Update all registries and fix some ut problems

mmdet3d/core/bbox/iou_calculators/iou3d_calculator.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
+from mmdet3d.registry import TASK_UTILS
 from mmdet.core.bbox import bbox_overlaps
-from mmdet.core.bbox.iou_calculators.builder import IOU_CALCULATORS
 from ..structures import get_box_type
 
 
-@IOU_CALCULATORS.register_module()
+@TASK_UTILS.register_module()
 class BboxOverlapsNearest3D(object):
     """Nearest 3D IoU Calculator.
 
@@ -54,7 +54,7 @@ class BboxOverlapsNearest3D(object):
         return repr_str
 
 
-@IOU_CALCULATORS.register_module()
+@TASK_UTILS.register_module()
 class BboxOverlaps3D(object):
     """3D IoU Calculator.
 
@@ -176,7 +176,7 @@ def bbox_overlaps_3d(bboxes1, bboxes2, mode='iou', coordinate='camera'):
     return bboxes1.overlaps(bboxes1, bboxes2, mode=mode)
 
 
-@IOU_CALCULATORS.register_module()
+@TASK_UTILS.register_module()
 class AxisAlignedBboxOverlaps3D(object):
     """Axis-aligned 3D Overlaps (IoU) Calculator."""
 

mmdet3d/core/bbox/samplers/iou_neg_piecewise_sampler.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
-from mmdet.core.bbox.builder import BBOX_SAMPLERS
+from mmdet3d.registry import TASK_UTILS
 from . import RandomSampler, SamplingResult
 
 
-@BBOX_SAMPLERS.register_module()
+@TASK_UTILS.register_module()
 class IoUNegPiecewiseSampler(RandomSampler):
     """IoU Piece-wise Sampling.
 

mmdet3d/datasets/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.datasets.builder import build_dataloader
 from .builder import DATASETS, PIPELINES, build_dataset
 from .custom_3d import Custom3DDataset
 from .custom_3d_seg import Custom3DSegDataset
@@ -29,17 +28,17 @@ from .utils import get_loading_pipeline
 from .waymo_dataset import WaymoDataset
 
 __all__ = [
-    'KittiDataset', 'KittiMonoDataset', 'build_dataloader', 'DATASETS',
-    'build_dataset', 'NuScenesDataset', 'NuScenesMonoDataset', 'LyftDataset',
-    'ObjectSample', 'RandomFlip3D', 'ObjectNoise', 'GlobalRotScaleTrans',
-    'PointShuffle', 'ObjectRangeFilter', 'PointsRangeFilter',
-    'LoadPointsFromFile', 'S3DISSegDataset', 'S3DISDataset',
-    'NormalizePointsColor', 'IndoorPatchPointSample', 'IndoorPointSample',
-    'PointSample', 'LoadAnnotations3D', 'GlobalAlignment', 'SUNRGBDDataset',
-    'ScanNetDataset', 'ScanNetSegDataset', 'ScanNetInstanceSegDataset',
-    'SemanticKITTIDataset', 'Custom3DDataset', 'Custom3DSegDataset',
-    'LoadPointsFromMultiSweeps', 'WaymoDataset', 'BackgroundPointsFilter',
-    'VoxelBasedPointSampler', 'get_loading_pipeline', 'RandomDropPointsColor',
-    'RandomJitterPoints', 'ObjectNameFilter', 'AffineResize',
-    'RandomShiftScale', 'LoadPointsFromDict', 'PIPELINES'
+    'KittiDataset', 'KittiMonoDataset', 'DATASETS', 'build_dataset',
+    'NuScenesDataset', 'NuScenesMonoDataset', 'LyftDataset', 'ObjectSample',
+    'RandomFlip3D', 'ObjectNoise', 'GlobalRotScaleTrans', 'PointShuffle',
+    'ObjectRangeFilter', 'PointsRangeFilter', 'LoadPointsFromFile',
+    'S3DISSegDataset', 'S3DISDataset', 'NormalizePointsColor',
+    'IndoorPatchPointSample', 'IndoorPointSample', 'PointSample',
+    'LoadAnnotations3D', 'GlobalAlignment', 'SUNRGBDDataset', 'ScanNetDataset',
+    'ScanNetSegDataset', 'ScanNetInstanceSegDataset', 'SemanticKITTIDataset',
+    'Custom3DDataset', 'Custom3DSegDataset', 'LoadPointsFromMultiSweeps',
+    'WaymoDataset', 'BackgroundPointsFilter', 'VoxelBasedPointSampler',
+    'get_loading_pipeline', 'RandomDropPointsColor', 'RandomJitterPoints',
+    'ObjectNameFilter', 'AffineResize', 'RandomShiftScale',
+    'LoadPointsFromDict', 'PIPELINES'
 ]

mmdet3d/datasets/builder.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import platform
 
-from mmcv.utils import build_from_cfg
-
-from mmdet3d.registry import DATASETS
+from mmdet3d.registry import DATASETS, TRANSFORMS
 from mmdet.datasets.builder import _concat_dataset
 
 if platform.system() != 'Windows':
@@ -15,9 +13,8 @@ if platform.system() != 'Windows':
     soft_limit = min(max(4096, base_soft_limit), hard_limit)
     resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
 
-OBJECTSAMPLERS = Registry('Object sampler')
-DATASETS = Registry('dataset')
-PIPELINES = Registry('pipeline')
+OBJECTSAMPLERS = TRANSFORMS
+PIPELINES = TRANSFORMS
 
 
 def build_dataset(cfg, default_args=None):
@@ -40,8 +37,7 @@ def build_dataset(cfg, default_args=None):
         dataset = CBGSDataset(build_dataset(cfg['dataset'], default_args))
     elif isinstance(cfg.get('ann_file'), (list, tuple)):
         dataset = _concat_dataset(cfg, default_args)
-    elif cfg['type'] in DATASETS._module_dict.keys():
-        dataset = build_from_cfg(cfg, DATASETS, default_args)
     else:
-        dataset = build_from_cfg(cfg, MMDET_DATASETS, default_args)
+        dataset = DATASETS.build(cfg, default_args=default_args)
+
     return dataset

mmdet3d/datasets/custom_3d.py

@@ -7,8 +7,8 @@ import mmcv
 import numpy as np
 from torch.utils.data import Dataset
 
+from mmdet3d.registry import DATASETS
 from ..core.bbox import get_box_type
-from .builder import DATASETS
 from .pipelines import Compose
 from .utils import extract_result_dict, get_loading_pipeline
 

mmdet3d/datasets/custom_3d_seg.py

@@ -7,14 +7,12 @@ import mmcv
 import numpy as np
 from torch.utils.data import Dataset
 
-from mmseg.datasets import DATASETS as SEG_DATASETS
-from .builder import DATASETS
+from mmdet3d.registry import DATASETS
 from .pipelines import Compose
 from .utils import extract_result_dict, get_loading_pipeline
 
 
 @DATASETS.register_module()
-@SEG_DATASETS.register_module()
 class Custom3DSegDataset(Dataset):
     """Customized 3D dataset for semantic segmentation task.
 

mmdet3d/datasets/dataset_wrappers.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 
-from .builder import DATASETS
+from mmdet3d.registry import DATASETS
 
 
 @DATASETS.register_module()

mmdet3d/datasets/kitti2d_dataset.py

@@ -2,8 +2,8 @@
 import mmcv
 import numpy as np
 
-from mmdet.datasets import CustomDataset
-from .builder import DATASETS
+from mmdet3d.datasets import CustomDataset
+from mmdet3d.registry import DATASETS
 
 
 @DATASETS.register_module()

mmdet3d/datasets/kitti_dataset.py

@@ -9,10 +9,10 @@ import numpy as np
 import torch
 from mmcv.utils import print_log
 
+from mmdet3d.registry import DATASETS
 from ..core import show_multi_modality_result, show_result
 from ..core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
                          LiDARInstance3DBoxes, points_cam2img)
-from .builder import DATASETS
 from .custom_3d import Custom3DDataset
 from .pipelines import Compose
 

mmdet3d/datasets/kitti_mono_dataset.py

@@ -8,8 +8,8 @@ import numpy as np
 import torch
 from mmcv.utils import print_log
 
+from mmdet3d.registry import DATASETS
 from ..core.bbox import Box3DMode, CameraInstance3DBoxes, points_cam2img
-from .builder import DATASETS
 from .nuscenes_mono_dataset import NuScenesMonoDataset
 
 
@@ -35,8 +35,6 @@ class KittiMonoDataset(NuScenesMonoDataset):
     def __init__(self,
                  data_root,
                  info_file,
-                 ann_file,
-                 pipeline,
                  load_interval=1,
                  with_velocity=False,
                  eval_version=None,
@@ -44,8 +42,6 @@ class KittiMonoDataset(NuScenesMonoDataset):
                  **kwargs):
         super().__init__(
             data_root=data_root,
-            ann_file=ann_file,
-            pipeline=pipeline,
             load_interval=load_interval,
             with_velocity=with_velocity,
             eval_version=eval_version,

mmdet3d/datasets/lyft_dataset.py

@@ -11,9 +11,9 @@ from lyft_dataset_sdk.utils.data_classes import Box as LyftBox
 from pyquaternion import Quaternion
 
 from mmdet3d.core.evaluation.lyft_eval import lyft_eval
+from mmdet3d.registry import DATASETS
 from ..core import show_result
 from ..core.bbox import Box3DMode, Coord3DMode, LiDARInstance3DBoxes
-from .builder import DATASETS
 from .custom_3d import Custom3DDataset
 from .pipelines import Compose
 

mmdet3d/datasets/nuscenes_dataset.py

@@ -7,9 +7,9 @@ import numpy as np
 import pyquaternion
 from nuscenes.utils.data_classes import Box as NuScenesBox
 
+from mmdet3d.registry import DATASETS
 from ..core import show_result
 from ..core.bbox import Box3DMode, Coord3DMode, LiDARInstance3DBoxes
-from .builder import DATASETS
 from .custom_3d import Custom3DDataset
 from .pipelines import Compose
 

mmdet3d/datasets/nuscenes_mono_dataset.py

@@ -11,10 +11,10 @@ import torch
 from nuscenes.utils.data_classes import Box as NuScenesBox
 
 from mmdet3d.core import bbox3d2result, box3d_multiclass_nms, xywhr2xyxyr
+from mmdet3d.registry import DATASETS
 from mmdet.datasets import CocoDataset
 from ..core import show_multi_modality_result
 from ..core.bbox import CameraInstance3DBoxes, get_box_type
-from .builder import DATASETS
 from .pipelines import Compose
 from .utils import extract_result_dict, get_loading_pipeline
 
@@ -77,8 +77,6 @@ class NuScenesMonoDataset(CocoDataset):
 
     def __init__(self,
                  data_root,
-                 ann_file,
-                 pipeline,
                  load_interval=1,
                  with_velocity=True,
                  modality=None,
@@ -86,46 +84,9 @@ class NuScenesMonoDataset(CocoDataset):
                  eval_version='detection_cvpr_2019',
                  use_valid_flag=False,
                  version='v1.0-trainval',
-                 classes=None,
-                 img_prefix='',
-                 seg_prefix=None,
-                 proposal_file=None,
-                 test_mode=False,
-                 filter_empty_gt=True,
-                 file_client_args=dict(backend='disk')):
-        self.ann_file = ann_file
+                 **kwargs):
+        super().__init__(**kwargs)
         self.data_root = data_root
-        self.img_prefix = img_prefix
-        self.seg_prefix = seg_prefix
-        self.proposal_file = proposal_file
-        self.test_mode = test_mode
-        self.filter_empty_gt = filter_empty_gt
-        self.CLASSES = self.get_classes(classes)
-        self.file_client = mmcv.FileClient(**file_client_args)
-
-        # load annotations (and proposals)
-        with self.file_client.get_local_path(self.ann_file) as local_path:
-            self.data_infos = self.load_annotations(local_path)
-
-        if self.proposal_file is not None:
-            with self.file_client.get_local_path(
-                    self.proposal_file) as local_path:
-                self.proposals = self.load_proposals(local_path)
-        else:
-            self.proposals = None
-
-        # filter images too small and containing no annotations
-        if not test_mode:
-            valid_inds = self._filter_imgs()
-            self.data_infos = [self.data_infos[i] for i in valid_inds]
-            if self.proposals is not None:
-                self.proposals = [self.proposals[i] for i in valid_inds]
-            # set group flag for the sampler
-            self._set_group_flag()
-
-        # processing pipeline
-        self.pipeline = Compose(pipeline)
-
         self.load_interval = load_interval
         self.with_velocity = with_velocity
         self.modality = modality

mmdet3d/datasets/pipelines/compose.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import collections
 
-from mmcv.utils import build_from_cfg
+from mmdet3d.registry import TRANSFORMS
 
-from mmdet.datasets.builder import PIPELINES as MMDET_PIPELINES
-from ..builder import PIPELINES
 
-
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class Compose:
-    """Compose multiple transforms sequentially. The pipeline registry of
-    mmdet3d separates with mmdet, however, sometimes we may need to use mmdet's
-    pipeline. So the class is rewritten to be able to use pipelines from both
-    mmdet3d and mmdet.
+    """Compose multiple transforms sequentially.
 
     Args:
         transforms (Sequence[dict | callable]): Sequence of transform object or
@@ -24,11 +18,7 @@ class Compose:
         self.transforms = []
         for transform in transforms:
             if isinstance(transform, dict):
-                _, key = PIPELINES.split_scope_key(transform['type'])
-                if key in PIPELINES._module_dict.keys():
-                    transform = build_from_cfg(transform, PIPELINES)
-                else:
-                    transform = build_from_cfg(transform, MMDET_PIPELINES)
+                transform = TRANSFORMS.build(transform)
                 self.transforms.append(transform)
             elif callable(transform):
                 self.transforms.append(transform)
@@ -54,7 +44,10 @@ class Compose:
     def __repr__(self):
         format_string = self.__class__.__name__ + '('
         for t in self.transforms:
+            str_ = t.__repr__()
+            if 'Compose(' in str_:
+                str_ = str_.replace('\n', '\n    ')
             format_string += '\n'
-            format_string += f'    {t}'
+            format_string += f'    {str_}'
         format_string += '\n)'
         return format_string

mmdet3d/datasets/pipelines/dbsampler.py

@@ -8,7 +8,7 @@ import numpy as np
 
 from mmdet3d.core.bbox import box_np_ops
 from mmdet3d.datasets.pipelines import data_augment_utils
-from ..builder import OBJECTSAMPLERS, PIPELINES
+from mmdet3d.registry import TRANSFORMS
 
 
 class BatchSampler:
@@ -78,7 +78,7 @@ class BatchSampler:
         return [self._sampled_list[i] for i in indices]
 
 
-@OBJECTSAMPLERS.register_module()
+@TRANSFORMS.register_module()
 class DataBaseSampler(object):
     """Class for sampling data from the ground truth database.
 

mmdet3d/datasets/pipelines/formating.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 from mmcv.parallel import DataContainer as DC
+from mmcv.transforms import to_tensor
 
 from mmdet3d.core.bbox import BaseInstance3DBoxes
 from mmdet3d.core.points import BasePoints
-from mmdet.datasets.pipelines import to_tensor
-from ..builder import PIPELINES
+from mmdet3d.registry import TRANSFORMS
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class DefaultFormatBundle(object):
     """Default formatting bundle.
 
@@ -79,7 +79,7 @@ class DefaultFormatBundle(object):
         return self.__class__.__name__
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class Collect3D(object):
     """Collect data from the loader relevant to the specific task.
 
@@ -170,7 +170,7 @@ class Collect3D(object):
             f'(keys={self.keys}, meta_keys={self.meta_keys})'
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class DefaultFormatBundle3D(DefaultFormatBundle):
     """Default formatting bundle.
 

mmdet3d/datasets/pipelines/loading.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import mmcv
 import numpy as np
+from mmcv.transforms import LoadImageFromFile
 
 from mmdet3d.core.points import BasePoints, get_points_type
-from mmdet.datasets.pipelines import LoadAnnotations, LoadImageFromFile
-from ..builder import PIPELINES
+from mmdet3d.registry import TRANSFORMS
+from mmdet.datasets.pipelines import LoadAnnotations
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class LoadMultiViewImageFromFiles(object):
     """Load multi channel images from a list of separate channel files.
 
@@ -72,7 +73,7 @@ class LoadMultiViewImageFromFiles(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class LoadImageFromFileMono3D(LoadImageFromFile):
     """Load an image from file in monocular 3D object detection. Compared to 2D
     detection, additional camera parameters need to be loaded.
@@ -96,7 +97,7 @@ class LoadImageFromFileMono3D(LoadImageFromFile):
         return results
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class LoadPointsFromMultiSweeps(object):
     """Load points from multiple sweeps.
 
@@ -238,7 +239,7 @@ class LoadPointsFromMultiSweeps(object):
         return f'{self.__class__.__name__}(sweeps_num={self.sweeps_num})'
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class PointSegClassMapping(object):
     """Map original semantic class to valid category ids.
 
@@ -293,7 +294,7 @@ class PointSegClassMapping(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class NormalizePointsColor(object):
     """Normalize color of points.
 
@@ -334,7 +335,7 @@ class NormalizePointsColor(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class LoadPointsFromFile(object):
     """Load Points From File.
 
@@ -460,7 +461,7 @@ class LoadPointsFromFile(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class LoadPointsFromDict(LoadPointsFromFile):
     """Load Points From Dict."""
 
@@ -469,7 +470,7 @@ class LoadPointsFromDict(LoadPointsFromFile):
         return results
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class LoadAnnotations3D(LoadAnnotations):
     """Load Annotations3D.
 

mmdet3d/datasets/pipelines/test_time_aug.py

@@ -4,118 +4,11 @@ from copy import deepcopy
 
 import mmcv
 
-from ..builder import PIPELINES
+from mmdet3d.registry import TRANSFORMS
 from .compose import Compose
 
 
-@PIPELINES.register_module()
-class MultiScaleFlipAug:
-    """Test-time augmentation with multiple scales and flipping. An example
-    configuration is as followed:
-
-    .. code-block::
-        img_scale=[(1333, 400), (1333, 800)],
-        flip=True,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ]
-    After MultiScaleFLipAug with above configuration, the results are wrapped
-    into lists of the same length as followed:
-    .. code-block::
-        dict(
-            img=[...],
-            img_shape=[...],
-            scale=[(1333, 400), (1333, 400), (1333, 800), (1333, 800)]
-            flip=[False, True, False, True]
-            ...
-        )
-    Args:
-        transforms (list[dict]): Transforms to apply in each augmentation.
-        img_scale (tuple | list[tuple] | None): Images scales for resizing.
-        scale_factor (float | list[float] | None): Scale factors for resizing.
-        flip (bool): Whether apply flip augmentation. Default: False.
-        flip_direction (str | list[str]): Flip augmentation directions,
-            options are "horizontal", "vertical" and "diagonal". If
-            flip_direction is a list, multiple flip augmentations will be
-            applied. It has no effect when flip == False. Default:
-            "horizontal".
-    """
-
-    def __init__(self,
-                 transforms,
-                 img_scale=None,
-                 scale_factor=None,
-                 flip=False,
-                 flip_direction='horizontal'):
-        self.transforms = Compose(transforms)
-        assert (img_scale is None) ^ (scale_factor is None), (
-            'Must have but only one variable can be set')
-        if img_scale is not None:
-            self.img_scale = img_scale if isinstance(img_scale,
-                                                     list) else [img_scale]
-            self.scale_key = 'scale'
-            assert mmcv.is_list_of(self.img_scale, tuple)
-        else:
-            self.img_scale = scale_factor if isinstance(
-                scale_factor, list) else [scale_factor]
-            self.scale_key = 'scale_factor'
-
-        self.flip = flip
-        self.flip_direction = flip_direction if isinstance(
-            flip_direction, list) else [flip_direction]
-        assert mmcv.is_list_of(self.flip_direction, str)
-        if not self.flip and self.flip_direction != ['horizontal']:
-            warnings.warn(
-                'flip_direction has no effect when flip is set to False')
-        if (self.flip
-                and not any([t['type'] == 'RandomFlip' for t in transforms])):
-            warnings.warn(
-                'flip has no effect when RandomFlip is not in transforms')
-
-    def __call__(self, results):
-        """Call function to apply test time augment transforms on results.
-
-        Args:
-            results (dict): Result dict contains the data to transform.
-        Returns:
-           dict[str: list]: The augmented data, where each value is wrapped
-               into a list.
-        """
-
-        aug_data = []
-        flip_args = [(False, None)]
-        if self.flip:
-            flip_args += [(True, direction)
-                          for direction in self.flip_direction]
-        for scale in self.img_scale:
-            for flip, direction in flip_args:
-                _results = results.copy()
-                _results[self.scale_key] = scale
-                _results['flip'] = flip
-                _results['flip_direction'] = direction
-                data = self.transforms(_results)
-                aug_data.append(data)
-        # list of dict to dict of list
-        aug_data_dict = {key: [] for key in aug_data[0]}
-        for data in aug_data:
-            for key, val in data.items():
-                aug_data_dict[key].append(val)
-        return aug_data_dict
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(transforms={self.transforms}, '
-        repr_str += f'img_scale={self.img_scale}, flip={self.flip}, '
-        repr_str += f'flip_direction={self.flip_direction})'
-        return repr_str
-
-
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class MultiScaleFlipAug3D(object):
     """Test-time augmentation with multiple scales and flipping.
 

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -12,13 +12,12 @@ from mmengine.registry import build_from_cfg
 from mmdet3d.core import VoxelGenerator
 from mmdet3d.core.bbox import (CameraInstance3DBoxes, DepthInstance3DBoxes,
                                LiDARInstance3DBoxes, box_np_ops)
-from mmdet3d.registry import OBJECTSAMPLERS, TRANSFORMS
-from mmdet.datasets.builder import PIPELINES
+from mmdet3d.registry import TRANSFORMS
 from mmdet.datasets.pipelines import RandomFlip
 from .data_augment_utils import noise_per_object_v3_
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class RandomDropPointsColor(object):
     r"""Randomly set the color of points to all zeros.
 
@@ -68,7 +67,7 @@ class RandomDropPointsColor(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class RandomFlip3D(RandomFlip):
     """Flip the points & bbox.
 
@@ -193,7 +192,7 @@ class RandomFlip3D(RandomFlip):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class RandomJitterPoints(object):
     """Randomly jitter point coordinates.
 
@@ -299,7 +298,7 @@ class ObjectSample(BaseTransform):
         self.sample_2d = sample_2d
         if 'type' not in db_sampler.keys():
             db_sampler['type'] = 'DataBaseSampler'
-        self.db_sampler = build_from_cfg(db_sampler, OBJECTSAMPLERS)
+        self.db_sampler = build_from_cfg(db_sampler, TRANSFORMS)
         self.use_ground_plane = use_ground_plane
 
     @staticmethod
@@ -471,7 +470,7 @@ class ObjectNoise(BaseTransform):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class GlobalAlignment(object):
     """Apply global alignment to 3D scene points by rotation and translation.
 
@@ -558,7 +557,7 @@ class GlobalAlignment(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class GlobalRotScaleTrans(object):
     """Apply global rotation, scaling and translation to a 3D scene.
 
@@ -724,7 +723,7 @@ class GlobalRotScaleTrans(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class PointShuffle(object):
     """Shuffle input points."""
 
@@ -756,7 +755,7 @@ class PointShuffle(object):
         return self.__class__.__name__
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class ObjectRangeFilter(object):
     """Filter objects by the range.
 
@@ -808,7 +807,7 @@ class ObjectRangeFilter(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class PointsRangeFilter(object):
     """Filter points by the range.
 
@@ -853,7 +852,7 @@ class PointsRangeFilter(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class ObjectNameFilter(object):
     """Filter GT objects by their names.
 
@@ -1009,7 +1008,7 @@ class PointSample(BaseTransform):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class IndoorPointSample(PointSample):
     """Indoor point sample.
 
@@ -1026,7 +1025,7 @@ class IndoorPointSample(PointSample):
         super(IndoorPointSample, self).__init__(*args, **kwargs)
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class IndoorPatchPointSample(object):
     r"""Indoor point sample within a patch. Modified from `PointNet++ <https://
     github.com/charlesq34/pointnet2/blob/master/scannet/scannet_dataset.py>`_.
@@ -1271,7 +1270,7 @@ class IndoorPatchPointSample(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class BackgroundPointsFilter(object):
     """Filter background points near the bounding box.
 
@@ -1336,7 +1335,7 @@ class BackgroundPointsFilter(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class VoxelBasedPointSampler(object):
     """Voxel based point sampler.
 
@@ -1478,7 +1477,7 @@ class VoxelBasedPointSampler(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class AffineResize(object):
     """Get the affine transform matrices to the target size.
 
@@ -1674,13 +1673,13 @@ class AffineResize(object):
         return repr_str
 
 
-@PIPELINES.register_module()
+@TRANSFORMS.register_module()
 class RandomShiftScale(object):
     """Random shift scale.
 
     Different from the normal shift and scale function, it doesn't
     directly shift or scale image. It can record the shift and scale
-    infos into loading pipelines. It's designed to be used with
+    infos into loading TRANSFORMS. It's designed to be used with
     AffineResize together.
 
     Args:

mmdet3d/datasets/s3dis_dataset.py

@@ -5,8 +5,7 @@ import numpy as np
 
 from mmdet3d.core import show_seg_result
 from mmdet3d.core.bbox import DepthInstance3DBoxes
-from mmseg.datasets import DATASETS as SEG_DATASETS
-from .builder import DATASETS
+from mmdet3d.registry import DATASETS
 from .custom_3d import Custom3DDataset
 from .custom_3d_seg import Custom3DSegDataset
 from .pipelines import Compose
@@ -308,7 +307,6 @@ class _S3DISSegDataset(Custom3DSegDataset):
 
 
 @DATASETS.register_module()
-@SEG_DATASETS.register_module()
 class S3DISSegDataset(_S3DISSegDataset):
     r"""S3DIS Dataset for Semantic Segmentation Task.