Release v1.0.0rc2

mmdet3d/models/builder.py

@@ -4,45 +4,78 @@ import warnings
 from mmcv.cnn import MODELS as MMCV_MODELS
 from mmcv.utils import Registry
 
-from mmdet.models.builder import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS,
-                                  ROI_EXTRACTORS, SHARED_HEADS)
-from mmseg.models.builder import SEGMENTORS
+from mmdet.models.builder import BACKBONES as MMDET_BACKBONES
+from mmdet.models.builder import DETECTORS as MMDET_DETECTORS
+from mmdet.models.builder import HEADS as MMDET_HEADS
+from mmdet.models.builder import LOSSES as MMDET_LOSSES
+from mmdet.models.builder import NECKS as MMDET_NECKS
+from mmdet.models.builder import ROI_EXTRACTORS as MMDET_ROI_EXTRACTORS
+from mmdet.models.builder import SHARED_HEADS as MMDET_SHARED_HEADS
+from mmseg.models.builder import LOSSES as MMSEG_LOSSES
 
 MODELS = Registry('models', parent=MMCV_MODELS)
 
+BACKBONES = MODELS
+NECKS = MODELS
+ROI_EXTRACTORS = MODELS
+SHARED_HEADS = MODELS
+HEADS = MODELS
+LOSSES = MODELS
+DETECTORS = MODELS
 VOXEL_ENCODERS = MODELS
 MIDDLE_ENCODERS = MODELS
 FUSION_LAYERS = MODELS
+SEGMENTORS = MODELS
 
 
 def build_backbone(cfg):
     """Build backbone."""
+    if cfg['type'] in BACKBONES._module_dict.keys():
         return BACKBONES.build(cfg)
+    else:
+        return MMDET_BACKBONES.build(cfg)
 
 
 def build_neck(cfg):
     """Build neck."""
+    if cfg['type'] in NECKS._module_dict.keys():
         return NECKS.build(cfg)
+    else:
+        return MMDET_NECKS.build(cfg)
 
 
 def build_roi_extractor(cfg):
     """Build RoI feature extractor."""
+    if cfg['type'] in NECKS._module_dict.keys():
         return ROI_EXTRACTORS.build(cfg)
+    else:
+        return MMDET_ROI_EXTRACTORS.build(cfg)
 
 
 def build_shared_head(cfg):
     """Build shared head of detector."""
+    if cfg['type'] in SHARED_HEADS._module_dict.keys():
         return SHARED_HEADS.build(cfg)
+    else:
+        return MMDET_SHARED_HEADS.build(cfg)
 
 
 def build_head(cfg):
     """Build head."""
+    if cfg['type'] in HEADS._module_dict.keys():
         return HEADS.build(cfg)
+    else:
+        return MMDET_HEADS.build(cfg)
 
 
 def build_loss(cfg):
     """Build loss function."""
+    if cfg['type'] in LOSSES._module_dict.keys():
         return LOSSES.build(cfg)
+    elif cfg['type'] in MMDET_LOSSES._module_dict.keys():
+        return MMDET_LOSSES.build(cfg)
+    else:
+        return MMSEG_LOSSES.build(cfg)
 
 
 def build_detector(cfg, train_cfg=None, test_cfg=None):
@@ -55,8 +88,12 @@ def build_detector(cfg, train_cfg=None, test_cfg=None):
         'train_cfg specified in both outer field and model field '
     assert cfg.get('test_cfg') is None or test_cfg is None, \
         'test_cfg specified in both outer field and model field '
+    if cfg['type'] in DETECTORS._module_dict.keys():
         return DETECTORS.build(
             cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
+    else:
+        return MMDET_DETECTORS.build(
+            cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
 
 
 def build_segmentor(cfg, train_cfg=None, test_cfg=None):

mmdet3d/models/decode_heads/dgcnn_head.py

@@ -2,7 +2,7 @@
 from mmcv.cnn.bricks import ConvModule
 
 from mmdet3d.ops import DGCNNFPModule
-from mmdet.models import HEADS
+from ..builder import HEADS
 from .decode_head import Base3DDecodeHead
 
 

mmdet3d/models/decode_heads/paconv_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.cnn.bricks import ConvModule
 
-from mmdet.models import HEADS
+from ..builder import HEADS
 from .pointnet2_head import PointNet2Head
 
 

mmdet3d/models/decode_heads/pointnet2_head.py

@@ -3,7 +3,7 @@ from mmcv.cnn.bricks import ConvModule
 from torch import nn as nn
 
 from mmdet3d.ops import PointFPModule
-from mmdet.models import HEADS
+from ..builder import HEADS
 from .decode_head import Base3DDecodeHead
 
 

mmdet3d/models/dense_heads/anchor3d_head.py

@@ -8,8 +8,7 @@ from mmdet3d.core import (PseudoSampler, box3d_multiclass_nms, limit_period,
                           xywhr2xyxyr)
 from mmdet.core import (build_assigner, build_bbox_coder,
                         build_prior_generator, build_sampler, multi_apply)
-from mmdet.models import HEADS
-from ..builder import build_loss
+from ..builder import HEADS, build_loss
 from .train_mixins import AnchorTrainMixin
 
 

mmdet3d/models/dense_heads/anchor_free_mono3d_head.py

@@ -7,7 +7,7 @@ from mmcv.runner import force_fp32
 from torch import nn as nn
 
 from mmdet.core import multi_apply
-from mmdet.models.builder import HEADS, build_loss
+from ..builder import HEADS, build_loss
 from .base_mono3d_dense_head import BaseMono3DDenseHead
 
 

mmdet3d/models/dense_heads/base_conv_bbox_head.py

@@ -4,7 +4,7 @@ from mmcv.cnn.bricks import build_conv_layer
 from mmcv.runner import BaseModule
 from torch import nn as nn
 
-from mmdet.models.builder import HEADS
+from ..builder import HEADS
 
 
 @HEADS.register_module()

mmdet3d/models/dense_heads/centerpoint_head.py

@@ -3,16 +3,16 @@ import copy
 
 import torch
 from mmcv.cnn import ConvModule, build_conv_layer
-from mmcv.ops import nms_bev as nms_gpu
 from mmcv.runner import BaseModule, force_fp32
 from torch import nn
 
 from mmdet3d.core import (circle_nms, draw_heatmap_gaussian, gaussian_radius,
                           xywhr2xyxyr)
+from mmdet3d.core.post_processing import nms_bev
 from mmdet3d.models import builder
-from mmdet3d.models.builder import HEADS, build_loss
 from mmdet3d.models.utils import clip_sigmoid
 from mmdet.core import build_bbox_coder, multi_apply
+from ..builder import HEADS, build_loss
 
 
 @HEADS.register_module()
@@ -747,9 +747,9 @@ class CenterHead(BaseModule):
         for i, (box_preds, cls_preds, cls_labels) in enumerate(
                 zip(batch_reg_preds, batch_cls_preds, batch_cls_labels)):
 
-            # Apply NMS in birdeye view
+            # Apply NMS in bird eye view
 
-            # get highest score per prediction, than apply nms
+            # get the highest score per prediction, then apply nms
             # to remove overlapped box.
             if num_class_with_bg == 1:
                 top_scores = cls_preds.squeeze(-1)
@@ -778,7 +778,7 @@ class CenterHead(BaseModule):
                     box_preds[:, :], self.bbox_coder.code_size).bev)
                 # the nms in 3d detection just remove overlap boxes.
 
-                selected = nms_gpu(
+                selected = nms_bev(
                     boxes_for_nms,
                     top_scores,
                     thresh=self.test_cfg['nms_thr'],

mmdet3d/models/dense_heads/fcos_mono3d_head.py

@@ -11,7 +11,7 @@ from mmdet3d.core import (box3d_multiclass_nms, limit_period, points_img2cam,
                           xywhr2xyxyr)
 from mmdet.core import multi_apply
 from mmdet.core.bbox.builder import build_bbox_coder
-from mmdet.models.builder import HEADS, build_loss
+from ..builder import HEADS, build_loss
 from .anchor_free_mono3d_head import AnchorFreeMono3DHead
 
 INF = 1e8

mmdet3d/models/dense_heads/free_anchor3d_head.py

@@ -4,7 +4,7 @@ from mmcv.runner import force_fp32
 from torch.nn import functional as F
 
 from mmdet3d.core.bbox import bbox_overlaps_nearest_3d
-from mmdet.models import HEADS
+from ..builder import HEADS
 from .anchor3d_head import Anchor3DHead
 from .train_mixins import get_direction_target
 

mmdet3d/models/dense_heads/groupfree3d_head.py

@@ -14,9 +14,8 @@ from torch import nn as nn
 from torch.nn import functional as F
 
 from mmdet3d.core.post_processing import aligned_3d_nms
-from mmdet3d.models.builder import build_loss
 from mmdet.core import build_bbox_coder, multi_apply
-from mmdet.models import HEADS
+from ..builder import HEADS, build_loss
 from .base_conv_bbox_head import BaseConvBboxHead
 
 EPS = 1e-6

mmdet3d/models/dense_heads/monoflex_head.py

@@ -9,11 +9,11 @@ from mmdet3d.models.utils import (filter_outside_objs, get_edge_indices,
                                   get_keypoints, handle_proj_objs)
 from mmdet.core import multi_apply
 from mmdet.core.bbox.builder import build_bbox_coder
-from mmdet.models.builder import HEADS, build_loss
 from mmdet.models.utils import gaussian_radius, gen_gaussian_target
 from mmdet.models.utils.gaussian_target import (get_local_maximum,
                                                 get_topk_from_heatmap,
                                                 transpose_and_gather_feat)
+from ..builder import HEADS, build_loss
 from .anchor_free_mono3d_head import AnchorFreeMono3DHead
 
 

mmdet3d/models/dense_heads/parta2_rpn_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from __future__ import division
-
 import numpy as np
 import torch
-from mmcv.ops import nms_bev as nms_gpu
-from mmcv.ops import nms_normal_bev as nms_normal_gpu
 from mmcv.runner import force_fp32
 
 from mmdet3d.core import limit_period, xywhr2xyxyr
-from mmdet.models import HEADS
+from mmdet3d.core.post_processing import nms_bev, nms_normal_bev
+from ..builder import HEADS
 from .anchor3d_head import Anchor3DHead
 
 
@@ -261,9 +258,9 @@ class PartA2RPNHead(Anchor3DHead):
         _scores = mlvl_max_scores[score_thr_inds]
         _bboxes_for_nms = mlvl_bboxes_for_nms[score_thr_inds, :]
         if cfg.use_rotate_nms:
-            nms_func = nms_gpu
+            nms_func = nms_bev
         else:
-            nms_func = nms_normal_gpu
+            nms_func = nms_normal_bev
         selected = nms_func(_bboxes_for_nms, _scores, cfg.nms_thr)
 
         _mlvl_bboxes = mlvl_bboxes[score_thr_inds, :]
@@ -288,7 +285,6 @@ class PartA2RPNHead(Anchor3DHead):
             scores = torch.cat(scores, dim=0)
             cls_scores = torch.cat(cls_scores, dim=0)
             labels = torch.cat(labels, dim=0)
-            dir_scores = torch.cat(dir_scores, dim=0)
             if bboxes.shape[0] > max_num:
                 _, inds = scores.sort(descending=True)
                 inds = inds[:max_num]

mmdet3d/models/dense_heads/pgd_head.py

@@ -9,7 +9,7 @@ from torch.nn import functional as F
 from mmdet3d.core import box3d_multiclass_nms, xywhr2xyxyr
 from mmdet3d.core.bbox import points_cam2img, points_img2cam
 from mmdet.core import distance2bbox, multi_apply
-from mmdet.models.builder import HEADS, build_loss
+from ..builder import HEADS, build_loss
 from .fcos_mono3d_head import FCOSMono3DHead
 
 

mmdet3d/models/dense_heads/point_rpn_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
-from mmcv.ops import nms_bev as nms_gpu
-from mmcv.ops import nms_normal_bev as nms_normal_gpu
 from mmcv.runner import BaseModule, force_fp32
 from torch import nn as nn
 
+from mmdet3d.core import xywhr2xyxyr
 from mmdet3d.core.bbox.structures import (DepthInstance3DBoxes,
                                           LiDARInstance3DBoxes)
+from mmdet3d.core.post_processing import nms_bev, nms_normal_bev
 from mmdet.core import build_bbox_coder, multi_apply
-from mmdet.models import HEADS, build_loss
+from ..builder import HEADS, build_loss
 
 
 @HEADS.register_module()
@@ -19,7 +19,7 @@ class PointRPNHead(BaseModule):
         num_classes (int): Number of classes.
         train_cfg (dict): Train configs.
         test_cfg (dict): Test configs.
-        pred_layer_cfg (dict, optional): Config of classfication and
+        pred_layer_cfg (dict, optional): Config of classification and
             regression prediction layers. Defaults to None.
         enlarge_width (float, optional): Enlarge bbox for each side to ignore
             close points. Defaults to 0.1.
@@ -121,7 +121,7 @@ class PointRPNHead(BaseModule):
             batch_size, -1, self._get_cls_out_channels())
         point_box_preds = self.reg_layers(feat_reg).reshape(
             batch_size, -1, self._get_reg_out_channels())
-        return (point_box_preds, point_cls_preds)
+        return point_box_preds, point_cls_preds
 
     @force_fp32(apply_to=('bbox_preds'))
     def loss(self,
@@ -159,7 +159,7 @@ class PointRPNHead(BaseModule):
         semantic_targets = mask_targets
         semantic_targets[negative_mask] = self.num_classes
         semantic_points_label = semantic_targets
-        # for ignore, but now we do not have ignore label
+        # for ignore, but now we do not have ignored label
         semantic_loss_weight = negative_mask.float() + positive_mask.float()
         semantic_loss = self.cls_loss(semantic_points,
                                       semantic_points_label.reshape(-1),
@@ -220,7 +220,7 @@ class PointRPNHead(BaseModule):
         gt_bboxes_3d = gt_bboxes_3d[valid_gt]
         gt_labels_3d = gt_labels_3d[valid_gt]
 
-        # transform the bbox coordinate to the pointcloud coordinate
+        # transform the bbox coordinate to the point cloud coordinate
         gt_bboxes_3d_tensor = gt_bboxes_3d.tensor.clone()
         gt_bboxes_3d_tensor[..., 2] += gt_bboxes_3d_tensor[..., 5] / 2
 
@@ -233,7 +233,6 @@ class PointRPNHead(BaseModule):
                                               points[..., 0:3], mask_targets)
 
         positive_mask = (points_mask.max(1)[0] > 0)
-        negative_mask = (points_mask.max(1)[0] == 0)
         # add ignore_mask
         extend_gt_bboxes_3d = gt_bboxes_3d.enlarged_box(self.enlarge_width)
         points_mask, _ = self._assign_targets_by_points_inside(
@@ -297,9 +296,9 @@ class PointRPNHead(BaseModule):
         nms_cfg = self.test_cfg.nms_cfg if not self.training \
             else self.train_cfg.nms_cfg
         if nms_cfg.use_rotate_nms:
-            nms_func = nms_gpu
+            nms_func = nms_bev
         else:
-            nms_func = nms_normal_gpu
+            nms_func = nms_normal_bev
 
         num_bbox = bbox.shape[0]
         bbox = input_meta['box_type_3d'](
@@ -322,29 +321,33 @@ class PointRPNHead(BaseModule):
         else:
             raise NotImplementedError('Unsupported bbox type!')
 
-        bbox = bbox.tensor[nonempty_box_mask]
+        bbox = bbox[nonempty_box_mask]
 
         if self.test_cfg.score_thr is not None:
             score_thr = self.test_cfg.score_thr
             keep = (obj_scores >= score_thr)
             obj_scores = obj_scores[keep]
             sem_scores = sem_scores[keep]
-            bbox = bbox[keep]
+            bbox = bbox.tensor[keep]
 
         if obj_scores.shape[0] > 0:
             topk = min(nms_cfg.nms_pre, obj_scores.shape[0])
             obj_scores_nms, indices = torch.topk(obj_scores, k=topk)
-            bbox_for_nms = bbox[indices]
+            bbox_for_nms = xywhr2xyxyr(bbox[indices].bev)
             sem_scores_nms = sem_scores[indices]
 
-            keep = nms_func(bbox_for_nms[:, 0:7], obj_scores_nms,
-                            nms_cfg.iou_thr)
+            keep = nms_func(bbox_for_nms, obj_scores_nms, nms_cfg.iou_thr)
             keep = keep[:nms_cfg.nms_post]
 
-            bbox_selected = bbox_for_nms[keep]
+            bbox_selected = bbox.tensor[indices][keep]
             score_selected = obj_scores_nms[keep]
             cls_preds = sem_scores_nms[keep]
             labels = torch.argmax(cls_preds, -1)
+        else:
+            bbox_selected = bbox.tensor
+            score_selected = obj_scores.new_zeros([0])
+            labels = obj_scores.new_zeros([0])
+            cls_preds = obj_scores.new_zeros([0, sem_scores.shape[-1]])
 
         return bbox_selected, score_selected, labels, cls_preds
 

mmdet3d/models/dense_heads/shape_aware_head.py

@@ -9,8 +9,7 @@ from torch import nn as nn
 
 from mmdet3d.core import box3d_multiclass_nms, limit_period, xywhr2xyxyr
 from mmdet.core import multi_apply
-from mmdet.models import HEADS
-from ..builder import build_head
+from ..builder import HEADS, build_head
 from .anchor3d_head import Anchor3DHead
 
 

mmdet3d/models/dense_heads/smoke_mono3d_head.py

@@ -4,11 +4,11 @@ from torch.nn import functional as F
 
 from mmdet.core import multi_apply
 from mmdet.core.bbox.builder import build_bbox_coder
-from mmdet.models.builder import HEADS
 from mmdet.models.utils import gaussian_radius, gen_gaussian_target
 from mmdet.models.utils.gaussian_target import (get_local_maximum,
                                                 get_topk_from_heatmap,
                                                 transpose_and_gather_feat)
+from ..builder import HEADS
 from .anchor_free_mono3d_head import AnchorFreeMono3DHead
 
 

mmdet3d/models/dense_heads/ssd_3d_head.py

@@ -7,9 +7,8 @@ from torch.nn import functional as F
 from mmdet3d.core.bbox.structures import (DepthInstance3DBoxes,
                                           LiDARInstance3DBoxes,
                                           rotation_3d_in_axis)
-from mmdet3d.models.builder import build_loss
 from mmdet.core import multi_apply
-from mmdet.models import HEADS
+from ..builder import HEADS, build_loss
 from .vote_head import VoteHead
 
 

mmdet3d/models/dense_heads/vote_head.py

@@ -6,12 +6,11 @@ from mmcv.runner import BaseModule, force_fp32
 from torch.nn import functional as F
 
 from mmdet3d.core.post_processing import aligned_3d_nms
-from mmdet3d.models.builder import build_loss
 from mmdet3d.models.losses import chamfer_distance
 from mmdet3d.models.model_utils import VoteModule
 from mmdet3d.ops import build_sa_module
 from mmdet.core import build_bbox_coder, multi_apply
-from mmdet.models import HEADS
+from ..builder import HEADS, build_loss
 from .base_conv_bbox_head import BaseConvBboxHead
 
 

mmdet3d/models/detectors/centerpoint.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 .mvx_two_stage import MVXTwoStageDetector