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[Relax][ONNX] Set max_output_boxes_per_class default value to 0 for NonMaxSuppression #19547

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[Relax][ONNX] Set max_output_boxes_per_class default value to 0 for NonMaxSuppression #19547

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13052ca
[Relax][ONNX] Set `max_output_boxes_per_class` default value to 0
cchung100m May 12, 2026
146054e
[Relax][ONNX] Set max_output_boxes_per_class default value to 0
cchung100m May 12, 2026
4c6e1aa
[Relax][ONNX] Add test case: test_nms_max_output_boxes_per_class_zero
cchung100m May 12, 2026
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8 changes: 4 additions & 4 deletions python/tvm/relax/frontend/onnx/onnx_frontend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4757,9 +4757,9 @@ def _impl_v10(cls, bb, inputs, attr, params):
_, param_value = params[1][var_name]
max_output_boxes_per_class = int(param_value.numpy().item())
else:
max_output_boxes_per_class = 100 # Default value
max_output_boxes_per_class = 0 # Default value
else:
max_output_boxes_per_class = 100 # Default value
max_output_boxes_per_class = 0 # Default value
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@gemini-code-assist gemini-code-assist Bot May 12, 2026

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The default value for max_output_boxes_per_class is updated to 0 here to align with the ONNX specification. However, AllClassNMS._impl_v1 (lines 4849 and 4851) still uses 100 as the default value. To maintain consistency across the ONNX frontend, AllClassNMS should also be updated to use 0 as the default value.


if iou_threshold is not None and isinstance(iou_threshold, relax.Constant):
iou_threshold = float(iou_threshold.data.numpy())
Expand Down Expand Up @@ -4846,9 +4846,9 @@ def _impl_v1(cls, bb, inputs, attr, params):
_, param_value = params[1][var_name]
max_output_boxes_per_class = int(param_value.numpy().item())
else:
max_output_boxes_per_class = 100 # Default value
max_output_boxes_per_class = 0 # Default value
else:
max_output_boxes_per_class = 100 # Default value
max_output_boxes_per_class = 0 # Default value

if iou_threshold is not None and isinstance(iou_threshold, relax.Constant):
iou_threshold = float(iou_threshold.data.numpy())
Expand Down
57 changes: 57 additions & 0 deletions tests/python/relax/test_frontend_onnx.py
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Expand Up @@ -4821,6 +4821,63 @@ def test_nms():
)


@pytest.mark.parametrize("with_explicit_max", [False, True])
def test_nms_max_output_boxes_per_class_zero(with_explicit_max: bool):
"""ONNX default for max_output_boxes_per_class is 0, yielding empty output."""
node_inputs = ["boxes", "scores"]
initializer = []
if with_explicit_max:
node_inputs.append("max_output_boxes_per_class")
initializer.append(
helper.make_tensor("max_output_boxes_per_class", TensorProto.INT64, [1], [0])
)

nms_node = helper.make_node(
"NonMaxSuppression",
node_inputs,
["selected_indices"],
center_point_box=0,
)

boxes_shape = [1, 4, 4]
scores_shape = [1, 1, 4]
graph = helper.make_graph(
[nms_node],
"nms_max_output_boxes_per_class_zero",
inputs=[
helper.make_tensor_value_info("boxes", TensorProto.FLOAT, boxes_shape),
helper.make_tensor_value_info("scores", TensorProto.FLOAT, scores_shape),
],
initializer=initializer,
outputs=[helper.make_tensor_value_info("selected_indices", TensorProto.INT64, [0, 3])],
)

model = helper.make_model(graph, producer_name="nms_max_output_boxes_per_class_zero")
model.ir_version = 8
model.opset_import[0].version = 11

inputs = {
"boxes": np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[2.0, 2.0, 3.0, 3.0],
[2.0, 2.1, 3.0, 3.1],
]
],
dtype=np.float32,
),
"scores": np.array([[[0.9, 0.8, 0.7, 0.6]]], dtype=np.float32),
}

check_correctness(model, inputs=inputs, opset=11)

tvm_out = run_in_tvm(model, inputs=inputs, opset=11)
tvm_selected = tvm_out[0].numpy() if isinstance(tvm_out, (list, tuple)) else tvm_out.numpy()
assert tvm_selected.shape == (0, 3)


def test_nms_algorithm_correctness():
"""Test NMS algorithm correctness with fixed data to verify suppression logic."""
nms_node = helper.make_node(
Expand Down
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