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[Fix] Fix potential gradient divergence of corner_loss in PointRCNN #1224

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Feb 16, 2022
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[Fix] Fix potential gradient divergence of corner_loss in PointRCNN #1224

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9 changes: 6 additions & 3 deletions configs/_base_/models/point_rcnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,23 +91,26 @@
pos_iou_thr=0.55,
neg_iou_thr=0.55,
min_pos_iou=0.55,
ignore_iof_thr=-1),
ignore_iof_thr=-1,
match_low_quality=False),
dict( # for Pedestrian
type='MaxIoUAssigner',
iou_calculator=dict(
type='BboxOverlaps3D', coordinate='lidar'),
pos_iou_thr=0.55,
neg_iou_thr=0.55,
min_pos_iou=0.55,
ignore_iof_thr=-1),
ignore_iof_thr=-1,
match_low_quality=False),
dict( # for Cyclist
type='MaxIoUAssigner',
iou_calculator=dict(
type='BboxOverlaps3D', coordinate='lidar'),
pos_iou_thr=0.55,
neg_iou_thr=0.55,
min_pos_iou=0.55,
ignore_iof_thr=-1)
ignore_iof_thr=-1,
match_low_quality=False)
],
sampler=dict(
type='IoUNegPiecewiseSampler',
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4 changes: 1 addition & 3 deletions mmdet3d/core/bbox/samplers/iou_neg_piecewise_sampler.py
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Expand Up @@ -60,9 +60,7 @@ def _sample_neg(self, assign_result, num_expected, **kwargs):
if neg_inds.numel() != 0:
neg_inds = neg_inds.squeeze(1)
if len(neg_inds) <= 0:
raise NotImplementedError(
'Not support sampling the negative samples when the length '
'of negative samples is 0')
return neg_inds.squeeze(1)
else:
neg_inds_choice = neg_inds.new_zeros([0])
extend_num = 0
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29 changes: 27 additions & 2 deletions mmdet3d/models/dense_heads/point_rpn_head.py
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Original file line number Diff line number Diff line change
Expand Up @@ -272,15 +272,17 @@ def get_bboxes(self,
bbox3d = self.bbox_coder.decode(bbox_preds[b], points[b, ..., :3],
object_class[b])
bbox_selected, score_selected, labels, cls_preds_selected = \
self.class_agnostic_nms(obj_scores[b], sem_scores[b], bbox3d)
self.class_agnostic_nms(obj_scores[b], sem_scores[b], bbox3d,
points[b, ..., :3], input_metas[b])
bbox = input_metas[b]['box_type_3d'](
bbox_selected.clone(),
box_dim=bbox_selected.shape[-1],
with_yaw=True)
results.append((bbox, score_selected, labels, cls_preds_selected))
return results

def class_agnostic_nms(self, obj_scores, sem_scores, bbox):
def class_agnostic_nms(self, obj_scores, sem_scores, bbox, points,
input_meta):
"""Class agnostic nms.

Args:
Expand All @@ -298,6 +300,29 @@ def class_agnostic_nms(self, obj_scores, sem_scores, bbox):
else:
nms_func = nms_normal_gpu

num_bbox = bbox.shape[0]
bbox = input_meta['box_type_3d'](
bbox.clone(),
box_dim=bbox.shape[-1],
with_yaw=True,
origin=(0.5, 0.5, 0.5))

if isinstance(bbox, LiDARInstance3DBoxes):
box_idx = bbox.points_in_boxes(points)
box_indices = box_idx.new_zeros([num_bbox + 1])
box_idx[box_idx == -1] = num_bbox
box_indices.scatter_add_(0, box_idx.long(),
box_idx.new_ones(box_idx.shape))
box_indices = box_indices[:-1]
nonempty_box_mask = box_indices >= 0
elif isinstance(bbox, DepthInstance3DBoxes):
box_indices = bbox.points_in_boxes(points)
nonempty_box_mask = box_indices.T.sum(1) >= 0
else:
raise NotImplementedError('Unsupported bbox type!')

bbox = bbox.tensor[nonempty_box_mask]

if self.test_cfg.score_thr is not None:
score_thr = self.test_cfg.score_thr
keep = (obj_scores >= score_thr)
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