting patch center and adding additional
            features. Currently support colors and normalized xyz as features.

        Args:
            coords (np.ndarray): Sampled 3D Points.
            patch_center (np.ndarray): Center coordinate of the selected patch.
            coord_max (np.ndarray): Max coordinate of all 3D Points.
            attributes (np.ndarray): features of input points.
            attribute_dims (dict): Dictionary to indicate the meaning of extra
                dimension.
            point_type (type): class of input points inherited from BasePoints.

        Returns:
            :obj:`BasePoints`: The generated input data.
        """
        # subtract patch center, the z dimension is not centered
        centered_coords = coords.copy()
        centered_coords[:, 0] -= patch_center[0]
        centered_coords[:, 1] -= patch_center[1]

        if self.use_normalized_coord:
            normalized_coord = coords / coord_max
            attributes = np.concatenate([attributes, normalized_coord], axis=1)
            if attribute_dims is None:
                attribute_dims = dict()
            attribute_dims.update(
                dict(normalized_coord=[
                    attributes.shape[1], attributes.shape[1] +
                    1, attributes.shape[1] + 2
                ]))

        points = np.concatenate([centered_coords, attributes], axis=1)
        points = point_type(
            points, points_dim=points.shape[1], attribute_dims=attribute_dims)

        return points

    def _patch_points_sampling(self, points, sem_mask):
        """Patch points sampling.

        First sample a valid patch.
        Then sample points within that patch to a certain number.

        Args:
            points (:obj:`BasePoints`): 3D Points.
            sem_mask (np.ndarray): semantic segmentation mask for input points.

        Returns:
            tuple[:obj:`BasePoints`, np.ndarray] | :obj:`BasePoints`:

                - points (:obj:`BasePoints`): 3D Points.
                - choices (np.ndarray): The generated random samples.
        """
        coords = points.coord.numpy()
        attributes = points.tensor[:, 3:].numpy()
        attribute_dims = points.attribute_dims
        point_type = type(points)

        coord_max = np.amax(coords, axis=0)
        coord_min = np.amin(coords, axis=0)

        for _ in range(self.num_try):
            # random sample a point as patch center
            cur_center = coords[np.random.choice(coords.shape[0])]

            # boundary of a patch, which would be enlarged by
            # `self.enlarge_size` as an augmentation
            cur_max = cur_center + np.array(
                [self.block_size / 2.0, self.block_size / 2.0, 0.0])
            cur_min = cur_center - np.array(
                [self.block_size / 2.0, self.block_size / 2.0, 0.0])
            cur_max[2] = coord_max[2]
            cur_min[2] = coord_min[2]
            cur_choice = np.sum(
                (coords >= (cur_min - self.enlarge_size)) *
                (coords <= (cur_max + self.enlarge_size)),
                axis=1) == 3

            if not cur_choice.any():  # no points in this patch
                continue

            cur_coords = coords[cur_choice, :]
            cur_sem_mask = sem_mask[cur_choice]
            point_idxs = np.where(cur_choice)[0]
            mask = np.sum(
                (cur_coords >= (cur_min - self.eps)) * (cur_coords <=
                                                        (cur_max + self.eps)),
                axis=1) == 3

            # two criteria for patch sampling, adopted from PointNet++
            # 1. selected patch should contain enough unique points
            if self.min_unique_num is None:
                # use PointNet++'s method as default
                # [31, 31, 62] are just some big values used to transform
                # coords from 3d array to 1d and then check their uniqueness
                # this is used in all the ScanNet code following PointNet++
                vidx = np.ceil(
                    (cur_coords[mask, :] - cur_min) / (cur_max - cur_min) *
                    np.array([31.0, 31.0, 62.0]))
                vidx = np.unique(vidx[:, 0] * 31.0 * 62.0 + vidx[:, 1] * 62.0 +
                                 vidx[:, 2])
                flag1 = len(vidx) / 31.0 / 31.0 / 62.0 >= 0.02
            else:
                # if `min_unique_num` is provided, directly compare with it
                flag1 = mask.sum() >= self.min_unique_num

            # 2. selected patch should contain enough annotated points
            if self.ignore_index is None:
                flag2 = True
            else:
                flag2 = np.sum(cur_sem_mask != self.ignore_index) / \
                               len(cur_sem_mask) >= 0.7

            if flag1 and flag2:
                break

        # sample idx to `self.num_points`
        if point_idxs.size >= self.num_points:
            # no duplicate in sub-sampling
            choices = np.random.choice(
                point_idxs, self.num_points, replace=False)
        else:
            # do not use random choice here to avoid some points not counted
            dup = np.random.choice(point_idxs.size,
                                   self.num_points - point_idxs.size)
            idx_dup = np.concatenate(
                [np.arange(point_idxs.size),
                 np.array(dup)], 0)
            choices = point_idxs[idx_dup]

        # construct model input
        points = self._input_generation(coords[choices], cur_center, coord_max,
                                        attributes[choices], attribute_dims,
                                        point_type)

        return points, choices

    def __call__(self, results):
        """Call function to sample points to in indoor scenes.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after sampling, 'points', 'pts_instance_mask'
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        points = results['points']

        assert 'pts_semantic_mask' in results.keys(), \
            'semantic mask should be provided in training and evaluation'
        pts_semantic_mask = results['pts_semantic_mask']

        points, choices = self._patch_points_sampling(points,
                                                      pts_semantic_mask)

        results['points'] = points
        results['pts_semantic_mask'] = pts_semantic_mask[choices]
        pts_instance_mask = results.get('pts_instance_mask', None)
        if pts_instance_mask is not None:
            results['pts_instance_mask'] = pts_instance_mask[choices]

        return results

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(num_points={self.num_points},'
        repr_str += f' block_size={self.block_size},'
        repr_str += f' ignore_index={self.ignore_index},'
        repr_str += f' use_normalized_coord={self.use_normalized_coord},'
        repr_str += f' num_try={self.num_try},'
        repr_str += f' enlarge_size={self.enlarge_size},'
        repr_str += f' min_unique_num={self.min_unique_num},'
        repr_str += f' eps={self.eps})'
        return repr_str


@PIPELINES.register_module()
class BackgroundPointsFilter(object):
    """Filter background points near the bounding box.

    Args:
        bbox_enlarge_range (tuple[float], float): Bbox enlarge range.
    """

    def __init__(self, bbox_enlarge_range):
        assert (is_tuple_of(bbox_enlarge_range, float)
                and len(bbox_enlarge_range) == 3) \
            or isinstance(bbox_enlarge_range, float), \
            f'Invalid arguments bbox_enlarge_range {bbox_enlarge_range}'

        if isinstance(bbox_enlarge_range, float):
            bbox_enlarge_range = [bbox_enlarge_range] * 3
        self.bbox_enlarge_range = np.array(
            bbox_enlarge_range, dtype=np.float32)[np.newaxis, :]

    def __call__(self, input_dict):
        """Call function to filter points by the range.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after filtering, 'points', 'pts_instance_mask'
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        points = input_dict['points']
        gt_bboxes_3d = input_dict['gt_bboxes_3d']

        # avoid groundtruth being modified
        gt_bboxes_3d_np = gt_bboxes_3d.tensor.clone().numpy()
        gt_bboxes_3d_np[:, :3] = gt_bboxes_3d.gravity_center.clone().numpy()

        enlarged_gt_bboxes_3d = gt_bboxes_3d_np.copy()
        enlarged_gt_bboxes_3d[:, 3:6] += self.bbox_enlarge_range
        points_numpy = points.tensor.clone().numpy()
        foreground_masks = box_np_ops.points_in_rbbox(
            points_numpy, gt_bboxes_3d_np, origin=(0.5, 0.5, 0.5))
        enlarge_foreground_masks = box_np_ops.points_in_rbbox(
            points_numpy, enlarged_gt_bboxes_3d, origin=(0.5, 0.5, 0.5))
        foreground_masks = foreground_masks.max(1)
        enlarge_foreground_masks = enlarge_foreground_masks.max(1)
        valid_masks = ~np.logical_and(~foreground_masks,
                                      enlarge_foreground_masks)

        input_dict['points'] = points[valid_masks]
        pts_instance_mask = input_dict.get('pts_instance_mask', None)
        if pts_instance_mask is not None:
            input_dict['pts_instance_mask'] = pts_instance_mask[valid_masks]

        pts_semantic_mask = input_dict.get('pts_semantic_mask', None)
        if pts_semantic_mask is not None:
            input_dict['pts_semantic_mask'] = pts_semantic_mask[valid_masks]
        return input_dict

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


@PIPELINES.register_module()
class VoxelBasedPointSampler(object):
    """Voxel based point sampler.

    Apply voxel sampling to multiple sweep points.

    Args:
        cur_sweep_cfg (dict): Config for sampling current points.
        prev_sweep_cfg (dict): Config for sampling previous points.
        time_dim (int): Index that indicate the time dimension
            for input points.
    """

    def __init__(self, cur_sweep_cfg, prev_sweep_cfg=None, time_dim=3):
        self.cur_voxel_generator = VoxelGenerator(**cur_sweep_cfg)
        self.cur_voxel_num = self.cur_voxel_generator._max_voxels
        self.time_dim = time_dim
        if prev_sweep_cfg is not None:
            assert prev_sweep_cfg['max_num_points'] == \
                cur_sweep_cfg['max_num_points']
            self.prev_voxel_generator = VoxelGenerator(**prev_sweep_cfg)
            self.prev_voxel_num = self.prev_voxel_generator._max_voxels
        else:
            self.prev_voxel_generator = None
            self.prev_voxel_num = 0

    def _sample_points(self, points, sampler, point_dim):
        """Sample points for each points subset.

        Args:
            points (np.ndarray): Points subset to be sampled.
            sampler (VoxelGenerator): Voxel based sampler for
                each points subset.
            point_dim (int): The dimension of each points

        Returns:
            np.ndarray: Sampled points.
        """
        voxels, coors, num_points_per_voxel = sampler.generate(points)
        if voxels.shape[0] < sampler._max_voxels:
            padding_points = np.zeros([
                sampler._max_voxels - voxels.shape[0], sampler._max_num_points,
                point_dim
            ],
                                      dtype=points.dtype)
            padding_points[:] = voxels[0]
            sample_points = np.concatenate([voxels, padding_points], axis=0)
        else:
            sample_points = voxels

        return sample_points

    def __call__(self, results):
        """Call function to sample points from multiple sweeps.

        Args:
            input_dict (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after sampling, 'points', 'pts_instance_mask'
                and 'pts_semantic_mask' keys are updated in the result dict.
        """
        points = results['points']
        original_dim = points.shape[1]

        # TODO: process instance and semantic mask while _max_num_points
        # is larger than 1
        # Extend points with seg and mask fields
        map_fields2dim = []
        start_dim = original_dim
        points_numpy = points.tensor.numpy()
        extra_channel = [points_numpy]
        for idx, key in enumerate(results['pts_mask_fields']):
            map_fields2dim.append((key, idx + start_dim))
            extra_channel.append(results[key][..., None])

        start_dim += len(results['pts_mask_fields'])
        for idx, key in enumerate(results['pts_seg_fields']):
            map_fields2dim.append((key, idx + start_dim))
            extra_channel.append(results[key][..., None])

        points_numpy = np.concatenate(extra_channel, axis=-1)

        # Split points into two part, current sweep points and
        # previous sweeps points.
        # TODO: support different sampling methods for next sweeps points
        # and previous sweeps points.
        cur_points_flag = (points_numpy[:, self.time_dim] == 0)
        cur_sweep_points = points_numpy[cur_points_flag]
        prev_sweeps_points = points_numpy[~cur_points_flag]
        if prev_sweeps_points.shape[0] == 0:
            prev_sweeps_points = cur_sweep_points

        # Shuffle points before sampling
        np.random.shuffle(cur_sweep_points)
        np.random.shuffle(prev_sweeps_points)

        cur_sweep_points = self._sample_points(cur_sweep_points,
                                               self.cur_voxel_generator,
                                               points_numpy.shape[1])
        if self.prev_voxel_generator is not None:
            prev_sweeps_points = self._sample_points(prev_sweeps_points,
                                                     self.prev_voxel_generator,
                                                     points_numpy.shape[1])

            points_numpy = np.concatenate(
                [cur_sweep_points, prev_sweeps_points], 0)
        else:
            points_numpy = cur_sweep_points

        if self.cur_voxel_generator._max_num_points == 1:
            points_numpy = points_numpy.squeeze(1)
        results['points'] = points.new_point(points_numpy[..., :original_dim])

        # Restore the corresponding seg and mask fields
        for key, dim_index in map_fields2dim:
            results[key] = points_numpy[..., dim_index]

        return results

    def __repr__(self):
        """str: Return a string that describes the module."""

        def _auto_indent(repr_str, indent):
            repr_str = repr_str.split('\n')
            repr_str = [' ' * indent + t + '\n' for t in repr_str]
            repr_str = ''.join(repr_str)[:-1]
            return repr_str

        repr_str = self.__class__.__name__
        indent = 4
        repr_str += '(\n'
        repr_str += ' ' * indent + f'num_cur_sweep={self.cur_voxel_num},\n'
        repr_str += ' ' * indent + f'num_prev_sweep={self.prev_voxel_num},\n'
        repr_str += ' ' * indent + f'time_dim={self.time_dim},\n'
        repr_str += ' ' * indent + 'cur_voxel_generator=\n'
        repr_str += f'{_auto_indent(repr(self.cur_voxel_generator), 8)},\n'
        repr_str += ' ' * indent + 'prev_voxel_generator=\n'
        repr_str += f'{_auto_indent(repr(self.prev_voxel_generator), 8)})'
        return repr_str


@PIPELINES.register_module()
class AffineResize(object):
    """Get the affine transform matrices to the target size.

    Different from :class:`RandomAffine` in MMDetection, this class can
    calculate the affine transform matrices while resizing the input image
    to a fixed size. The affine transform matrices include: 1) matrix
    transforming original image to the network input image size. 2) matrix
    transforming original image to the network output feature map size.

    Args:
        img_scale (tuple): Images scales for resizing.
        down_ratio (int): The down ratio of feature map.
            Actually the arg should be >= 1.
        bbox_clip_border (bool, optional): Whether clip the objects
            outside the border of the image. Defaults to True.
    """

    def __init__(self, img_scale, down_ratio, bbox_clip_border=True):

        self.img_scale = img_scale
        self.down_ratio = down_ratio
        self.bbox_clip_border = bbox_clip_border

    def __call__(self, results):
        """Call function to do affine transform to input image and labels.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after affine resize, 'affine_aug', 'trans_mat'
                keys are added in the result dict.
        """
        # The results have gone through RandomShiftScale before AffineResize
        if 'center' not in results:
            img = results['img']
            height, width = img.shape[:2]
            center = np.array([width / 2, height / 2], dtype=np.float32)
            size = np.array([width, height], dtype=np.float32)
            results['affine_aug'] = False
        else:
            # The results did not go through RandomShiftScale before
            # AffineResize
            img = results['img']
            center = results['center']
            size = results['size']

        trans_affine = self._get_transform_matrix(center, size, self.img_scale)

        img = cv2.warpAffine(img, trans_affine[:2, :], self.img_scale)

        if isinstance(self.down_ratio, tuple):
            trans_mat = [
                self._get_transform_matrix(
                    center, size,
                    (self.img_scale[0] // ratio, self.img_scale[1] // ratio))
                for ratio in self.down_ratio
            ]  # (3, 3)
        else:
            trans_mat = self._get_transform_matrix(
                center, size, (self.img_scale[0] // self.down_ratio,
                               self.img_scale[1] // self.down_ratio))

        results['img'] = img
        results['img_shape'] = img.shape
        results['pad_shape'] = img.shape
        results['trans_mat'] = trans_mat

        self._affine_bboxes(results, trans_affine)

        if 'centers2d' in results:
            centers2d = self._affine_transform(results['centers2d'],
                                               trans_affine)
            valid_index = (centers2d[:, 0] >
                           0) & (centers2d[:, 0] <
                                 self.img_scale[0]) & (centers2d[:, 1] > 0) & (
                                     centers2d[:, 1] < self.img_scale[1])
            results['centers2d'] = centers2d[valid_index]

            for key in results.get('bbox_fields', []):
                if key in ['gt_bboxes']:
                    results[key] = results[key][valid_index]
                    if 'gt_labels' in results:
                        results['gt_labels'] = results['gt_labels'][
                            valid_index]
                    if 'gt_masks' in results:
                        raise NotImplementedError(
                            'AffineResize only supports bbox.')

            for key in results.get('bbox3d_fields', []):
                if key in ['gt_bboxes_3d']:
                    results[key].tensor = results[key].tensor[valid_index]
                    if 'gt_labels_3d' in results:
                        results['gt_labels_3d'] = results['gt_labels_3d'][
                            valid_index]

            results['depths'] = results['depths'][valid_index]

        return results

    def _affine_bboxes(self, results, matrix):
        """Affine transform bboxes to input image.

        Args:
            results (dict): Result dict from loading pipeline.
            matrix (np.ndarray): Matrix transforming original
                image to the network input image size.
                shape: (3, 3)
        """

        for key in results.get('bbox_fields', []):
            bboxes = results[key]
            bboxes[:, :2] = self._affine_transform(bboxes[:, :2], matrix)
            bboxes[:, 2:] = self._affine_transform(bboxes[:, 2:], matrix)
            if self.bbox_clip_border:
                bboxes[:,
                       [0, 2]] = bboxes[:,
                                        [0, 2]].clip(0, self.img_scale[0] - 1)
                bboxes[:,
                       [1, 3]] = bboxes[:,
                                        [1, 3]].clip(0, self.img_scale[1] - 1)
            results[key] = bboxes

    def _affine_transform(self, points, matrix):
        """Affine transform bbox points to input image.

        Args:
            points (np.ndarray): Points to be transformed.
                shape: (N, 2)
            matrix (np.ndarray): Affine transform matrix.
                shape: (3, 3)

        Returns:
            np.ndarray: Transformed points.
        """
        num_points = points.shape[0]
        hom_points_2d = np.concatenate((points, np.ones((num_points, 1))),
                                       axis=1)
        hom_points_2d = hom_points_2d.T
        affined_points = np.matmul(matrix, hom_points_2d).T
        return affined_points[:, :2]

    def _get_transform_matrix(self, center, scale, output_scale):
        """Get affine transform matrix.

        Args:
            center (tuple): Center of current image.
            scale (tuple): Scale of current image.
            output_scale (tuple[float]): The transform target image scales.

        Returns:
            np.ndarray: Affine transform matrix.
        """
        # TODO: further add rot and shift here.
        src_w = scale[0]
        dst_w = output_scale[0]
        dst_h = output_scale[1]

        src_dir = np.array([0, src_w * -0.5])
        dst_dir = np.array([0, dst_w * -0.5])

        src = np.zeros((3, 2), dtype=np.float32)
        dst = np.zeros((3, 2), dtype=np.float32)
        src[0, :] = center
        src[1, :] = center + src_dir
        dst[0, :] = np.array([dst_w * 0.5, dst_h * 0.5])
        dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir

        src[2, :] = self._get_ref_point(src[0, :], src[1, :])
        dst[2, :] = self._get_ref_point(dst[0, :], dst[1, :])

        get_matrix = cv2.getAffineTransform(src, dst)

        matrix = np.concatenate((get_matrix, [[0., 0., 1.]]))

        return matrix.astype(np.float32)

    def _get_ref_point(self, ref_point1, ref_point2):
        """Get reference point to calculate affine transform matrix.

        While using opencv to calculate the affine matrix, we need at least
        three corresponding points separately on original image and target
        image. Here we use two points to get the the third reference point.
        """
        d = ref_point1 - ref_point2
        ref_point3 = ref_point2 + np.array([-d[1], d[0]])
        return ref_point3

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(img_scale={self.img_scale}, '
        repr_str += f'down_ratio={self.down_ratio}) '
        return repr_str


@PIPELINES.register_module()
class RandomShiftScale(object):
    """Random shift scale.

    Different from the normal shift and scale function, it doesn't
    directly shift or scale image. It can record the shift and scale
    infos into loading pipelines. It's designed to be used with
    AffineResize together.

    Args:
        shift_scale (tuple[float]): Shift and scale range.
        aug_prob (float): The shifting and scaling probability.
    """

    def __init__(self, shift_scale, aug_prob):

        self.shift_scale = shift_scale
        self.aug_prob = aug_prob

    def __call__(self, results):
        """Call function to record random shift and scale infos.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Results after random shift and scale, 'center', 'size'
                and 'affine_aug' keys are added in the result dict.
        """
        img = results['img']

        height, width = img.shape[:2]

        center = np.array([width / 2, height / 2], dtype=np.float32)
        size = np.array([width, height], dtype=np.float32)

        if random.random() < self.aug_prob:
            shift, scale = self.shift_scale[0], self.shift_scale[1]
            shift_ranges = np.arange(-shift, shift + 0.1, 0.1)
            center[0] += size[0] * random.choice(shift_ranges)
            center[1] += size[1] * random.choice(shift_ranges)
            scale_ranges = np.arange(1 - scale, 1 + scale + 0.1, 0.1)
            size *= random.choice(scale_ranges)
            results['affine_aug'] = True
        else:
            results['affine_aug'] = False

        results['center'] = center
        results['size'] = size

        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(shift_scale={self.shift_scale}, '
        repr_str += f'aug_prob={self.aug_prob}) '
        return repr_str