[Enhance] Update Registry in MMDet3D (#1412)

* Update Registry in MMDet3D

* fix compose pipeline bug

* update registry

* fix some bugs

* fix comments

* fix comments

mmdet3d/datasets/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from mmdet.datasets.builder import build_dataloader
-from .builder import DATASETS, build_dataset
+from .builder import DATASETS, PIPELINES, build_dataset
 from .custom_3d import Custom3DDataset
 from .custom_3d_seg import Custom3DSegDataset
 from .kitti_dataset import KittiDataset
@@ -41,5 +41,5 @@ __all__ = [
     'LoadPointsFromMultiSweeps', 'WaymoDataset', 'BackgroundPointsFilter',
     'VoxelBasedPointSampler', 'get_loading_pipeline', 'RandomDropPointsColor',
     'RandomJitterPoints', 'ObjectNameFilter', 'AffineResize',
-    'RandomShiftScale', 'LoadPointsFromDict'
+    'RandomShiftScale', 'LoadPointsFromDict', 'PIPELINES'
 ]

mmdet3d/datasets/builder.py

@@ -3,7 +3,6 @@ import platform
 
 from mmcv.utils import Registry, build_from_cfg
 
-from mmdet.datasets import DATASETS
 from mmdet.datasets.builder import _concat_dataset
 
 if platform.system() != 'Windows':
@@ -16,6 +15,8 @@ if platform.system() != 'Windows':
     resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
 
 OBJECTSAMPLERS = Registry('Object sampler')
+DATASETS = Registry('dataset')
+PIPELINES = Registry('pipeline')
 
 
 def build_dataset(cfg, default_args=None):

mmdet3d/datasets/custom_3d.py

@@ -7,8 +7,8 @@ import mmcv
 import numpy as np
 from torch.utils.data import Dataset
 
-from mmdet.datasets 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

@@ -7,8 +7,8 @@ import mmcv
 import numpy as np
 from torch.utils.data import Dataset
 
-from mmdet.datasets import DATASETS
 from mmseg.datasets import DATASETS as SEG_DATASETS
+from .builder import DATASETS
 from .pipelines import Compose
 from .utils import extract_result_dict, get_loading_pipeline
 

mmdet3d/datasets/kitti2d_dataset.py

@@ -2,7 +2,8 @@
 import mmcv
 import numpy as np
 
-from mmdet.datasets import DATASETS, CustomDataset
+from mmdet.datasets import CustomDataset
+from .builder 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 mmdet.datasets 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 mmdet.datasets import DATASETS
 from ..core.bbox import Box3DMode, CameraInstance3DBoxes, points_cam2img
+from .builder import DATASETS
 from .nuscenes_mono_dataset import NuScenesMonoDataset
 
 
@@ -35,6 +35,8 @@ class KittiMonoDataset(NuScenesMonoDataset):
     def __init__(self,
                  data_root,
                  info_file,
+                 ann_file,
+                 pipeline,
                  load_interval=1,
                  with_velocity=False,
                  eval_version=None,
@@ -42,6 +44,8 @@ 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 mmdet.datasets 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 mmdet.datasets 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
 
@@ -125,8 +125,7 @@ class NuScenesDataset(Custom3DDataset):
                  filter_empty_gt=True,
                  test_mode=False,
                  eval_version='detection_cvpr_2019',
-                 use_valid_flag=False,
-                 **kwargs):
+                 use_valid_flag=False):
         self.load_interval = load_interval
         self.use_valid_flag = use_valid_flag
         super().__init__(
@@ -137,8 +136,7 @@ class NuScenesDataset(Custom3DDataset):
             modality=modality,
             box_type_3d=box_type_3d,
             filter_empty_gt=filter_empty_gt,
-            test_mode=test_mode,
-            **kwargs)
+            test_mode=test_mode)
 
         self.with_velocity = with_velocity
         self.eval_version = eval_version
@@ -186,7 +184,6 @@ class NuScenesDataset(Custom3DDataset):
         Returns:
             list[dict]: List of annotations sorted by timestamps.
         """
-        # loading data from a file-like object needs file format
         data = mmcv.load(ann_file, file_format='pkl')
         data_infos = list(sorted(data['infos'], key=lambda e: e['timestamp']))
         data_infos = data_infos[::self.load_interval]

mmdet3d/datasets/nuscenes_mono_dataset.py

@@ -11,9 +11,10 @@ import torch
 from nuscenes.utils.data_classes import Box as NuScenesBox
 
 from mmdet3d.core import bbox3d2result, box3d_multiclass_nms, xywhr2xyxyr
-from mmdet.datasets import DATASETS, CocoDataset
+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
 
@@ -76,6 +77,8 @@ class NuScenesMonoDataset(CocoDataset):
 
     def __init__(self,
                  data_root,
+                 ann_file,
+                 pipeline,
                  load_interval=1,
                  with_velocity=True,
                  modality=None,
@@ -83,9 +86,46 @@ class NuScenesMonoDataset(CocoDataset):
                  eval_version='detection_cvpr_2019',
                  use_valid_flag=False,
                  version='v1.0-trainval',
-                 **kwargs):
-        super().__init__(**kwargs)
+                 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
         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/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.datasets.pipelines import Compose
+from .compose import Compose
 from .dbsampler import DataBaseSampler
 from .formating import Collect3D, DefaultFormatBundle, DefaultFormatBundle3D
 from .loading import (LoadAnnotations3D, LoadImageFromFileMono3D,

mmdet3d/datasets/pipelines/compose.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import collections
+
+from mmcv.utils import build_from_cfg
+
+from mmdet.datasets.builder import PIPELINES as MMDET_PIPELINES
+from ..builder import PIPELINES
+
+
+@PIPELINES.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.
+
+    Args:
+        transforms (Sequence[dict | callable]): Sequence of transform object or
+            config dict to be composed.
+    """
+
+    def __init__(self, transforms):
+        assert isinstance(transforms, collections.abc.Sequence)
+        self.transforms = []
+        for transform in transforms:
+            if isinstance(transform, dict):
+                if transform['type'] in PIPELINES._module_dict.keys():
+                    transform = build_from_cfg(transform, PIPELINES)
+                else:
+                    transform = build_from_cfg(transform, MMDET_PIPELINES)
+                self.transforms.append(transform)
+            elif callable(transform):
+                self.transforms.append(transform)
+            else:
+                raise TypeError('transform must be callable or a dict')
+
+    def __call__(self, data):
+        """Call function to apply transforms sequentially.
+
+        Args:
+            data (dict): A result dict contains the data to transform.
+
+        Returns:
+           dict: Transformed data.
+        """
+
+        for t in self.transforms:
+            data = t(data)
+            if data is None:
+                return None
+        return data
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + '('
+        for t in self.transforms:
+            format_string += '\n'
+            format_string += f'    {t}'
+        format_string += '\n)'
+        return format_string

mmdet3d/datasets/pipelines/dbsampler.py

@@ -8,8 +8,7 @@ import numpy as np
 
 from mmdet3d.core.bbox import box_np_ops
 from mmdet3d.datasets.pipelines import data_augment_utils
-from mmdet.datasets import PIPELINES
-from ..builder import OBJECTSAMPLERS
+from ..builder import OBJECTSAMPLERS, PIPELINES
 
 
 class BatchSampler:

mmdet3d/datasets/pipelines/formating.py

@@ -4,10 +4,8 @@ from mmcv.parallel import DataContainer as DC
 
 from mmdet3d.core.bbox import BaseInstance3DBoxes
 from mmdet3d.core.points import BasePoints
-from mmdet.datasets.builder import PIPELINES
 from mmdet.datasets.pipelines import to_tensor
-
-PIPELINES._module_dict.pop('DefaultFormatBundle')
+from ..builder import PIPELINES
 
 
 @PIPELINES.register_module()

mmdet3d/datasets/pipelines/loading.py

@@ -3,8 +3,8 @@ import mmcv
 import numpy as np
 
 from mmdet3d.core.points import BasePoints, get_points_type
-from mmdet.datasets.builder import PIPELINES
 from mmdet.datasets.pipelines import LoadAnnotations, LoadImageFromFile
+from ..builder import PIPELINES
 
 
 @PIPELINES.register_module()

mmdet3d/datasets/pipelines/test_time_aug.py

@@ -4,8 +4,8 @@ from copy import deepcopy
 
 import mmcv
 
-from mmdet.datasets.builder import PIPELINES
-from mmdet.datasets.pipelines import Compose
+from ..builder import PIPELINES
+from .compose import Compose
 
 
 @PIPELINES.register_module()

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -10,9 +10,8 @@ from mmcv.utils import build_from_cfg
 from mmdet3d.core import VoxelGenerator
 from mmdet3d.core.bbox import (CameraInstance3DBoxes, DepthInstance3DBoxes,
                                LiDARInstance3DBoxes, box_np_ops)
-from mmdet.datasets.builder import PIPELINES
 from mmdet.datasets.pipelines import RandomFlip
-from ..builder import OBJECTSAMPLERS
+from ..builder import OBJECTSAMPLERS, PIPELINES
 from .data_augment_utils import noise_per_object_v3_
 
 

mmdet3d/datasets/s3dis_dataset.py

@@ -5,8 +5,8 @@ import numpy as np
 
 from mmdet3d.core import show_seg_result
 from mmdet3d.core.bbox import DepthInstance3DBoxes
-from mmdet.datasets import DATASETS
 from mmseg.datasets import DATASETS as SEG_DATASETS
+from .builder import DATASETS
 from .custom_3d import Custom3DDataset
 from .custom_3d_seg import Custom3DSegDataset
 from .pipelines import Compose

mmdet3d/datasets/scannet_dataset.py

@@ -7,8 +7,8 @@ import numpy as np
 
 from mmdet3d.core import instance_seg_eval, show_result, show_seg_result
 from mmdet3d.core.bbox import DepthInstance3DBoxes
-from mmdet.datasets import DATASETS
 from mmseg.datasets import DATASETS as SEG_DATASETS
+from .builder import DATASETS
 from .custom_3d import Custom3DDataset
 from .custom_3d_seg import Custom3DSegDataset
 from .pipelines import Compose

mmdet3d/datasets/semantickitti_dataset.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from os import path as osp
 
-from mmdet.datasets import DATASETS
+from .builder import DATASETS
 from .custom_3d import Custom3DDataset