Update all registries and fix some ut problems

mmdet3d/models/detectors/mvx_two_stage.py

@@ -11,13 +11,12 @@ from torch.nn import functional as F
 
 from mmdet3d.core import (Box3DMode, Coord3DMode, bbox3d2result,
                           merge_aug_bboxes_3d, show_result)
+from mmdet3d.registry import MODELS
 from mmdet.core import multi_apply
-from .. import builder
-from ..builder import DETECTORS
 from .base import Base3DDetector
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class MVXTwoStageDetector(Base3DDetector):
     """Base class of Multi-modality VoxelNet."""
 
@@ -42,33 +41,30 @@ class MVXTwoStageDetector(Base3DDetector):
         if pts_voxel_layer:
             self.pts_voxel_layer = Voxelization(**pts_voxel_layer)
         if pts_voxel_encoder:
-            self.pts_voxel_encoder = builder.build_voxel_encoder(
-                pts_voxel_encoder)
+            self.pts_voxel_encoder = MODELS.build(pts_voxel_encoder)
         if pts_middle_encoder:
-            self.pts_middle_encoder = builder.build_middle_encoder(
-                pts_middle_encoder)
+            self.pts_middle_encoder = MODELS.build(pts_middle_encoder)
         if pts_backbone:
-            self.pts_backbone = builder.build_backbone(pts_backbone)
+            self.pts_backbone = MODELS.build(pts_backbone)
         if pts_fusion_layer:
-            self.pts_fusion_layer = builder.build_fusion_layer(
-                pts_fusion_layer)
+            self.pts_fusion_layer = MODELS.build(pts_fusion_layer)
         if pts_neck is not None:
-            self.pts_neck = builder.build_neck(pts_neck)
+            self.pts_neck = MODELS.build(pts_neck)
         if pts_bbox_head:
             pts_train_cfg = train_cfg.pts if train_cfg else None
             pts_bbox_head.update(train_cfg=pts_train_cfg)
             pts_test_cfg = test_cfg.pts if test_cfg else None
             pts_bbox_head.update(test_cfg=pts_test_cfg)
-            self.pts_bbox_head = builder.build_head(pts_bbox_head)
+            self.pts_bbox_head = MODELS.build(pts_bbox_head)
 
         if img_backbone:
-            self.img_backbone = builder.build_backbone(img_backbone)
+            self.img_backbone = MODELS.build(img_backbone)
         if img_neck is not None:
-            self.img_neck = builder.build_neck(img_neck)
+            self.img_neck = MODELS.build(img_neck)
         if img_rpn_head is not None:
-            self.img_rpn_head = builder.build_head(img_rpn_head)
+            self.img_rpn_head = MODELS.build(img_rpn_head)
         if img_roi_head is not None:
-            self.img_roi_head = builder.build_head(img_roi_head)
+            self.img_roi_head = MODELS.build(img_roi_head)
 
         self.train_cfg = train_cfg
         self.test_cfg = test_cfg

mmdet3d/models/detectors/parta2.py

@@ -3,12 +3,11 @@ import torch
 from mmcv.ops import Voxelization
 from torch.nn import functional as F
 
-from .. import builder
-from ..builder import DETECTORS
+from mmdet3d.registry import MODELS
 from .two_stage import TwoStage3DDetector
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class PartA2(TwoStage3DDetector):
     r"""Part-A2 detector.
 
@@ -37,8 +36,8 @@ class PartA2(TwoStage3DDetector):
             pretrained=pretrained,
             init_cfg=init_cfg)
         self.voxel_layer = Voxelization(**voxel_layer)
-        self.voxel_encoder = builder.build_voxel_encoder(voxel_encoder)
-        self.middle_encoder = builder.build_middle_encoder(middle_encoder)
+        self.voxel_encoder = MODELS.build(voxel_encoder)
+        self.middle_encoder = MODELS.build(middle_encoder)
 
     def extract_feat(self, points, img_metas):
         """Extract features from points."""

mmdet3d/models/detectors/point_rcnn.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
-from ..builder import DETECTORS
+from mmdet3d.registry import MODELS
 from .two_stage import TwoStage3DDetector
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class PointRCNN(TwoStage3DDetector):
     r"""PointRCNN detector.
 

mmdet3d/models/detectors/single_stage.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from ..builder import DETECTORS, build_backbone, build_head, build_neck
+from mmdet3d.registry import MODELS
 from .base import Base3DDetector
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class SingleStage3DDetector(Base3DDetector):
     """SingleStage3DDetector.
 
@@ -30,12 +30,12 @@ class SingleStage3DDetector(Base3DDetector):
                  init_cfg=None,
                  pretrained=None):
         super(SingleStage3DDetector, self).__init__(init_cfg)
-        self.backbone = build_backbone(backbone)
+        self.backbone = MODELS.build(backbone)
         if neck is not None:
-            self.neck = build_neck(neck)
+            self.neck = MODELS.build(neck)
         bbox_head.update(train_cfg=train_cfg)
         bbox_head.update(test_cfg=test_cfg)
-        self.bbox_head = build_head(bbox_head)
+        self.bbox_head = MODELS.build(bbox_head)
         self.train_cfg = train_cfg
         self.test_cfg = test_cfg
 

mmdet3d/models/detectors/single_stage_mono3d.py

 # Copyright (c) OpenMMLab. All rights reserved.
-import warnings
 from os import path as osp
 
 import mmcv
@@ -9,11 +8,11 @@ from mmcv.parallel import DataContainer as DC
 
 from mmdet3d.core import (CameraInstance3DBoxes, bbox3d2result,
                           show_multi_modality_result)
-from mmdet.models.detectors import SingleStageDetector
-from ..builder import DETECTORS, build_backbone, build_head, build_neck
+from mmdet3d.registry import MODELS
+from mmdet.models.detectors.single_stage import SingleStageDetector
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class SingleStageMono3DDetector(SingleStageDetector):
     """Base class for monocular 3D single-stage detectors.
 
@@ -21,28 +20,6 @@ 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 ..builder import DETECTORS
+from mmdet3d.registry import MODELS
 from .single_stage_mono3d import SingleStageMono3DDetector
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class SMOKEMono3D(SingleStageMono3DDetector):
     r"""SMOKE <https://arxiv.org/abs/2002.10111>`_ for monocular 3D object
         detection.

mmdet3d/models/detectors/ssd3dnet.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from ..builder import DETECTORS
+from mmdet3d.registry import MODELS
 from .votenet import VoteNet
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class SSD3DNet(VoteNet):
     """3DSSDNet model.
 

mmdet3d/models/detectors/two_stage.py

 # Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
+from mmdet3d.registry import MODELS
 from mmdet.models import TwoStageDetector
-from ..builder import DETECTORS, build_backbone, build_head, build_neck
 from .base import Base3DDetector
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class TwoStage3DDetector(Base3DDetector, TwoStageDetector):
     """Base class of two-stage 3D detector.
 
@@ -15,37 +13,5 @@ class TwoStage3DDetector(Base3DDetector, TwoStageDetector):
     two-stage 3D detectors.
     """
 
-    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)
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-        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)
+    def __init__(self, **kwargs):
+        super(TwoStage3DDetector, self).__init__(**kwargs)

mmdet3d/models/detectors/votenet.py

@@ -2,11 +2,11 @@
 import torch
 
 from mmdet3d.core import bbox3d2result, merge_aug_bboxes_3d
-from ..builder import DETECTORS
+from mmdet3d.registry import MODELS
 from .single_stage import SingleStage3DDetector
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class VoteNet(SingleStage3DDetector):
     r"""`VoteNet <https://arxiv.org/pdf/1904.09664.pdf>`_ for 3D detection."""
 

mmdet3d/models/detectors/voxelnet.py

@@ -5,12 +5,11 @@ from mmcv.runner import force_fp32
 from torch.nn import functional as F
 
 from mmdet3d.core import bbox3d2result, merge_aug_bboxes_3d
-from .. import builder
-from ..builder import DETECTORS
+from mmdet3d.registry import MODELS
 from .single_stage import SingleStage3DDetector
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class VoxelNet(SingleStage3DDetector):
     r"""`VoxelNet <https://arxiv.org/abs/1711.06396>`_ for 3D detection."""
 
@@ -34,8 +33,8 @@ class VoxelNet(SingleStage3DDetector):
             init_cfg=init_cfg,
             pretrained=pretrained)
         self.voxel_layer = Voxelization(**voxel_layer)
-        self.voxel_encoder = builder.build_voxel_encoder(voxel_encoder)
-        self.middle_encoder = builder.build_middle_encoder(middle_encoder)
+        self.voxel_encoder = MODELS.build(voxel_encoder)
+        self.middle_encoder = MODELS.build(middle_encoder)
 
     def extract_feat(self, points, img_metas=None):
         """Extract features from points."""

mmdet3d/models/fusion_layers/point_fusion.py

@@ -7,7 +7,7 @@ from torch.nn import functional as F
 
 from mmdet3d.core.bbox.structures import (get_proj_mat_by_coord_type,
                                           points_cam2img)
-from ..builder import FUSION_LAYERS
+from mmdet3d.registry import MODELS
 from . import apply_3d_transformation
 
 
@@ -91,7 +91,7 @@ def point_sample(img_meta,
     return point_features.squeeze().t()
 
 
-@FUSION_LAYERS.register_module()
+@MODELS.register_module()
 class PointFusion(BaseModule):
     """Fuse image features from multi-scale features.
 

mmdet3d/models/fusion_layers/vote_fusion.py

@@ -3,13 +3,13 @@ import torch
 from torch import nn as nn
 
 from mmdet3d.core.bbox import points_cam2img
-from ..builder import FUSION_LAYERS
+from mmdet3d.registry import MODELS
 from . import apply_3d_transformation, bbox_2d_transform, coord_2d_transform
 
 EPS = 1e-6
 
 
-@FUSION_LAYERS.register_module()
+@MODELS.register_module()
 class VoteFusion(nn.Module):
     """Fuse 2d features from 3d seeds.
 

mmdet3d/models/losses/axis_aligned_iou_loss.py

@@ -2,9 +2,9 @@
 import torch
 from torch import nn as nn
 
+from mmdet3d.registry import MODELS
 from mmdet.models.losses.utils import weighted_loss
 from ...core.bbox import AxisAlignedBboxOverlaps3D
-from ..builder import LOSSES
 
 
 @weighted_loss
@@ -26,7 +26,7 @@ def axis_aligned_iou_loss(pred, target):
     return iou_loss
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class AxisAlignedIoULoss(nn.Module):
     """Calculate the IoU loss (1-IoU) of axis aligned bounding boxes.
 

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 ..builder import LOSSES
+from mmdet3d.registry import MODELS
 
 
 def chamfer_distance(src,
@@ -72,7 +72,7 @@ def chamfer_distance(src,
     return loss_src, loss_dst, indices1, indices2
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class ChamferDistance(nn.Module):
     """Calculate Chamfer Distance of two sets.
 

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 mmdet3d.registry import MODELS
 from mmdet.models.losses.utils import weighted_loss
-from ..builder import LOSSES
 
 
 @weighted_loss
@@ -57,7 +57,7 @@ def multibin_loss(pred_orientations, gt_orientations, num_dir_bins=4):
         return cls_losses / num_dir_bins + reg_losses / reg_cnt
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class MultiBinLoss(nn.Module):
     """Multi-Bin Loss for orientation.
 

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 mmdet3d.registry import MODELS
 from mmdet.models.losses.utils import weight_reduce_loss
-from ..builder import LOSSES
 
 
 def weight_correlation(conv):
@@ -68,7 +68,7 @@ def paconv_regularization_loss(modules, reduction):
     return corr_loss
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class PAConvRegularizationLoss(nn.Module):
     """Calculate correlation loss of kernel weights in PAConv's weight bank.
 

mmdet3d/models/losses/uncertain_smooth_l1_loss.py

@@ -2,8 +2,8 @@
 import torch
 from torch import nn as nn
 
+from mmdet3d.registry import MODELS
 from mmdet.models.losses.utils import weighted_loss
-from ..builder import LOSSES
 
 
 @weighted_loss
@@ -58,7 +58,7 @@ def uncertain_l1_loss(pred, target, sigma, alpha=1.0):
     return loss
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class UncertainSmoothL1Loss(nn.Module):
     r"""Smooth L1 loss with uncertainty.
 
@@ -122,7 +122,7 @@ class UncertainSmoothL1Loss(nn.Module):
         return loss_bbox
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class UncertainL1Loss(nn.Module):
     """L1 loss with uncertainty.
 

mmdet3d/models/middle_encoders/pillar_scatter.py

@@ -3,10 +3,10 @@ import torch
 from mmcv.runner import auto_fp16
 from torch import nn
 
-from ..builder import MIDDLE_ENCODERS
+from mmdet3d.registry import MODELS
 
 
-@MIDDLE_ENCODERS.register_module()
+@MODELS.register_module()
 class PointPillarsScatter(nn.Module):
     """Point Pillar's Scatter.
 

mmdet3d/models/middle_encoders/sparse_encoder.py

@@ -6,8 +6,7 @@ from torch import nn as nn
 
 from mmdet3d.ops import SparseBasicBlock, make_sparse_convmodule
 from mmdet3d.ops.spconv import IS_SPCONV2_AVAILABLE
-from mmdet.models.losses import sigmoid_focal_loss, smooth_l1_loss
-from ..builder import MIDDLE_ENCODERS
+from mmdet3d.registry import MODELS
 
 if IS_SPCONV2_AVAILABLE:
     from spconv.pytorch import SparseConvTensor, SparseSequential
@@ -15,7 +14,7 @@ else:
     from mmcv.ops import SparseConvTensor, SparseSequential
 
 
-@MIDDLE_ENCODERS.register_module()
+@MODELS.register_module()
 class SparseEncoder(nn.Module):
     r"""Sparse encoder for SECOND and Part-A2.
 

mmdet3d/models/middle_encoders/sparse_unet.py

@@ -15,7 +15,7 @@ from mmdet3d.ops.sparse_block import replace_feature
 from ..builder import MIDDLE_ENCODERS
 
 
-@MIDDLE_ENCODERS.register_module()
+@MODELS.register_module()
 class SparseUNet(BaseModule):
     r"""SparseUNet for PartA^2.