# Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from os import path as osp
from typing import Dict, List, Optional, Sequence, Tuple, Union

import mmcv
import numpy as np
import pyquaternion
import torch
from mmengine.evaluator import BaseMetric
from mmengine.logging import MMLogger
from nuscenes.eval.detection.config import config_factory
from nuscenes.eval.detection.data_classes import DetectionConfig
from nuscenes.utils.data_classes import Box as NuScenesBox

from mmdet3d.models.layers import box3d_multiclass_nms
from mmdet3d.registry import METRICS
from mmdet3d.structures import (CameraInstance3DBoxes, LiDARInstance3DBoxes,
                                bbox3d2result, xywhr2xyxyr)


@METRICS.register_module()
class NuScenesMetric(BaseMetric):
    """Nuscenes evaluation metric.

    Args:
        data_root (str): Path of dataset root.
        ann_file (str): Path of annotation file.
        metric (str | list[str]): Metrics to be evaluated.
            Default to 'bbox'.
        modality (dict): Modality to specify the sensor data used
            as input. Defaults to dict(use_camera=False, use_lidar=True).
        prefix (str, optional): The prefix that will be added in the metric
            names to disambiguate homonymous metrics of different evaluators.
            If prefix is not provided in the argument, self.default_prefix
            will be used instead. Defaults to None.
        jsonfile_prefix (str, optional): The prefix of json files including
            the file path and the prefix of filename, e.g., "a/b/prefix".
            If not specified, a temp file will be created. Default: None.
        eval_version (str): Configuration version of evaluation.
            Defaults to  'detection_cvpr_2019'.
        collect_device (str): Device name used for collecting results
            from different ranks during distributed training. Must be 'cpu' or
            'gpu'. Defaults to 'cpu'.
    """
    NameMapping = {
        'movable_object.barrier': 'barrier',
        'vehicle.bicycle': 'bicycle',
        'vehicle.bus.bendy': 'bus',
        'vehicle.bus.rigid': 'bus',
        'vehicle.car': 'car',
        'vehicle.construction': 'construction_vehicle',
        'vehicle.motorcycle': 'motorcycle',
        'human.pedestrian.adult': 'pedestrian',
        'human.pedestrian.child': 'pedestrian',
        'human.pedestrian.construction_worker': 'pedestrian',
        'human.pedestrian.police_officer': 'pedestrian',
        'movable_object.trafficcone': 'traffic_cone',
        'vehicle.trailer': 'trailer',
        'vehicle.truck': 'truck'
    }
    DefaultAttribute = {
        'car': 'vehicle.parked',
        'pedestrian': 'pedestrian.moving',
        'trailer': 'vehicle.parked',
        'truck': 'vehicle.parked',
        'bus': 'vehicle.moving',
        'motorcycle': 'cycle.without_rider',
        'construction_vehicle': 'vehicle.parked',
        'bicycle': 'cycle.without_rider',
        'barrier': '',
        'traffic_cone': '',
    }
    # https://github.com/nutonomy/nuscenes-devkit/blob/57889ff20678577025326cfc24e57424a829be0a/python-sdk/nuscenes/eval/detection/evaluate.py#L222 # noqa
    ErrNameMapping = {
        'trans_err': 'mATE',
        'scale_err': 'mASE',
        'orient_err': 'mAOE',
        'vel_err': 'mAVE',
        'attr_err': 'mAAE'
    }

    def __init__(self,
                 data_root: str,
                 ann_file: str,
                 metric: Union[str, List[str]] = 'bbox',
                 modality: Dict = dict(use_camera=False, use_lidar=True),
                 prefix: Optional[str] = None,
                 jsonfile_prefix: Optional[str] = None,
                 eval_version: str = 'detection_cvpr_2019',
                 collect_device: str = 'cpu') -> None:
        self.default_prefix = 'NuScenes metric'
        super(NuScenesMetric, self).__init__(
            collect_device=collect_device, prefix=prefix)
        if modality is None:
            modality = dict(
                use_camera=False,
                use_lidar=True,
            )
        self.ann_file = ann_file
        self.data_root = data_root
        self.modality = modality
        self.jsonfile_prefix = jsonfile_prefix

        self.metrics = metric if isinstance(metric, list) else [metric]

        self.eval_version = eval_version
        self.eval_detection_configs = config_factory(self.eval_version)

    def load_annotations(self, ann_file: str) -> list:
        """Load annotations from ann_file.

        Args:
            ann_file (str): Path of the annotation file.

        Returns:
            list[dict]: List of annotations.
        """
        # loading data from a pkl file
        return mmcv.load(ann_file, file_format='pkl')

    def process(self, data_batch: Sequence[dict],
                predictions: Sequence[dict]) -> None:
        """Process one batch of data samples and predictions.

        The processed results should be stored in ``self.results``,
        which will be used to compute the metrics when all batches
        have been processed.

        Args:
            data_batch (Sequence[dict]): A batch of data
                from the dataloader.
            predictions (Sequence[dict]): A batch of outputs from
                the model.
        """
        assert len(data_batch) == len(predictions)
        for data, pred in zip(data_batch, predictions):
            result = dict()
            for pred_result in pred:
                if pred[pred_result] is not None:
                    for attr_name in pred[pred_result]:
                        pred[pred_result][attr_name] = pred[pred_result][
                            attr_name].to(self.collect_device)
                    result[pred_result] = pred[pred_result]
                sample_idx = data['data_sample']['sample_idx']
                result['sample_idx'] = sample_idx
        self.results.append(result)

    def compute_metrics(self, results: list) -> Dict[str, float]:
        """Compute the metrics from processed results.

        Args:
            results (list): The processed results of each batch.

        Returns:
            Dict[str, float]: The computed metrics. The keys are the names of
            the metrics, and the values are corresponding results.
        """
        logger: MMLogger = MMLogger.get_current_instance()

        classes = self.dataset_meta['CLASSES']
        self.version = self.dataset_meta['version']
        # load annotations
        self.data_infos = self.load_annotations(self.ann_file)['data_list']
        result_dict, tmp_dir = self.format_results(results, classes,
                                                   self.jsonfile_prefix)

        metric_dict = {}
        for metric in self.metrics:
            ap_dict = self.nus_evaluate(
                result_dict, classes=classes, metric=metric, logger=logger)
            for result in ap_dict:
                metric_dict[result] = ap_dict[result]

        if tmp_dir is not None:
            tmp_dir.cleanup()
        return metric_dict

    def nus_evaluate(self,
                     result_dict: dict,
                     metric: str = 'bbox',
                     classes: List[str] = None,
                     logger: logging.Logger = None) -> dict:
        """Evaluation in Nuscenes protocol.

        Args:
            result_dict (dict): Formatted results of the dataset.
            metric (str): Metrics to be evaluated.
                Default: None.
            classes (list[String], optional): A list of class name. Defaults
                to None.
            logger (MMLogger, optional): Logger used for printing
                related information during evaluation. Default: None.

        Returns:
            dict[str, float]: Results of each evaluation metric.
        """
        metric_dict = dict()
        for name in result_dict:
            print(f'Evaluating bboxes of {name}')
            ret_dict = self._evaluate_single(
                result_dict[name], classes=classes, result_name=name)
        metric_dict.update(ret_dict)
        return metric_dict

    def _evaluate_single(self,
                         result_path: str,
                         classes: List[None] = None,
                         result_name: str = 'pred_instances_3d') -> dict:
        """Evaluation for a single model in nuScenes protocol.

        Args:
            result_path (str): Path of the result file.
                Default: 'bbox'.
            classes (list[String], optional): A list of class name. Defaults
                to None.
            result_name (str): Result name in the metric prefix.
                Default: 'pred_instances_3d'.

        Returns:
            dict: Dictionary of evaluation details.
        """
        from nuscenes import NuScenes
        from nuscenes.eval.detection.evaluate import NuScenesEval

        output_dir = osp.join(*osp.split(result_path)[:-1])
        nusc = NuScenes(
            version=self.version, dataroot=self.data_root, verbose=False)
        eval_set_map = {
            'v1.0-mini': 'mini_val',
            'v1.0-trainval': 'val',
        }
        nusc_eval = NuScenesEval(
            nusc,
            config=self.eval_detection_configs,
            result_path=result_path,
            eval_set=eval_set_map[self.version],
            output_dir=output_dir,
            verbose=False)
        nusc_eval.main(render_curves=False)

        # record metrics
        metrics = mmcv.load(osp.join(output_dir, 'metrics_summary.json'))
        detail = dict()
        metric_prefix = f'{result_name}_NuScenes'
        for name in classes:
            for k, v in metrics['label_aps'][name].items():
                val = float('{:.4f}'.format(v))
                detail[f'{metric_prefix}/{name}_AP_dist_{k}'] = val
            for k, v in metrics['label_tp_errors'][name].items():
                val = float('{:.4f}'.format(v))
                detail[f'{metric_prefix}/{name}_{k}'] = val
            for k, v in metrics['tp_errors'].items():
                val = float('{:.4f}'.format(v))
                detail[f'{metric_prefix}/{self.ErrNameMapping[k]}'] = val

        detail[f'{metric_prefix}/NDS'] = metrics['nd_score']
        detail[f'{metric_prefix}/mAP'] = metrics['mean_ap']
        return detail

    def format_results(self,
                       results: List[dict],
                       classes: List[str] = None,
                       jsonfile_prefix: str = None) -> Tuple:
        """Format the mmdet3d results to standard NuScenes json file.

        Args:
            results (list[dict]): Testing results of the dataset.
            classes (list[String], optional): A list of class name. Defaults
                to None.
            jsonfile_prefix (str, optional): The prefix of json files. It
                includes the file path and the prefix of filename, e.g.,
                "a/b/prefix". If not specified, a temp file will be created.
                Default: None.

        Returns:
            tuple: Returns (result_dict, tmp_dir), where `result_dict` is a
                dict containing the json filepaths, `tmp_dir` is the temporal
                directory created for saving json files when
                `jsonfile_prefix` is not specified.
        """
        assert isinstance(results, list), 'results must be a list'

        if jsonfile_prefix is None:
            tmp_dir = tempfile.TemporaryDirectory()
            jsonfile_prefix = osp.join(tmp_dir.name, 'results')
        else:
            tmp_dir = None
        result_dict = dict()
        sample_id_list = [result['sample_idx'] for result in results]

        for name in results[0]:
            if 'pred' in name and '3d' in name and name[0] != '_':
                print(f'\nFormating bboxes of {name}')
                results_ = [out[name] for out in results]
                tmp_file_ = osp.join(jsonfile_prefix, name)
                box_type_3d = type(results_[0]['bbox_3d'])
                if box_type_3d == LiDARInstance3DBoxes:
                    result_dict[name] = self._format_lidar_bbox(
                        results_, sample_id_list, classes, tmp_file_)
                elif box_type_3d == CameraInstance3DBoxes:
                    result_dict[name] = self._format_camera_bbox(
                        results_, sample_id_list, classes, tmp_file_)

        return result_dict, tmp_dir

    def _format_camera_bbox(self,
                            results: List[dict],
                            sample_id_list: List[int],
                            classes: List[str] = None,
                            jsonfile_prefix: str = None) -> str:
        """Convert the results to the standard format.

        Args:
            results (list[dict]): Testing results of the dataset.
            jsonfile_prefix (str): The prefix of the output jsonfile.
                You can specify the output directory/filename by
                modifying the jsonfile_prefix. Default: None.

        Returns:
            str: Path of the output json file.
        """
        nusc_annos = {}

        print('Start to convert detection format...')

        # Camera types in Nuscenes datasets
        camera_types = [
            'CAM_FRONT',
            'CAM_FRONT_RIGHT',
            'CAM_FRONT_LEFT',
            'CAM_BACK',
            'CAM_BACK_LEFT',
            'CAM_BACK_RIGHT',
        ]

        CAM_NUM = 6

        for i, det in enumerate(mmcv.track_iter_progress(results)):

            sample_id = sample_id_list[i]

            camera_type_id = sample_id % CAM_NUM

            if camera_type_id == 0:
                boxes_per_frame = []
                attrs_per_frame = []

            # need to merge results from images of the same sample
            annos = []
            boxes, attrs = output_to_nusc_box(det)
            sample_token = self.data_infos[sample_id]['token']
            camera_type = camera_types[camera_type_id]
            boxes, attrs = cam_nusc_box_to_global(
                self.data_infos[sample_id - camera_type_id], boxes, attrs,
                camera_type, classes, self.eval_detection_configs)
            boxes_per_frame.extend(boxes)
            attrs_per_frame.extend(attrs)
            # Remove redundant predictions caused by overlap of images
            if (sample_id + 1) % CAM_NUM != 0:
                continue
            boxes = global_nusc_box_to_cam(
                self.data_infos[sample_id + 1 - CAM_NUM], boxes_per_frame,
                classes, self.eval_detection_configs)
            cam_boxes3d, scores, labels = nusc_box_to_cam_box3d(boxes)
            # box nms 3d over 6 images in a frame
            # TODO: move this global setting into config
            nms_cfg = dict(
                use_rotate_nms=True,
                nms_across_levels=False,
                nms_pre=4096,
                nms_thr=0.05,
                score_thr=0.01,
                min_bbox_size=0,
                max_per_frame=500)
            from mmcv import Config
            nms_cfg = Config(nms_cfg)
            cam_boxes3d_for_nms = xywhr2xyxyr(cam_boxes3d.bev)
            boxes3d = cam_boxes3d.tensor
            # generate attr scores from attr labels
            attrs = labels.new_tensor([attr for attr in attrs_per_frame])
            boxes3d, scores, labels, attrs = box3d_multiclass_nms(
                boxes3d,
                cam_boxes3d_for_nms,
                scores,
                nms_cfg.score_thr,
                nms_cfg.max_per_frame,
                nms_cfg,
                mlvl_attr_scores=attrs)
            cam_boxes3d = CameraInstance3DBoxes(boxes3d, box_dim=9)
            det = bbox3d2result(cam_boxes3d, scores, labels, attrs)
            boxes, attrs = output_to_nusc_box(det)
            boxes, attrs = cam_nusc_box_to_global(
                self.data_infos[sample_id + 1 - CAM_NUM], boxes, attrs,
                classes, self.eval_detection_configs)

            for i, box in enumerate(boxes):
                name = classes[box.label]
                attr = self.get_attr_name(attrs[i], name)
                nusc_anno = dict(
                    sample_token=sample_token,
                    translation=box.center.tolist(),
                    size=box.wlh.tolist(),
                    rotation=box.orientation.elements.tolist(),
                    velocity=box.velocity[:2].tolist(),
                    detection_name=name,
                    detection_score=box.score,
                    attribute_name=attr)
                annos.append(nusc_anno)
            # other views results of the same frame should be concatenated
            if sample_token in nusc_annos:
                nusc_annos[sample_token].extend(annos)
            else:
                nusc_annos[sample_token] = annos

        nusc_submissions = {
            'meta': self.modality,
            'results': nusc_annos,
        }

        mmcv.mkdir_or_exist(jsonfile_prefix)
        res_path = osp.join(jsonfile_prefix, 'results_nusc.json')
        print('Results writes to', res_path)
        mmcv.dump(nusc_submissions, res_path)
        return res_path

    def _format_lidar_bbox(self,
                           results: List[dict],
                           sample_id_list: List[int],
                           classes: List[str] = None,
                           jsonfile_prefix: str = None) -> str:
        """Convert the results to the standard format.

        Args:
            results (list[dict]): Testing results of the dataset.
            sample_id_list (list[int]): List of result sample id.
            classes (list[String], optional): A list of class name. Defaults
                to None.
            jsonfile_prefix (str, optional): The prefix of the output jsonfile.
                You can specify the output directory/filename by
                modifying the jsonfile_prefix. Default: None.

        Returns:
            str: Path of the output json file.
        """
        nusc_annos = {}

        print('Start to convert detection format...')
        for i, det in enumerate(mmcv.track_iter_progress(results)):
            annos = []
            boxes = output_to_nusc_box(det)
            sample_id = sample_id_list[i]
            sample_token = self.data_infos[sample_id]['token']
            boxes = lidar_nusc_box_to_global(self.data_infos[sample_id], boxes,
                                             classes,
                                             self.eval_detection_configs)
            for i, box in enumerate(boxes):
                name = classes[box.label]
                if np.sqrt(box.velocity[0]**2 + box.velocity[1]**2) > 0.2:
                    if name in [
                            'car',
                            'construction_vehicle',
                            'bus',
                            'truck',
                            'trailer',
                    ]:
                        attr = 'vehicle.moving'
                    elif name in ['bicycle', 'motorcycle']:
                        attr = 'cycle.with_rider'
                    else:
                        attr = self.DefaultAttribute[name]
                else:
                    if name in ['pedestrian']:
                        attr = 'pedestrian.standing'
                    elif name in ['bus']:
                        attr = 'vehicle.stopped'
                    else:
                        attr = self.DefaultAttribute[name]

                nusc_anno = dict(
                    sample_token=sample_token,
                    translation=box.center.tolist(),
                    size=box.wlh.tolist(),
                    rotation=box.orientation.elements.tolist(),
                    velocity=box.velocity[:2].tolist(),
                    detection_name=name,
                    detection_score=box.score,
                    attribute_name=attr)
                annos.append(nusc_anno)
            nusc_annos[sample_token] = annos
        nusc_submissions = {
            'meta': self.modality,
            'results': nusc_annos,
        }
        mmcv.mkdir_or_exist(jsonfile_prefix)
        res_path = osp.join(jsonfile_prefix, 'results_nusc.json')
        print('Results writes to', res_path)
        mmcv.dump(nusc_submissions, res_path)
        return res_path


def output_to_nusc_box(detection: dict) -> List[NuScenesBox]:
    """Convert the output to the box class in the nuScenes.

    Args:
        detection (dict): Detection results.

            - bbox_3d (:obj:`BaseInstance3DBoxes`): Detection bbox.
            - scores_3d (torch.Tensor): Detection scores.
            - labels_3d (torch.Tensor): Predicted box labels.

    Returns:
        list[:obj:`NuScenesBox`]: List of standard NuScenesBoxes.
    """
    bbox3d = detection['bbox_3d']
    scores = detection['scores_3d'].numpy()
    labels = detection['labels_3d'].numpy()
    attrs = None
    if 'attr_labels' in detection:
        attrs = detection['attr_labels'].numpy()

    box_gravity_center = bbox3d.gravity_center.numpy()
    box_dims = bbox3d.dims.numpy()
    box_yaw = bbox3d.yaw.numpy()

    box_list = []

    if type(bbox3d) == LiDARInstance3DBoxes:
        # our LiDAR coordinate system -> nuScenes box coordinate system
        nus_box_dims = box_dims[:, [1, 0, 2]]
        for i in range(len(bbox3d)):
            quat = pyquaternion.Quaternion(axis=[0, 0, 1], radians=box_yaw[i])
            velocity = (*bbox3d.tensor[i, 7:9], 0.0)
            # velo_val = np.linalg.norm(box3d[i, 7:9])
            # velo_ori = box3d[i, 6]
            # velocity = (
            # velo_val * np.cos(velo_ori), velo_val * np.sin(velo_ori), 0.0)
            box = NuScenesBox(
                box_gravity_center[i],
                nus_box_dims[i],
                quat,
                label=labels[i],
                score=scores[i],
                velocity=velocity)
            box_list.append(box)
    elif type(bbox3d) == CameraInstance3DBoxes:
        # our Camera coordinate system -> nuScenes box coordinate system
        # convert the dim/rot to nuscbox convention
        nus_box_dims = box_dims[:, [2, 0, 1]]
        nus_box_yaw = -box_yaw
        for i in range(len(bbox3d)):
            q1 = pyquaternion.Quaternion(
                axis=[0, 0, 1], radians=nus_box_yaw[i])
            q2 = pyquaternion.Quaternion(axis=[1, 0, 0], radians=np.pi / 2)
            quat = q2 * q1
            velocity = (bbox3d.tensor[i, 7], 0.0, bbox3d.tensor[i, 8])
            box = NuScenesBox(
                box_gravity_center[i],
                nus_box_dims[i],
                quat,
                label=labels[i],
                score=scores[i],
                velocity=velocity)
            box_list.append(box)
    else:
        raise NotImplementedError(
            f'Do not support convert {type(bbox3d)} bboxes'
            'to standard NuScenesBoxes.')

    return box_list, attrs


def lidar_nusc_box_to_global(
        info: dict, boxes: List[NuScenesBox], classes: List[str],
        eval_configs: DetectionConfig) -> List[NuScenesBox]:
    """Convert the box from ego to global coordinate.

    Args:
        info (dict): Info for a specific sample data, including the
            calibration information.
        boxes (list[:obj:`NuScenesBox`]): List of predicted NuScenesBoxes.
        classes (list[str]): Mapped classes in the evaluation.
        eval_configs (object): Evaluation configuration object.

    Returns:
        list: List of standard NuScenesBoxes in the global
            coordinate.
    """
    box_list = []
    for box in boxes:
        # Move box to ego vehicle coord system
        lidar2ego = np.array(info['lidar_points']['lidar2ego'])
        box.rotate(
            pyquaternion.Quaternion(matrix=lidar2ego, rtol=1e-05, atol=1e-07))
        box.translate(lidar2ego[:3, 3])
        # filter det in ego.
        cls_range_map = eval_configs.class_range
        radius = np.linalg.norm(box.center[:2], 2)
        det_range = cls_range_map[classes[box.label]]
        if radius > det_range:
            continue
        # Move box to global coord system
        ego2global = np.array(info['ego2global'])
        box.rotate(
            pyquaternion.Quaternion(matrix=ego2global, rtol=1e-05, atol=1e-07))
        box.translate(ego2global[:3, 3])
        box_list.append(box)
    return box_list


def cam_nusc_box_to_global(info: dict, boxes: List[NuScenesBox],
                           attrs: List[str], camera_type: str,
                           classes: List[str],
                           eval_configs: DetectionConfig) -> List[NuScenesBox]:
    """Convert the box from camera to global coordinate.

    Args:
        info (dict): Info for a specific sample data, including the
            calibration information.
        boxes (list[:obj:`NuScenesBox`]): List of predicted NuScenesBoxes.
        attrs (list[str]): List of attributes.
        camera_type (str): Type of camera.
        classes (list[str]): Mapped classes in the evaluation.
        eval_configs (object): Evaluation configuration object.

    Returns:
        list: List of standard NuScenesBoxes in the global
            coordinate.
    """
    box_list = []
    attr_list = []
    for (box, attr) in zip(boxes, attrs):
        # Move box to ego vehicle coord system
        cam2ego = np.array(info['images'][camera_type]['cam2ego'])
        box.rotate(
            pyquaternion.Quaternion(matrix=cam2ego, rtol=1e-05, atol=1e-07))
        box.translate(cam2ego[:3, 3])
        # filter det in ego.
        cls_range_map = eval_configs.class_range
        radius = np.linalg.norm(box.center[:2], 2)
        det_range = cls_range_map[classes[box.label]]
        if radius > det_range:
            continue
        # Move box to global coord system
        ego2global = np.array(info['ego2global'])
        box.rotate(
            pyquaternion.Quaternion(matrix=ego2global, rtol=1e-05, atol=1e-07))
        box.translate(ego2global[:3, 3])
        box_list.append(box)
        attr_list.append(attr)
    return box_list, attr_list


def global_nusc_box_to_cam(info: dict, boxes: List[NuScenesBox],
                           classes: List[str],
                           eval_configs: DetectionConfig) -> List[NuScenesBox]:
    """Convert the box from global to camera coordinate.

    Args:
        info (dict): Info for a specific sample data, including the
            calibration information.
        boxes (list[:obj:`NuScenesBox`]): List of predicted NuScenesBoxes.
        classes (list[str]): Mapped classes in the evaluation.
        eval_configs (object): Evaluation configuration object.

    Returns:
        list: List of standard NuScenesBoxes in the global
            coordinate.
    """
    box_list = []
    for box in boxes:
        # Move box to ego vehicle coord system
        ego2global = np.array(info['ego2global'])
        box.translate(-ego2global[:3, 3])
        box.rotate(
            pyquaternion.Quaternion(matrix=ego2global, rtol=1e-05,
                                    atol=1e-07).inverse)
        # filter det in ego.
        cls_range_map = eval_configs.class_range
        radius = np.linalg.norm(box.center[:2], 2)
        det_range = cls_range_map[classes[box.label]]
        if radius > det_range:
            continue
        # Move box to camera coord system
        cam2ego = np.array(info['images']['CAM_FRONT']['cam2ego'])
        box.translate(-cam2ego[:3, :3])
        box.rotate(
            pyquaternion.Quaternion(matrix=cam2ego, rtol=1e-05,
                                    atol=1e-07).inverse)
        box_list.append(box)
    return box_list


def nusc_box_to_cam_box3d(boxes: List[NuScenesBox]):
    """Convert boxes from :obj:`NuScenesBox` to :obj:`CameraInstance3DBoxes`.

    Args:
        boxes (list[:obj:`NuScenesBox`]): List of predicted NuScenesBoxes.

    Returns:
        tuple (:obj:`CameraInstance3DBoxes` | torch.Tensor | torch.Tensor):
            Converted 3D bounding boxes, scores and labels.
    """
    locs = torch.Tensor([b.center for b in boxes]).view(-1, 3)
    dims = torch.Tensor([b.wlh for b in boxes]).view(-1, 3)
    rots = torch.Tensor([b.orientation.yaw_pitch_roll[0]
                         for b in boxes]).view(-1, 1)
    velocity = torch.Tensor([b.velocity[0::2] for b in boxes]).view(-1, 2)

    # convert nusbox to cambox convention
    dims[:, [0, 1, 2]] = dims[:, [1, 2, 0]]
    rots = -rots

    boxes_3d = torch.cat([locs, dims, rots, velocity], dim=1).cuda()
    cam_boxes3d = CameraInstance3DBoxes(
        boxes_3d, box_dim=9, origin=(0.5, 0.5, 0.5))
    scores = torch.Tensor([b.score for b in boxes]).cuda()
    labels = torch.LongTensor([b.label for b in boxes]).cuda()
    nms_scores = scores.new_zeros(scores.shape[0], 10 + 1)
    indices = labels.new_tensor(list(range(scores.shape[0])))
    nms_scores[indices, labels] = scores
    return cam_boxes3d, nms_scores, labels
