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Fix batched_nms for ndarray inputs #8901

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Fix batched_nms for ndarray inputs #8901

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281edab
Fix batched_nms for ndarray inputs
aymuos15 Jun 7, 2026
ee66c9e
Add regression test for batched_nms ndarray inputs
aymuos15 Jun 7, 2026
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2 changes: 1 addition & 1 deletion monai/data/box_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1200,7 +1200,7 @@ def batched_nms(
# from different classes do not overlap
max_coordinate = boxes_t.max()
offsets = labels_t.to(boxes_t) * (max_coordinate + 1)
boxes_for_nms = boxes + offsets[:, None]
boxes_for_nms = boxes_t + offsets[:, None]
keep = non_max_suppression(boxes_for_nms, scores_t, nms_thresh, max_proposals, box_overlap_metric)

# convert tensor back to numpy if needed
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11 changes: 11 additions & 0 deletions tests/data/test_box_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
CornerCornerModeTypeB,
CornerCornerModeTypeC,
CornerSizeMode,
batched_nms,
box_area,
box_centers,
box_giou,
Expand Down Expand Up @@ -269,5 +270,15 @@ def test_integer_truncation_bug(self):
self.assertGreater(iou[0, 0], 0.0, "IoU should not be truncated to 0")


class TestBatchedNms(unittest.TestCase):
@parameterized.expand(TEST_NDARRAYS)
def test_batched_nms_backend(self, p):
boxes = p(np.array([[0, 0, 10, 10], [1, 1, 11, 11], [100, 100, 110, 110]], dtype=np.float32))
scores = p(np.array([0.9, 0.8, 0.7], dtype=np.float32))
labels = p(np.array([0, 0, 1]))
keep = batched_nms(boxes, scores, labels, nms_thresh=0.5)
assert_allclose(keep, [0, 2], type_test=False)


if __name__ == "__main__":
unittest.main()
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