[Enhance] Update Registry in MMDet3D (#1412)

* Update Registry in MMDet3D

* fix compose pipeline bug

* update registry

* fix some bugs

* fix comments

* fix comments

mmdet3d/models/detectors/single_stage.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.models import DETECTORS, build_backbone, build_head, build_neck
+from ..builder import DETECTORS, build_backbone, build_head, build_neck
 from .base import Base3DDetector
 
 

mmdet3d/models/detectors/single_stage_mono3d.py

 # Copyright (c) OpenMMLab. All rights reserved.
+import warnings
 from os import path as osp
 
 import mmcv
@@ -8,8 +9,8 @@ from mmcv.parallel import DataContainer as DC
 
 from mmdet3d.core import (CameraInstance3DBoxes, bbox3d2result,
                           show_multi_modality_result)
-from mmdet.models.builder import DETECTORS
-from mmdet.models.detectors.single_stage import SingleStageDetector
+from mmdet.models.detectors import SingleStageDetector
+from ..builder import DETECTORS, build_backbone, build_head, build_neck
 
 
 @DETECTORS.register_module()
@@ -20,6 +21,28 @@ class SingleStageMono3DDetector(SingleStageDetector):
     output features of the backbone+neck.
     """
 
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 bbox_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(SingleStageDetector, self).__init__(init_cfg)
+        if pretrained:
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            backbone.pretrained = pretrained
+        self.backbone = build_backbone(backbone)
+        if neck is not None:
+            self.neck = build_neck(neck)
+        bbox_head.update(train_cfg=train_cfg)
+        bbox_head.update(test_cfg=test_cfg)
+        self.bbox_head = build_head(bbox_head)
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
     def extract_feats(self, imgs):
         """Directly extract features from the backbone+neck."""
         assert isinstance(imgs, list)

mmdet3d/models/detectors/smoke_mono3d.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.models.builder import DETECTORS
+from ..builder import DETECTORS
 from .single_stage_mono3d import SingleStageMono3DDetector
 
 

mmdet3d/models/detectors/ssd3dnet.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.models import DETECTORS
+from ..builder import DETECTORS
 from .votenet import VoteNet
 
 

mmdet3d/models/detectors/two_stage.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.models import DETECTORS, TwoStageDetector
+import warnings
+
+from mmdet.models import TwoStageDetector
+from ..builder import DETECTORS, build_backbone, build_head, build_neck
 from .base import Base3DDetector
 
 
@@ -12,5 +15,36 @@ class TwoStage3DDetector(Base3DDetector, TwoStageDetector):
     two-stage 3D detectors.
     """
 
-    def __init__(self, **kwargs):
-        super(TwoStage3DDetector, self).__init__(**kwargs)
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 rpn_head=None,
+                 roi_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(TwoStageDetector, self).__init__(init_cfg)
+        if pretrained:
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            backbone.pretrained = pretrained
+        self.backbone = build_backbone(backbone)
+
+        if neck is not None:
+            self.neck = build_neck(neck)
+
+        if rpn_head is not None:
+            rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None
+            rpn_head_ = rpn_head.copy()
+            rpn_head_.update(train_cfg=rpn_train_cfg, test_cfg=test_cfg.rpn)
+            self.rpn_head = build_head(rpn_head_)
+
+        if roi_head is not None:
+            # update train and test cfg here for now
+            # TODO: refactor assigner & sampler
+            rcnn_train_cfg = train_cfg.rcnn if train_cfg is not None else None
+            roi_head.update(train_cfg=rcnn_train_cfg)
+            roi_head.update(test_cfg=test_cfg.rcnn)
+            roi_head.pretrained = pretrained
+            self.roi_head = build_head(roi_head)

mmdet3d/models/detectors/votenet.py

@@ -2,7 +2,7 @@
 import torch
 
 from mmdet3d.core import bbox3d2result, merge_aug_bboxes_3d
-from mmdet.models import DETECTORS
+from ..builder import DETECTORS
 from .single_stage import SingleStage3DDetector
 
 

mmdet3d/models/detectors/voxelnet.py

@@ -5,8 +5,8 @@ from mmcv.runner import force_fp32
 from torch.nn import functional as F
 
 from mmdet3d.core import bbox3d2result, merge_aug_bboxes_3d
-from mmdet.models import DETECTORS
 from .. import builder
+from ..builder import DETECTORS
 from .single_stage import SingleStage3DDetector
 
 

mmdet3d/models/losses/axis_aligned_iou_loss.py

@@ -2,9 +2,9 @@
 import torch
 from torch import nn as nn
 
-from mmdet.models.builder import LOSSES
 from mmdet.models.losses.utils import weighted_loss
 from ...core.bbox import AxisAlignedBboxOverlaps3D
+from ..builder import LOSSES
 
 
 @weighted_loss

mmdet3d/models/losses/chamfer_distance.py

@@ -3,7 +3,7 @@ import torch
 from torch import nn as nn
 from torch.nn.functional import l1_loss, mse_loss, smooth_l1_loss
 
-from mmdet.models.builder import LOSSES
+from ..builder import LOSSES
 
 
 def chamfer_distance(src,

mmdet3d/models/losses/multibin_loss.py

@@ -3,8 +3,8 @@ import torch
 from torch import nn as nn
 from torch.nn import functional as F
 
-from mmdet.models.builder import LOSSES
 from mmdet.models.losses.utils import weighted_loss
+from ..builder import LOSSES
 
 
 @weighted_loss

mmdet3d/models/losses/paconv_regularization_loss.py

@@ -3,8 +3,8 @@ import torch
 from torch import nn as nn
 
 from mmdet3d.ops import PAConv, PAConvCUDA
-from mmdet.models.builder import LOSSES
 from mmdet.models.losses.utils import weight_reduce_loss
+from ..builder import LOSSES
 
 
 def weight_correlation(conv):

mmdet3d/models/losses/uncertain_smooth_l1_loss.py

@@ -2,8 +2,8 @@
 import torch
 from torch import nn as nn
 
-from mmdet.models.builder import LOSSES
 from mmdet.models.losses.utils import weighted_loss
+from ..builder import LOSSES
 
 
 @weighted_loss

mmdet3d/models/necks/dla_neck.py

@@ -6,7 +6,7 @@ from mmcv.cnn import ConvModule, build_conv_layer
 from mmcv.runner import BaseModule
 from torch import nn as nn
 
-from mmdet.models.builder import NECKS
+from ..builder import NECKS
 
 
 def fill_up_weights(up):

mmdet3d/models/necks/imvoxel_neck.py

@@ -2,7 +2,7 @@
 from mmcv.cnn import ConvModule
 from torch import nn
 
-from mmdet.models import NECKS
+from ..builder import NECKS
 
 
 @NECKS.register_module()

mmdet3d/models/necks/pointnet2_fp_neck.py

@@ -3,7 +3,7 @@ from mmcv.runner import BaseModule
 from torch import nn as nn
 
 from mmdet3d.ops import PointFPModule
-from mmdet.models import NECKS
+from ..builder import NECKS
 
 
 @NECKS.register_module()

mmdet3d/models/necks/second_fpn.py

@@ -5,7 +5,7 @@ from mmcv.cnn import build_conv_layer, build_norm_layer, build_upsample_layer
 from mmcv.runner import BaseModule, auto_fp16
 from torch import nn as nn
 
-from mmdet.models import NECKS
+from ..builder import NECKS
 
 
 @NECKS.register_module()

mmdet3d/models/roi_heads/bbox_heads/h3d_bbox_head.py

@@ -7,11 +7,10 @@ from torch.nn import functional as F
 
 from mmdet3d.core.bbox import DepthInstance3DBoxes
 from mmdet3d.core.post_processing import aligned_3d_nms
-from mmdet3d.models.builder import build_loss
+from mmdet3d.models.builder import HEADS, build_loss
 from mmdet3d.models.losses import chamfer_distance
 from mmdet3d.ops import build_sa_module
 from mmdet.core import build_bbox_coder, multi_apply
-from mmdet.models import HEADS
 
 
 @HEADS.register_module()

mmdet3d/models/roi_heads/bbox_heads/parta2_bbox_head.py

@@ -9,10 +9,9 @@ from torch import nn as nn
 from mmdet3d.core.bbox.structures import (LiDARInstance3DBoxes,
                                           rotation_3d_in_axis, xywhr2xyxyr)
 from mmdet3d.core.post_processing import nms_bev, nms_normal_bev
-from mmdet3d.models.builder import build_loss
+from mmdet3d.models.builder import HEADS, build_loss
 from mmdet3d.ops import make_sparse_convmodule
 from mmdet.core import build_bbox_coder, multi_apply
-from mmdet.models import HEADS
 
 
 @HEADS.register_module()

mmdet3d/models/roi_heads/bbox_heads/point_rcnn_bbox_head.py

@@ -9,10 +9,9 @@ from torch import nn as nn
 from mmdet3d.core.bbox.structures import (LiDARInstance3DBoxes,
                                           rotation_3d_in_axis, xywhr2xyxyr)
 from mmdet3d.core.post_processing import nms_bev, nms_normal_bev
-from mmdet3d.models.builder import build_loss
+from mmdet3d.models.builder import HEADS, build_loss
 from mmdet3d.ops import build_sa_module
 from mmdet.core import build_bbox_coder, multi_apply
-from mmdet.models import HEADS
 
 
 @HEADS.register_module()

mmdet3d/models/roi_heads/h3d_roi_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from mmdet3d.core.bbox import bbox3d2result
-from mmdet.models import HEADS
-from ..builder import build_head
+from ..builder import HEADS, build_head
 from .base_3droi_head import Base3DRoIHead