Merge branch 'add_docstring' into 'master'

Add docstrings

See merge request open-mmlab/mmdet.3d!72

mmdet3d/datasets/kitti_dataset.py

@@ -339,7 +339,7 @@ class KittiDataset(Custom3DDataset):
                             class_names,
                             pklfile_prefix=None,
                             submission_prefix=None):
-        """Convert results to kitti format for evaluation and test submission
+        """Convert results to kitti format for evaluation and test submission.
 
         Args:
             net_outputs (List[array]): list of array storing the bbox and score
@@ -348,7 +348,7 @@ class KittiDataset(Custom3DDataset):
             submission_prefix (str | None): The prefix of submission file.
 
         Return:
-            List([dict]): A list of dict have the kitti format
+            List[dict]: A list of dict have the kitti format
         """
         assert len(net_outputs) == len(self.data_infos)
 

mmdet3d/models/dense_heads/anchor3d_head.py

@@ -134,10 +134,10 @@ class Anchor3DHead(nn.Module, AnchorTrainMixin):
         """Forward function on a single-scale feature map.
 
         Args:
-            x (Tensor): Input features.
+            x (torch.Tensor): Input features.
 
         Returns:
-            tuple[Tensor]: Contain score of each class, bbox predictions
+            tuple[torch.Tensor]: Contain score of each class, bbox predictions
                 and class predictions of direction.
         """
         cls_score = self.conv_cls(x)
@@ -151,11 +151,11 @@ class Anchor3DHead(nn.Module, AnchorTrainMixin):
         """Forward pass.
 
         Args:
-            feats (list[Tensor]): Multi-level features, e.g.,
+            feats (list[torch.Tensor]): Multi-level features, e.g.,
                 features produced by FPN.
 
         Returns:
-            tuple[list[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)
@@ -185,20 +185,21 @@ class Anchor3DHead(nn.Module, AnchorTrainMixin):
         """Calculate loss of Single-level results.
 
         Args:
-            cls_score (Tensor): Class score in single-level.
-            bbox_pred (Tensor): Bbox prediction in single-level.
-            dir_cls_preds (Tensor): Predictions of direction class
+            cls_score (torch.Tensor): Class score in single-level.
+            bbox_pred (torch.Tensor): Bbox prediction in single-level.
+            dir_cls_preds (torch.Tensor): Predictions of direction class
                 in single-level.
-            labels (Tensor): Labels of class.
-            label_weights (Tensor): Weights of class loss.
-            bbox_targets (Tensor): Targets of bbox predictions.
-            bbox_weights (Tensor): Weights of bbox loss.
-            dir_targets (Tensor): Targets of direction predictions.
-            dir_weights (Tensor): Weights of direction loss.
+            labels (torch.Tensor): Labels of class.
+            label_weights (torch.Tensor): Weights of class loss.
+            bbox_targets (torch.Tensor): Targets of bbox predictions.
+            bbox_weights (torch.Tensor): Weights of bbox loss.
+            dir_targets (torch.Tensor): Targets of direction predictions.
+            dir_weights (torch.Tensor): Weights of direction loss.
             num_total_samples (int): The number of valid samples.
 
         Returns:
-            tuple[Tensor]: losses of class, bbox and direction, respectively.
+            tuple[torch.Tensor]: losses of class, bbox
+                and direction, respectively.
         """
         # classification loss
         if num_total_samples is None:
@@ -246,10 +247,10 @@ class Anchor3DHead(nn.Module, AnchorTrainMixin):
         """Convert the rotation difference to difference in sine function
 
         Args:
-            boxes1 (Tensor): shape (NxC), where C>=7 and the 7th dimension is
-                rotation dimension
-            boxes2 (Tensor): shape (NxC), where C>=7 and the 7th dimension is
-                rotation dimension
+            boxes1 (torch.Tensor): shape (NxC), where C>=7 and
+                the 7th dimension is rotation dimension
+            boxes2 (torch.Tensor): shape (NxC), where C>=7 and the 7th
+                dimension is rotation dimension
 
         Returns:
             tuple: (boxes1, boxes2) whose 7th dimensions are changed
@@ -275,15 +276,16 @@ class Anchor3DHead(nn.Module, AnchorTrainMixin):
         """Calculate losses.
 
         Args:
-            cls_scores (list[Tensor]): Multi-level class scores.
-            bbox_preds (list[Tensor]): Multi-level bbox predictions.
-            dir_cls_preds (list[Tensor]): Multi-level direction
+            cls_scores (list[torch.Tensor]): Multi-level class scores.
+            bbox_preds (list[torch.Tensor]): Multi-level bbox predictions.
+            dir_cls_preds (list[torch.Tensor]): Multi-level direction
                 class predictions.
             gt_bboxes (list[:obj:BaseInstance3DBoxes]): Gt bboxes
                 of each sample.
-            gt_labels (list[Tensor]): Gt labels 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[Tensor]): Specify which bounding.
+            gt_bboxes_ignore (None | list[torch.Tensor]): Specify
+                which bounding.
 
         Returns:
             dict: Contain class, bbox and direction losses of each level.
@@ -338,13 +340,13 @@ class Anchor3DHead(nn.Module, AnchorTrainMixin):
         """Get bboxes of anchor head.
 
         Args:
-            cls_scores (list[Tensor]): Multi-level class scores.
-            bbox_preds (list[Tensor]): Multi-level bbox predictions.
-            dir_cls_preds (list[Tensor]): Multi-level direction
+            cls_scores (list[torch.Tensor]): Multi-level class scores.
+            bbox_preds (list[torch.Tensor]): Multi-level bbox predictions.
+            dir_cls_preds (list[torch.Tensor]): Multi-level direction
                 class predictions.
             input_metas (list[dict]): Contain pcd and img's meta info.
             cfg (None | ConfigDict): Training or testing config.
-            rescale (list[Tensor]): whether th rescale bbox.
+            rescale (list[torch.Tensor]): whether th rescale bbox.
 
         Returns:
             list[tuple]: prediction resultes of batches.
@@ -390,20 +392,21 @@ class Anchor3DHead(nn.Module, AnchorTrainMixin):
         """Get bboxes of single branch.
 
         Args:
-            cls_scores (Tensor): Class score in single batch.
-            bbox_preds (Tensor): Bbox prediction in single batch.
-            dir_cls_preds (Tensor): Predictions of direction class
+            cls_scores (torch.Tensor): Class score in single batch.
+            bbox_preds (torch.Tensor): Bbox prediction in single batch.
+            dir_cls_preds (torch.Tensor): Predictions of direction class
+                in single batch.
+            mlvl_anchors (List[torch.Tensor]): Multi-level anchors
                 in single batch.
-            mlvl_anchors (List[Tensor]): Multi-level anchors in single batch.
             input_meta (list[dict]): Contain pcd and img's meta info.
             cfg (None | ConfigDict): Training or testing config.
-            rescale (list[Tensor]): whether th rescale bbox.
+            rescale (list[torch.Tensor]): whether th rescale bbox.
 
         Returns:
             tuple: Contain predictions of single batch.
                 - bboxes (:obj:BaseInstance3DBoxes): Predicted 3d bboxes.
-                - scores (Tensor): Class score of each bbox.
-                - labels (Tensor): Label of each bbox.
+                - scores (torch.Tensor): Class score of each bbox.
+                - labels (torch.Tensor): Label of each bbox.
         """
         cfg = self.test_cfg if cfg is None else cfg
         assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)

mmdet3d/models/dense_heads/parta2_rpn_head.py

@@ -109,21 +109,22 @@ class PartA2RPNHead(Anchor3DHead):
         """Get bboxes of single branch.
 
         Args:
-            cls_scores (Tensor): Class score in single batch.
-            bbox_preds (Tensor): Bbox prediction in single batch.
-            dir_cls_preds (Tensor): Predictions of direction class
+            cls_scores (torch.Tensor): Class score in single batch.
+            bbox_preds (torch.Tensor): Bbox prediction in single batch.
+            dir_cls_preds (torch.Tensor): Predictions of direction class
+                in single batch.
+            mlvl_anchors (List[torch.Tensor]): Multi-level anchors
                 in single batch.
-            mlvl_anchors (List[Tensor]): Multi-level anchors in single batch.
             input_meta (list[dict]): Contain pcd and img's meta info.
             cfg (None | ConfigDict): Training or testing config.
-            rescale (list[Tensor]): whether th rescale bbox.
+            rescale (list[torch.Tensor]): whether th rescale bbox.
 
         Returns:
             dict: Predictions of single batch. Contain the keys:
                 - boxes_3d (:obj:BaseInstance3DBoxes): Predicted 3d bboxes.
-                - scores_3d (Tensor): Score of each bbox.
-                - labels_3d (Tensor): Label of each bbox.
-                - cls_preds (Tensor): Class score of each bbox.
+                - scores_3d (torch.Tensor): Score of each bbox.
+                - labels_3d (torch.Tensor): Label of each bbox.
+                - cls_preds (torch.Tensor): Class score of each bbox.
         """
         assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
         mlvl_bboxes = []
@@ -198,13 +199,16 @@ class PartA2RPNHead(Anchor3DHead):
         """Class agnostic nms for single batch.
 
         Args:
-            mlvl_bboxes (Tensor): Bboxes from Multi-level.
-            mlvl_bboxes_for_nms (Tensor): Bboxes for nms (bev or minmax boxes)
-                from Multi-level.
-            mlvl_max_scores (Tensor): Max scores of Multi-level bbox.
-            mlvl_label_pred (Tensor): Class predictions of Multi-level bbox.
-            mlvl_cls_score (Tensor): Class scores of Multi-level bbox.
-            mlvl_dir_scores (Tensor): Direction scores of Multi-level bbox.
+            mlvl_bboxes (torch.Tensor): Bboxes from Multi-level.
+            mlvl_bboxes_for_nms (torch.Tensor): Bboxes for nms
+                (bev or minmax boxes) from Multi-level.
+            mlvl_max_scores (torch.Tensor): Max scores of Multi-level bbox.
+            mlvl_label_pred (torch.Tensor): Class predictions
+                of Multi-level bbox.
+            mlvl_cls_score (torch.Tensor): Class scores of
+                Multi-level bbox.
+            mlvl_dir_scores (torch.Tensor): Direction scores of
+                Multi-level bbox.
             score_thr (int): Score threshold.
             max_num (int): Max number of bboxes after nms.
             cfg (None | ConfigDict): Training or testing config.
@@ -213,9 +217,9 @@ class PartA2RPNHead(Anchor3DHead):
         Returns:
             dict: Predictions of single batch. Contain the keys:
                 - boxes_3d (:obj:BaseInstance3DBoxes): Predicted 3d bboxes.
-                - scores_3d (Tensor): Score of each bbox.
-                - labels_3d (Tensor): Label of each bbox.
-                - cls_preds (Tensor): Class score of each bbox.
+                - scores_3d (torch.Tensor): Score of each bbox.
+                - labels_3d (torch.Tensor): Label of each bbox.
+                - cls_preds (torch.Tensor): Class score of each bbox.
         """
         bboxes = []
         scores = []

mmdet3d/models/dense_heads/train_mixins.py

@@ -24,7 +24,7 @@ class AnchorTrainMixin(object):
                 bboxes of each image.
             input_metas (list[dict]): Meta info of each image.
             gt_bboxes_ignore_list (None | list): Ignore list of gt bboxes.
-            gt_labels_list (list[Tensor]): Gt labels of batches.
+            gt_labels_list (list[torch.Tensor]): Gt labels of batches.
             label_channels (int): The channel of labels.
             num_classes (int): The number of classes.
             sampling (bool): Whether to sample anchors.
@@ -95,10 +95,10 @@ class AnchorTrainMixin(object):
         """Compute targets of anchors in single batch.
 
         Args:
-            anchors (Tensor): Concatenated multi-level anchor.
+            anchors (torch.Tensor): Concatenated multi-level anchor.
             gt_bboxes (:obj:BaseInstance3DBoxes): Gt bboxes.
-            gt_bboxes_ignore (Tensor): Ignored gt bboxes.
-            gt_labels (Tensor): Gt class labels.
+            gt_bboxes_ignore (torch.Tensor): Ignored gt bboxes.
+            gt_labels (torch.Tensor): Gt class labels.
             input_meta (dict): Meta info of each image.
             label_channels (int): The channel of labels.
             num_classes (int): The number of classes.
@@ -184,10 +184,10 @@ class AnchorTrainMixin(object):
 
         Args:
             bbox_assigner (BaseAssigner): assign positive and negative boxes.
-            anchors (Tensor): Concatenated multi-level anchor.
+            anchors (torch.Tensor): Concatenated multi-level anchor.
             gt_bboxes (:obj:BaseInstance3DBoxes): Gt bboxes.
-            gt_bboxes_ignore (Tensor): Ignored gt bboxes.
-            gt_labels (Tensor): Gt class labels.
+            gt_bboxes_ignore (torch.Tensor): Ignored gt bboxes.
+            gt_labels (torch.Tensor): Gt class labels.
             input_meta (dict): Meta info of each image.
             label_channels (int): The channel of labels.
             num_classes (int): The number of classes.
@@ -260,14 +260,14 @@ def get_direction_target(anchors,
     """Encode direction to 0 ~ num_bins-1.
 
     Args:
-        anchors (Tensor): Concatenated multi-level anchor.
-        reg_targets (Tensor): Bbox regression targets.
+        anchors (torch.Tensor): Concatenated multi-level anchor.
+        reg_targets (torch.Tensor): Bbox regression targets.
         dir_offset (int): Direction offset.
         num_bins (int): Number of bins to divide 2*PI.
         one_hot (bool): Whether to encode as one hot.
 
     Returns:
-        Tensor: Encoded direction targets.
+        torch.Tensor: Encoded direction targets.
     """
     rot_gt = reg_targets[..., 6] + anchors[..., 6]
     offset_rot = box_torch_ops.limit_period(rot_gt - dir_offset, 0, 2 * np.pi)

mmdet3d/models/dense_heads/vote_head.py

@@ -188,14 +188,17 @@ class VoteHead(nn.Module):
 
         Args:
             bbox_preds (dict): Predictions from forward of vote head.
-            points (list[Tensor]): Input points.
+            points (list[torch.Tensor]): Input points.
             gt_bboxes_3d (list[:obj:BaseInstance3DBoxes]): Gt bboxes
                 of each sample.
-            gt_labels_3d (list[Tensor]): Gt labels of each sample.
-            pts_semantic_mask (None | list[Tensor]): Point-wise semantic mask.
-            pts_instance_mask (None | list[Tensor]): Point-wise instance mask.
+            gt_labels_3d (list[torch.Tensor]): Gt labels of each sample.
+            pts_semantic_mask (None | list[torch.Tensor]): Point-wise
+                semantic mask.
+            pts_instance_mask (None | list[torch.Tensor]): Point-wise
+                instance mask.
             img_metas (list[dict]): Contain pcd and img's meta info.
-            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding.
+            gt_bboxes_ignore (None | list[torch.Tensor]): Specify
+                which bounding.
 
         Returns:
             dict: Losses of Votenet.
@@ -292,15 +295,15 @@ class VoteHead(nn.Module):
         """Generate targets of vote head.
 
         Args:
-            points (list[Tensor]): Points of each batch.
+            points (list[torch.Tensor]): Points of each batch.
             gt_bboxes_3d (list[:obj:BaseInstance3DBoxes]): gt bboxes of
                 each batch.
-            gt_labels_3d (list[Tensor]): gt class labels of each batch.
-            pts_semantic_mask (None | list[Tensor]): point-wise semantic
+            gt_labels_3d (list[torch.Tensor]): gt class labels of each batch.
+            pts_semantic_mask (None | list[torch.Tensor]): point-wise semantic
                 label of each batch.
-            pts_instance_mask (None | list[Tensor]): point-wise instance
+            pts_instance_mask (None | list[torch.Tensor]): point-wise instance
                 label of each batch.
-            bbox_preds (Tensor): Bbox predictions of vote head.
+            bbox_preds (torch.Tensor): Bbox predictions of vote head.
 
         Returns:
             tuple: Targets of vote head.
@@ -378,14 +381,14 @@ class VoteHead(nn.Module):
         """Generate targets of vote head for single batch.
 
         Args:
-            points (Tensor): Points of each batch.
+            points (torch.Tensor): Points of each batch.
             gt_bboxes_3d (:obj:BaseInstance3DBoxes): gt bboxes of each batch.
-            gt_labels_3d (Tensor): gt class labels of each batch.
-            pts_semantic_mask (None | Tensor): point-wise semantic
+            gt_labels_3d (torch.Tensor): gt class labels of each batch.
+            pts_semantic_mask (None | torch.Tensor): point-wise semantic
                 label of each batch.
-            pts_instance_mask (None | Tensor): point-wise instance
+            pts_instance_mask (None | torch.Tensor): point-wise instance
                 label of each batch.
-            aggregated_points (Tensor): Aggregated points from
+            aggregated_points (torch.Tensor): Aggregated points from
                 vote aggregation layer.
 
         Returns:
@@ -491,13 +494,13 @@ class VoteHead(nn.Module):
         """Generate bboxes from vote head predictions.
 
         Args:
-            points (Tensor): Input points.
+            points (torch.Tensor): Input points.
             bbox_preds (dict): Predictions from vote head.
             input_metas (list[dict]): Contain pcd and img's meta info.
             rescale (bool): Whether to rescale bboxes.
 
         Returns:
-            list[tuple[Tensor]]: Contain bbox, scores and labels.
+            list[tuple[torch.Tensor]]: Contain bbox, scores and labels.
         """
         # decode boxes
         obj_scores = F.softmax(bbox_preds['obj_scores'], dim=-1)[..., -1]
@@ -523,14 +526,14 @@ class VoteHead(nn.Module):
         """multi-class nms in single batch.
 
         Args:
-            obj_scores (Tensor): Objectness score of bboxes.
-            sem_scores (Tensor): semantic class score of bboxes.
-            bbox (Tensor): Predicted bbox.
-            points (Tensor): Input points.
+            obj_scores (torch.Tensor): Objectness score of bboxes.
+            sem_scores (torch.Tensor): semantic class score of bboxes.
+            bbox (torch.Tensor): Predicted bbox.
+            points (torch.Tensor): Input points.
             input_meta (dict): Contain pcd and img's meta info.
 
         Returns:
-            tuple[Tensor]: Contain bbox, scores and labels.
+            tuple[torch.Tensor]: Contain bbox, scores and labels.
         """
         bbox = input_meta['box_type_3d'](
             bbox,

mmdet3d/models/detectors/base.py

@@ -7,15 +7,16 @@ class Base3DDetector(BaseDetector):
     def forward_test(self, points, img_metas, img=None, **kwargs):
         """
         Args:
-            points (List[Tensor]): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxC,
+            points (list[torch.Tensor]): the outer list indicates test-time
+                augmentations and inner torch.Tensor should have a shape NxC,
                 which contains all points in the batch.
-            img_metas (List[List[dict]]): the outer list indicates test-time
+            img_metas (list[list[dict]]): the outer list indicates test-time
                 augs (multiscale, flip, etc.) and the inner list indicates
                 images in a batch
-            img (List[Tensor], optional): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxCxHxW,
-                which contains all images in the batch. Defaults to None.
+            img (list[torch.Tensor], optional): the outer
+                list indicates test-time augmentations and inner
+                torch.Tensor should have a shape NxCxHxW, which contains
+                all images in the batch. Defaults to None.
         """
         for var, name in [(points, 'points'), (img_metas, 'img_metas')]:
             if not isinstance(var, list):
@@ -42,9 +43,9 @@ class Base3DDetector(BaseDetector):
         Calls either forward_train or forward_test depending on whether
         return_loss=True. Note this setting will change the expected inputs.
         When `return_loss=True`, img and img_metas are single-nested (i.e.
-        Tensor and List[dict]), and when `resturn_loss=False`, img and
+        torch.Tensor and list[dict]), and when `resturn_loss=False`, img and
         img_metas should be double nested
-        (i.e.  List[Tensor], List[List[dict]]), with the outer list
+        (i.e.  list[torch.Tensor], list[list[dict]]), with the outer list
         indicating test time augmentations.
         """
         if return_loss:

mmdet3d/models/detectors/single_stage.py

@@ -52,7 +52,10 @@ class SingleStage3DDetector(Base3DDetector):
         self.bbox_head.init_weights()
 
     def extract_feat(self, points, img_metas=None):
-        """Directly extract features from the backbone+neck
+        """Directly extract features from the backbone+neck.
+
+        Args:
+            points (torch.Tensor): Input points.
         """
         x = self.backbone(points)
         if self.with_neck:

mmdet3d/models/detectors/votenet.py

@@ -36,15 +36,16 @@ class VoteNet(SingleStage3DDetector):
         """Forward of training.
 
         Args:
-            points (list[Tensor]): Points of each batch.
+            points (list[torch.Tensor]): Points of each batch.
             img_metas (list): Image metas.
             gt_bboxes_3d (:obj:BaseInstance3DBoxes): gt bboxes of each batch.
-            gt_labels_3d (list[Tensor]): gt class labels of each batch.
-            pts_semantic_mask (None | list[Tensor]): point-wise semantic
+            gt_labels_3d (list[torch.Tensor]): gt class labels of each batch.
+            pts_semantic_mask (None | list[torch.Tensor]): point-wise semantic
                 label of each batch.
-            pts_instance_mask (None | list[Tensor]): point-wise instance
+            pts_instance_mask (None | list[torch.Tensor]): point-wise instance
                 label of each batch.
-            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding.
+            gt_bboxes_ignore (None | list[torch.Tensor]): Specify
+                which bounding.
 
         Returns:
             dict: Losses.
@@ -63,7 +64,7 @@ class VoteNet(SingleStage3DDetector):
         """Forward of testing.
 
         Args:
-            points (list[Tensor]): Points of each sample.
+            points (list[torch.Tensor]): Points of each sample.
             img_metas (list): Image metas.
             rescale (bool): Whether to rescale results.
 

tools/data_converter/create_gt_database.py

@@ -120,6 +120,27 @@ def create_groundtruth_database(dataset_class_name,
                                 bev_only=False,
                                 coors_range=None,
                                 with_mask=False):
+    """Given the raw data, generate the ground truth database.
+
+    Args:
+        dataset_class_name （str): Name of the input dataset.
+        data_path (str): Path of the data.
+        info_prefix (str): Prefix of the info file.
+        info_path (str): Path of the info file.
+            Default: None.
+        mask_anno_path (str): Path of the mask_anno.
+            Default: None.
+        used_classes (list[str]): Classes have been used.
+            Default: None.
+        database_save_path (str): Path to save database.
+            Default: None.
+        db_info_save_path (str): Path to save db_info.
+            Default: None.
+        relative_path (bool): Whether to use relative path.
+            Default: True.
+        with_mask (bool): Whether to use mask.
+            Default: False.
+    """
     print(f'Create GT Database of {dataset_class_name}')
     dataset_cfg = dict(
         type=dataset_class_name,

tools/data_converter/kitti_converter.py

@@ -10,6 +10,9 @@ from .kitti_data_utils import get_kitti_image_info
 
 def convert_to_kitti_info_version2(info):
     """convert kitti info v1 to v2 if possible.
+
+    Args:
+        info (dict): Info of the input kitti data.
     """
     if 'image' not in info or 'calib' not in info or 'point_cloud' not in info:
         info['image'] = {
@@ -78,6 +81,16 @@ def create_kitti_info_file(data_path,
                            pkl_prefix='kitti_',
                            save_path=None,
                            relative_path=True):
+    """Create info file of KITTI dataset.
+
+    Given the raw data, generate its related info file in pkl format.
+
+    Args:
+        data_path (str): Path of the data root.
+        pkl_prefix (str): Prefix of the info file to be generated.
+        save_path (str): Path to save the info file.
+        relative_path (bool): Whether to use relative path.
+    """
     imageset_folder = Path(data_path) / 'ImageSets'
     train_img_ids = _read_imageset_file(
         str(imageset_folder / 'train_6014.txt'))

tools/data_converter/nuscenes_converter.py

@@ -20,6 +20,18 @@ def create_nuscenes_infos(root_path,
                           info_prefix,
                           version='v1.0-trainval',
                           max_sweeps=10):
+    """Create info file of nuscene dataset.
+
+    Given the raw data, generate its related info file in pkl format.
+
+    Args:
+        root_path (str): Path of the data root.
+        info_prefix (str): Prefix of the info file to be generated.
+        version (str): Version of the data.
+            Default: 'v1.0-trainval'
+        max_sweeps (int): Max number of sweeps.
+            Default: 10
+    """
     from nuscenes.nuscenes import NuScenes
     nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
     from nuscenes.utils import splits