refactor directory

mmdet3d/datasets/s3dis_dataset.py

@@ -4,11 +4,11 @@ from typing import Callable, List, Optional, Union
 
 import numpy as np
 
-from mmdet3d.core.bbox import DepthInstance3DBoxes
 from mmdet3d.registry import DATASETS
+from mmdet3d.structures import DepthInstance3DBoxes
 from .det3d_dataset import Det3DDataset
-from .pipelines import Compose
 from .seg3d_dataset import Seg3DDataset
+from .transforms import Compose
 
 
 @DATASETS.register_module()
@@ -118,7 +118,7 @@ class S3DISDataset(Det3DDataset):
 
         Returns:
             dict: Data information that will be passed to the data
-                preprocessing pipelines. It includes the following keys:
+                preprocessing transforms. It includes the following keys:
 
                 - pts_filename (str): Filename of point clouds.
                 - file_name (str): Filename of point clouds.

mmdet3d/datasets/scannet_dataset.py

@@ -5,8 +5,8 @@ from typing import Callable, List, Optional, Union
 
 import numpy as np
 
-from mmdet3d.core.bbox import DepthInstance3DBoxes
 from mmdet3d.registry import DATASETS
+from mmdet3d.structures import DepthInstance3DBoxes
 from .det3d_dataset import Det3DDataset
 from .seg3d_dataset import Seg3DDataset
 
@@ -111,7 +111,7 @@ class ScanNetDataset(Det3DDataset):
 
         Returns:
             dict: Data information that will be passed to the data
-            preprocessing pipelines. It includes the following keys:
+            preprocessing transforms. It includes the following keys:
         """
         info['axis_align_matrix'] = self._get_axis_align_matrix(info)
         info['pts_instance_mask_path'] = osp.join(

mmdet3d/datasets/seg3d_dataset.py

@@ -242,7 +242,7 @@ class Seg3DDataset(BaseDataset):
         # use it in PointSegClassMapping pipeline
         info['label_mapping'] = self.label_mapping
 
-        # 'eval_ann_info' will be updated in loading pipelines
+        # 'eval_ann_info' will be updated in loading transforms
         if self.test_mode and self.load_eval_anns:
             info['eval_ann_info'] = dict()
 

mmdet3d/datasets/sunrgbd_dataset.py

@@ -5,8 +5,8 @@ from typing import Callable, List, Optional, Union
 
 import numpy as np
 
-from mmdet3d.core.bbox import DepthInstance3DBoxes
 from mmdet3d.registry import DATASETS
+from mmdet3d.structures import DepthInstance3DBoxes
 from .det3d_dataset import Det3DDataset
 
 

mmdet3d/datasets/pipelines/data_augment_utils.py → mmdet3d/datasets/transforms/data_augment_utils.py

@@ -5,7 +5,7 @@ import numba
 import numpy as np
 from numba.core.errors import NumbaPerformanceWarning
 
-from mmdet3d.core.bbox import box_np_ops
+from mmdet3d.structures.ops import box_np_ops
 
 warnings.filterwarnings('ignore', category=NumbaPerformanceWarning)
 

mmdet3d/datasets/pipelines/dbsampler.py → mmdet3d/datasets/transforms/dbsampler.py

@@ -6,9 +6,9 @@ import warnings
 import mmcv
 import numpy as np
 
-from mmdet3d.core.bbox import box_np_ops
-from mmdet3d.datasets.pipelines import data_augment_utils
+from mmdet3d.datasets.transforms import data_augment_utils
 from mmdet3d.registry import TRANSFORMS
+from mmdet3d.structures.ops import box_np_ops
 
 
 class BatchSampler:

mmdet3d/datasets/pipelines/formating.py → mmdet3d/datasets/transforms/formating.py

@@ -8,10 +8,9 @@ from mmcv import BaseTransform
 from mmengine import InstanceData
 from numpy import dtype
 
-from mmdet3d.core import Det3DDataSample, PointData
-from mmdet3d.core.bbox import BaseInstance3DBoxes
-from mmdet3d.core.points import BasePoints
 from mmdet3d.registry import TRANSFORMS
+from mmdet3d.structures import BaseInstance3DBoxes, Det3DDataSample, PointData
+from mmdet3d.structures.points import BasePoints
 
 
 def to_tensor(

mmdet3d/datasets/pipelines/loading.py → mmdet3d/datasets/transforms/loading.py

@@ -6,9 +6,9 @@ import numpy as np
 from mmcv.transforms import LoadImageFromFile
 from mmcv.transforms.base import BaseTransform
 
-from mmdet3d.core.points import BasePoints, get_points_type
 from mmdet3d.registry import TRANSFORMS
-from mmdet.datasets.pipelines import LoadAnnotations
+from mmdet3d.structures.points import BasePoints, get_points_type
+from mmdet.datasets.transforms import LoadAnnotations
 
 
 @TRANSFORMS.register_module()
@@ -760,12 +760,6 @@ class LoadAnnotations3D(LoadAnnotations):
         else:
             results['gt_bboxes'] = np.array(
                 gt_bboxes, dtype=np.float32).reshape((-1, 4))
-        if self.denorm_bbox:
-            bbox_num = results['gt_bboxes'].shape[0]
-            if bbox_num != 0:
-                h, w = results['img_shape']
-                results['gt_bboxes'][:, 0::2] *= w
-                results['gt_bboxes'][:, 1::2] *= h
 
         if 'eval_ann_info' in results:
             results['eval_ann_info']['gt_bboxes'] = results['gt_bboxes']

mmdet3d/datasets/pipelines/transforms_3d.py → mmdet3d/datasets/transforms/transforms_3d.py

@@ -8,12 +8,13 @@ import numpy as np
 from mmcv import is_tuple_of
 from mmcv.transforms import BaseTransform
 
-from mmdet3d.core import VoxelGenerator
-from mmdet3d.core.bbox import (CameraInstance3DBoxes, DepthInstance3DBoxes,
-                               LiDARInstance3DBoxes, box_np_ops)
-from mmdet3d.core.points import BasePoints
+from mmdet3d.models.task_modules import VoxelGenerator
 from mmdet3d.registry import TRANSFORMS
-from mmdet.datasets.pipelines import RandomFlip
+from mmdet3d.structures import (CameraInstance3DBoxes, DepthInstance3DBoxes,
+                                LiDARInstance3DBoxes)
+from mmdet3d.structures.ops import box_np_ops
+from mmdet3d.structures.points import BasePoints
+from mmdet.datasets.transforms import RandomFlip
 from .data_augment_utils import noise_per_object_v3_
 
 

mmdet3d/datasets/utils.py

@@ -5,16 +5,15 @@ from mmcv.transforms import LoadImageFromFile
 from pyquaternion import Quaternion
 
 # yapf: disable
-from mmdet3d.datasets.pipelines import (LoadAnnotations3D,
-                                        LoadImageFromFileMono3D,
-                                        LoadMultiViewImageFromFiles,
-                                        LoadPointsFromFile,
-                                        LoadPointsFromMultiSweeps,
-                                        MultiScaleFlipAug3D, Pack3DDetInputs,
-                                        PointSegClassMapping)
+from mmdet3d.datasets.transforms import (LoadAnnotations3D,
+                                         LoadImageFromFileMono3D,
+                                         LoadMultiViewImageFromFiles,
+                                         LoadPointsFromFile,
+                                         LoadPointsFromMultiSweeps,
+                                         MultiScaleFlipAug3D, Pack3DDetInputs,
+                                         PointSegClassMapping)
 # yapf: enable
 from mmdet3d.registry import TRANSFORMS
-from mmdet.datasets.pipelines import MultiScaleFlipAug
 
 
 def is_loading_function(transform):
@@ -41,12 +40,12 @@ def is_loading_function(transform):
             return False
         if obj_cls in loading_functions:
             return True
-        if obj_cls in (MultiScaleFlipAug3D, MultiScaleFlipAug):
+        if obj_cls in (MultiScaleFlipAug3D, ):
             return None
     elif callable(transform):
         if isinstance(transform, loading_functions):
             return True
-        if isinstance(transform, (MultiScaleFlipAug3D, MultiScaleFlipAug)):
+        if isinstance(transform, (MultiScaleFlipAug3D)):
             return None
     return False
 
@@ -63,7 +62,7 @@ def get_loading_pipeline(pipeline):
             keep loading image, points and annotations related configuration.
 
     Examples:
-        >>> pipelines = [
+        >>> transforms = [
         ...    dict(type='LoadPointsFromFile',
         ...         coord_type='LIDAR', load_dim=4, use_dim=4),
         ...    dict(type='LoadImageFromFile'),
@@ -94,7 +93,7 @@ def get_loading_pipeline(pipeline):
         ...         keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
         ...    ]
         >>> assert expected_pipelines == \
-        ...        get_loading_pipeline(pipelines)
+        ...        get_loading_pipeline(transforms)
     """
     loading_pipeline = []
     for transform in pipeline:

mmdet3d/datasets/waymo_dataset.py

@@ -9,7 +9,7 @@ import torch
 from mmcv.utils import print_log
 
 from mmdet3d.registry import DATASETS
-from ..core.bbox import Box3DMode, points_cam2img
+from mmdet3d.structures import Box3DMode, points_cam2img
 from .kitti_dataset import KittiDataset
 
 
@@ -188,7 +188,7 @@ class WaymoDataset(KittiDataset):
                                                   pklfile_prefix,
                                                   submission_prefix)
         if 'waymo' in data_format:
-            from ..core.evaluation.waymo_utils.prediction_kitti_to_waymo import \
+            from mmdet3d.evaluation.functional.waymo_utils import \
                 KITTI2Waymo  # noqa
             waymo_root = osp.join(
                 self.data_root.split('kitti_format')[0], 'waymo_format')
@@ -257,7 +257,7 @@ class WaymoDataset(KittiDataset):
                 pklfile_prefix,
                 submission_prefix,
                 data_format='kitti')
-            from mmdet3d.core.evaluation import kitti_eval
+            from mmdet3d.evaluation import kitti_eval
             gt_annos = [info['annos'] for info in self.data_infos]
 
             if isinstance(result_files, dict):

mmdet3d/engine/__init__.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from .scheduler import BetasSchedulerMixin, CosineAnnealingBetas
+
+__all__ = ['BetasSchedulerMixin', 'CosineAnnealingBetas']

mmdet3d/evaluation/__init__.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet3d.evaluation.functional.kitti_utils import (do_eval, eval_class,
+                                                       kitti_eval,
+                                                       kitti_eval_coco_style)
+from .functional import (aggregate_predictions, average_precision,
+                         eval_det_cls, eval_map_recall, fast_hist, get_acc,
+                         get_acc_cls, get_classwise_aps, get_single_class_aps,
+                         indoor_eval, instance_seg_eval, load_lyft_gts,
+                         load_lyft_predictions, lyft_eval, per_class_iou,
+                         rename_gt, seg_eval)
+from .metrics import (IndoorMetric, InstanceSegMetric, KittiMetric, LyftMetric,
+                      NuScenesMetric, SegMetric)
+
+__all__ = [
+    'kitti_eval_coco_style', 'kitti_eval', 'indoor_eval', 'lyft_eval',
+    'seg_eval', 'instance_seg_eval', 'average_precision', 'eval_det_cls',
+    'eval_map_recall', 'indoor_eval', 'aggregate_predictions', 'rename_gt',
+    'instance_seg_eval', 'load_lyft_gts', 'load_lyft_predictions', 'lyft_eval',
+    'get_classwise_aps', 'get_single_class_aps', 'fast_hist', 'per_class_iou',
+    'get_acc', 'get_acc_cls', 'seg_eval', 'KittiMetric', 'NuScenesMetric',
+    'IndoorMetric', 'LyftMetric', 'SegMetric', 'InstanceSegMetric',
+    'eval_class', 'do_eval'
+]

mmdet3d/evaluation/functional/__init__.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from .indoor_eval import (average_precision, eval_det_cls, eval_map_recall,
+                          indoor_eval)
+from .instance_seg_eval import (aggregate_predictions, instance_seg_eval,
+                                rename_gt)
+from .kitti_utils import do_eval, kitti_eval, kitti_eval_coco_style
+from .lyft_eval import (get_classwise_aps, get_single_class_aps, load_lyft_gts,
+                        load_lyft_predictions, lyft_eval)
+from .scannet_utils import evaluate_matches, scannet_eval
+from .seg_eval import fast_hist, get_acc, get_acc_cls, per_class_iou, seg_eval
+
+__all__ = [
+    'average_precision', 'eval_det_cls', 'eval_map_recall', 'indoor_eval',
+    'aggregate_predictions', 'rename_gt', 'instance_seg_eval', 'load_lyft_gts',
+    'load_lyft_predictions', 'lyft_eval', 'get_classwise_aps',
+    'get_single_class_aps', 'fast_hist', 'per_class_iou', 'get_acc',
+    'get_acc_cls', 'seg_eval', 'kitti_eval', 'kitti_eval_coco_style',
+    'scannet_eval', 'evaluate_matches', 'do_eval'
+]