def test_ml_nms_rotated(self):
from mmcv.ops import nms_rotated
np_boxes = np.array(
[[6.0, 3.0, 8.0, 7.0, 0.5, 0.7], [3.0, 6.0, 9.0, 11.0, 0.6, 0.8],
[3.0, 7.0, 10.0, 12.0, 0.3, 0.5], [1.0, 4.0, 13.0, 7.0, 0.6, 0.9]
],
dtype=np.float32)
np_labels = np.array([1, 0, 1, 0], dtype=np.float32)
np_expect_dets = np.array(
[[1.0, 4.0, 13.0, 7.0, 0.6], [3.0, 6.0, 9.0, 11.0, 0.6],
[6.0, 3.0, 8.0, 7.0, 0.5]],
dtype=np.float32)
np_expect_keep_inds = np.array([3, 1, 0], dtype=np.int64)
boxes = torch.from_numpy(np_boxes).cuda()
labels = torch.from_numpy(np_labels).cuda()
# test cw angle definition
dets, keep_inds = nms_rotated(boxes[:, :5], boxes[:, -1], 0.5, labels)
assert np.allclose(dets.cpu().numpy()[:, :5], np_expect_dets)
assert np.allclose(keep_inds.cpu().numpy(), np_expect_keep_inds)
# test ccw angle definition
boxes[..., -2] *= -1
dets, keep_inds = nms_rotated(boxes[:, :5],
boxes[:, -1],
0.5,
labels,
clockwise=False)
dets[..., -2] *= -1
assert np.allclose(dets.cpu().numpy()[:, :5], np_expect_dets)
assert np.allclose(keep_inds.cpu().numpy(), np_expect_keep_inds)
def test_nms_rotated(self):
from mmcv.ops import nms_rotated
np_boxes = np.array(
[[6.0, 3.0, 8.0, 7.0, 0.5, 0.7], [3.0, 6.0, 9.0, 11.0, 0.6, 0.8],
[3.0, 7.0, 10.0, 12.0, 0.3, 0.5], [1.0, 4.0, 13.0, 7.0, 0.6, 0.9]
],
dtype=np.float32)
np_expect_dets = np.array(
[[1.0, 4.0, 13.0, 7.0, 0.6], [3.0, 6.0, 9.0, 11.0, 0.6],
[6.0, 3.0, 8.0, 7.0, 0.5]],
dtype=np.float32)
np_expect_keep_inds = np.array([3, 1, 0], dtype=np.int64)
boxes = torch.from_numpy(np_boxes).cuda()
# test cw angle definition
dets, keep_inds = nms_rotated(boxes[:, :5], boxes[:, -1], 0.5)
assert np.allclose(dets.cpu().numpy()[:, :5], np_expect_dets)
assert np.allclose(keep_inds.cpu().numpy(), np_expect_keep_inds)
# test ccw angle definition
boxes[..., -2] *= -1
dets, keep_inds = nms_rotated(boxes[:, :5],
boxes[:, -1],
0.5,
clockwise=False)
dets[..., -2] *= -1
assert np.allclose(dets.cpu().numpy()[:, :5], np_expect_dets)
assert np.allclose(keep_inds.cpu().numpy(), np_expect_keep_inds)
# test batched_nms with nms_rotated
from mmcv.ops import batched_nms
nms_cfg = dict(type='nms_rotated', iou_threshold=0.5)
boxes, keep = batched_nms(torch.from_numpy(np_boxes[:, :5]),
torch.from_numpy(np_boxes[:, -1]),
torch.from_numpy(np.array([0, 0, 0, 0])),
nms_cfg,
class_agnostic=False)
assert np.allclose(boxes.cpu().numpy()[:, :5], np_expect_dets)
assert np.allclose(keep.cpu().numpy(), np_expect_keep_inds)
def test_nms_rotated(self):
from mmcv.ops import nms_rotated
np_boxes = np.array(
[[6.0, 3.0, 8.0, 7.0, 0.5, 0.7], [3.0, 6.0, 9.0, 11.0, 0.6, 0.8],
[3.0, 7.0, 10.0, 12.0, 0.3, 0.5], [1.0, 4.0, 13.0, 7.0, 0.6, 0.9]
],
dtype=np.float32)
np_expect_dets = np.array(
[[1.0, 4.0, 13.0, 7.0, 0.6], [3.0, 6.0, 9.0, 11.0, 0.6],
[6.0, 3.0, 8.0, 7.0, 0.5]],
dtype=np.float32)
np_expect_keep_inds = np.array([3, 1, 0], dtype=np.int64)
boxes = torch.from_numpy(np_boxes).cuda()
dets, keep_inds = nms_rotated(boxes[:, :5], boxes[:, -1], 0.5)
assert np.allclose(dets.cpu().numpy()[:, :5], np_expect_dets)
assert np.allclose(keep_inds.cpu().numpy(), np_expect_keep_inds)
def test_ml_nms_rotated(self):
if not torch.cuda.is_available():
return
from mmcv.ops import nms_rotated
np_boxes = np.array(
[[6.0, 3.0, 8.0, 7.0, 0.5, 0.7], [3.0, 6.0, 9.0, 11.0, 0.6, 0.8],
[3.0, 7.0, 10.0, 12.0, 0.3, 0.5], [1.0, 4.0, 13.0, 7.0, 0.6, 0.9]
],
dtype=np.float32)
np_labels = np.array([1, 0, 1, 0], dtype=np.float32)
np_expect_dets = np.array(
[[1.0, 4.0, 13.0, 7.0, 0.6], [3.0, 6.0, 9.0, 11.0, 0.6],
[6.0, 3.0, 8.0, 7.0, 0.5]],
dtype=np.float32)
np_expect_keep_inds = np.array([3, 1, 0], dtype=np.int64)
boxes = torch.from_numpy(np_boxes).cuda()
labels = torch.from_numpy(np_labels).cuda()
dets, keep_inds = nms_rotated(boxes, 0.5, labels, True)
assert np.allclose(dets.cpu().numpy(), np_expect_dets)
assert np.allclose(keep_inds.cpu().numpy(), np_expect_keep_inds)
