Source code for mmdet3d.datasets.nuscenes_dataset
# Copyright (c) OpenMMLab. All rights reserved.
import tempfile
from os import path as osp
import mmcv
import numpy as np
import pyquaternion
from nuscenes.utils.data_classes import Box as NuScenesBox
from ..core import show_result
from ..core.bbox import Box3DMode, Coord3DMode, LiDARInstance3DBoxes
from .builder import DATASETS
from .custom_3d import Custom3DDataset
from .pipelines import Compose
[docs]@DATASETS.register_module()
class NuScenesDataset(Custom3DDataset):
r"""NuScenes Dataset.
This class serves as the API for experiments on the NuScenes Dataset.
Please refer to `NuScenes Dataset <https://www.nuscenes.org/download>`_
for data downloading.
Args:
ann_file (str): Path of annotation file.
pipeline (list[dict], optional): Pipeline used for data processing.
Defaults to None.
data_root (str): Path of dataset root.
classes (tuple[str], optional): Classes used in the dataset.
Defaults to None.
load_interval (int, optional): Interval of loading the dataset. It is
used to uniformly sample the dataset. Defaults to 1.
with_velocity (bool, optional): Whether include velocity prediction
into the experiments. Defaults to True.
modality (dict, optional): Modality to specify the sensor data used
as input. Defaults to None.
box_type_3d (str, optional): Type of 3D box of this dataset.
Based on the `box_type_3d`, the dataset will encapsulate the box
to its original format then converted them to `box_type_3d`.
Defaults to 'LiDAR' in this dataset. Available options includes.
- 'LiDAR': Box in LiDAR coordinates.
- 'Depth': Box in depth coordinates, usually for indoor dataset.
- 'Camera': Box in camera coordinates.
filter_empty_gt (bool, optional): Whether to filter empty GT.
Defaults to True.
test_mode (bool, optional): Whether the dataset is in test mode.
Defaults to False.
eval_version (bool, optional): Configuration version of evaluation.
Defaults to 'detection_cvpr_2019'.
use_valid_flag (bool, optional): Whether to use `use_valid_flag` key
in the info file as mask to filter gt_boxes and gt_names.
Defaults to False.
"""
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': '',
}
AttrMapping = {
'cycle.with_rider': 0,
'cycle.without_rider': 1,
'pedestrian.moving': 2,
'pedestrian.standing': 3,
'pedestrian.sitting_lying_down': 4,
'vehicle.moving': 5,
'vehicle.parked': 6,
'vehicle.stopped': 7,
}
AttrMapping_rev = [
'cycle.with_rider',
'cycle.without_rider',
'pedestrian.moving',
'pedestrian.standing',
'pedestrian.sitting_lying_down',
'vehicle.moving',
'vehicle.parked',
'vehicle.stopped',
]
# 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'
}
CLASSES = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
'barrier')
def __init__(self,
ann_file,
pipeline=None,
data_root=None,
classes=None,
load_interval=1,
with_velocity=True,
modality=None,
box_type_3d='LiDAR',
filter_empty_gt=True,
test_mode=False,
eval_version='detection_cvpr_2019',
use_valid_flag=False):
self.load_interval = load_interval
self.use_valid_flag = use_valid_flag
super().__init__(
data_root=data_root,
ann_file=ann_file,
pipeline=pipeline,
classes=classes,
modality=modality,
box_type_3d=box_type_3d,
filter_empty_gt=filter_empty_gt,
test_mode=test_mode)
self.with_velocity = with_velocity
self.eval_version = eval_version
from nuscenes.eval.detection.config import config_factory
self.eval_detection_configs = config_factory(self.eval_version)
if self.modality is None:
self.modality = dict(
use_camera=False,
use_lidar=True,
use_radar=False,
use_map=False,
use_external=False,
)
[docs] def get_cat_ids(self, idx):
"""Get category distribution of single scene.
Args:
idx (int): Index of the data_info.
Returns:
dict[list]: for each category, if the current scene
contains such boxes, store a list containing idx,
otherwise, store empty list.
"""
info = self.data_infos[idx]
if self.use_valid_flag:
mask = info['valid_flag']
gt_names = set(info['gt_names'][mask])
else:
gt_names = set(info['gt_names'])
cat_ids = []
for name in gt_names:
if name in self.CLASSES:
cat_ids.append(self.cat2id[name])
return cat_ids
[docs] def load_annotations(self, ann_file):
"""Load annotations from ann_file.
Args:
ann_file (str): Path of the annotation file.
Returns:
list[dict]: List of annotations sorted by timestamps.
"""
data = mmcv.load(ann_file, file_format='pkl')
data_infos = list(sorted(data['infos'], key=lambda e: e['timestamp']))
data_infos = data_infos[::self.load_interval]
self.metadata = data['metadata']
self.version = self.metadata['version']
return data_infos
[docs] def get_data_info(self, index):
"""Get data info according to the given index.
Args:
index (int): Index of the sample data to get.
Returns:
dict: Data information that will be passed to the data
preprocessing pipelines. It includes the following keys:
- sample_idx (str): Sample index.
- pts_filename (str): Filename of point clouds.
- sweeps (list[dict]): Infos of sweeps.
- timestamp (float): Sample timestamp.
- img_filename (str, optional): Image filename.
- lidar2img (list[np.ndarray], optional): Transformations
from lidar to different cameras.
- ann_info (dict): Annotation info.
"""
info = self.data_infos[index]
# standard protocol modified from SECOND.Pytorch
input_dict = dict(
sample_idx=info['token'],
pts_filename=info['lidar_path'],
sweeps=info['sweeps'],
timestamp=info['timestamp'] / 1e6,
)
if self.modality['use_camera']:
image_paths = []
lidar2img_rts = []
for cam_type, cam_info in info['cams'].items():
image_paths.append(cam_info['data_path'])
# obtain lidar to image transformation matrix
lidar2cam_r = np.linalg.inv(cam_info['sensor2lidar_rotation'])
lidar2cam_t = cam_info[
'sensor2lidar_translation'] @ lidar2cam_r.T
lidar2cam_rt = np.eye(4)
lidar2cam_rt[:3, :3] = lidar2cam_r.T
lidar2cam_rt[3, :3] = -lidar2cam_t
intrinsic = cam_info['cam_intrinsic']
viewpad = np.eye(4)
viewpad[:intrinsic.shape[0], :intrinsic.shape[1]] = intrinsic
lidar2img_rt = (viewpad @ lidar2cam_rt.T)
lidar2img_rts.append(lidar2img_rt)
input_dict.update(
dict(
img_filename=image_paths,
lidar2img=lidar2img_rts,
))
if not self.test_mode:
annos = self.get_ann_info(index)
input_dict['ann_info'] = annos
return input_dict
[docs] def get_ann_info(self, index):
"""Get annotation info according to the given index.
Args:
index (int): Index of the annotation data to get.
Returns:
dict: Annotation information consists of the following keys:
- gt_bboxes_3d (:obj:`LiDARInstance3DBoxes`):
3D ground truth bboxes
- gt_labels_3d (np.ndarray): Labels of ground truths.
- gt_names (list[str]): Class names of ground truths.
"""
info = self.data_infos[index]
# filter out bbox containing no points
if self.use_valid_flag:
mask = info['valid_flag']
else:
mask = info['num_lidar_pts'] > 0
gt_bboxes_3d = info['gt_boxes'][mask]
gt_names_3d = info['gt_names'][mask]
gt_labels_3d = []
for cat in gt_names_3d:
if cat in self.CLASSES:
gt_labels_3d.append(self.CLASSES.index(cat))
else:
gt_labels_3d.append(-1)
gt_labels_3d = np.array(gt_labels_3d)
if self.with_velocity:
gt_velocity = info['gt_velocity'][mask]
nan_mask = np.isnan(gt_velocity[:, 0])
gt_velocity[nan_mask] = [0.0, 0.0]
gt_bboxes_3d = np.concatenate([gt_bboxes_3d, gt_velocity], axis=-1)
# the nuscenes box center is [0.5, 0.5, 0.5], we change it to be
# the same as KITTI (0.5, 0.5, 0)
gt_bboxes_3d = LiDARInstance3DBoxes(
gt_bboxes_3d,
box_dim=gt_bboxes_3d.shape[-1],
origin=(0.5, 0.5, 0.5)).convert_to(self.box_mode_3d)
anns_results = dict(
gt_bboxes_3d=gt_bboxes_3d,
gt_labels_3d=gt_labels_3d,
gt_names=gt_names_3d)
return anns_results
def _format_bbox(self, results, jsonfile_prefix=None):
"""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 = {}
mapped_class_names = self.CLASSES
print('Start to convert detection format...')
for sample_id, det in enumerate(mmcv.track_iter_progress(results)):
annos = []
boxes = output_to_nusc_box(det, self.with_velocity)
sample_token = self.data_infos[sample_id]['token']
boxes = lidar_nusc_box_to_global(self.data_infos[sample_id], boxes,
mapped_class_names,
self.eval_detection_configs,
self.eval_version)
for i, box in enumerate(boxes):
name = mapped_class_names[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 = NuScenesDataset.DefaultAttribute[name]
else:
if name in ['pedestrian']:
attr = 'pedestrian.standing'
elif name in ['bus']:
attr = 'vehicle.stopped'
else:
attr = NuScenesDataset.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 _evaluate_single(self,
result_path,
logger=None,
metric='bbox',
result_name='pts_bbox'):
"""Evaluation for a single model in nuScenes protocol.
Args:
result_path (str): Path of the result file.
logger (logging.Logger | str, optional): Logger used for printing
related information during evaluation. Default: None.
metric (str, optional): Metric name used for evaluation.
Default: 'bbox'.
result_name (str, optional): Result name in the metric prefix.
Default: 'pts_bbox'.
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 self.CLASSES:
for k, v in metrics['label_aps'][name].items():
val = float('{:.4f}'.format(v))
detail['{}/{}_AP_dist_{}'.format(metric_prefix, name, k)] = val
for k, v in metrics['label_tp_errors'][name].items():
val = float('{:.4f}'.format(v))
detail['{}/{}_{}'.format(metric_prefix, name, k)] = val
for k, v in metrics['tp_errors'].items():
val = float('{:.4f}'.format(v))
detail['{}/{}'.format(metric_prefix,
self.ErrNameMapping[k])] = val
detail['{}/NDS'.format(metric_prefix)] = metrics['nd_score']
detail['{}/mAP'.format(metric_prefix)] = metrics['mean_ap']
return detail
[docs] def format_results(self, results, jsonfile_prefix=None):
"""Format the results to json (standard format for COCO evaluation).
Args:
results (list[dict]): Testing results of the dataset.
jsonfile_prefix (str): 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_files, tmp_dir), where `result_files` 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'
assert len(results) == len(self), (
'The length of results is not equal to the dataset len: {} != {}'.
format(len(results), len(self)))
if jsonfile_prefix is None:
tmp_dir = tempfile.TemporaryDirectory()
jsonfile_prefix = osp.join(tmp_dir.name, 'results')
else:
tmp_dir = None
# currently the output prediction results could be in two formats
# 1. list of dict('boxes_3d': ..., 'scores_3d': ..., 'labels_3d': ...)
# 2. list of dict('pts_bbox' or 'img_bbox':
# dict('boxes_3d': ..., 'scores_3d': ..., 'labels_3d': ...))
# this is a workaround to enable evaluation of both formats on nuScenes
# refer to https://github.com/open-mmlab/mmdetection3d/issues/449
if not ('pts_bbox' in results[0] or 'img_bbox' in results[0]):
result_files = self._format_bbox(results, jsonfile_prefix)
else:
# should take the inner dict out of 'pts_bbox' or 'img_bbox' dict
result_files = dict()
for name in results[0]:
print(f'\nFormating bboxes of {name}')
results_ = [out[name] for out in results]
tmp_file_ = osp.join(jsonfile_prefix, name)
result_files.update(
{name: self._format_bbox(results_, tmp_file_)})
return result_files, tmp_dir
[docs] def evaluate(self,
results,
metric='bbox',
logger=None,
jsonfile_prefix=None,
result_names=['pts_bbox'],
show=False,
out_dir=None,
pipeline=None):
"""Evaluation in nuScenes protocol.
Args:
results (list[dict]): Testing results of the dataset.
metric (str | list[str], optional): Metrics to be evaluated.
Default: 'bbox'.
logger (logging.Logger | str, optional): Logger used for printing
related information during evaluation. Default: 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.
show (bool, optional): Whether to visualize.
Default: False.
out_dir (str, optional): Path to save the visualization results.
Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
Returns:
dict[str, float]: Results of each evaluation metric.
"""
result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
if isinstance(result_files, dict):
results_dict = dict()
for name in result_names:
print('Evaluating bboxes of {}'.format(name))
ret_dict = self._evaluate_single(result_files[name])
results_dict.update(ret_dict)
elif isinstance(result_files, str):
results_dict = self._evaluate_single(result_files)
if tmp_dir is not None:
tmp_dir.cleanup()
if show or out_dir:
self.show(results, out_dir, show=show, pipeline=pipeline)
return results_dict
def _build_default_pipeline(self):
"""Build the default pipeline for this dataset."""
pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=dict(backend='disk')),
dict(
type='DefaultFormatBundle3D',
class_names=self.CLASSES,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
return Compose(pipeline)
[docs] def show(self, results, out_dir, show=False, pipeline=None):
"""Results visualization.
Args:
results (list[dict]): List of bounding boxes results.
out_dir (str): Output directory of visualization result.
show (bool): Whether to visualize the results online.
Default: False.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
"""
assert out_dir is not None, 'Expect out_dir, got none.'
pipeline = self._get_pipeline(pipeline)
for i, result in enumerate(results):
if 'pts_bbox' in result.keys():
result = result['pts_bbox']
data_info = self.data_infos[i]
pts_path = data_info['lidar_path']
file_name = osp.split(pts_path)[-1].split('.')[0]
points = self._extract_data(i, pipeline, 'points').numpy()
# for now we convert points into depth mode
points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR,
Coord3DMode.DEPTH)
inds = result['scores_3d'] > 0.1
gt_bboxes = self.get_ann_info(i)['gt_bboxes_3d'].tensor.numpy()
show_gt_bboxes = Box3DMode.convert(gt_bboxes, Box3DMode.LIDAR,
Box3DMode.DEPTH)
pred_bboxes = result['boxes_3d'][inds].tensor.numpy()
show_pred_bboxes = Box3DMode.convert(pred_bboxes, Box3DMode.LIDAR,
Box3DMode.DEPTH)
show_result(points, show_gt_bboxes, show_pred_bboxes, out_dir,
file_name, show)
def output_to_nusc_box(detection, with_velocity=True):
"""Convert the output to the box class in the nuScenes.
Args:
detection (dict): Detection results.
- boxes_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.
"""
box3d = detection['boxes_3d']
scores = detection['scores_3d'].numpy()
labels = detection['labels_3d'].numpy()
box_gravity_center = box3d.gravity_center.numpy()
box_dims = box3d.dims.numpy()
box_yaw = box3d.yaw.numpy()
# our LiDAR coordinate system -> nuScenes box coordinate system
nus_box_dims = box_dims[:, [1, 0, 2]]
box_list = []
for i in range(len(box3d)):
quat = pyquaternion.Quaternion(axis=[0, 0, 1], radians=box_yaw[i])
if with_velocity:
velocity = (*box3d.tensor[i, 7:9], 0.0)
else:
velocity = (0, 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)
return box_list
def lidar_nusc_box_to_global(info,
boxes,
classes,
eval_configs,
eval_version='detection_cvpr_2019'):
"""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.
eval_version (str, optional): Evaluation version.
Default: 'detection_cvpr_2019'
Returns:
list: List of standard NuScenesBoxes in the global
coordinate.
"""
box_list = []
for box in boxes:
# Move box to ego vehicle coord system
box.rotate(pyquaternion.Quaternion(info['lidar2ego_rotation']))
box.translate(np.array(info['lidar2ego_translation']))
# 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
box.rotate(pyquaternion.Quaternion(info['ego2global_rotation']))
box.translate(np.array(info['ego2global_translation']))
box_list.append(box)
return box_list