[Enhance] Remove mmcv.iou3d from mmdetecion3d (#1403)

* remove iou3d_boxes_overlap_bev_forward

* remove nms ops from mmcv.ops.iou3d from mmdetection3d

mmdet3d/core/bbox/structures/base_box3d.py

@@ -4,10 +4,9 @@
 
 import numpy as np
 import torch
-from mmcv._ext import iou3d_boxes_overlap_bev_forward as boxes_overlap_bev_gpu
-from mmcv.ops import points_in_boxes_all, points_in_boxes_part
+from mmcv.ops import box_iou_rotated, points_in_boxes_all, points_in_boxes_part
 
-from .utils import limit_period, xywhr2xyxyr
+from .utils import limit_period
 
 
 class BaseInstance3DBoxes(object):
@@ -447,7 +446,7 @@ def overlaps(cls, boxes1, boxes2, mode='iou'):
             mode (str, optional): Mode of iou calculation. Defaults to 'iou'.
 
         Returns:
-            torch.Tensor: Calculated iou of boxes' heights.
+            torch.Tensor: Calculated 3D overlaps of the boxes.
         """
         assert isinstance(boxes1, BaseInstance3DBoxes)
         assert isinstance(boxes2, BaseInstance3DBoxes)
@@ -464,15 +463,13 @@ def overlaps(cls, boxes1, boxes2, mode='iou'):
         # height overlap
         overlaps_h = cls.height_overlaps(boxes1, boxes2)
 
-        # obtain BEV boxes in XYXYR format
-        boxes1_bev = xywhr2xyxyr(boxes1.bev)
-        boxes2_bev = xywhr2xyxyr(boxes2.bev)
-
         # bev overlap
-        overlaps_bev = boxes1_bev.new_zeros(
-            (boxes1_bev.shape[0], boxes2_bev.shape[0])).cuda()  # (N, M)
-        boxes_overlap_bev_gpu(boxes1_bev.contiguous().cuda(),
-                              boxes2_bev.contiguous().cuda(), overlaps_bev)
+        iou2d = box_iou_rotated(boxes1.bev, boxes2.bev)
+        areas1 = (boxes1.bev[:, 2] * boxes1.bev[:, 3]).unsqueeze(1).expand(
+            rows, cols)
+        areas2 = (boxes2.bev[:, 2] * boxes2.bev[:, 3]).unsqueeze(0).expand(
+            rows, cols)
+        overlaps_bev = iou2d * (areas1 + areas2) / (1 + iou2d)
 
         # 3d overlaps
         overlaps_3d = overlaps_bev.to(boxes1.device) * overlaps_h

mmdet3d/core/post_processing/__init__.py

@@ -2,11 +2,13 @@
 from mmdet.core.post_processing import (merge_aug_bboxes, merge_aug_masks,
                                         merge_aug_proposals, merge_aug_scores,
                                         multiclass_nms)
-from .box3d_nms import aligned_3d_nms, box3d_multiclass_nms, circle_nms
+from .box3d_nms import (aligned_3d_nms, box3d_multiclass_nms, circle_nms,
+                        nms_bev, nms_normal_bev)
 from .merge_augs import merge_aug_bboxes_3d
 
 __all__ = [
     'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes',
     'merge_aug_scores', 'merge_aug_masks', 'box3d_multiclass_nms',
-    'aligned_3d_nms', 'merge_aug_bboxes_3d', 'circle_nms'
+    'aligned_3d_nms', 'merge_aug_bboxes_3d', 'circle_nms', 'nms_bev',
+    'nms_normal_bev'
 ]

mmdet3d/core/post_processing/box3d_nms.py

@@ -2,8 +2,9 @@
 import numba
 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.ops import nms, nms_rotated
+
+from ..bbox import xywhr2xyxyr
 
 
 def box3d_multiclass_nms(mlvl_bboxes,
@@ -61,9 +62,9 @@ def box3d_multiclass_nms(mlvl_bboxes,
         _bboxes_for_nms = mlvl_bboxes_for_nms[cls_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[cls_inds, :]
@@ -224,3 +225,63 @@ def circle_nms(dets, thresh, post_max_size=83):
         return keep[:post_max_size]
 
     return keep
+
+
+# This function duplicates functionality of mmcv.ops.iou_3d.nms_bev
+# from mmcv<=1.5, but using cuda ops from mmcv.ops.nms.nms_rotated.
+# Nms api will be unified in mmdetection3d one day.
+def nms_bev(boxes, scores, thresh, pre_max_size=None, post_max_size=None):
+    """NMS function GPU implementation (for BEV boxes). The overlap of two
+    boxes for IoU calculation is defined as the exact overlapping area of the
+    two boxes. In this function, one can also set ``pre_max_size`` and
+    ``post_max_size``.
+
+    Args:
+        boxes (torch.Tensor): Input boxes with the shape of [N, 5]
+            ([x1, y1, x2, y2, ry]).
+        scores (torch.Tensor): Scores of boxes with the shape of [N].
+        thresh (float): Overlap threshold of NMS.
+        pre_max_size (int, optional): Max size of boxes before NMS.
+            Default: None.
+        post_max_size (int, optional): Max size of boxes after NMS.
+            Default: None.
+
+    Returns:
+        torch.Tensor: Indexes after NMS.
+    """
+    assert boxes.size(1) == 5, 'Input boxes shape should be [N, 5]'
+    order = scores.sort(0, descending=True)[1]
+    if pre_max_size is not None:
+        order = order[:pre_max_size]
+    boxes = boxes[order].contiguous()
+    # xyxyr -> back to xywhr
+    # note: better skip this step before nms_bev call in the future
+    boxes = torch.stack(
+        ((boxes[:, 0] + boxes[:, 2]) / 2, (boxes[:, 1] + boxes[:, 3]) / 2,
+         boxes[:, 2] - boxes[:, 0], boxes[:, 3] - boxes[:, 1], boxes[:, 4]),
+        dim=-1)
+
+    keep = nms_rotated(boxes, scores, thresh)[1]
+    if post_max_size is not None:
+        keep = keep[:post_max_size]
+    return keep
+
+
+# This function duplicates functionality of mmcv.ops.iou_3d.nms_normal_bev
+# from mmcv<=1.5, but using cuda ops from mmcv.ops.nms.nms.
+# Nms api will be unified in mmdetection3d one day.
+def nms_normal_bev(boxes, scores, thresh):
+    """Normal NMS function GPU implementation (for BEV boxes). The overlap of
+    two boxes for IoU calculation is defined as the exact overlapping area of
+    the two boxes WITH their yaw angle set to 0.
+
+    Args:
+        boxes (torch.Tensor): Input boxes with shape (N, 5).
+        scores (torch.Tensor): Scores of predicted boxes with shape (N).
+        thresh (float): Overlap threshold of NMS.
+
+    Returns:
+        torch.Tensor: Remaining indices with scores in descending order.
+    """
+    assert boxes.shape[1] == 5, 'Input boxes shape should be [N, 5]'
+    return nms(xywhr2xyxyr(boxes)[:, :-1], scores, thresh)[1]

mmdet3d/core/post_processing/merge_augs.py

@@ -1,8 +1,7 @@
 # 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 mmdet3d.core.post_processing import nms_bev, nms_normal_bev
 from ..bbox import bbox3d2result, bbox3d_mapping_back, xywhr2xyxyr
 
 
@@ -52,9 +51,9 @@ def merge_aug_bboxes_3d(aug_results, img_metas, test_cfg):
 
     # TODO: use a more elegent way to deal with nms
     if test_cfg.use_rotate_nms:
-        nms_func = nms_gpu
+        nms_func = nms_bev
     else:
-        nms_func = nms_normal_gpu
+        nms_func = nms_normal_bev
 
     merged_bboxes = []
     merged_scores = []

mmdet3d/models/dense_heads/centerpoint_head.py

@@ -3,12 +3,12 @@
 
 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
@@ -747,9 +747,9 @@ def get_task_detections(self, num_class_with_bg, batch_cls_preds,
         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 @@ def get_task_detections(self, num_class_with_bg, batch_cls_preds,
                     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/parta2_rpn_head.py

@@ -1,13 +1,10 @@
 # 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 mmdet3d.core.post_processing import nms_bev, nms_normal_bev
 from mmdet.models import HEADS
 from .anchor3d_head import Anchor3DHead
 
@@ -261,9 +258,9 @@ def class_agnostic_nms(self, mlvl_bboxes, mlvl_bboxes_for_nms,
         _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 @@ def class_agnostic_nms(self, mlvl_bboxes, mlvl_bboxes_for_nms,
             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/point_rpn_head.py

@@ -1,12 +1,11 @@
 # 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.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
 
@@ -19,7 +18,7 @@ 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 +120,7 @@ def forward(self, feat_dict):
             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 +158,7 @@ def loss(self,
         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 +219,7 @@ def get_targets_single(self, points, gt_bboxes_3d, gt_labels_3d):
         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 +232,6 @@ def get_targets_single(self, points, gt_bboxes_3d, gt_labels_3d):
                                               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 +295,9 @@ def class_agnostic_nms(self, obj_scores, sem_scores, bbox, points,
         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'](

mmdet3d/models/roi_heads/bbox_heads/parta2_bbox_head.py

@@ -3,13 +3,12 @@
 import torch
 from mmcv.cnn import ConvModule, normal_init
 from mmcv.ops import SparseConvTensor, SparseMaxPool3d, SparseSequential
-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
 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.ops import make_sparse_convmodule
 from mmdet.core import build_bbox_coder, multi_apply
@@ -582,9 +581,9 @@ def multi_class_nms(self,
             torch.Tensor: Selected indices.
         """
         if use_rotate_nms:
-            nms_func = nms_gpu
+            nms_func = nms_bev
         else:
-            nms_func = nms_normal_gpu
+            nms_func = nms_normal_bev
 
         assert box_probs.shape[
             1] == self.num_classes, f'box_probs shape: {str(box_probs.shape)}'

mmdet3d/models/roi_heads/bbox_heads/point_rcnn_bbox_head.py

@@ -3,13 +3,12 @@
 import torch
 from mmcv.cnn import ConvModule, normal_init
 from mmcv.cnn.bricks import build_conv_layer
-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
 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.ops import build_sa_module
 from mmdet.core import build_bbox_coder, multi_apply
@@ -239,7 +238,7 @@ def forward(self, feats):
         rcnn_reg = self.conv_reg(x_reg)
         rcnn_cls = rcnn_cls.transpose(1, 2).contiguous().squeeze(dim=1)
         rcnn_reg = rcnn_reg.transpose(1, 2).contiguous().squeeze(dim=1)
-        return (rcnn_cls, rcnn_reg)
+        return rcnn_cls, rcnn_reg
 
     def loss(self, cls_score, bbox_pred, rois, labels, bbox_targets,
              pos_gt_bboxes, reg_mask, label_weights, bbox_weights):
@@ -483,7 +482,7 @@ def get_bboxes(self,
         local_roi_boxes[..., 0:3] = 0
         rcnn_boxes3d = self.bbox_coder.decode(local_roi_boxes, bbox_pred)
         rcnn_boxes3d[..., 0:3] = rotation_3d_in_axis(
-            rcnn_boxes3d[..., 0:3].unsqueeze(1), (roi_ry), axis=2).squeeze(1)
+            rcnn_boxes3d[..., 0:3].unsqueeze(1), roi_ry, axis=2).squeeze(1)
         rcnn_boxes3d[:, 0:3] += roi_xyz
 
         # post processing
@@ -492,7 +491,6 @@ def get_bboxes(self,
             cur_class_labels = class_labels[batch_id]
             cur_cls_score = cls_score[roi_batch_id == batch_id].view(-1)
 
-            cur_box_prob = cls_score[batch_id]
             cur_box_prob = cur_cls_score.unsqueeze(1)
             cur_rcnn_boxes3d = rcnn_boxes3d[roi_batch_id == batch_id]
             keep = self.multi_class_nms(cur_box_prob, cur_rcnn_boxes3d,
@@ -524,7 +522,7 @@ def multi_class_nms(self,
             merging these two functions in the future.
 
         Args:
-            box_probs (torch.Tensor): Predicted boxes probabitilies in
+            box_probs (torch.Tensor): Predicted boxes probabilities in
                 shape (N,).
             box_preds (torch.Tensor): Predicted boxes in shape (N, 7+C).
             score_thr (float): Threshold of scores.
@@ -537,9 +535,9 @@ def multi_class_nms(self,
             torch.Tensor: Selected indices.
         """
         if use_rotate_nms:
-            nms_func = nms_gpu
+            nms_func = nms_bev
         else:
-            nms_func = nms_normal_gpu
+            nms_func = nms_normal_bev
 
         assert box_probs.shape[
             1] == self.num_classes, f'box_probs shape: {str(box_probs.shape)}'