refactor directory

configs/_base_/datasets/s3dis-3d-5class.py

 # dataset settings
+# TODO refactor S3DISDataset
 dataset_type = 'S3DISDataset'
 data_root = './data/s3dis/'
 class_names = ('table', 'chair', 'sofa', 'bookcase', 'board')

docs/en/tutorials/config.md

@@ -116,7 +116,7 @@ model = dict(
                         [0.50867593, 0.50656086, 0.30136237],
                         [1.1511526, 1.0546296, 0.49706793],
                         [0.47535285, 0.49249494, 0.5802117]]),  # Mean sizes for each class, the order is consistent with class_names.
-        vote_moudule_cfg=dict(  # Config of vote module branch, refer to mmdet3d.models.model_utils for more details
+        vote_moudule_cfg=dict(  # Config of vote module branch, refer to mmdet3d.models.layers for more details
             in_channels=256,  # Input channels for vote_module
             vote_per_seed=1,  # Number of votes to generate for each seed
             gt_per_seed=3,  # Number of gts for each seed
@@ -186,77 +186,77 @@ class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
                'bookshelf', 'picture', 'counter', 'desk', 'curtain',
                'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
                'garbagebin')  # Names of classes
-train_pipeline = [  # Training pipeline, refer to mmdet3d.datasets.pipelines for more details
+train_pipeline = [  # Training pipeline, refer to mmdet3d.datasets.transforms for more details
     dict(
-        type='LoadPointsFromFile',  # First pipeline to load points, refer to mmdet3d.datasets.pipelines.indoor_loading for more details
+        type='LoadPointsFromFile',  # First pipeline to load points, refer to mmdet3d.datasets.transforms.indoor_loading for more details
         shift_height=True,  # Whether to use shifted height
         load_dim=6,  # The dimension of the loaded points
         use_dim=[0, 1, 2]),  # Which dimensions of the points to be used
     dict(
-        type='LoadAnnotations3D',  # Second pipeline to load annotations, refer to mmdet3d.datasets.pipelines.indoor_loading for more details
+        type='LoadAnnotations3D',  # Second pipeline to load annotations, refer to mmdet3d.datasets.transforms.indoor_loading for more details
         with_bbox_3d=True,  # Whether to load 3D boxes
         with_label_3d=True,  # Whether to load 3D labels corresponding to each 3D box
         with_mask_3d=True,  # Whether to load 3D instance masks
         with_seg_3d=True),  # Whether to load 3D semantic masks
     dict(
-        type='PointSegClassMapping',  # Declare valid categories, refer to mmdet3d.datasets.pipelines.point_seg_class_mapping for more details
+        type='PointSegClassMapping',  # Declare valid categories, refer to mmdet3d.datasets.transforms.point_seg_class_mapping for more details
         valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
                        36, 39),  # all valid categories ids
         max_cat_id=40),  # max possible category id in input segmentation mask
-    dict(type='PointSample',  # Sample points, refer to mmdet3d.datasets.pipelines.transforms_3d for more details
+    dict(type='PointSample',  # Sample points, refer to mmdet3d.datasets.transforms.transforms_3d for more details
             num_points=40000),  # Number of points to be sampled
     dict(type='IndoorFlipData',  # Augmentation pipeline that flip points and 3d boxes
         flip_ratio_yz=0.5,  # Probability of being flipped along yz plane
         flip_ratio_xz=0.5),  # Probability of being flipped along xz plane
     dict(
-        type='IndoorGlobalRotScale',  # Augmentation pipeline that rotate and scale points and 3d boxes, refer to mmdet3d.datasets.pipelines.indoor_augment for more details
+        type='IndoorGlobalRotScale',  # Augmentation pipeline that rotate and scale points and 3d boxes, refer to mmdet3d.datasets.transforms.indoor_augment for more details
         shift_height=True,  # Whether the loaded points use `shift_height` attribute
         rot_range=[-0.027777777777777776, 0.027777777777777776],  # Range of rotation
         scale_range=None),  # Range of scale
     dict(
-        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formatting for more details
+        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.transforms.formatting for more details
         class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                      'window', 'bookshelf', 'picture', 'counter', 'desk',
                      'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                      'sink', 'bathtub', 'garbagebin')),
     dict(
-        type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formatting for more details
+        type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.transforms.formatting for more details
         keys=[
             'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
             'pts_instance_mask'
         ])
 ]
-test_pipeline = [  # Testing pipeline, refer to mmdet3d.datasets.pipelines for more details
+test_pipeline = [  # Testing pipeline, refer to mmdet3d.datasets.transforms for more details
     dict(
-        type='LoadPointsFromFile',  # First pipeline to load points, refer to mmdet3d.datasets.pipelines.indoor_loading for more details
+        type='LoadPointsFromFile',  # First pipeline to load points, refer to mmdet3d.datasets.transforms.indoor_loading for more details
         shift_height=True,  # Whether to use shifted height
         load_dim=6,  # The dimension of the loaded points
         use_dim=[0, 1, 2]),  # Which dimensions of the points to be used
-    dict(type='PointSample',  # Sample points, refer to mmdet3d.datasets.pipelines.transforms_3d for more details
+    dict(type='PointSample',  # Sample points, refer to mmdet3d.datasets.transforms.transforms_3d for more details
         num_points=40000),  # Number of points to be sampled
     dict(
-        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formatting for more details
+        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.transforms.formatting for more details
         class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                      'window', 'bookshelf', 'picture', 'counter', 'desk',
                      'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                      'sink', 'bathtub', 'garbagebin')),
-    dict(type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formatting for more details
+    dict(type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.transforms.formatting for more details
         keys=['points'])
 ]
-eval_pipeline = [  # Pipeline used for evaluation or visualization, refer to mmdet3d.datasets.pipelines for more details
+eval_pipeline = [  # Pipeline used for evaluation or visualization, refer to mmdet3d.datasets.transforms for more details
     dict(
-        type='LoadPointsFromFile',  # First pipeline to load points, refer to mmdet3d.datasets.pipelines.indoor_loading for more details
+        type='LoadPointsFromFile',  # First pipeline to load points, refer to mmdet3d.datasets.transforms.indoor_loading for more details
         shift_height=True,  # Whether to use shifted height
         load_dim=6,  # The dimension of the loaded points
         use_dim=[0, 1, 2]),  # Which dimensions of the points to be used
     dict(
-        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formatting for more details
+        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.transforms.formatting for more details
         class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                      'window', 'bookshelf', 'picture', 'counter', 'desk',
                      'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                      'sink', 'bathtub', 'garbagebin')),
         with_label=False),
-    dict(type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formatting for more details
+    dict(type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.transforms.formatting for more details
         keys=['points'])
 ]
 data = dict(

docs/zh_cn/tutorials/config.md

@@ -117,7 +117,7 @@ model = dict(
                         [0.50867593, 0.50656086, 0.30136237],
                         [1.1511526, 1.0546296, 0.49706793],
                         [0.47535285, 0.49249494, 0.5802117]]),  # 每一类的平均尺寸，其顺序与类名顺序相同
-        vote_moudule_cfg=dict(  # 投票 (vote) 模块的配置，更多细节请参考 mmdet3d.models.model_utils
+        vote_moudule_cfg=dict(  # 投票 (vote) 模块的配置，更多细节请参考 mmdet3d.models.layers
             in_channels=256,  # 投票模块的输入通道数
             vote_per_seed=1,  # 对于每个种子点生成的投票数
             gt_per_seed=3,  # 每个种子点的真实标签个数
@@ -187,77 +187,77 @@ class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
                'bookshelf', 'picture', 'counter', 'desk', 'curtain',
                'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
                'garbagebin')  # 类的名称
-train_pipeline = [  # 训练流水线，更多细节请参考 mmdet3d.datasets.pipelines
+train_pipeline = [  # 训练流水线，更多细节请参考 mmdet3d.datasets.transforms
     dict(
-        type='LoadPointsFromFile',  # 第一个流程，用于读取点，更多细节请参考 mmdet3d.datasets.pipelines.indoor_loading
+        type='LoadPointsFromFile',  # 第一个流程，用于读取点，更多细节请参考 mmdet3d.datasets.transforms.indoor_loading
         shift_height=True,  # 是否使用变换高度
         load_dim=6,  # 读取的点的维度
         use_dim=[0, 1, 2]),  # 使用所读取点的哪些维度
     dict(
-        type='LoadAnnotations3D',  # 第二个流程，用于读取标注，更多细节请参考 mmdet3d.datasets.pipelines.indoor_loading
+        type='LoadAnnotations3D',  # 第二个流程，用于读取标注，更多细节请参考 mmdet3d.datasets.transforms.indoor_loading
         with_bbox_3d=True,  # 是否读取 3D 框
         with_label_3d=True,  # 是否读取 3D 框对应的类别标签
         with_mask_3d=True,  # 是否读取 3D 实例分割掩码
         with_seg_3d=True),  # 是否读取 3D 语义分割掩码
     dict(
-        type='PointSegClassMapping',  # 选取有效的类别，更多细节请参考 mmdet3d.datasets.pipelines.point_seg_class_mapping
+        type='PointSegClassMapping',  # 选取有效的类别，更多细节请参考 mmdet3d.datasets.transforms.point_seg_class_mapping
         valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
                        36, 39),  # 所有有效类别的编号
         max_cat_id=40),  # 输入语义分割掩码中可能存在的最大类别编号
-    dict(type='PointSample',  # 室内点采样，更多细节请参考 mmdet3d.datasets.pipelines.indoor_sample
+    dict(type='PointSample',  # 室内点采样，更多细节请参考 mmdet3d.datasets.transforms.indoor_sample
             num_points=40000),  # 采样的点的数量
     dict(type='IndoorFlipData',  # 数据增广流程，随机翻转点和 3D 框
         flip_ratio_yz=0.5,  # 沿着 yz 平面被翻转的概率
         flip_ratio_xz=0.5),  # 沿着 xz 平面被翻转的概率
     dict(
-        type='IndoorGlobalRotScale',  # 数据增广流程，旋转并放缩点和 3D 框，更多细节请参考 mmdet3d.datasets.pipelines.indoor_augment
+        type='IndoorGlobalRotScale',  # 数据增广流程，旋转并放缩点和 3D 框，更多细节请参考 mmdet3d.datasets.transforms.indoor_augment
         shift_height=True,  # 读取的点是否有高度这一属性
         rot_range=[-0.027777777777777776, 0.027777777777777776],  # 旋转角范围
         scale_range=None),  # 缩放尺寸范围
     dict(
-        type='DefaultFormatBundle3D',  # 默认格式打包以收集读取的所有数据，更多细节请参考 mmdet3d.datasets.pipelines.formatting
+        type='DefaultFormatBundle3D',  # 默认格式打包以收集读取的所有数据，更多细节请参考 mmdet3d.datasets.transforms.formatting
         class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                      'window', 'bookshelf', 'picture', 'counter', 'desk',
                      'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                      'sink', 'bathtub', 'garbagebin')),
     dict(
-        type='Collect3D',  # 最后一个流程，决定哪些键值对应的数据会被输入给检测器，更多细节请参考 mmdet3d.datasets.pipelines.formatting
+        type='Collect3D',  # 最后一个流程，决定哪些键值对应的数据会被输入给检测器，更多细节请参考 mmdet3d.datasets.transforms.formatting
         keys=[
             'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
             'pts_instance_mask'
         ])
 ]
-test_pipeline = [  # 测试流水线，更多细节请参考 mmdet3d.datasets.pipelines
+test_pipeline = [  # 测试流水线，更多细节请参考 mmdet3d.datasets.transforms
     dict(
-        type='LoadPointsFromFile',  # 第一个流程，用于读取点，更多细节请参考 mmdet3d.datasets.pipelines.indoor_loading
+        type='LoadPointsFromFile',  # 第一个流程，用于读取点，更多细节请参考 mmdet3d.datasets.transforms.indoor_loading
         shift_height=True,  # 是否使用变换高度
         load_dim=6,  # 读取的点的维度
         use_dim=[0, 1, 2]),  # 使用所读取点的哪些维度
-    dict(type='PointSample',  # 室内点采样，更多细节请参考 mmdet3d.datasets.pipelines.indoor_sample
+    dict(type='PointSample',  # 室内点采样，更多细节请参考 mmdet3d.datasets.transforms.indoor_sample
             num_points=40000),  # 采样的点的数量
     dict(
-        type='DefaultFormatBundle3D',  # 默认格式打包以收集读取的所有数据，更多细节请参考 mmdet3d.datasets.pipelines.formatting
+        type='DefaultFormatBundle3D',  # 默认格式打包以收集读取的所有数据，更多细节请参考 mmdet3d.datasets.transforms.formatting
         class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                      'window', 'bookshelf', 'picture', 'counter', 'desk',
                      'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                      'sink', 'bathtub', 'garbagebin')),
-    dict(type='Collect3D',  # 最后一个流程，决定哪些键值对应的数据会被输入给检测器，更多细节请参考 mmdet3d.datasets.pipelines.formatting
+    dict(type='Collect3D',  # 最后一个流程，决定哪些键值对应的数据会被输入给检测器，更多细节请参考 mmdet3d.datasets.transforms.formatting
         keys=['points'])
 ]
-eval_pipeline = [  # 模型验证或可视化所使用的流水线，更多细节请参考 mmdet3d.datasets.pipelines
+eval_pipeline = [  # 模型验证或可视化所使用的流水线，更多细节请参考 mmdet3d.datasets.transforms
     dict(
-        type='LoadPointsFromFile',  # 第一个流程，用于读取点，更多细节请参考 mmdet3d.datasets.pipelines.indoor_loading
+        type='LoadPointsFromFile',  # 第一个流程，用于读取点，更多细节请参考 mmdet3d.datasets.transforms.indoor_loading
         shift_height=True,  # 是否使用变换高度
         load_dim=6,  # 读取的点的维度
         use_dim=[0, 1, 2]),  # 使用所读取点的哪些维度
     dict(
-        type='DefaultFormatBundle3D',  # 默认格式打包以收集读取的所有数据，更多细节请参考 mmdet3d.datasets.pipelines.formatting
+        type='DefaultFormatBundle3D',  # 默认格式打包以收集读取的所有数据，更多细节请参考 mmdet3d.datasets.transforms.formatting
         class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                      'window', 'bookshelf', 'picture', 'counter', 'desk',
                      'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                      'sink', 'bathtub', 'garbagebin')),
         with_label=False),
-    dict(type='Collect3D',  # 最后一个流程，决定哪些键值对应的数据会被输入给检测器，更多细节请参考 mmdet3d.datasets.pipelines.formatting
+    dict(type='Collect3D',  # 最后一个流程，决定哪些键值对应的数据会被输入给检测器，更多细节请参考 mmdet3d.datasets.transforms.formatting
         keys=['points'])
 ]
 data = dict(

mmdet3d/apis/inference.py

@@ -12,9 +12,9 @@ import torch.nn as nn
 from mmengine.dataset import Compose
 from mmengine.runner import load_checkpoint
 
-from mmdet3d.core import Box3DMode, Det3DDataSample, SampleList
-from mmdet3d.core.bbox import get_box_type
-from mmdet3d.models import build_model
+from mmdet3d.registry import MODELS
+from mmdet3d.structures import Box3DMode, Det3DDataSample, get_box_type
+from mmdet3d.structures.det3d_data_sample import SampleList
 
 
 def convert_SyncBN(config):
@@ -55,7 +55,7 @@ def init_model(config, checkpoint=None, device='cuda:0'):
     config.model.pretrained = None
     convert_SyncBN(config.model)
     config.model.train_cfg = None
-    model = build_model(config.model)
+    model = MODELS.build(config.model)
     if checkpoint is not None:
         checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
         if 'CLASSES' in checkpoint['meta']:

mmdet3d/core/__init__.py

-# Copyright (c) OpenMMLab. All rights reserved.
-from .anchor import *  # noqa: F401, F403
-from .bbox import *  # noqa: F401, F403
-from .data_structures import *  # noqa: F401, F403
-from .evaluation import *  # noqa: F401, F403
-from .points import *  # noqa: F401, F403
-from .post_processing import *  # noqa: F401, F403
-from .utils import *  # noqa: F401, F403
-from .visualization import *  # noqa: F401, F403
-from .voxel import *  # noqa: F401, F403

mmdet3d/core/bbox/__init__.py

-# Copyright (c) OpenMMLab. All rights reserved.
-from .assigners import AssignResult, BaseAssigner, Max3DIoUAssigner
-# from .bbox_target import bbox_target
-from .builder import build_assigner, build_bbox_coder, build_sampler
-from .coders import DeltaXYZWLHRBBoxCoder
-from .iou_calculators import (AxisAlignedBboxOverlaps3D, BboxOverlaps3D,
-                              BboxOverlapsNearest3D,
-                              axis_aligned_bbox_overlaps_3d, bbox_overlaps_3d,
-                              bbox_overlaps_nearest_3d)
-from .samplers import (BaseSampler, CombinedSampler,
-                       InstanceBalancedPosSampler, IoUBalancedNegSampler,
-                       PseudoSampler, RandomSampler, SamplingResult)
-from .structures import (BaseInstance3DBoxes, Box3DMode, CameraInstance3DBoxes,
-                         Coord3DMode, DepthInstance3DBoxes,
-                         LiDARInstance3DBoxes, get_box_type, limit_period,
-                         mono_cam_box2vis, points_cam2img, points_img2cam,
-                         xywhr2xyxyr)
-from .transforms import bbox3d2result, bbox3d2roi, bbox3d_mapping_back
-
-__all__ = [
-    'BaseSampler', 'AssignResult', 'BaseAssigner', 'Max3DIoUAssigner',
-    'PseudoSampler', 'RandomSampler', 'InstanceBalancedPosSampler',
-    'IoUBalancedNegSampler', 'CombinedSampler', 'SamplingResult',
-    'DeltaXYZWLHRBBoxCoder', 'BboxOverlapsNearest3D', 'BboxOverlaps3D',
-    'bbox_overlaps_nearest_3d', 'bbox_overlaps_3d',
-    'AxisAlignedBboxOverlaps3D', 'axis_aligned_bbox_overlaps_3d', 'Box3DMode',
-    'LiDARInstance3DBoxes', 'CameraInstance3DBoxes', 'bbox3d2roi',
-    'bbox3d2result', 'DepthInstance3DBoxes', 'BaseInstance3DBoxes',
-    'bbox3d_mapping_back', 'xywhr2xyxyr', 'limit_period', 'points_cam2img',
-    'points_img2cam', 'get_box_type', 'Coord3DMode', 'mono_cam_box2vis',
-    'build_assigner', 'build_bbox_coder', 'build_sampler'
-]

mmdet3d/core/bbox/iou_calculators/__init__.py

-# Copyright (c) OpenMMLab. All rights reserved.
-from .iou3d_calculator import (AxisAlignedBboxOverlaps3D, BboxOverlaps3D,
-                               BboxOverlapsNearest3D,
-                               axis_aligned_bbox_overlaps_3d, bbox_overlaps_3d,
-                               bbox_overlaps_nearest_3d)
-
-__all__ = [
-    'BboxOverlapsNearest3D', 'BboxOverlaps3D', 'bbox_overlaps_nearest_3d',
-    'bbox_overlaps_3d', 'AxisAlignedBboxOverlaps3D',
-    'axis_aligned_bbox_overlaps_3d'
-]

mmdet3d/core/data_structures/__init__.py

-# Copyright (c) OpenMMLab. All rights reserved.
-from .det3d_data_sample import Det3DDataSample
-from .point_data import PointData
-
-__all__ = ['Det3DDataSample', 'PointData']

mmdet3d/core/evaluation/__init__.py

-# Copyright (c) OpenMMLab. All rights reserved.
-from .indoor_eval import indoor_eval
-from .instance_seg_eval import instance_seg_eval
-from .kitti_utils import kitti_eval, kitti_eval_coco_style
-from .lyft_eval import lyft_eval
-from .seg_eval import seg_eval
-
-__all__ = [
-    'kitti_eval_coco_style', 'kitti_eval', 'indoor_eval', 'lyft_eval',
-    'seg_eval', 'instance_seg_eval'
-]

mmdet3d/core/post_processing/__init__.py

-# Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.core.post_processing import (merge_aug_bboxes, merge_aug_masks,
-                                        merge_aug_proposals, merge_aug_scores,
-                                        multiclass_nms)
-from .box3d_nms import (aligned_3d_nms, box3d_multiclass_nms, circle_nms,
-                        nms_bev, nms_normal_bev)
-from .merge_augs import merge_aug_bboxes_3d
-
-__all__ = [
-    'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes',
-    'merge_aug_scores', 'merge_aug_masks', 'box3d_multiclass_nms',
-    'aligned_3d_nms', 'merge_aug_bboxes_3d', 'circle_nms', 'nms_bev',
-    'nms_normal_bev'
-]

mmdet3d/core/utils/__init__.py

-# Copyright (c) OpenMMLab. All rights reserved.
-from .array_converter import ArrayConverter, array_converter
-from .gaussian import (draw_heatmap_gaussian, ellip_gaussian2D, gaussian_2d,
-                       gaussian_radius, get_ellip_gaussian_2D)
-from .misc import add_prefix
-from .typing import (ConfigType, ForwardResults, InstanceList, MultiConfig,
-                     OptConfigType, OptInstanceList, OptMultiConfig,
-                     OptSampleList, OptSamplingResultList, SampleList,
-                     SamplingResultList)
-
-__all__ = [
-    'gaussian_2d', 'gaussian_radius', 'draw_heatmap_gaussian',
-    'ArrayConverter', 'array_converter', 'ellip_gaussian2D',
-    'get_ellip_gaussian_2D', 'ConfigType', 'OptConfigType', 'MultiConfig',
-    'OptMultiConfig', 'InstanceList', 'OptInstanceList', 'SampleList',
-    'OptSampleList', 'SamplingResultList', 'ForwardResults',
-    'OptSamplingResultList', 'add_prefix'
-]

mmdet3d/datasets/__init__.py

@@ -4,21 +4,8 @@ from .convert_utils import get_2d_boxes
 from .dataset_wrappers import CBGSDataset
 from .det3d_dataset import Det3DDataset
 from .kitti_dataset import KittiDataset
-from .kitti_mono_dataset import KittiMonoDataset
 from .lyft_dataset import LyftDataset
 from .nuscenes_dataset import NuScenesDataset
-from .nuscenes_mono_dataset import NuScenesMonoDataset
-# yapf: disable
-from .pipelines import (AffineResize, BackgroundPointsFilter, GlobalAlignment,
-                        GlobalRotScaleTrans, IndoorPatchPointSample,
-                        IndoorPointSample, LoadAnnotations3D,
-                        LoadPointsFromDict, LoadPointsFromFile,
-                        LoadPointsFromMultiSweeps, NormalizePointsColor,
-                        ObjectNameFilter, ObjectNoise, ObjectRangeFilter,
-                        ObjectSample, PointSample, PointShuffle,
-                        PointsRangeFilter, RandomDropPointsColor, RandomFlip3D,
-                        RandomJitterPoints, RandomShiftScale,
-                        VoxelBasedPointSampler)
 # yapf: enable
 from .s3dis_dataset import S3DISDataset, S3DISSegDataset
 from .scannet_dataset import (ScanNetDataset, ScanNetInstanceSegDataset,
@@ -26,12 +13,23 @@ from .scannet_dataset import (ScanNetDataset, ScanNetInstanceSegDataset,
 from .seg3d_dataset import Seg3DDataset
 from .semantickitti_dataset import SemanticKITTIDataset
 from .sunrgbd_dataset import SUNRGBDDataset
+# yapf: disable
+from .transforms import (AffineResize, BackgroundPointsFilter, GlobalAlignment,
+                         GlobalRotScaleTrans, IndoorPatchPointSample,
+                         IndoorPointSample, LoadAnnotations3D,
+                         LoadPointsFromDict, LoadPointsFromFile,
+                         LoadPointsFromMultiSweeps, NormalizePointsColor,
+                         ObjectNameFilter, ObjectNoise, ObjectRangeFilter,
+                         ObjectSample, PointSample, PointShuffle,
+                         PointsRangeFilter, RandomDropPointsColor,
+                         RandomFlip3D, RandomJitterPoints, RandomShiftScale,
+                         VoxelBasedPointSampler)
 from .utils import get_loading_pipeline
 from .waymo_dataset import WaymoDataset
 
 __all__ = [
-    'KittiDataset', 'KittiMonoDataset', 'DATASETS', 'CBGSDataset',
-    'build_dataset', 'NuScenesDataset', 'NuScenesMonoDataset', 'LyftDataset',
+    'KittiDataset', 'DATASETS', 'CBGSDataset',
+    'build_dataset', 'NuScenesDataset', 'LyftDataset',
     'ObjectSample', 'RandomFlip3D', 'ObjectNoise', 'GlobalRotScaleTrans',
     'PointShuffle', 'ObjectRangeFilter', 'PointsRangeFilter',
     'LoadPointsFromFile', 'S3DISSegDataset', 'S3DISDataset',

mmdet3d/datasets/builder.py

@@ -18,9 +18,9 @@ PIPELINES = TRANSFORMS
 
 
 def build_dataset(cfg, default_args=None):
+    from mmengine import ClassBalancedDataset, ConcatDataset, RepeatDataset
+
     from mmdet3d.datasets.dataset_wrappers import CBGSDataset
-    from mmdet.datasets.dataset_wrappers import (ClassBalancedDataset,
-                                                 ConcatDataset, RepeatDataset)
     if isinstance(cfg, (list, tuple)):
         dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
     elif cfg['type'] == 'ConcatDataset':

mmdet3d/datasets/convert_utils.py

@@ -7,7 +7,7 @@ from nuscenes.utils.geometry_utils import view_points
 from pyquaternion import Quaternion
 from shapely.geometry import MultiPoint, box
 
-from mmdet3d.core.bbox import points_cam2img
+from mmdet3d.structures import points_cam2img
 
 nus_categories = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                   'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',

mmdet3d/datasets/det3d_dataset.py

@@ -8,7 +8,7 @@ import numpy as np
 from mmengine.dataset import BaseDataset
 
 from mmdet3d.datasets import DATASETS
-from ..core.bbox import get_box_type
+from mmdet3d.structures import get_box_type
 
 
 @DATASETS.register_module()
@@ -139,7 +139,10 @@ class Det3DDataset(BaseDataset):
         img_filtered_annotations = {}
         filter_mask = ann_info['gt_labels_3d'] > -1
         for key in ann_info.keys():
-            img_filtered_annotations[key] = (ann_info[key][filter_mask])
+            if key != 'instances':
+                img_filtered_annotations[key] = (ann_info[key][filter_mask])
+            else:
+                img_filtered_annotations[key] = ann_info[key]
         return img_filtered_annotations
 
     def get_ann_info(self, index: int) -> dict:
@@ -180,7 +183,7 @@ class Det3DDataset(BaseDataset):
         # add s or gt prefix for most keys after concat
         # we only process 3d annotations here, the corresponding
         # 2d annotation process is in the `LoadAnnotations3D`
-        # in `pipelines`
+        # in `transforms`
         name_mapping = {
             'bbox_label_3d': 'gt_labels_3d',
             'bbox_3d': 'gt_bboxes_3d',
@@ -197,17 +200,16 @@ class Det3DDataset(BaseDataset):
             keys = list(instances[0].keys())
             ann_info = dict()
             for ann_name in keys:
+                temp_anns = [item[ann_name] for item in instances]
+                # map the original dataset label to training label
+                if 'label' in ann_name:
+                    temp_anns = [
+                        self.label_mapping[item] for item in temp_anns
+                    ]
                 if ann_name in name_mapping:
-                    temp_anns = [item[ann_name] for item in instances]
-                    # map the original dataset label to training label
-                    if 'label' in ann_name:
-                        temp_anns = [
-                            self.label_mapping[item] for item in temp_anns
-                        ]
-                    temp_anns = np.array(temp_anns)
-
                     ann_name = name_mapping[ann_name]
-                    ann_info[ann_name] = temp_anns
+                temp_anns = np.array(temp_anns)
+                ann_info[ann_name] = temp_anns
             ann_info['instances'] = info['instances']
         return ann_info
 

mmdet3d/datasets/kitti2d_dataset.py

@@ -213,7 +213,7 @@ class Kitti2DDataset(Det3DDataset):
         Returns:
             list[dict]: A list of dictionaries with the kitti 2D format.
         """
-        from mmdet3d.core.bbox.transforms import bbox2result_kitti2d
+        from mmdet3d.structures.ops.transforms import bbox2result_kitti2d
         sample_idx = [info['image']['image_idx'] for info in self.data_infos]
         result_files = bbox2result_kitti2d(outputs, self.CLASSES, sample_idx,
                                            out)
@@ -231,7 +231,7 @@ class Kitti2DDataset(Det3DDataset):
             tuple (str, dict): Average precision results in str format
                 and average precision results in dict format.
         """
-        from mmdet3d.core.evaluation import kitti_eval
+        from mmdet3d.evaluation import kitti_eval
         eval_types = ['bbox'] if not eval_types else eval_types
         assert eval_types in ('bbox', ['bbox'
                                        ]), 'KITTI data set only evaluate bbox'

mmdet3d/datasets/kitti_dataset.py

@@ -4,7 +4,7 @@ from typing import Callable, List, Optional, Union
 import numpy as np
 
 from mmdet3d.datasets import DATASETS
-from ..core.bbox import CameraInstance3DBoxes
+from mmdet3d.structures import CameraInstance3DBoxes
 from .det3d_dataset import Det3DDataset
 
 
@@ -119,7 +119,7 @@ class KittiDataset(Det3DDataset):
         Returns:
             dict: annotation information consists of the following keys:
 
-                - bboxes_3d (:obj:`LiDARInstance3DBoxes`):
+                - gt_bboxes_3d (:obj:`LiDARInstance3DBoxes`):
                     3D ground truth bboxes.
                 - bbox_labels_3d (np.ndarray): Labels of ground truths.
                 - gt_bboxes (np.ndarray): 2D ground truth bboxes.

mmdet3d/datasets/lyft_dataset.py

@@ -4,7 +4,7 @@ from typing import Dict, List
 import numpy as np
 
 from mmdet3d.registry import DATASETS
-from ..core.bbox import LiDARInstance3DBoxes
+from mmdet3d.structures import LiDARInstance3DBoxes
 from .det3d_dataset import Det3DDataset
 
 

mmdet3d/datasets/nuscenes_dataset.py

@@ -4,9 +4,9 @@ from typing import Dict, List
 
 import numpy as np
 
-from mmdet3d.core.bbox.structures.cam_box3d import CameraInstance3DBoxes
 from mmdet3d.registry import DATASETS
-from ..core.bbox import LiDARInstance3DBoxes
+from mmdet3d.structures import LiDARInstance3DBoxes
+from mmdet3d.structures.bbox_3d.cam_box3d import CameraInstance3DBoxes
 from .det3d_dataset import Det3DDataset