transforms_3d.py

# Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings
from typing import List, Optional, Sequence, Tuple, Union

import cv2
import mmcv
import numpy as np
import torch
from mmcv.transforms import BaseTransform, Compose, RandomResize, Resize
from mmdet.datasets.transforms import (PhotoMetricDistortion, RandomCrop,
                                       RandomFlip)
from mmengine import is_list_of, is_tuple_of

from mmdet3d.models.task_modules import VoxelGenerator
from mmdet3d.registry import TRANSFORMS
from mmdet3d.structures import (CameraInstance3DBoxes, DepthInstance3DBoxes,
                                LiDARInstance3DBoxes)
from mmdet3d.structures.ops import box_np_ops
from mmdet3d.structures.points import BasePoints
from .data_augment_utils import noise_per_object_v3_


@TRANSFORMS.register_module()
class RandomDropPointsColor(BaseTransform):
    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): The probability of dropping point colors.
            Defaults to 0.2.
    """

    def __init__(self, drop_ratio: float = 0.2) -> None:
        assert isinstance(drop_ratio, (int, float)) and 0 <= drop_ratio <= 1, \
            f'invalid drop_ratio value {drop_ratio}'
        self.drop_ratio = drop_ratio

    def transform(self, input_dict: dict) -> 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:
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(drop_ratio={self.drop_ratio})'
        return repr_str


@TRANSFORMS.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.

    Required Keys:

    - points (np.float32)
    - gt_bboxes_3d (np.float32)

    Modified Keys:

    - points (np.float32)
    - gt_bboxes_3d (np.float32)

    Added Keys:

    - points (np.float32)
    - pcd_trans (np.float32)
    - pcd_rotation (np.float32)
    - pcd_rotation_angle (np.float32)
    - pcd_scale_factor (np.float32)

    Args:
        sync_2d (bool): 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): The flipping probability
            in horizontal direction. Defaults to 0.0.
        flip_ratio_bev_vertical (float): The flipping probability
            in vertical direction. Defaults to 0.0.
        flip_box3d (bool): Whether to flip bounding box. In most of the case,
            the box should be fliped. In cam-based bev detection, this is set
            to False, since the flip of 2D images does not influence the 3D
            box. Defaults to True.
    """

    def __init__(self,
                 sync_2d: bool = True,
                 flip_ratio_bev_horizontal: float = 0.0,
                 flip_ratio_bev_vertical: float = 0.0,
                 flip_box3d: bool = True,
                 **kwargs) -> None:
        # `flip_ratio_bev_horizontal` is equal to
        # for flip prob of 2d image when
        # `sync_2d` is True
        super(RandomFlip3D, self).__init__(
            prob=flip_ratio_bev_horizontal, direction='horizontal', **kwargs)
        self.sync_2d = sync_2d
        self.flip_ratio_bev_horizontal = flip_ratio_bev_horizontal
        self.flip_ratio_bev_vertical = flip_ratio_bev_vertical
        self.flip_box3d = flip_box3d
        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: dict,
                            direction: str = 'horizontal') -> None:
        """Flip 3D data randomly.

        `random_flip_data_3d` should take these situations into consideration:

        - 1. LIDAR-based 3d detection
        - 2. LIDAR-based 3d segmentation
        - 3. vision-only detection
        - 4. multi-modality 3d detection.

        Args:
            input_dict (dict): Result dict from loading pipeline.
            direction (str): Flip direction. Defaults to 'horizontal'.

        Returns:
            dict: Flipped results, 'points', 'bbox3d_fields' keys are
            updated in the result dict.
        """
        assert direction in ['horizontal', 'vertical']
        if self.flip_box3d:
            if 'gt_bboxes_3d' in input_dict:
                if 'points' in input_dict:
                    input_dict['points'] = input_dict['gt_bboxes_3d'].flip(
                        direction, points=input_dict['points'])
                else:
                    # vision-only detection
                    input_dict['gt_bboxes_3d'].flip(direction)
            else:
                input_dict['points'].flip(direction)

        if 'centers_2d' 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['img_shape'][1]
            input_dict['centers_2d'][..., 0] = \
                w - input_dict['centers_2d'][..., 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 _flip_on_direction(self, results: dict) -> None:
        """Function to flip images, bounding boxes, semantic segmentation map
        and keypoints.

        Add the override feature that if 'flip' is already in results, use it
        to do the augmentation.
        """
        if 'flip' not in results:
            cur_dir = self._choose_direction()
        else:
            # `flip_direction` works only when `flip` is True.
            # For example, in `MultiScaleFlipAug3D`, `flip_direction` is
            # 'horizontal' but `flip` is False.
            if results['flip']:
                assert 'flip_direction' in results, 'flip and flip_direction '
                'must exist simultaneously'
                cur_dir = results['flip_direction']
            else:
                cur_dir = None
        if cur_dir is None:
            results['flip'] = False
            results['flip_direction'] = None
        else:
            results['flip'] = True
            results['flip_direction'] = cur_dir
            self._flip(results)

    def transform(self, input_dict: dict) -> 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
        if 'img' in input_dict:
            super(RandomFlip3D, self).transform(input_dict)

        if self.sync_2d and 'img' in input_dict:
            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_bev_horizontal 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:
        """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


@TRANSFORMS.register_module()
class RandomJitterPoints(BaseTransform):
    """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: List[float] = [0.01, 0.01, 0.01],
                 clip_range: List[float] = [-0.05, 0.05]) -> None:
        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 transform(self, input_dict: dict) -> 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:
        """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


@TRANSFORMS.register_module()
class ObjectSample(BaseTransform):
    """Sample GT objects to the data.

    Required Keys:

    - points
    - ann_info
    - gt_bboxes_3d
    - gt_labels_3d
    - img (optional)
    - gt_bboxes (optional)

    Modified Keys:

    - points
    - gt_bboxes_3d
    - gt_labels_3d
    - img (optional)
    - gt_bboxes (optional)

    Added Keys:

    - plane (optional)

    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.
        use_ground_plane (bool): Whether to use ground plane to adjust the
            3D labels. Defaults to False.
    """

    def __init__(self,
                 db_sampler: dict,
                 sample_2d: bool = False,
                 use_ground_plane: bool = False) -> None:
        self.sampler_cfg = db_sampler
        self.sample_2d = sample_2d
        if 'type' not in db_sampler.keys():
            db_sampler['type'] = 'DataBaseSampler'
        self.db_sampler = TRANSFORMS.build(db_sampler)
        self.use_ground_plane = use_ground_plane
        self.disabled = False

    @staticmethod
    def remove_points_in_boxes(points: BasePoints,
                               boxes: np.ndarray) -> np.ndarray:
        """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 transform(self, input_dict: dict) -> dict:
        """Transform 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.
        """
        if self.disabled:
            return input_dict

        gt_bboxes_3d = input_dict['gt_bboxes_3d']
        gt_labels_3d = input_dict['gt_labels_3d']

        if self.use_ground_plane:
            ground_plane = input_dict.get('plane', None)
            assert ground_plane is not None, '`use_ground_plane` is True ' \
                                             'but find plane is None'
        else:
            ground_plane = None
        # 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.numpy(),
                gt_labels_3d,
                gt_bboxes_2d=gt_bboxes_2d,
                img=img)
        else:
            sampled_dict = self.db_sampler.sample_all(
                gt_bboxes_3d.numpy(),
                gt_labels_3d,
                img=None,
                ground_plane=ground_plane)

        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.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.int64)
        input_dict['points'] = points

        return input_dict

    def __repr__(self) -> str:
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(db_sampler={self.db_sampler},'
        repr_str += f' sample_2d={self.sample_2d},'
        repr_str += f' use_ground_plane={self.use_ground_plane})'
        return repr_str


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

    Required Keys:

    - points
    - gt_bboxes_3d

    Modified Keys:

    - points
    - gt_bboxes_3d

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

    def __init__(self,
                 translation_std: List[float] = [0.25, 0.25, 0.25],
                 global_rot_range: List[float] = [0.0, 0.0],
                 rot_range: List[float] = [-0.15707963267, 0.15707963267],
                 num_try: int = 100) -> None:
        self.translation_std = translation_std
        self.global_rot_range = global_rot_range
        self.rot_range = rot_range
        self.num_try = num_try

    def transform(self, input_dict: dict) -> dict:
        """Transform 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.numpy()
        numpy_points = points.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:
        """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


@TRANSFORMS.register_module()
class GlobalAlignment(BaseTransform):
    """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: int) -> None:
        self.rotation_axis = rotation_axis

    def _trans_points(self, results: dict, trans_factor: np.ndarray) -> None:
        """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.
        """
        results['points'].translate(trans_factor)

    def _rot_points(self, results: dict, rot_mat: np.ndarray) -> None:
        """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
        results['points'].rotate(rot_mat.T)

    def _check_rot_mat(self, rot_mat: np.ndarray) -> None:
        """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 transform(self, results: dict) -> 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 results, \
            'axis_align_matrix is not provided in GlobalAlignment'

        axis_align_matrix = results['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(results, rot_mat)
        self._trans_points(results, trans_vec)

        return results

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


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

    Required Keys:

    - points (np.float32)
    - gt_bboxes_3d (np.float32)

    Modified Keys:

    - points (np.float32)
    - gt_bboxes_3d (np.float32)

    Added Keys:

    - points (np.float32)
    - pcd_trans (np.float32)
    - pcd_rotation (np.float32)
    - pcd_rotation_angle (np.float32)
    - pcd_scale_factor (np.float32)

    Args:
        rot_range (list[float]): Range of rotation angle.
            Defaults to [-0.78539816, 0.78539816] (close to [-pi/4, pi/4]).
        scale_ratio_range (list[float]): Range of scale ratio.
            Defaults to [0.95, 1.05].
        translation_std (list[float]): 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): Whether to shift height.
            (the fourth dimension of indoor points) when scaling.
            Defaults to False.
    """

    def __init__(self,
                 rot_range: List[float] = [-0.78539816, 0.78539816],
                 scale_ratio_range: List[float] = [0.95, 1.05],
                 translation_std: List[int] = [0, 0, 0],
                 shift_height: bool = False) -> None:
        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: dict) -> None:
        """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 `gt_bboxes_3d` is 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
        if 'gt_bboxes_3d' in input_dict:
            input_dict['gt_bboxes_3d'].translate(trans_factor)

    def _rot_bbox_points(self, input_dict: dict) -> None:
        """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 `gt_bboxes_3d` is updated in the result dict.
        """
        rotation = self.rot_range
        noise_rotation = np.random.uniform(rotation[0], rotation[1])

        if 'gt_bboxes_3d' in input_dict and \
                len(input_dict['gt_bboxes_3d'].tensor) != 0:
            # rotate points with bboxes
            points, rot_mat_T = input_dict['gt_bboxes_3d'].rotate(
                noise_rotation, input_dict['points'])
            input_dict['points'] = points
        else:
            # if no bbox in input_dict, only rotate points
            rot_mat_T = input_dict['points'].rotate(noise_rotation)

        input_dict['pcd_rotation'] = rot_mat_T
        input_dict['pcd_rotation_angle'] = noise_rotation

    def _scale_bbox_points(self, input_dict: dict) -> None:
        """Private function to scale bounding boxes and points.

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

        Returns:
            dict: Results after scaling, 'points' and
            `gt_bboxes_3d` is 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

        if 'gt_bboxes_3d' in input_dict and \
                len(input_dict['gt_bboxes_3d'].tensor) != 0:
            input_dict['gt_bboxes_3d'].scale(scale)

    def _random_scale(self, input_dict: dict) -> None:
        """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 transform(self, input_dict: dict) -> 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 `gt_bboxes_3d` 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:
        """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


@TRANSFORMS.register_module()
class PointShuffle(BaseTransform):
    """Shuffle input points."""

    def transform(self, input_dict: dict) -> 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) -> str:
        """str: Return a string that describes the module."""
        return self.__class__.__name__


@TRANSFORMS.register_module()
class ObjectRangeFilter(BaseTransform):
    """Filter objects by the range.

    Required Keys:

    - gt_bboxes_3d

    Modified Keys:

    - gt_bboxes_3d

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

    def __init__(self, point_cloud_range: List[float]) -> None:
        self.pcd_range = np.array(point_cloud_range, dtype=np.float32)

    def transform(self, input_dict: dict) -> dict:
        """Transform 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(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:
        """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


@TRANSFORMS.register_module()
class PointsRangeFilter(BaseTransform):
    """Filter points by the range.

    Required Keys:

    - points
    - pts_instance_mask (optional)

    Modified Keys:

    - points
    - pts_instance_mask (optional)

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

    def __init__(self, point_cloud_range: List[float]) -> None:
        self.pcd_range = np.array(point_cloud_range, dtype=np.float32)

    def transform(self, input_dict: dict) -> dict:
        """Transform 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:
        """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


@TRANSFORMS.register_module()
class ObjectNameFilter(BaseTransform):
    """Filter GT objects by their names.

    Required Keys:

    - gt_labels_3d

    Modified Keys:

    - gt_labels_3d

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

    def __init__(self, classes: List[str]) -> None:
        self.classes = classes
        self.labels = list(range(len(self.classes)))

    def transform(self, input_dict: dict) -> dict:
        """Transform 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=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:
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(classes={self.classes})'
        return repr_str


@TRANSFORMS.register_module()
class PointSample(BaseTransform):
    """Point sample.

    Sampling data to a certain number.

    Required Keys:

    - points
    - pts_instance_mask (optional)
    - pts_semantic_mask (optional)

    Modified Keys:

    - points
    - pts_instance_mask (optional)
    - pts_semantic_mask (optional)

    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): Whether the sampling is with or without replacement.
            Defaults to False.
    """

    def __init__(self,
                 num_points: int,
                 sample_range: Optional[float] = None,
                 replace: bool = False) -> None:
        self.num_points = num_points
        self.sample_range = sample_range
        self.replace = replace

    def _points_random_sampling(
        self,
        points: BasePoints,
        num_samples: Union[int, float],
        sample_range: Optional[float] = None,
        replace: bool = False,
        return_choices: bool = False
    ) -> Union[Tuple[BasePoints, np.ndarray], BasePoints]:
        """Points random sampling.

        Sample points to a certain number.

        Args:
            points (:obj:`BasePoints`): 3D Points.
            num_samples (int, float): Number of samples to be sampled. If
                float, we sample random fraction of points from num_points
                to 100%.
            sample_range (float, optional): Indicating the range where the
                points will be sampled. Defaults to None.
            replace (bool): Sampling with or without replacement.
                Defaults to False.
            return_choices (bool): Whether return choice. Defaults to False.

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

                - points (:obj:`BasePoints`): 3D Points.
                - choices (np.ndarray, optional): The generated random samples.
        """
        if isinstance(num_samples, float):
            assert num_samples < 1
            num_samples = int(
                np.random.uniform(self.num_points, 1.) * points.shape[0])

        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
            dist = np.linalg.norm(points.coord.numpy(), axis=1)
            far_inds = np.where(dist >= sample_range)[0]
            near_inds = np.where(dist < 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 transform(self, input_dict: dict) -> dict:
        """Transform 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 = input_dict['points']
        points, choices = self._points_random_sampling(
            points,
            self.num_points,
            self.sample_range,
            self.replace,
            return_choices=True)
        input_dict['points'] = points

        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:
            pts_instance_mask = pts_instance_mask[choices]
            input_dict['pts_instance_mask'] = pts_instance_mask

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

        return input_dict

    def __repr__(self) -> str:
        """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


@TRANSFORMS.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)


@TRANSFORMS.register_module()
class IndoorPatchPointSample(BaseTransform):
    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): 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): Whether to use normalized xyz as
            additional features. Defaults to False.
        num_try (int): Number of times to try if the patch selected is invalid.
            Defaults to 10.
        enlarge_size (float): 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): 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: int,
                 block_size: float = 1.5,
                 sample_rate: Optional[float] = None,
                 ignore_index: Optional[int] = None,
                 use_normalized_coord: bool = False,
                 num_try: int = 10,
                 enlarge_size: float = 0.2,
                 min_unique_num: Optional[int] = None,
                 eps: float = 1e-2) -> None:
        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: np.ndarray, patch_center: np.ndarray,
                          coord_max: np.ndarray, attributes: np.ndarray,
                          attribute_dims: dict,
                          point_type: type) -> BasePoints:
        """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: BasePoints,
            sem_mask: np.ndarray) -> Tuple[BasePoints, np.ndarray]:
        """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]:

                - 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 transform(self, input_dict: dict) -> dict:
        """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 = input_dict['points']

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

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

        input_dict['points'] = points
        input_dict['pts_semantic_mask'] = pts_semantic_mask[choices]

        # 'eval_ann_info' will be passed to evaluator
        if 'eval_ann_info' in input_dict:
            input_dict['eval_ann_info']['pts_semantic_mask'] = \
                pts_semantic_mask[choices]

        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[choices]
            # 'eval_ann_info' will be passed to evaluator
            if 'eval_ann_info' in input_dict:
                input_dict['eval_ann_info']['pts_instance_mask'] = \
                    pts_instance_mask[choices]

        return input_dict

    def __repr__(self) -> str:
        """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


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

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

    def __init__(self, bbox_enlarge_range: Union[Tuple[float], float]) -> None:
        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 transform(self, input_dict: dict) -> 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:
        """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


@TRANSFORMS.register_module()
class VoxelBasedPointSampler(BaseTransform):
    """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, optional): Config for sampling previous points.
            Defaults to None.
        time_dim (int): Index that indicate the time dimension
            for input points. Defaults to 3.
    """

    def __init__(self,
                 cur_sweep_cfg: dict,
                 prev_sweep_cfg: Optional[dict] = None,
                 time_dim: int = 3) -> None:
        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: np.ndarray, sampler: VoxelGenerator,
                       point_dim: int) -> np.ndarray:
        """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 transform(self, results: dict) -> dict:
        """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.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:
        """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


@TRANSFORMS.register_module()
class AffineResize(BaseTransform):
    """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): Whether clip the objects
            outside the border of the image. Defaults to True.
    """

    def __init__(self,
                 img_scale: Tuple,
                 down_ratio: int,
                 bbox_clip_border: bool = True) -> None:

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

    def transform(self, results: dict) -> dict:
        """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

        if 'gt_bboxes' in results:
            self._affine_bboxes(results, trans_affine)

        if 'centers_2d' in results:
            centers2d = self._affine_transform(results['centers_2d'],
                                               trans_affine)
            valid_index = (centers2d[:, 0] >
                           0) & (centers2d[:, 0] <
                                 self.img_scale[0]) & (centers2d[:, 1] > 0) & (
                                     centers2d[:, 1] < self.img_scale[1])
            results['centers_2d'] = centers2d[valid_index]

            if 'gt_bboxes' in results:
                results['gt_bboxes'] = results['gt_bboxes'][valid_index]
                if 'gt_bboxes_labels' in results:
                    results['gt_bboxes_labels'] = results['gt_bboxes_labels'][
                        valid_index]
                if 'gt_masks' in results:
                    raise NotImplementedError(
                        'AffineResize only supports bbox.')

            if 'gt_bboxes_3d' in results:
                results['gt_bboxes_3d'].tensor = results[
                    'gt_bboxes_3d'].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: dict, matrix: np.ndarray) -> None:
        """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)
        """

        bboxes = results['gt_bboxes']
        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['gt_bboxes'] = bboxes

    def _affine_transform(self, points: np.ndarray,
                          matrix: np.ndarray) -> np.ndarray:
        """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: Tuple, scale: Tuple,
                              output_scale: Tuple[float]) -> np.ndarray:
        """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: np.ndarray,
                       ref_point2: np.ndarray) -> np.ndarray:
        """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) -> str:
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(img_scale={self.img_scale}, '
        repr_str += f'down_ratio={self.down_ratio}) '
        return repr_str


@TRANSFORMS.register_module()
class RandomShiftScale(BaseTransform):
    """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 TRANSFORMS. 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: Tuple[float], aug_prob: float) -> None:

        self.shift_scale = shift_scale
        self.aug_prob = aug_prob

    def transform(self, results: dict) -> dict:
        """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) -> str:
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(shift_scale={self.shift_scale}, '
        repr_str += f'aug_prob={self.aug_prob}) '
        return repr_str


@TRANSFORMS.register_module()
class Resize3D(Resize):

    def _resize_3d(self, results: dict) -> None:
        """Resize centers_2d and modify camera intrinisc with
        ``results['scale']``."""
        if 'centers_2d' in results:
            results['centers_2d'] *= results['scale_factor'][:2]
        results['cam2img'][0] *= np.array(results['scale_factor'][0])
        results['cam2img'][1] *= np.array(results['scale_factor'][1])

    def transform(self, results: dict) -> dict:
        """Transform function to resize images, bounding boxes, semantic
        segmentation map and keypoints.

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

        Returns:
            dict: Resized results, 'img', 'gt_bboxes', 'gt_seg_map',
            'gt_keypoints', 'scale', 'scale_factor', 'img_shape',
            and 'keep_ratio' keys are updated in result dict.
        """

        super(Resize3D, self).transform(results)
        self._resize_3d(results)
        return results


@TRANSFORMS.register_module()
class RandomResize3D(RandomResize):
    """The difference between RandomResize3D and RandomResize:

    1. Compared to RandomResize, this class would further
        check if scale is already set in results.
    2. During resizing, this class would modify the centers_2d
        and cam2img with ``results['scale']``.
    """

    def _resize_3d(self, results: dict) -> None:
        """Resize centers_2d and modify camera intrinisc with
        ``results['scale']``."""
        if 'centers_2d' in results:
            results['centers_2d'] *= results['scale_factor'][:2]
        results['cam2img'][0] *= np.array(results['scale_factor'][0])
        results['cam2img'][1] *= np.array(results['scale_factor'][1])

    def transform(self, results: dict) -> dict:
        """Transform function to resize images, bounding boxes, masks, semantic
        segmentation map. Compared to RandomResize, this function would further
        check if scale is already set in results.

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

        Returns:
            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor',
            'keep_ratio' keys are added into result dict.
        """
        if 'scale' not in results:
            results['scale'] = self._random_scale()
        self.resize.scale = results['scale']
        results = self.resize(results)
        self._resize_3d(results)

        return results


@TRANSFORMS.register_module()
class RandomCrop3D(RandomCrop):
    """3D version of RandomCrop. RamdomCrop3D supports the modifications of
    camera intrinsic matrix and using predefined randomness variable to do the
    augmentation.

    The absolute ``crop_size`` is sampled based on ``crop_type`` and
    ``image_size``, then the cropped results are generated.

    Required Keys:

    - img
    - gt_bboxes (np.float32) (optional)
    - gt_bboxes_labels (np.int64) (optional)
    - gt_masks (BitmapMasks | PolygonMasks) (optional)
    - gt_ignore_flags (bool) (optional)
    - gt_seg_map (np.uint8) (optional)

    Modified Keys:

    - img
    - img_shape
    - gt_bboxes (optional)
    - gt_bboxes_labels (optional)
    - gt_masks (optional)
    - gt_ignore_flags (optional)
    - gt_seg_map (optional)

    Added Keys:

    - homography_matrix

    Args:
        crop_size (tuple): The relative ratio or absolute pixels of
            height and width.
        crop_type (str): One of "relative_range", "relative",
            "absolute", "absolute_range". "relative" randomly crops
            (h * crop_size[0], w * crop_size[1]) part from an input of size
            (h, w). "relative_range" uniformly samples relative crop size from
            range [crop_size[0], 1] and [crop_size[1], 1] for height and width
            respectively. "absolute" crops from an input with absolute size
            (crop_size[0], crop_size[1]). "absolute_range" uniformly samples
            crop_h in range [crop_size[0], min(h, crop_size[1])] and crop_w
            in range [crop_size[0], min(w, crop_size[1])].
            Defaults to "absolute".
        allow_negative_crop (bool): Whether to allow a crop that does
            not contain any bbox area. Defaults to False.
        recompute_bbox (bool): Whether to re-compute the boxes based
            on cropped instance masks. Defaults to False.
        bbox_clip_border (bool): Whether clip the objects outside
            the border of the image. Defaults to True.
        rel_offset_h (tuple): The cropping interval of image height. Defaults
            to (0., 1.).
        rel_offset_w (tuple): The cropping interval of image width. Defaults
            to (0., 1.).

    Note:
        - If the image is smaller than the absolute crop size, return the
          original image.
        - The keys for bboxes, labels and masks must be aligned. That is,
          ``gt_bboxes`` corresponds to ``gt_labels`` and ``gt_masks``, and
          ``gt_bboxes_ignore`` corresponds to ``gt_labels_ignore`` and
          ``gt_masks_ignore``.
        - If the crop does not contain any gt-bbox region and
          ``allow_negative_crop`` is set to False, skip this image.
    """

    def __init__(
        self,
        crop_size: tuple,
        crop_type: str = 'absolute',
        allow_negative_crop: bool = False,
        recompute_bbox: bool = False,
        bbox_clip_border: bool = True,
        rel_offset_h: tuple = (0., 1.),
        rel_offset_w: tuple = (0., 1.)
    ) -> None:
        super().__init__(
            crop_size=crop_size,
            crop_type=crop_type,
            allow_negative_crop=allow_negative_crop,
            recompute_bbox=recompute_bbox,
            bbox_clip_border=bbox_clip_border)
        # rel_offset specifies the relative offset range of cropping origin
        # [0., 1.] means starting from 0*margin to 1*margin + 1
        self.rel_offset_h = rel_offset_h
        self.rel_offset_w = rel_offset_w

    def _crop_data(self,
                   results: dict,
                   crop_size: tuple,
                   allow_negative_crop: bool = False) -> dict:
        """Function to randomly crop images, bounding boxes, masks, semantic
        segmentation maps.

        Args:
            results (dict): Result dict from loading pipeline.
            crop_size (tuple): Expected absolute size after cropping, (h, w).
            allow_negative_crop (bool): Whether to allow a crop that does not
                contain any bbox area. Defaults to False.

        Returns:
            dict: Randomly cropped results, 'img_shape' key in result dict is
            updated according to crop size.
        """
        assert crop_size[0] > 0 and crop_size[1] > 0
        for key in results.get('img_fields', ['img']):
            img = results[key]
            if 'img_crop_offset' not in results:
                margin_h = max(img.shape[0] - crop_size[0], 0)
                margin_w = max(img.shape[1] - crop_size[1], 0)
                # TOCHECK: a little different from LIGA implementation
                offset_h = np.random.randint(
                    self.rel_offset_h[0] * margin_h,
                    self.rel_offset_h[1] * margin_h + 1)
                offset_w = np.random.randint(
                    self.rel_offset_w[0] * margin_w,
                    self.rel_offset_w[1] * margin_w + 1)
            else:
                offset_w, offset_h = results['img_crop_offset']

            crop_h = min(crop_size[0], img.shape[0])
            crop_w = min(crop_size[1], img.shape[1])
            crop_y1, crop_y2 = offset_h, offset_h + crop_h
            crop_x1, crop_x2 = offset_w, offset_w + crop_w

            # crop the image
            img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
            img_shape = img.shape
            results[key] = img
        results['img_shape'] = img_shape

        # crop bboxes accordingly and clip to the image boundary
        for key in results.get('bbox_fields', []):
            # e.g. gt_bboxes and gt_bboxes_ignore
            bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h],
                                   dtype=np.float32)
            bboxes = results[key] - bbox_offset
            if self.bbox_clip_border:
                bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1])
                bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0])
            valid_inds = (bboxes[:, 2] > bboxes[:, 0]) & (
                bboxes[:, 3] > bboxes[:, 1])
            # If the crop does not contain any gt-bbox area and
            # allow_negative_crop is False, skip this image.
            if (key == 'gt_bboxes' and not valid_inds.any()
                    and not allow_negative_crop):
                return None
            results[key] = bboxes[valid_inds, :]
            # label fields. e.g. gt_labels and gt_labels_ignore
            label_key = self.bbox2label.get(key)
            if label_key in results:
                results[label_key] = results[label_key][valid_inds]

            # mask fields, e.g. gt_masks and gt_masks_ignore
            mask_key = self.bbox2mask.get(key)
            if mask_key in results:
                results[mask_key] = results[mask_key][
                    valid_inds.nonzero()[0]].crop(
                        np.asarray([crop_x1, crop_y1, crop_x2, crop_y2]))
                if self.recompute_bbox:
                    results[key] = results[mask_key].get_bboxes()

        # crop semantic seg
        for key in results.get('seg_fields', []):
            results[key] = results[key][crop_y1:crop_y2, crop_x1:crop_x2]

        # manipulate camera intrinsic matrix
        # needs to apply offset to K instead of P2 (on KITTI)
        if isinstance(results['cam2img'], list):
            # TODO ignore this, but should handle it in the future
            pass
        else:
            K = results['cam2img'][:3, :3].copy()
            inv_K = np.linalg.inv(K)
            T = np.matmul(inv_K, results['cam2img'][:3])
            K[0, 2] -= crop_x1
            K[1, 2] -= crop_y1
            offset_cam2img = np.matmul(K, T)
            results['cam2img'][:offset_cam2img.shape[0], :offset_cam2img.
                               shape[1]] = offset_cam2img

        results['img_crop_offset'] = [offset_w, offset_h]

        return results

    def transform(self, results: dict) -> dict:
        """Transform function to randomly crop images, bounding boxes, masks,
        semantic segmentation maps.

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

        Returns:
            dict: Randomly cropped results, 'img_shape' key in result dict is
            updated according to crop size.
        """
        image_size = results['img'].shape[:2]
        if 'crop_size' not in results:
            crop_size = self._get_crop_size(image_size)
            results['crop_size'] = crop_size
        else:
            crop_size = results['crop_size']
        results = self._crop_data(results, crop_size, self.allow_negative_crop)
        return results

    def __repr__(self) -> dict:
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(crop_size={self.crop_size}, '
        repr_str += f'crop_type={self.crop_type}, '
        repr_str += f'allow_negative_crop={self.allow_negative_crop}, '
        repr_str += f'bbox_clip_border={self.bbox_clip_border}), '
        repr_str += f'rel_offset_h={self.rel_offset_h}), '
        repr_str += f'rel_offset_w={self.rel_offset_w})'
        return repr_str


@TRANSFORMS.register_module()
class PhotoMetricDistortion3D(PhotoMetricDistortion):
    """Apply photometric distortion to image sequentially, every transformation
    is applied with a probability of 0.5. The position of random contrast is in
    second or second to last.

    PhotoMetricDistortion3D further support using predefined randomness
    variable to do the augmentation.

    1. random brightness
    2. random contrast (mode 0)
    3. convert color from BGR to HSV
    4. random saturation
    5. random hue
    6. convert color from HSV to BGR
    7. random contrast (mode 1)
    8. randomly swap channels

    Required Keys:

    - img (np.uint8)

    Modified Keys:

    - img (np.float32)

    Args:
        brightness_delta (int): delta of brightness.
        contrast_range (sequence): range of contrast.
        saturation_range (sequence): range of saturation.
        hue_delta (int): delta of hue.
    """

    def transform(self, results: dict) -> dict:
        """Transform function to perform photometric distortion on images.

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

        Returns:
            dict: Result dict with images distorted.
        """
        assert 'img' in results, '`img` is not found in results'
        img = results['img']
        img = img.astype(np.float32)
        if 'photometric_param' not in results:
            photometric_param = self._random_flags()
            results['photometric_param'] = photometric_param
        else:
            photometric_param = results['photometric_param']

        (mode, brightness_flag, contrast_flag, saturation_flag, hue_flag,
         swap_flag, delta_value, alpha_value, saturation_value, hue_value,
         swap_value) = photometric_param

        # random brightness
        if brightness_flag:
            img += delta_value

        # mode == 0 --> do random contrast first
        # mode == 1 --> do random contrast last
        if mode == 1:
            if contrast_flag:
                img *= alpha_value

        # convert color from BGR to HSV
        img = mmcv.bgr2hsv(img)

        # random saturation
        if saturation_flag:
            img[..., 1] *= saturation_value

        # random hue
        if hue_flag:
            img[..., 0] += hue_value
            img[..., 0][img[..., 0] > 360] -= 360
            img[..., 0][img[..., 0] < 0] += 360

        # convert color from HSV to BGR
        img = mmcv.hsv2bgr(img)

        # random contrast
        if mode == 0:
            if contrast_flag:
                img *= alpha_value

        # randomly swap channels
        if swap_flag:
            img = img[..., swap_value]

        results['img'] = img
        return results


@TRANSFORMS.register_module()
class MultiViewWrapper(BaseTransform):
    """Wrap transformation from single-view into multi-view.

    The wrapper processes the images from multi-view one by one. For each
    image, it constructs a pseudo dict according to the keys specified by the
    'process_fields' parameter. After the transformation is finished, desired
    information can be collected by specifying the keys in the 'collected_keys'
    parameter. Multi-view images share the same transformation parameters
    but do not share the same magnitude when a random transformation is
    conducted.

    Args:
        transforms (list[dict]): A list of dict specifying the transformations
            for the monocular situation.
        override_aug_config (bool): flag of whether to use the same aug config
            for multiview image. Defaults to True.
        process_fields (list): Desired keys that the transformations should
            be conducted on. Defaults to ['img', 'cam2img', 'lidar2cam'].
        collected_keys (list): Collect information in transformation
            like rotate angles, crop roi, and flip state. Defaults to
                ['scale', 'scale_factor', 'crop',
                 'crop_offset', 'ori_shape',
                 'pad_shape', 'img_shape',
                 'pad_fixed_size', 'pad_size_divisor',
                 'flip', 'flip_direction', 'rotate'].
        randomness_keys (list): The keys that related to the randomness
            in transformation. Defaults to
                    ['scale', 'scale_factor', 'crop_size', 'flip',
                     'flip_direction', 'photometric_param']
    """

    def __init__(
        self,
        transforms: dict,
        override_aug_config: bool = True,
        process_fields: list = ['img', 'cam2img', 'lidar2cam'],
        collected_keys: list = [
            'scale', 'scale_factor', 'crop', 'img_crop_offset', 'ori_shape',
            'pad_shape', 'img_shape', 'pad_fixed_size', 'pad_size_divisor',
            'flip', 'flip_direction', 'rotate'
        ],
        randomness_keys: list = [
            'scale', 'scale_factor', 'crop_size', 'img_crop_offset', 'flip',
            'flip_direction', 'photometric_param'
        ]
    ) -> None:
        self.transforms = Compose(transforms)
        self.override_aug_config = override_aug_config
        self.collected_keys = collected_keys
        self.process_fields = process_fields
        self.randomness_keys = randomness_keys

    def transform(self, input_dict: dict) -> dict:
        """Transform function to do the transform for multiview image.

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

        Returns:
            dict: output dict after transformtaion
        """
        # store the augmentation related keys for each image.
        for key in self.collected_keys:
            if key not in input_dict or \
                    not isinstance(input_dict[key], list):
                input_dict[key] = []
        prev_process_dict = {}
        for img_id in range(len(input_dict['img'])):
            process_dict = {}

            # override the process dict (e.g. scale in random scale,
            # crop_size in random crop, flip, flip_direction in
            # random flip)
            if img_id != 0 and self.override_aug_config:
                for key in self.randomness_keys:
                    if key in prev_process_dict:
                        process_dict[key] = prev_process_dict[key]

            for key in self.process_fields:
                if key in input_dict:
                    process_dict[key] = input_dict[key][img_id]
            process_dict = self.transforms(process_dict)
            # store the randomness variable in transformation.
            prev_process_dict = process_dict

            # store the related results to results_dict
            for key in self.process_fields:
                if key in process_dict:
                    input_dict[key][img_id] = process_dict[key]
            # update the keys
            for key in self.collected_keys:
                if key in process_dict:
                    if len(input_dict[key]) == img_id + 1:
                        input_dict[key][img_id] = process_dict[key]
                    else:
                        input_dict[key].append(process_dict[key])

        for key in self.collected_keys:
            if len(input_dict[key]) == 0:
                input_dict.pop(key)
        return input_dict


@TRANSFORMS.register_module()
class PolarMix(BaseTransform):
    """PolarMix data augmentation.

    The polarmix transform steps are as follows:

        1. Another random point cloud is picked by dataset.
        2. Exchange sectors of two point clouds that are cut with certain
           azimuth angles.
        3. Cut point instances from picked point cloud, rotate them by multiple
           azimuth angles, and paste the cut and rotated instances.

    Required Keys:

    - points (:obj:`BasePoints`)
    - pts_semantic_mask (np.int64)
    - dataset (:obj:`BaseDataset`)

    Modified Keys:

    - points (:obj:`BasePoints`)
    - pts_semantic_mask (np.int64)

    Args:
        instance_classes (List[int]): Semantic masks which represent the
            instance.
        swap_ratio (float): Swap ratio of two point cloud. Defaults to 0.5.
        rotate_paste_ratio (float): Rotate paste ratio. Defaults to 1.0.
        pre_transform (Sequence[dict], optional): Sequence of transform object
            or config dict to be composed. Defaults to None.
        prob (float): The transformation probability. Defaults to 1.0.
    """

    def __init__(self,
                 instance_classes: List[int],
                 swap_ratio: float = 0.5,
                 rotate_paste_ratio: float = 1.0,
                 pre_transform: Optional[Sequence[dict]] = None,
                 prob: float = 1.0) -> None:
        assert is_list_of(instance_classes, int), \
            'instance_classes should be a list of int'
        self.instance_classes = instance_classes
        self.swap_ratio = swap_ratio
        self.rotate_paste_ratio = rotate_paste_ratio

        self.prob = prob
        if pre_transform is None:
            self.pre_transform = None
        else:
            self.pre_transform = Compose(pre_transform)

    def polar_mix_transform(self, input_dict: dict, mix_results: dict) -> dict:
        """PolarMix transform function.

        Args:
            input_dict (dict): Result dict from loading pipeline.
            mix_results (dict): Mixed dict picked from dataset.

        Returns:
            dict: output dict after transformation.
        """
        mix_points = mix_results['points']
        mix_pts_semantic_mask = mix_results['pts_semantic_mask']

        points = input_dict['points']
        pts_semantic_mask = input_dict['pts_semantic_mask']

        # 1. swap point cloud
        if np.random.random() < self.swap_ratio:
            start_angle = (np.random.random() - 1) * np.pi  # -pi~0
            end_angle = start_angle + np.pi
            # calculate horizontal angle for each point
            yaw = -torch.atan2(points.coord[:, 1], points.coord[:, 0])
            mix_yaw = -torch.atan2(mix_points.coord[:, 1], mix_points.coord[:,
                                                                            0])

            # select points in sector
            idx = (yaw <= start_angle) | (yaw >= end_angle)
            mix_idx = (mix_yaw > start_angle) & (mix_yaw < end_angle)

            # swap
            points = points.cat([points[idx], mix_points[mix_idx]])
            pts_semantic_mask = np.concatenate(
                (pts_semantic_mask[idx.numpy()],
                 mix_pts_semantic_mask[mix_idx.numpy()]),
                axis=0)

        # 2. rotate-pasting
        if np.random.random() < self.rotate_paste_ratio:
            # extract instance points
            instance_points, instance_pts_semantic_mask = [], []
            for instance_class in self.instance_classes:
                mix_idx = mix_pts_semantic_mask == instance_class
                instance_points.append(mix_points[mix_idx])
                instance_pts_semantic_mask.append(
                    mix_pts_semantic_mask[mix_idx])
            instance_points = mix_points.cat(instance_points)
            instance_pts_semantic_mask = np.concatenate(
                instance_pts_semantic_mask, axis=0)

            # rotate-copy
            copy_points = [instance_points]
            copy_pts_semantic_mask = [instance_pts_semantic_mask]
            angle_list = [
                np.random.random() * np.pi * 2 / 3,
                (np.random.random() + 1) * np.pi * 2 / 3
            ]
            for angle in angle_list:
                new_points = instance_points.clone()
                new_points.rotate(angle)
                copy_points.append(new_points)
                copy_pts_semantic_mask.append(instance_pts_semantic_mask)
            copy_points = instance_points.cat(copy_points)
            copy_pts_semantic_mask = np.concatenate(
                copy_pts_semantic_mask, axis=0)

            points = points.cat([points, copy_points])
            pts_semantic_mask = np.concatenate(
                (pts_semantic_mask, copy_pts_semantic_mask), axis=0)

        input_dict['points'] = points
        input_dict['pts_semantic_mask'] = pts_semantic_mask
        return input_dict

    def transform(self, input_dict: dict) -> dict:
        """PolarMix transform function.

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

        Returns:
            dict: output dict after transformation.
        """
        if np.random.rand() > self.prob:
            return input_dict

        assert 'dataset' in input_dict, \
            '`dataset` is needed to pass through PolarMix, while not found.'
        dataset = input_dict['dataset']

        # get index of other point cloud
        index = np.random.randint(0, len(dataset))

        mix_results = dataset.get_data_info(index)

        if self.pre_transform is not None:
            # pre_transform may also require dataset
            mix_results.update({'dataset': dataset})
            # before polarmix need to go through
            # the necessary pre_transform
            mix_results = self.pre_transform(mix_results)
            mix_results.pop('dataset')

        input_dict = self.polar_mix_transform(input_dict, mix_results)

        return input_dict

    def __repr__(self) -> str:
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(instance_classes={self.instance_classes}, '
        repr_str += f'swap_ratio={self.swap_ratio}, '
        repr_str += f'rotate_paste_ratio={self.rotate_paste_ratio}, '
        repr_str += f'pre_transform={self.pre_transform}, '
        repr_str += f'prob={self.prob})'
        return repr_str


@TRANSFORMS.register_module()
class LaserMix(BaseTransform):
    """LaserMix data augmentation.

    The lasermix transform steps are as follows:

        1. Another random point cloud is picked by dataset.
        2. Divide the point cloud into several regions according to pitch
           angles and combine the areas crossly.

    Required Keys:

    - points (:obj:`BasePoints`)
    - pts_semantic_mask (np.int64)
    - dataset (:obj:`BaseDataset`)

    Modified Keys:

    - points (:obj:`BasePoints`)
    - pts_semantic_mask (np.int64)

    Args:
        num_areas (List[int]): A list of area numbers will be divided into.
        pitch_angles (Sequence[float]): Pitch angles used to divide areas.
        pre_transform (Sequence[dict], optional): Sequence of transform object
            or config dict to be composed. Defaults to None.
        prob (float): The transformation probability. Defaults to 1.0.
    """

    def __init__(self,
                 num_areas: List[int],
                 pitch_angles: Sequence[float],
                 pre_transform: Optional[Sequence[dict]] = None,
                 prob: float = 1.0) -> None:
        assert is_list_of(num_areas, int), \
            'num_areas should be a list of int.'
        self.num_areas = num_areas

        assert len(pitch_angles) == 2, \
            'The length of pitch_angles should be 2, ' \
            f'but got {len(pitch_angles)}.'
        assert pitch_angles[1] > pitch_angles[0], \
            'pitch_angles[1] should be larger than pitch_angles[0].'
        self.pitch_angles = pitch_angles

        self.prob = prob
        if pre_transform is None:
            self.pre_transform = None
        else:
            self.pre_transform = Compose(pre_transform)

    def laser_mix_transform(self, input_dict: dict, mix_results: dict) -> dict:
        """LaserMix transform function.

        Args:
            input_dict (dict): Result dict from loading pipeline.
            mix_results (dict): Mixed dict picked from dataset.

        Returns:
            dict: output dict after transformation.
        """
        mix_points = mix_results['points']
        mix_pts_semantic_mask = mix_results['pts_semantic_mask']

        points = input_dict['points']
        pts_semantic_mask = input_dict['pts_semantic_mask']

        rho = torch.sqrt(points.coord[:, 0]**2 + points.coord[:, 1]**2)
        pitch = torch.atan2(points.coord[:, 2], rho)
        pitch = torch.clamp(pitch, self.pitch_angles[0] + 1e-5,
                            self.pitch_angles[1] - 1e-5)

        mix_rho = torch.sqrt(mix_points.coord[:, 0]**2 +
                             mix_points.coord[:, 1]**2)
        mix_pitch = torch.atan2(mix_points.coord[:, 2], mix_rho)
        mix_pitch = torch.clamp(mix_pitch, self.pitch_angles[0] + 1e-5,
                                self.pitch_angles[1] - 1e-5)

        num_areas = np.random.choice(self.num_areas, size=1)[0]
        angle_list = np.linspace(self.pitch_angles[1], self.pitch_angles[0],
                                 num_areas + 1)
        out_points = []
        out_pts_semantic_mask = []
        for i in range(num_areas):
            # convert angle to radian
            start_angle = angle_list[i + 1] / 180 * np.pi
            end_angle = angle_list[i] / 180 * np.pi
            if i % 2 == 0:  # pick from original point cloud
                idx = (pitch > start_angle) & (pitch <= end_angle)
                out_points.append(points[idx])
                out_pts_semantic_mask.append(pts_semantic_mask[idx.numpy()])
            else:  # pickle from mixed point cloud
                idx = (mix_pitch > start_angle) & (mix_pitch <= end_angle)
                out_points.append(mix_points[idx])
                out_pts_semantic_mask.append(
                    mix_pts_semantic_mask[idx.numpy()])
        out_points = points.cat(out_points)
        out_pts_semantic_mask = np.concatenate(out_pts_semantic_mask, axis=0)
        input_dict['points'] = out_points
        input_dict['pts_semantic_mask'] = out_pts_semantic_mask
        return input_dict

    def transform(self, input_dict: dict) -> dict:
        """LaserMix transform function.

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

        Returns:
            dict: output dict after transformation.
        """
        if np.random.rand() > self.prob:
            return input_dict

        assert 'dataset' in input_dict, \
            '`dataset` is needed to pass through LaserMix, while not found.'
        dataset = input_dict['dataset']

        # get index of other point cloud
        index = np.random.randint(0, len(dataset))

        mix_results = dataset.get_data_info(index)

        if self.pre_transform is not None:
            # pre_transform may also require dataset
            mix_results.update({'dataset': dataset})
            # before lasermix need to go through
            # the necessary pre_transform
            mix_results = self.pre_transform(mix_results)
            mix_results.pop('dataset')

        input_dict = self.laser_mix_transform(input_dict, mix_results)

        return input_dict

    def __repr__(self) -> str:
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f'(num_areas={self.num_areas}, '
        repr_str += f'pitch_angles={self.pitch_angles}, '
        repr_str += f'pre_transform={self.pre_transform}, '
        repr_str += f'prob={self.prob})'
        return repr_str