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Commit 53435c62 authored by Yezhen Cong's avatar Yezhen Cong Committed by Tai-Wang
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[Refactor] Refactor code structure and docstrings (#803) 

* refactor points_in_boxes

* Merge same functions of three boxes

* More docstring fixes and unify x/y/z size

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Remove None in function param type

* Fix unittest

* Add comments for NMS functions

* Merge methods of Points

* Add unittest

* Add optional and default value

* Fix box conversion and add unittest

* Fix comments

* Add unit test

* Indent

* Fix CI

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Add unit test for box bev

* More unit tests and refine docstrings in box_np_ops

* Fix comment

* Add deprecation warning
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mmdet3d/datasets/lyft_dataset.py
View file @ 53435c62
......@@ -129,7 +129,7 @@ class LyftDataset(Custom3DDataset):
index (int): Index of the sample data to get.
Returns:
dict: Data information that will be passed to the data \
dict: Data information that will be passed to the data
preprocessing pipelines. It includes the following keys:
- sample_idx (str): sample index
......@@ -137,7 +137,7 @@ class LyftDataset(Custom3DDataset):
- sweeps (list[dict]): infos of sweeps
- timestamp (float): sample timestamp
- img_filename (str, optional): image filename
- lidar2img (list[np.ndarray], optional): transformations \
- lidar2img (list[np.ndarray], optional): transformations
from lidar to different cameras
- ann_info (dict): annotation info
"""
......@@ -190,7 +190,7 @@ class LyftDataset(Custom3DDataset):
Returns:
dict: Annotation information consists of the following keys:
- gt_bboxes_3d (:obj:`LiDARInstance3DBoxes`): \
- 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.
......@@ -275,10 +275,11 @@ class LyftDataset(Custom3DDataset):
Args:
result_path (str): Path of the result file.
logger (logging.Logger | str | None): Logger used for printing
logger (logging.Logger | str, optional): Logger used for printing
related information during evaluation. Default: None.
metric (str): Metric name used for evaluation. Default: 'bbox'.
result_name (str): Result name in the metric prefix.
metric (str, optional): Metric name used for evaluation.
Default: 'bbox'.
result_name (str, optional): Result name in the metric prefix.
Default: 'pts_bbox'.
Returns:
......@@ -312,18 +313,18 @@ class LyftDataset(Custom3DDataset):
Args:
results (list[dict]): Testing results of the dataset.
jsonfile_prefix (str | None): The prefix of json files. It includes
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.
csv_savepath (str | None): The path for saving csv files.
csv_savepath (str): The path for saving csv files.
It includes the file path and the csv filename,
e.g., "a/b/filename.csv". If not specified,
the result will not be converted to csv file.
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 \
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'
......@@ -372,19 +373,22 @@ class LyftDataset(Custom3DDataset):
Args:
results (list[dict]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | str | None): Logger used for printing
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 | None): The prefix of json files. It includes
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.
csv_savepath (str | None): The path for saving csv files.
csv_savepath (str, optional): The path for saving csv files.
It includes the file path and the csv filename,
e.g., "a/b/filename.csv". If not specified,
the result will not be converted to csv file.
show (bool): Whether to visualize.
result_names (list[str], optional): Result names in the
metric prefix. Default: ['pts_bbox'].
show (bool, optional): Whether to visualize.
Default: False.
out_dir (str): Path to save the visualization results.
out_dir (str, optional): Path to save the visualization results.
Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
......
mmdet3d/datasets/nuscenes_dataset.py
View file @ 53435c62
......@@ -48,8 +48,9 @@ class NuScenesDataset(Custom3DDataset):
Defaults to False.
eval_version (bool, optional): Configuration version of evaluation.
Defaults to 'detection_cvpr_2019'.
use_valid_flag (bool): Whether to use `use_valid_flag` key in the info
file as mask to filter gt_boxes and gt_names. Defaults to False.
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',
......@@ -196,7 +197,7 @@ class NuScenesDataset(Custom3DDataset):
index (int): Index of the sample data to get.
Returns:
dict: Data information that will be passed to the data \
dict: Data information that will be passed to the data
preprocessing pipelines. It includes the following keys:
- sample_idx (str): Sample index.
......@@ -204,7 +205,7 @@ class NuScenesDataset(Custom3DDataset):
- sweeps (list[dict]): Infos of sweeps.
- timestamp (float): Sample timestamp.
- img_filename (str, optional): Image filename.
- lidar2img (list[np.ndarray], optional): Transformations \
- lidar2img (list[np.ndarray], optional): Transformations
from lidar to different cameras.
- ann_info (dict): Annotation info.
"""
......@@ -256,7 +257,7 @@ class NuScenesDataset(Custom3DDataset):
Returns:
dict: Annotation information consists of the following keys:
- gt_bboxes_3d (:obj:`LiDARInstance3DBoxes`): \
- 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.
......@@ -374,10 +375,11 @@ class NuScenesDataset(Custom3DDataset):
Args:
result_path (str): Path of the result file.
logger (logging.Logger | str | None): Logger used for printing
logger (logging.Logger | str, optional): Logger used for printing
related information during evaluation. Default: None.
metric (str): Metric name used for evaluation. Default: 'bbox'.
result_name (str): Result name in the metric prefix.
metric (str, optional): Metric name used for evaluation.
Default: 'bbox'.
result_name (str, optional): Result name in the metric prefix.
Default: 'pts_bbox'.
Returns:
......@@ -427,14 +429,14 @@ class NuScenesDataset(Custom3DDataset):
Args:
results (list[dict]): Testing results of the dataset.
jsonfile_prefix (str | None): The prefix of json files. It includes
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 \
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'
......@@ -480,15 +482,16 @@ class NuScenesDataset(Custom3DDataset):
Args:
results (list[dict]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | str | None): Logger used for printing
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 | None): The prefix of json files. It includes
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): Whether to visualize.
show (bool, optional): Whether to visualize.
Default: False.
out_dir (str): Path to save the visualization results.
out_dir (str, optional): Path to save the visualization results.
Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
......@@ -624,7 +627,7 @@ def lidar_nusc_box_to_global(info,
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): Evaluation version.
eval_version (str, optional): Evaluation version.
Default: 'detection_cvpr_2019'
Returns:
......
mmdet3d/datasets/nuscenes_mono_dataset.py
View file @ 53435c62
......@@ -44,8 +44,9 @@ class NuScenesMonoDataset(CocoDataset):
- 'Camera': Box in camera coordinates.
eval_version (str, optional): Configuration version of evaluation.
Defaults to 'detection_cvpr_2019'.
use_valid_flag (bool): Whether to use `use_valid_flag` key in the info
file as mask to filter gt_boxes and gt_names. Defaults to False.
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.
version (str, optional): Dataset version. Defaults to 'v1.0-trainval'.
"""
CLASSES = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
......@@ -140,8 +141,8 @@ class NuScenesMonoDataset(CocoDataset):
ann_info (list[dict]): Annotation info of an image.
Returns:
dict: A dict containing the following keys: bboxes, labels, \
gt_bboxes_3d, gt_labels_3d, attr_labels, centers2d, \
dict: A dict containing the following keys: bboxes, labels,
gt_bboxes_3d, gt_labels_3d, attr_labels, centers2d,
depths, bboxes_ignore, masks, seg_map
"""
gt_bboxes = []
......@@ -394,10 +395,11 @@ class NuScenesMonoDataset(CocoDataset):
Args:
result_path (str): Path of the result file.
logger (logging.Logger | str | None): Logger used for printing
logger (logging.Logger | str, optional): Logger used for printing
related information during evaluation. Default: None.
metric (str): Metric name used for evaluation. Default: 'bbox'.
result_name (str): Result name in the metric prefix.
metric (str, optional): Metric name used for evaluation.
Default: 'bbox'.
result_name (str, optional): Result name in the metric prefix.
Default: 'img_bbox'.
Returns:
......@@ -448,13 +450,13 @@ class NuScenesMonoDataset(CocoDataset):
Args:
results (list[tuple | numpy.ndarray]): Testing results of the
dataset.
jsonfile_prefix (str | None): The prefix of json files. It includes
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: (result_files, tmp_dir), result_files is a dict containing \
the json filepaths, tmp_dir is the temporal directory created \
tuple: (result_files, tmp_dir), 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'
......@@ -504,15 +506,18 @@ class NuScenesMonoDataset(CocoDataset):
Args:
results (list[dict]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | str | None): Logger used for printing
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 | None): The prefix of json files. It includes
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.
show (bool): Whether to visualize.
result_names (list[str], optional): Result names in the
metric prefix. Default: ['img_bbox'].
show (bool, optional): Whether to visualize.
Default: False.
out_dir (str): Path to save the visualization results.
out_dir (str, optional): Path to save the visualization results.
Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
......@@ -576,7 +581,7 @@ class NuScenesMonoDataset(CocoDataset):
"""Get data loading pipeline in self.show/evaluate function.
Args:
pipeline (list[dict] | None): Input pipeline. If None is given, \
pipeline (list[dict]): Input pipeline. If None is given,
get from self.pipeline.
"""
if pipeline is None:
......@@ -696,7 +701,7 @@ def cam_nusc_box_to_global(info,
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): Evaluation version.
eval_version (str, optional): Evaluation version.
Default: 'detection_cvpr_2019'
Returns:
......@@ -736,7 +741,7 @@ def global_nusc_box_to_cam(info,
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): Evaluation version.
eval_version (str, optional): Evaluation version.
Default: 'detection_cvpr_2019'
Returns:
......@@ -769,7 +774,7 @@ def nusc_box_to_cam_box3d(boxes):
boxes (list[:obj:`NuScenesBox`]): List of predicted NuScenesBoxes.
Returns:
tuple (:obj:`CameraInstance3DBoxes` | torch.Tensor | torch.Tensor): \
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)
......
mmdet3d/datasets/pipelines/data_augment_utils.py
View file @ 53435c62
......@@ -34,8 +34,8 @@ def box_collision_test(boxes, qboxes, clockwise=True):
Args:
boxes (np.ndarray): Corners of current boxes.
qboxes (np.ndarray): Boxes to be avoid colliding.
clockwise (bool): Whether the corners are in clockwise order.
Default: True.
clockwise (bool, optional): Whether the corners are in
clockwise order. Default: True.
"""
N = boxes.shape[0]
K = qboxes.shape[0]
......@@ -317,7 +317,7 @@ def box3d_transform_(boxes, loc_transform, rot_transform, valid_mask):
boxes (np.ndarray): 3D boxes to be transformed.
loc_transform (np.ndarray): Location transform to be applied.
rot_transform (np.ndarray): Rotation transform to be applied.
valid_mask (np.ndarray | None): Mask to indicate which boxes are valid.
valid_mask (np.ndarray): Mask to indicate which boxes are valid.
"""
num_box = boxes.shape[0]
for i in range(num_box):
......@@ -338,16 +338,17 @@ def noise_per_object_v3_(gt_boxes,
Args:
gt_boxes (np.ndarray): Ground truth boxes with shape (N, 7).
points (np.ndarray | None): Input point cloud with shape (M, 4).
Default: None.
valid_mask (np.ndarray | None): Mask to indicate which boxes are valid.
Default: None.
rotation_perturb (float): Rotation perturbation. Default: pi / 4.
center_noise_std (float): Center noise standard deviation.
points (np.ndarray, optional): Input point cloud with
shape (M, 4). Default: None.
valid_mask (np.ndarray, optional): Mask to indicate which
boxes are valid. Default: None.
rotation_perturb (float, optional): Rotation perturbation.
Default: pi / 4.
center_noise_std (float, optional): Center noise standard deviation.
Default: 1.0.
global_random_rot_range (float): Global random rotation range.
Default: pi/4.
num_try (int): Number of try. Default: 100.
global_random_rot_range (float, optional): Global random rotation
range. Default: pi/4.
num_try (int, optional): Number of try. Default: 100.
"""
num_boxes = gt_boxes.shape[0]
if not isinstance(rotation_perturb, (list, tuple, np.ndarray)):
......
mmdet3d/datasets/pipelines/dbsampler.py
View file @ 53435c62
......@@ -15,10 +15,10 @@ class BatchSampler:
Args:
sample_list (list[dict]): List of samples.
name (str | None): The category of samples. Default: None.
epoch (int | None): Sampling epoch. Default: None.
shuffle (bool): Whether to shuffle indices. Default: False.
drop_reminder (bool): Drop reminder. Default: False.
name (str, optional): The category of samples. Default: None.
epoch (int, optional): Sampling epoch. Default: None.
shuffle (bool, optional): Whether to shuffle indices. Default: False.
drop_reminder (bool, optional): Drop reminder. Default: False.
"""
def __init__(self,
......@@ -87,9 +87,9 @@ class DataBaseSampler(object):
rate (float): Rate of actual sampled over maximum sampled number.
prepare (dict): Name of preparation functions and the input value.
sample_groups (dict): Sampled classes and numbers.
classes (list[str]): List of classes. Default: None.
points_loader(dict): Config of points loader. Default: dict(
type='LoadPointsFromFile', load_dim=4, use_dim=[0,1,2,3])
classes (list[str], optional): List of classes. Default: None.
points_loader(dict, optional): Config of points loader. Default:
dict(type='LoadPointsFromFile', load_dim=4, use_dim=[0,1,2,3])
"""
def __init__(self,
......@@ -198,9 +198,9 @@ class DataBaseSampler(object):
Returns:
dict: Dict of sampled 'pseudo ground truths'.
- gt_labels_3d (np.ndarray): ground truths labels \
- gt_labels_3d (np.ndarray): ground truths labels
of sampled objects.
- gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): \
- gt_bboxes_3d (:obj:`BaseInstance3DBoxes`):
sampled ground truth 3D bounding boxes
- points (np.ndarray): sampled points
- group_ids (np.ndarray): ids of sampled ground truths
......
mmdet3d/datasets/pipelines/formating.py
View file @ 53435c62
......@@ -24,7 +24,7 @@ class DefaultFormatBundle(object):
- gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
- gt_labels: (1)to tensor, (2)to DataContainer
- gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
- gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, \
- gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor,
(3)to DataContainer (stack=True)
"""
......@@ -92,8 +92,8 @@ class Collect3D(object):
The "img_meta" item is always populated. The contents of the "img_meta"
dictionary depends on "meta_keys". By default this includes:
- 'img_shape': shape of the image input to the network as a tuple \
(h, w, c). Note that images may be zero padded on the \
- 'img_shape': shape of the image input to the network as a tuple
(h, w, c). Note that images may be zero padded on the
bottom/right if the batch tensor is larger than this shape.
- 'scale_factor': a float indicating the preprocessing scale
- 'flip': a boolean indicating if image flip transform was used
......@@ -103,9 +103,9 @@ class Collect3D(object):
- 'lidar2img': transform from lidar to image
- 'depth2img': transform from depth to image
- 'cam2img': transform from camera to image
- 'pcd_horizontal_flip': a boolean indicating if point cloud is \
- 'pcd_horizontal_flip': a boolean indicating if point cloud is
flipped horizontally
- 'pcd_vertical_flip': a boolean indicating if point cloud is \
- 'pcd_vertical_flip': a boolean indicating if point cloud is
flipped vertically
- 'box_mode_3d': 3D box mode
- 'box_type_3d': 3D box type
......@@ -130,16 +130,15 @@ class Collect3D(object):
'sample_idx', 'pcd_scale_factor', 'pcd_rotation', 'pts_filename')
"""
def __init__(self,
keys,
meta_keys=('filename', 'ori_shape', 'img_shape', 'lidar2img',
'depth2img', 'cam2img', 'pad_shape',
'scale_factor', 'flip', 'pcd_horizontal_flip',
'pcd_vertical_flip', 'box_mode_3d', 'box_type_3d',
'img_norm_cfg', 'pcd_trans', 'sample_idx',
'pcd_scale_factor', 'pcd_rotation',
'pcd_rotation_angle', 'pts_filename',
'transformation_3d_flow')):
def __init__(
self,
keys,
meta_keys=('filename', 'ori_shape', 'img_shape', 'lidar2img',
'depth2img', 'cam2img', 'pad_shape', 'scale_factor', 'flip',
'pcd_horizontal_flip', 'pcd_vertical_flip', 'box_mode_3d',
'box_type_3d', 'img_norm_cfg', 'pcd_trans', 'sample_idx',
'pcd_scale_factor', 'pcd_rotation', 'pcd_rotation_angle',
'pts_filename', 'transformation_3d_flow')):
self.keys = keys
self.meta_keys = meta_keys
......
mmdet3d/datasets/pipelines/loading.py
View file @ 53435c62
......@@ -14,9 +14,10 @@ class LoadMultiViewImageFromFiles(object):
Expects results['img_filename'] to be a list of filenames.
Args:
to_float32 (bool): Whether to convert the img to float32.
to_float32 (bool, optional): Whether to convert the img to float32.
Defaults to False.
color_type (str): Color type of the file. Defaults to 'unchanged'.
color_type (str, optional): Color type of the file.
Defaults to 'unchanged'.
"""
def __init__(self, to_float32=False, color_type='unchanged'):
......@@ -30,7 +31,7 @@ class LoadMultiViewImageFromFiles(object):
results (dict): Result dict containing multi-view image filenames.
Returns:
dict: The result dict containing the multi-view image data. \
dict: The result dict containing the multi-view image data.
Added keys and values are described below.
- filename (str): Multi-view image filenames.
......@@ -77,7 +78,7 @@ class LoadImageFromFileMono3D(LoadImageFromFile):
detection, additional camera parameters need to be loaded.
Args:
kwargs (dict): Arguments are the same as those in \
kwargs (dict): Arguments are the same as those in
:class:`LoadImageFromFile`.
"""
......@@ -102,17 +103,20 @@ class LoadPointsFromMultiSweeps(object):
This is usually used for nuScenes dataset to utilize previous sweeps.
Args:
sweeps_num (int): Number of sweeps. Defaults to 10.
load_dim (int): Dimension number of the loaded points. Defaults to 5.
use_dim (list[int]): Which dimension to use. Defaults to [0, 1, 2, 4].
file_client_args (dict): Config dict of file clients, refer to
sweeps_num (int, optional): Number of sweeps. Defaults to 10.
load_dim (int, optional): Dimension number of the loaded points.
Defaults to 5.
use_dim (list[int], optional): Which dimension to use.
Defaults to [0, 1, 2, 4].
file_client_args (dict, optional): Config dict of file clients,
refer to
https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
for more details. Defaults to dict(backend='disk').
pad_empty_sweeps (bool): Whether to repeat keyframe when
pad_empty_sweeps (bool, optional): Whether to repeat keyframe when
sweeps is empty. Defaults to False.
remove_close (bool): Whether to remove close points.
remove_close (bool, optional): Whether to remove close points.
Defaults to False.
test_mode (bool): If test_model=True used for testing, it will not
test_mode (bool, optional): If `test_mode=True`, it will not
randomly sample sweeps but select the nearest N frames.
Defaults to False.
"""
......@@ -161,7 +165,7 @@ class LoadPointsFromMultiSweeps(object):
Args:
points (np.ndarray | :obj:`BasePoints`): Sweep points.
radius (float): Radius below which points are removed.
radius (float, optional): Radius below which points are removed.
Defaults to 1.0.
Returns:
......@@ -182,14 +186,14 @@ class LoadPointsFromMultiSweeps(object):
"""Call function to load multi-sweep point clouds from files.
Args:
results (dict): Result dict containing multi-sweep point cloud \
results (dict): Result dict containing multi-sweep point cloud
filenames.
Returns:
dict: The result dict containing the multi-sweep points data. \
dict: The result dict containing the multi-sweep points data.
Added key and value are described below.
- points (np.ndarray | :obj:`BasePoints`): Multi-sweep point \
- points (np.ndarray | :obj:`BasePoints`): Multi-sweep point
cloud arrays.
"""
points = results['points']
......@@ -243,8 +247,8 @@ class PointSegClassMapping(object):
Args:
valid_cat_ids (tuple[int]): A tuple of valid category.
max_cat_id (int): The max possible cat_id in input segmentation mask.
Defaults to 40.
max_cat_id (int, optional): The max possible cat_id in input
segmentation mask. Defaults to 40.
"""
def __init__(self, valid_cat_ids, max_cat_id=40):
......@@ -268,7 +272,7 @@ class PointSegClassMapping(object):
results (dict): Result dict containing point semantic masks.
Returns:
dict: The result dict containing the mapped category ids. \
dict: The result dict containing the mapped category ids.
Updated key and value are described below.
- pts_semantic_mask (np.ndarray): Mapped semantic masks.
......@@ -307,7 +311,7 @@ class NormalizePointsColor(object):
results (dict): Result dict containing point clouds data.
Returns:
dict: The result dict containing the normalized points. \
dict: The result dict containing the normalized points.
Updated key and value are described below.
- points (:obj:`BasePoints`): Points after color normalization.
......@@ -342,14 +346,17 @@ class LoadPointsFromFile(object):
- 'LIDAR': Points in LiDAR coordinates.
- 'DEPTH': Points in depth coordinates, usually for indoor dataset.
- 'CAMERA': Points in camera coordinates.
load_dim (int): The dimension of the loaded points.
load_dim (int, optional): The dimension of the loaded points.
Defaults to 6.
use_dim (list[int]): Which dimensions of the points to be used.
use_dim (list[int], optional): Which dimensions of the points to use.
Defaults to [0, 1, 2]. For KITTI dataset, set use_dim=4
or use_dim=[0, 1, 2, 3] to use the intensity dimension.
shift_height (bool): Whether to use shifted height. Defaults to False.
use_color (bool): Whether to use color features. Defaults to False.
file_client_args (dict): Config dict of file clients, refer to
shift_height (bool, optional): Whether to use shifted height.
Defaults to False.
use_color (bool, optional): Whether to use color features.
Defaults to False.
file_client_args (dict, optional): Config dict of file clients,
refer to
https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
for more details. Defaults to dict(backend='disk').
"""
......@@ -405,7 +412,7 @@ class LoadPointsFromFile(object):
results (dict): Result dict containing point clouds data.
Returns:
dict: The result dict containing the point clouds data. \
dict: The result dict containing the point clouds data.
Added key and value are described below.
- points (:obj:`BasePoints`): Point clouds data.
......
mmdet3d/datasets/pipelines/test_time_aug.py
View file @ 53435c62
......@@ -16,18 +16,19 @@ class MultiScaleFlipAug3D(object):
img_scale (tuple | list[tuple]: Images scales for resizing.
pts_scale_ratio (float | list[float]): Points scale ratios for
resizing.
flip (bool): Whether apply flip augmentation. Defaults to False.
flip_direction (str | list[str]): Flip augmentation directions
for images, options are "horizontal" and "vertical".
flip (bool, optional): Whether apply flip augmentation.
Defaults to False.
flip_direction (str | list[str], optional): Flip augmentation
directions for images, options are "horizontal" and "vertical".
If flip_direction is list, multiple flip augmentations will
be applied. It has no effect when ``flip == False``.
Defaults to "horizontal".
pcd_horizontal_flip (bool): Whether apply horizontal flip augmentation
to point cloud. Defaults to True. Note that it works only when
'flip' is turned on.
pcd_vertical_flip (bool): Whether apply vertical flip augmentation
to point cloud. Defaults to True. Note that it works only when
'flip' is turned on.
pcd_horizontal_flip (bool, optional): Whether apply horizontal
flip augmentation to point cloud. Defaults to True.
Note that it works only when 'flip' is turned on.
pcd_vertical_flip (bool, optional): Whether apply vertical flip
augmentation to point cloud. Defaults to True.
Note that it works only when 'flip' is turned on.
"""
def __init__(self,
......@@ -70,7 +71,7 @@ class MultiScaleFlipAug3D(object):
results (dict): Result dict contains the data to augment.
Returns:
dict: The result dict contains the data that is augmented with \
dict: The result dict contains the data that is augmented with
different scales and flips.
"""
aug_data = []
......
mmdet3d/datasets/pipelines/transforms_3d.py
View file @ 53435c62
......@@ -22,7 +22,7 @@ class RandomDropPointsColor(object):
util/transform.py#L223>`_ for more details.
Args:
drop_ratio (float): The probability of dropping point colors.
drop_ratio (float, optional): The probability of dropping point colors.
Defaults to 0.2.
"""
......@@ -38,7 +38,7 @@ class RandomDropPointsColor(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after color dropping, \
dict: Results after color dropping,
'points' key is updated in the result dict.
"""
points = input_dict['points']
......@@ -105,10 +105,11 @@ class RandomFlip3D(RandomFlip):
Args:
input_dict (dict): Result dict from loading pipeline.
direction (str): Flip direction. Default: horizontal.
direction (str, optional): Flip direction.
Default: 'horizontal'.
Returns:
dict: Flipped results, 'points', 'bbox3d_fields' keys are \
dict: Flipped results, 'points', 'bbox3d_fields' keys are
updated in the result dict.
"""
assert direction in ['horizontal', 'vertical']
......@@ -137,15 +138,15 @@ class RandomFlip3D(RandomFlip):
input_dict['cam2img'][0][2] = w - input_dict['cam2img'][0][2]
def __call__(self, input_dict):
"""Call function to flip points, values in the ``bbox3d_fields`` and \
"""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 \
dict: Flipped results, 'flip', 'flip_direction',
'pcd_horizontal_flip' and 'pcd_vertical_flip' keys are added
into result dict.
"""
# filp 2D image and its annotations
......@@ -187,20 +188,20 @@ class RandomFlip3D(RandomFlip):
class RandomJitterPoints(object):
"""Randomly jitter point coordinates.
Different from the global translation in ``GlobalRotScaleTrans``, here we \
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 \
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] | None): Clip the randomly generated jitter \
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 \
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`.
"""
......@@ -229,7 +230,7 @@ class RandomJitterPoints(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after adding noise to each point, \
dict: Results after adding noise to each point,
'points' key is updated in the result dict.
"""
points = input_dict['points']
......@@ -291,8 +292,8 @@ class ObjectSample(object):
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 \
dict: Results after object sampling augmentation,
'points', 'gt_bboxes_3d', 'gt_labels_3d' keys are updated
in the result dict.
"""
gt_bboxes_3d = input_dict['gt_bboxes_3d']
......@@ -388,7 +389,7 @@ class ObjectNoise(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after adding noise to each object, \
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']
......@@ -428,10 +429,10 @@ class GlobalAlignment(object):
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 \
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 \
For example, ScanNet 3D detection task uses aligned ground-truth
bounding boxes for evaluation.
"""
......@@ -483,7 +484,7 @@ class GlobalAlignment(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after global alignment, 'points' and keys in \
dict: Results after global alignment, 'points' and keys in
input_dict['bbox3d_fields'] are updated in the result dict.
"""
assert 'axis_align_matrix' in input_dict['ann_info'].keys(), \
......@@ -512,15 +513,15 @@ class GlobalRotScaleTrans(object):
"""Apply global rotation, scaling and translation to a 3D scene.
Args:
rot_range (list[float]): Range of rotation angle.
rot_range (list[float], optional): Range of rotation angle.
Defaults to [-0.78539816, 0.78539816] (close to [-pi/4, pi/4]).
scale_ratio_range (list[float]): Range of scale ratio.
scale_ratio_range (list[float], optional): Range of scale ratio.
Defaults to [0.95, 1.05].
translation_std (list[float]): The standard deviation of translation
noise. This applies random translation to a scene by a noise, which
translation_std (list[float], optional): The standard deviation of
translation noise applied to a scene, which
is sampled from a gaussian distribution whose standard deviation
is set by ``translation_std``. Defaults to [0, 0, 0]
shift_height (bool): Whether to shift height.
shift_height (bool, optional): Whether to shift height.
(the fourth dimension of indoor points) when scaling.
Defaults to False.
"""
......@@ -559,8 +560,8 @@ class GlobalRotScaleTrans(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after translation, 'points', 'pcd_trans' \
and keys in input_dict['bbox3d_fields'] are updated \
dict: Results after translation, 'points', 'pcd_trans'
and keys in input_dict['bbox3d_fields'] are updated
in the result dict.
"""
translation_std = np.array(self.translation_std, dtype=np.float32)
......@@ -578,8 +579,8 @@ class GlobalRotScaleTrans(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after rotation, 'points', 'pcd_rotation' \
and keys in input_dict['bbox3d_fields'] are updated \
dict: Results after rotation, 'points', 'pcd_rotation'
and keys in input_dict['bbox3d_fields'] are updated
in the result dict.
"""
rotation = self.rot_range
......@@ -608,7 +609,7 @@ class GlobalRotScaleTrans(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after scaling, 'points'and keys in \
dict: Results after scaling, 'points'and keys in
input_dict['bbox3d_fields'] are updated in the result dict.
"""
scale = input_dict['pcd_scale_factor']
......@@ -630,7 +631,7 @@ class GlobalRotScaleTrans(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after scaling, 'pcd_scale_factor' are updated \
dict: Results after scaling, 'pcd_scale_factor' are updated
in the result dict.
"""
scale_factor = np.random.uniform(self.scale_ratio_range[0],
......@@ -638,7 +639,7 @@ class GlobalRotScaleTrans(object):
input_dict['pcd_scale_factor'] = scale_factor
def __call__(self, input_dict):
"""Private function to rotate, scale and translate bounding boxes and \
"""Private function to rotate, scale and translate bounding boxes and
points.
Args:
......@@ -646,7 +647,7 @@ class GlobalRotScaleTrans(object):
Returns:
dict: Results after scaling, 'points', 'pcd_rotation',
'pcd_scale_factor', 'pcd_trans' and keys in \
'pcd_scale_factor', 'pcd_trans' and keys in
input_dict['bbox3d_fields'] are updated in the result dict.
"""
if 'transformation_3d_flow' not in input_dict:
......@@ -684,7 +685,7 @@ class PointShuffle(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after filtering, 'points', 'pts_instance_mask' \
dict: Results after filtering, 'points', 'pts_instance_mask'
and 'pts_semantic_mask' keys are updated in the result dict.
"""
idx = input_dict['points'].shuffle()
......@@ -723,7 +724,7 @@ class ObjectRangeFilter(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d' \
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
......@@ -775,7 +776,7 @@ class PointsRangeFilter(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after filtering, 'points', 'pts_instance_mask' \
dict: Results after filtering, 'points', 'pts_instance_mask'
and 'pts_semantic_mask' keys are updated in the result dict.
"""
points = input_dict['points']
......@@ -821,7 +822,7 @@ class ObjectNameFilter(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d' \
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']
......@@ -913,7 +914,7 @@ class PointSample(object):
Args:
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after sampling, 'points', 'pts_instance_mask' \
dict: Results after sampling, 'points', 'pts_instance_mask'
and 'pts_semantic_mask' keys are updated in the result dict.
"""
points = results['points']
......@@ -994,10 +995,10 @@ class IndoorPatchPointSample(object):
additional features. Defaults to False.
num_try (int, optional): Number of times to try if the patch selected
is invalid. Defaults to 10.
enlarge_size (float | None, optional): Enlarge the sampled patch to
enlarge_size (float, optional): Enlarge the sampled patch to
[-block_size / 2 - enlarge_size, block_size / 2 + enlarge_size] as
an augmentation. If None, set it as 0. Defaults to 0.2.
min_unique_num (int | None, optional): Minimum number of unique points
min_unique_num (int, optional): Minimum number of unique points
the sampled patch should contain. If None, use PointNet++'s method
to judge uniqueness. Defaults to None.
eps (float, optional): A value added to patch boundary to guarantee
......@@ -1038,7 +1039,7 @@ class IndoorPatchPointSample(object):
attribute_dims, point_type):
"""Generating model input.
Generate input by subtracting patch center and adding additional \
Generate input by subtracting patch center and adding additional
features. Currently support colors and normalized xyz as features.
Args:
......@@ -1182,7 +1183,7 @@ class IndoorPatchPointSample(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after sampling, 'points', 'pts_instance_mask' \
dict: Results after sampling, 'points', 'pts_instance_mask'
and 'pts_semantic_mask' keys are updated in the result dict.
"""
points = results['points']
......@@ -1242,7 +1243,7 @@ class BackgroundPointsFilter(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after filtering, 'points', 'pts_instance_mask' \
dict: Results after filtering, 'points', 'pts_instance_mask'
and 'pts_semantic_mask' keys are updated in the result dict.
"""
points = input_dict['points']
......@@ -1340,7 +1341,7 @@ class VoxelBasedPointSampler(object):
input_dict (dict): Result dict from loading pipeline.
Returns:
dict: Results after sampling, 'points', 'pts_instance_mask' \
dict: Results after sampling, 'points', 'pts_instance_mask'
and 'pts_semantic_mask' keys are updated in the result dict.
"""
points = results['points']
......
mmdet3d/datasets/scannet_dataset.py
View file @ 53435c62
......@@ -78,13 +78,13 @@ class ScanNetDataset(Custom3DDataset):
index (int): Index of the sample data to get.
Returns:
dict: Data information that will be passed to the data \
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.
- file_name (str): Filename of point clouds.
- img_prefix (str | None, optional): Prefix of image files.
- img_prefix (str, optional): Prefix of image files.
- img_info (dict, optional): Image info.
- ann_info (dict): Annotation info.
"""
......@@ -129,12 +129,12 @@ class ScanNetDataset(Custom3DDataset):
Returns:
dict: annotation information consists of the following keys:
- gt_bboxes_3d (:obj:`DepthInstance3DBoxes`): \
- gt_bboxes_3d (:obj:`DepthInstance3DBoxes`):
3D ground truth bboxes
- gt_labels_3d (np.ndarray): Labels of ground truths.
- pts_instance_mask_path (str): Path of instance masks.
- pts_semantic_mask_path (str): Path of semantic masks.
- axis_align_matrix (np.ndarray): Transformation matrix for \
- axis_align_matrix (np.ndarray): Transformation matrix for
global scene alignment.
"""
# Use index to get the annos, thus the evalhook could also use this api
......@@ -172,7 +172,7 @@ class ScanNetDataset(Custom3DDataset):
def prepare_test_data(self, index):
"""Prepare data for testing.
We should take axis_align_matrix from self.data_infos since we need \
We should take axis_align_matrix from self.data_infos since we need
to align point clouds.
Args:
......@@ -272,7 +272,7 @@ class ScanNetSegDataset(Custom3DSegDataset):
as input. Defaults to None.
test_mode (bool, optional): Whether the dataset is in test mode.
Defaults to False.
ignore_index (int, optional): The label index to be ignored, e.g. \
ignore_index (int, optional): The label index to be ignored, e.g.
unannotated points. If None is given, set to len(self.CLASSES).
Defaults to None.
scene_idxs (np.ndarray | str, optional): Precomputed index to load
......@@ -424,7 +424,7 @@ class ScanNetSegDataset(Custom3DSegDataset):
Args:
outputs (list[dict]): Testing results of the dataset.
txtfile_prefix (str | None): The prefix of saved files. It includes
txtfile_prefix (str): The prefix of saved 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.
......
mmdet3d/datasets/sunrgbd_dataset.py
View file @ 53435c62
......@@ -74,13 +74,13 @@ class SUNRGBDDataset(Custom3DDataset):
index (int): Index of the sample data to get.
Returns:
dict: Data information that will be passed to the data \
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, optional): Filename of point clouds.
- file_name (str, optional): Filename of point clouds.
- img_prefix (str | None, optional): Prefix of image files.
- img_prefix (str, optional): Prefix of image files.
- img_info (dict, optional): Image info.
- calib (dict, optional): Camera calibration info.
- ann_info (dict): Annotation info.
......@@ -125,7 +125,7 @@ class SUNRGBDDataset(Custom3DDataset):
Returns:
dict: annotation information consists of the following keys:
- gt_bboxes_3d (:obj:`DepthInstance3DBoxes`): \
- gt_bboxes_3d (:obj:`DepthInstance3DBoxes`):
3D ground truth bboxes
- gt_labels_3d (np.ndarray): Labels of ground truths.
- pts_instance_mask_path (str): Path of instance masks.
......@@ -239,12 +239,15 @@ class SUNRGBDDataset(Custom3DDataset):
Args:
results (list[dict]): List of results.
metric (str | list[str]): Metrics to be evaluated.
iou_thr (list[float]): AP IoU thresholds.
iou_thr_2d (list[float]): AP IoU thresholds for 2d evaluation.
show (bool): Whether to visualize.
metric (str | list[str], optional): Metrics to be evaluated.
Default: None.
iou_thr (list[float], optional): AP IoU thresholds for 3D
evaluation. Default: (0.25, 0.5).
iou_thr_2d (list[float], optional): AP IoU thresholds for 2D
evaluation. Default: (0.5, ).
show (bool, optional): Whether to visualize.
Default: False.
out_dir (str): Path to save the visualization results.
out_dir (str, optional): Path to save the visualization results.
Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
......
mmdet3d/datasets/utils.py
View file @ 53435c62
......@@ -25,7 +25,7 @@ def is_loading_function(transform):
transform (dict | :obj:`Pipeline`): A transform config or a function.
Returns:
bool | None: Whether it is a loading function. None means can't judge.
bool: Whether it is a loading function. None means can't judge.
When transform is `MultiScaleFlipAug3D`, we return None.
"""
# TODO: use more elegant way to distinguish loading modules
......@@ -92,7 +92,7 @@ def get_loading_pipeline(pipeline):
... dict(type='Collect3D',
... keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
... ]
>>> assert expected_pipelines ==\
>>> assert expected_pipelines == \
... get_loading_pipeline(pipelines)
"""
loading_pipeline = []
......@@ -126,7 +126,7 @@ def extract_result_dict(results, key):
key (str): Key of the desired data.
Returns:
np.ndarray | torch.Tensor | None: Data term.
np.ndarray | torch.Tensor: Data term.
"""
if key not in results.keys():
return None
......
mmdet3d/datasets/waymo_dataset.py
View file @ 53435c62
......@@ -46,8 +46,9 @@ class WaymoDataset(KittiDataset):
Defaults to True.
test_mode (bool, optional): Whether the dataset is in test mode.
Defaults to False.
pcd_limit_range (list): The range of point cloud used to filter
invalid predicted boxes. Default: [-85, -85, -5, 85, 85, 5].
pcd_limit_range (list(float), optional): The range of point cloud used
to filter invalid predicted boxes.
Default: [-85, -85, -5, 85, 85, 5].
"""
CLASSES = ('Car', 'Cyclist', 'Pedestrian')
......@@ -100,7 +101,7 @@ class WaymoDataset(KittiDataset):
- sample_idx (str): sample index
- pts_filename (str): filename of point clouds
- img_prefix (str | None): prefix of image files
- img_prefix (str): prefix of image files
- img_info (dict): image info
- lidar2img (list[np.ndarray], optional): transformations from
lidar to different cameras
......@@ -140,15 +141,15 @@ class WaymoDataset(KittiDataset):
Args:
outputs (list[dict]): Testing results of the dataset.
pklfile_prefix (str | None): The prefix of pkl files. It includes
pklfile_prefix (str): The prefix of pkl 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.
submission_prefix (str | None): The prefix of submitted files. It
submission_prefix (str): The prefix of submitted 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.
data_format (str | None): Output data format. Default: 'waymo'.
Another supported choice is 'kitti'.
data_format (str, optional): Output data format.
Default: 'waymo'. Another supported choice is 'kitti'.
Returns:
tuple: (result_files, tmp_dir), result_files is a dict containing
......@@ -226,18 +227,18 @@ class WaymoDataset(KittiDataset):
Args:
results (list[dict]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
metric (str | list[str], optional): Metrics to be evaluated.
Default: 'waymo'. Another supported metric is 'kitti'.
logger (logging.Logger | str | None): Logger used for printing
logger (logging.Logger | str, optional): Logger used for printing
related information during evaluation. Default: None.
pklfile_prefix (str | None): The prefix of pkl files. It includes
pklfile_prefix (str, optional): The prefix of pkl 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.
submission_prefix (str | None): The prefix of submission datas.
submission_prefix (str, optional): The prefix of submission datas.
If not specified, the submission data will not be generated.
show (bool): Whether to visualize.
show (bool, optional): Whether to visualize.
Default: False.
out_dir (str): Path to save the visualization results.
out_dir (str, optional): Path to save the visualization results.
Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
......@@ -364,8 +365,8 @@ class WaymoDataset(KittiDataset):
net_outputs (List[np.ndarray]): list of array storing the
bbox and score
class_nanes (List[String]): A list of class names
pklfile_prefix (str | None): The prefix of pkl file.
submission_prefix (str | None): The prefix of submission file.
pklfile_prefix (str): The prefix of pkl file.
submission_prefix (str): The prefix of submission file.
Returns:
List[dict]: A list of dict have the kitti 3d format
......
mmdet3d/models/backbones/pointnet2_sa_msg.py
View file @ 53435c62
......@@ -134,7 +134,7 @@ class PointNet2SAMSG(BasePointNet):
- sa_xyz (torch.Tensor): The coordinates of sa features.
- sa_features (torch.Tensor): The features from the
last Set Aggregation Layers.
- sa_indices (torch.Tensor): Indices of the \
- sa_indices (torch.Tensor): Indices of the
input points.
"""
xyz, features = self._split_point_feats(points)
......
mmdet3d/models/backbones/pointnet2_sa_ssg.py
View file @ 53435c62
......@@ -97,11 +97,11 @@ class PointNet2SASSG(BasePointNet):
Returns:
dict[str, list[torch.Tensor]]: Outputs after SA and FP modules.
- fp_xyz (list[torch.Tensor]): The coordinates of \
- fp_xyz (list[torch.Tensor]): The coordinates of
each fp features.
- fp_features (list[torch.Tensor]): The features \
- fp_features (list[torch.Tensor]): The features
from each Feature Propagate Layers.
- fp_indices (list[torch.Tensor]): Indices of the \
- fp_indices (list[torch.Tensor]): Indices of the
input points.
"""
xyz, features = self._split_point_feats(points)
......
mmdet3d/models/decode_heads/decode_head.py
View file @ 53435c62
......@@ -13,17 +13,18 @@ class Base3DDecodeHead(BaseModule, metaclass=ABCMeta):
Args:
channels (int): Channels after modules, before conv_seg.
num_classes (int): Number of classes.
dropout_ratio (float): Ratio of dropout layer. Default: 0.5.
conv_cfg (dict|None): Config of conv layers.
dropout_ratio (float, optional): Ratio of dropout layer. Default: 0.5.
conv_cfg (dict, optional): Config of conv layers.
Default: dict(type='Conv1d').
norm_cfg (dict|None): Config of norm layers.
norm_cfg (dict, optional): Config of norm layers.
Default: dict(type='BN1d').
act_cfg (dict): Config of activation layers.
act_cfg (dict, optional): Config of activation layers.
Default: dict(type='ReLU').
loss_decode (dict): Config of decode loss.
loss_decode (dict, optional): Config of decode loss.
Default: dict(type='CrossEntropyLoss').
ignore_index (int | None): The label index to be ignored. When using
masked BCE loss, ignore_index should be set to None. Default: 255.
ignore_index (int, optional): The label index to be ignored.
When using masked BCE loss, ignore_index should be set to None.
Default: 255.
"""
def __init__(self,
......@@ -110,9 +111,9 @@ class Base3DDecodeHead(BaseModule, metaclass=ABCMeta):
"""Compute semantic segmentation loss.
Args:
seg_logit (torch.Tensor): Predicted per-point segmentation logits \
seg_logit (torch.Tensor): Predicted per-point segmentation logits
of shape [B, num_classes, N].
seg_label (torch.Tensor): Ground-truth segmentation label of \
seg_label (torch.Tensor): Ground-truth segmentation label of
shape [B, N].
"""
loss = dict()
......
mmdet3d/models/decode_heads/paconv_head.py
View file @ 53435c62
......@@ -14,7 +14,7 @@ class PAConvHead(PointNet2Head):
Args:
fp_channels (tuple[tuple[int]]): Tuple of mlp channels in FP modules.
fp_norm_cfg (dict|None): Config of norm layers used in FP modules.
fp_norm_cfg (dict): Config of norm layers used in FP modules.
Default: dict(type='BN2d').
"""
......
mmdet3d/models/decode_heads/pointnet2_head.py
View file @ 53435c62
......@@ -16,7 +16,7 @@ class PointNet2Head(Base3DDecodeHead):
Args:
fp_channels (tuple[tuple[int]]): Tuple of mlp channels in FP modules.
fp_norm_cfg (dict|None): Config of norm layers used in FP modules.
fp_norm_cfg (dict): Config of norm layers used in FP modules.
Default: dict(type='BN2d').
"""
......
mmdet3d/models/dense_heads/anchor3d_head.py
View file @ 53435c62
......@@ -145,7 +145,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
x (torch.Tensor): Input features.
Returns:
tuple[torch.Tensor]: Contain score of each class, bbox \
tuple[torch.Tensor]: Contain score of each class, bbox
regression and direction classification predictions.
"""
cls_score = self.conv_cls(x)
......@@ -163,7 +163,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
features produced by FPN.
Returns:
tuple[list[torch.Tensor]]: Multi-level class score, bbox \
tuple[list[torch.Tensor]]: Multi-level class score, bbox
and direction predictions.
"""
return multi_apply(self.forward_single, feats)
......@@ -177,7 +177,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
device (str): device of current module.
Returns:
list[list[torch.Tensor]]: Anchors of each image, valid flags \
list[list[torch.Tensor]]: Anchors of each image, valid flags
of each image.
"""
num_imgs = len(input_metas)
......@@ -207,7 +207,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
num_total_samples (int): The number of valid samples.
Returns:
tuple[torch.Tensor]: Losses of class, bbox \
tuple[torch.Tensor]: Losses of class, bbox
and direction, respectively.
"""
# classification loss
......@@ -285,7 +285,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
the 7th dimension is rotation dimension.
Returns:
tuple[torch.Tensor]: ``boxes1`` and ``boxes2`` whose 7th \
tuple[torch.Tensor]: ``boxes1`` and ``boxes2`` whose 7th
dimensions are changed.
"""
rad_pred_encoding = torch.sin(boxes1[..., 6:7]) * torch.cos(
......@@ -318,16 +318,16 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
of each sample.
gt_labels (list[torch.Tensor]): Gt labels of each sample.
input_metas (list[dict]): Contain pcd and img's meta info.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
which bounding.
gt_bboxes_ignore (list[torch.Tensor]): Specify
which bounding boxes to ignore.
Returns:
dict[str, list[torch.Tensor]]: Classification, bbox, and \
dict[str, list[torch.Tensor]]: Classification, bbox, and
direction losses of each level.
- loss_cls (list[torch.Tensor]): Classification losses.
- loss_bbox (list[torch.Tensor]): Box regression losses.
- loss_dir (list[torch.Tensor]): Direction classification \
- loss_dir (list[torch.Tensor]): Direction classification
losses.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
......@@ -385,7 +385,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
dir_cls_preds (list[torch.Tensor]): Multi-level direction
class predictions.
input_metas (list[dict]): Contain pcd and img's meta info.
cfg (None | :obj:`ConfigDict`): Training or testing config.
cfg (:obj:`ConfigDict`): Training or testing config.
rescale (list[torch.Tensor]): Whether th rescale bbox.
Returns:
......@@ -439,7 +439,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
mlvl_anchors (List[torch.Tensor]): Multi-level anchors
in single batch.
input_meta (list[dict]): Contain pcd and img's meta info.
cfg (None | :obj:`ConfigDict`): Training or testing config.
cfg (:obj:`ConfigDict`): Training or testing config.
rescale (list[torch.Tensor]): whether th rescale bbox.
Returns:
......
mmdet3d/models/dense_heads/anchor_free_mono3d_head.py
View file @ 53435c62
......@@ -18,35 +18,45 @@ class AnchorFreeMono3DHead(BaseMono3DDenseHead):
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
feat_channels (int): Number of hidden channels. Used in child classes.
stacked_convs (int): Number of stacking convs of the head.
strides (tuple): Downsample factor of each feature map.
dcn_on_last_conv (bool): If true, use dcn in the last layer of
towers. Default: False.
conv_bias (bool | str): If specified as `auto`, it will be decided by
the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
None, otherwise False. Default: "auto".
background_label (int | None): Label ID of background, set as 0 for
RPN and num_classes for other heads. It will automatically set as
num_classes if None is given.
use_direction_classifier (bool): Whether to add a direction classifier.
diff_rad_by_sin (bool): Whether to change the difference into sin
difference for box regression loss.
loss_cls (dict): Config of classification loss.
loss_bbox (dict): Config of localization loss.
loss_dir (dict): Config of direction classifier loss.
loss_attr (dict): Config of attribute classifier loss, which is only
active when pred_attrs=True.
bbox_code_size (int): Dimensions of predicted bounding boxes.
pred_attrs (bool): Whether to predict attributes. Default to False.
num_attrs (int): The number of attributes to be predicted. Default: 9.
pred_velo (bool): Whether to predict velocity. Default to False.
pred_bbox2d (bool): Whether to predict 2D boxes. Default to False.
group_reg_dims (tuple[int]): The dimension of each regression target
group. Default: (2, 1, 3, 1, 2).
cls_branch (tuple[int]): Channels for classification branch.
feat_channels (int, optional): Number of hidden channels.
Used in child classes. Defaults to 256.
stacked_convs (int, optional): Number of stacking convs of the head.
strides (tuple, optional): Downsample factor of each feature map.
dcn_on_last_conv (bool, optional): If true, use dcn in the last
layer of towers. Default: False.
conv_bias (bool | str, optional): If specified as `auto`, it will be
decided by the norm_cfg. Bias of conv will be set as True
if `norm_cfg` is None, otherwise False. Default: 'auto'.
background_label (int, optional): Label ID of background,
set as 0 for RPN and num_classes for other heads.
It will automatically set as `num_classes` if None is given.
use_direction_classifier (bool, optional):
Whether to add a direction classifier.
diff_rad_by_sin (bool, optional): Whether to change the difference
into sin difference for box regression loss. Defaults to True.
dir_offset (float, optional): Parameter used in direction
classification. Defaults to 0.
dir_limit_offset (float, optional): Parameter used in direction
classification. Defaults to 0.
loss_cls (dict, optional): Config of classification loss.
loss_bbox (dict, optional): Config of localization loss.
loss_dir (dict, optional): Config of direction classifier loss.
loss_attr (dict, optional): Config of attribute classifier loss,
which is only active when `pred_attrs=True`.
bbox_code_size (int, optional): Dimensions of predicted bounding boxes.
pred_attrs (bool, optional): Whether to predict attributes.
Defaults to False.
num_attrs (int, optional): The number of attributes to be predicted.
Default: 9.
pred_velo (bool, optional): Whether to predict velocity.
Defaults to False.
pred_bbox2d (bool, optional): Whether to predict 2D boxes.
Defaults to False.
group_reg_dims (tuple[int], optional): The dimension of each regression
target group. Default: (2, 1, 3, 1, 2).
cls_branch (tuple[int], optional): Channels for classification branch.
Default: (128, 64).
reg_branch (tuple[tuple]): Channels for regression branch.
reg_branch (tuple[tuple], optional): Channels for regression branch.
Default: (
(128, 64), # offset
(128, 64), # depth
......@@ -54,14 +64,16 @@ class AnchorFreeMono3DHead(BaseMono3DDenseHead):
(64, ), # rot
() # velo
),
dir_branch (tuple[int]): Channels for direction classification branch.
dir_branch (tuple[int], optional): Channels for direction
classification branch. Default: (64, ).
attr_branch (tuple[int], optional): Channels for classification branch.
Default: (64, ).
attr_branch (tuple[int]): Channels for classification branch.
Default: (64, ).
conv_cfg (dict): Config dict for convolution layer. Default: None.
norm_cfg (dict): Config dict for normalization layer. Default: None.
train_cfg (dict): Training config of anchor head.
test_cfg (dict): Testing config of anchor head.
conv_cfg (dict, optional): Config dict for convolution layer.
Default: None.
norm_cfg (dict, optional): Config dict for normalization layer.
Default: None.
train_cfg (dict, optional): Training config of anchor head.
test_cfg (dict, optional): Testing config of anchor head.
""" # noqa: W605
_version = 1
......@@ -126,6 +138,7 @@ class AnchorFreeMono3DHead(BaseMono3DDenseHead):
self.use_direction_classifier = use_direction_classifier
self.diff_rad_by_sin = diff_rad_by_sin
self.dir_offset = dir_offset
self.dir_limit_offset = dir_limit_offset
self.loss_cls = build_loss(loss_cls)
self.loss_bbox = build_loss(loss_bbox)
self.loss_dir = build_loss(loss_dir)
......@@ -290,7 +303,7 @@ class AnchorFreeMono3DHead(BaseMono3DDenseHead):
a 4D-tensor.
Returns:
tuple: Usually contain classification scores, bbox predictions, \
tuple: Usually contain classification scores, bbox predictions,
and direction class predictions.
cls_scores (list[Tensor]): Box scores for each scale level,
each is a 4D-tensor, the channel number is
......@@ -402,7 +415,7 @@ class AnchorFreeMono3DHead(BaseMono3DDenseHead):
corresponding to each box
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
gt_bboxes_ignore (list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
"""
......
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