def d3_box_overlap(boxes, qboxes, criterion=-1, z_axis=1, z_center=1.0):
"""kitti camera format z_axis=1.
"""
bev_axes = list(range(7))
bev_axes.pop(z_axis + 3)
bev_axes.pop(z_axis)
rinc = rotate_iou_gpu_eval(boxes[:, bev_axes], qboxes[:, bev_axes], 2)
d3_box_overlap_kernel(boxes, qboxes, rinc, criterion, z_axis, z_center)
return rinc
def box3d_overlap(boxes, qboxes, criterion=-1, z_axis=1, z_center=1.0):
"""kitti camera format z_axis=1.
"""
bev_axes = list(range(7))
bev_axes.pop(z_axis + 3)
bev_axes.pop(z_axis)
# t = time.time()
# rinc = box_np_ops.rinter_cc(boxes[:, bev_axes], qboxes[:, bev_axes])
rinc = rotate_iou_gpu_eval(boxes[:, bev_axes], qboxes[:, bev_axes], 2)
# print("riou time", time.time() - t)
box3d_overlap_kernel(boxes, qboxes, rinc, criterion, z_axis, z_center)
return rinc
def riou3d(boxes, qboxes):
rinc = rotate_iou_gpu_eval(boxes[:, [0, 1, 3, 4, 6]],
qboxes[:, [0, 1, 3, 4, 6]], 2)
riou3d_lidar_kernel(boxes, qboxes, rinc)
return rinc
def d3_box_overlap(boxes, qboxes, criterion=-1):
rinc = rotate_iou_gpu_eval(boxes[:, [0, 2, 3, 5, 6]],
qboxes[:, [0, 2, 3, 5, 6]], 2)
d3_box_overlap_kernel(boxes, qboxes, rinc, criterion)
return rinc
def bev_box_overlap(boxes, qboxes, criterion=-1):
riou = rotate_iou_gpu_eval(boxes, qboxes, criterion)
return riou
def bev_box_overlap(boxes, qboxes, criterion=-1, stable=True):
# riou = box_np_ops.riou_cc(boxes, qboxes)
riou = rotate_iou_gpu_eval(boxes, qboxes, criterion)
return riou
def boxes_iou_3d(targets_bbox3d,
anchors_bbox3d,
aug_thickness=None,
criterion=-1,
only_xy=False,
flag=''):
'''
about criterion check:
/home/z/Research/Detection_3D/second/core/non_max_suppression/nms_gpu.py devRotateIoUEval
# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
# with slight modifications
'''
if flag == 'rpn_label_generation':
assert aug_thickness['anchor_Y'] == 0
assert aug_thickness['target_Y'] >= 0.3
elif flag == 'roi_label_generation':
assert aug_thickness['anchor_Y'] >= 0.3
assert aug_thickness['target_Y'] >= 0.3
elif flag == 'eval':
assert aug_thickness['anchor_Y'] <= 0.3
assert aug_thickness['target_Y'] <= 0.3
elif flag == 'rpn_post' or flag == 'roi_post':
assert aug_thickness is None
else:
print(flag)
print(aug_thickness)
raise NotImplementedError
if aug_thickness is None:
ma = 0.0
aug_thickness = {
'target_Y': ma,
'target_Z': ma,
'anchor_Y': ma,
'anchor_Z': ma
}
#print(f'{flag}\n{aug_thickness}\n')
targets_bbox3d = targets_bbox3d.clone().detach()
anchors_bbox3d = anchors_bbox3d.clone().detach()
targets_bbox3d[:, 3] = torch.clamp(targets_bbox3d[:, 3],
min=aug_thickness['target_Y'])
anchors_bbox3d[:, 3] = torch.clamp(anchors_bbox3d[:, 3],
min=aug_thickness['anchor_Y'])
targets_bbox3d[:, 5] = torch.clamp(targets_bbox3d[:, 5],
min=aug_thickness['target_Z'])
anchors_bbox3d[:, 5] = torch.clamp(anchors_bbox3d[:, 5],
min=aug_thickness['anchor_Z'])
iouz = iou_one_dim(targets_bbox3d[:, [2, 5]], anchors_bbox3d[:, [2, 5]])
cuda_index = targets_bbox3d.device.index
anchors_2d = anchors_bbox3d[:, [0, 1, 3, 4, 6]].cpu().data.numpy()
targets_2d = targets_bbox3d[:, [0, 1, 3, 4, 6]].cpu().data.numpy()
#print(f"targets yaw : {targets_2d[:,-1].min()} , {targets_2d[:,-1].max()}")
#print(f"anchors yaw : {anchors_2d[:,-1].min()} , {anchors_2d[:,-1].max()}")
# aug thickness. When thickness==0, iou is wrong
#targets_2d[:,2] = np.clip(targets_2d[:,2], a_min=aug_thickness['target'], a_max=None)
#anchors_2d[:,2] = np.clip(anchors_2d[:,2], a_min=aug_thickness['anchor'], a_max=None)
#aug_th_mask = (targets_2d[:,2] < 0.3).astype(np.float32)
#targets_2d[:,2] += aug_thickness['target'] * aug_th_mask # 0.25
#aug_th_mask = (anchors_2d[:,2] < 0.3).astype(np.float32)
#anchors_2d[:,2] += aug_thickness['anchor'] * aug_th_mask
# criterion=1: use targets_2d as ref
iou2d = rotate_iou_gpu_eval(targets_2d,
anchors_2d,
criterion=criterion,
device_id=cuda_index)
iou2d = torch.from_numpy(iou2d)
iou2d = iou2d.to(targets_bbox3d.device)
if only_xy:
iou3d = iou2d
else:
iou3d = iou2d * iouz
if DEBUG and flag == 'eval':
if iou3d.max() < 1:
return iou3d
mask = iou3d == iou3d.max()
t_i, a_i = torch.nonzero(mask)[0]
t = targets[t_i]
a = anchors[a_i]
print(f"max iou: {iou3d.max()}")
a.show_together(t)
if iou3d.max() > 1:
torch.set_printoptions(precision=16)
print(a.bbox3d)
print(t.bbox3d)
#np.set_printoptions(precision=10)
#print(a.bbox3d.cpu().data.numpy())
#print(t.bbox3d.cpu().data.numpy())
areas = rotate_iou_gpu_eval(targets_2d,
anchors_2d,
criterion=3,
device_id=cuda_index)
ious0 = rotate_iou_gpu_eval(targets_2d,
anchors_2d,
criterion=0,
device_id=cuda_index)
ious1 = rotate_iou_gpu_eval(targets_2d,
anchors_2d,
criterion=1,
device_id=cuda_index)
import pdb
pdb.set_trace() # XXX BREAKPOINT
areas_max = areas[t_i, a_i]
import pdb
pdb.set_trace() # XXX BREAKPOINT
iou_preds = iou3d.max(0)[0]
mask = iou3d == iou_preds.min()
t_i, a_i = torch.nonzero(mask)[0]
t = targets[t_i]
a = anchors[a_i]
print(f"min pred iou: {iou_preds.min()}")
a.show_together(t)
anchors.show_highlight([a_i])
targets.show_highlight([t_i])
import pdb
pdb.set_trace() # XXX BREAKPOINT
pass
return iou3d
def bev_box_overlap(boxes, qboxes, criterion=-1, stable=False):
if stable:
riou = box_np_ops.riou_cc(boxes, qboxes)
else:
riou = rotate_iou_gpu_eval(boxes, qboxes, criterion)
return riou
