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[Relax][ONNX] Accept 1-D scalar inputs in NonMaxSuppression #19843

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[Relax][ONNX] Accept 1-D scalar inputs in NonMaxSuppression #19843

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26 changes: 20 additions & 6 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 @@ -4913,7 +4913,7 @@ def _impl_v10(cls, bb, inputs, attr, params):
if max_output_boxes_per_class is not None and isinstance(
max_output_boxes_per_class, relax.Constant
):
max_output_boxes_per_class = int(max_output_boxes_per_class.data.numpy())
max_output_boxes_per_class = int(max_output_boxes_per_class.data.numpy().item())
elif max_output_boxes_per_class is not None and isinstance(
max_output_boxes_per_class, relax.Var
):
Expand All @@ -4927,12 +4927,19 @@ def _impl_v10(cls, bb, inputs, attr, params):
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())
iou_threshold = float(iou_threshold.data.numpy().item())
elif iou_threshold is not None and isinstance(iou_threshold, relax.Var):
var_name = iou_threshold.name_hint
if var_name in params[1]:
_, param_value = params[1][var_name]
iou_threshold = float(param_value.numpy().item())
else:
iou_threshold = 0.5 # Default value
else:
iou_threshold = 0.5 # Default value
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if score_threshold is not None and isinstance(score_threshold, relax.Constant):
score_threshold = float(score_threshold.data.numpy())
score_threshold = float(score_threshold.data.numpy().item())
elif score_threshold is not None and isinstance(score_threshold, relax.Var):
var_name = score_threshold.name_hint
if var_name in params[1]:
Expand Down Expand Up @@ -5002,7 +5009,7 @@ def _impl_v1(cls, bb, inputs, attr, params):
if max_output_boxes_per_class is not None and isinstance(
max_output_boxes_per_class, relax.Constant
):
max_output_boxes_per_class = int(max_output_boxes_per_class.data.numpy())
max_output_boxes_per_class = int(max_output_boxes_per_class.data.numpy().item())
elif max_output_boxes_per_class is not None and isinstance(
max_output_boxes_per_class, relax.Var
):
Expand All @@ -5016,12 +5023,19 @@ def _impl_v1(cls, bb, inputs, attr, params):
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())
iou_threshold = float(iou_threshold.data.numpy().item())
elif iou_threshold is not None and isinstance(iou_threshold, relax.Var):
var_name = iou_threshold.name_hint
if var_name in params[1]:
_, param_value = params[1][var_name]
iou_threshold = float(param_value.numpy().item())
else:
iou_threshold = 0.5 # Default value
else:
iou_threshold = 0.5 # Default value
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if score_threshold is not None and isinstance(score_threshold, relax.Constant):
score_threshold = float(score_threshold.data.numpy())
score_threshold = float(score_threshold.data.numpy().item())
elif score_threshold is not None and isinstance(score_threshold, relax.Var):
var_name = score_threshold.name_hint
if var_name in params[1]:
Expand Down
26 changes: 26 additions & 0 deletions tests/python/relax/test_frontend_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5190,6 +5190,32 @@ def test_nms():
)


def test_nms_scalar_shape1_constants():
"""Scalar params given as 1-D single-element constants must import (NumPy 2.x cast)."""
nms_node = helper.make_node(
"NonMaxSuppression",
["boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold"],
["selected_indices"],
)
graph = helper.make_graph(
[nms_node],
"nms_scalar_shape1",
inputs=[
helper.make_tensor_value_info("boxes", TensorProto.FLOAT, [1, 5, 4]),
helper.make_tensor_value_info("scores", TensorProto.FLOAT, [1, 1, 5]),
],
initializer=[
helper.make_tensor("max_output_boxes_per_class", TensorProto.INT64, [1], [3]),
helper.make_tensor("iou_threshold", TensorProto.FLOAT, [1], [0.5]),
helper.make_tensor("score_threshold", TensorProto.FLOAT, [1], [0.0]),
],
outputs=[helper.make_tensor_value_info("selected_indices", TensorProto.INT64, [0, 3])],
)
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 18)])
# Default import folds initializers to relax.Constant, exercising the scalar-cast path.
from_onnx(model)


@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."""
Expand Down
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