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new ec0daad082 [Relax][ONNX] Support Resize dynamic ROI via TOPI (#18963)
ec0daad082 is described below
commit ec0daad082aa1c35c6d8e4bcc6cd450038cc994c
Author: Dayuxiaoshui <[email protected]>
AuthorDate: Thu Apr 2 11:25:40 2026 +0800
[Relax][ONNX] Support Resize dynamic ROI via TOPI (#18963)
The ONNX Resize converter previously rejected non-constant ROI inputs,
which blocked models where ROI is provided at runtime. This change adds
a dynamic-ROI path lowered through TOPI resize kernels while preserving
the existing relax.image.resize* path for static ROI.
Specifically:
- add reusable helper to convert ONNX full ROI ([starts..., ends...])
into spatial ROI vector
- add reusable helper to emit topi.image.resize1d/2d/3d for dynamic ROI
- keep static ROI fast path for relax.image.resize2d/resize3d
- normalize dynamic ROI expr before emit_te to ensure struct_info is
populated
- handle optional Resize inputs (roi/scales/sizes) more defensively
- add frontend test coverage with graph-input ROI:
test_resize_dynamic_roi_tf_crop_and_resize
Ref: apache/tvm#18945
---
python/tvm/relax/frontend/onnx/onnx_frontend.py | 164 ++++++++++++++++++++----
tests/python/relax/test_frontend_onnx.py | 58 +++++++++
2 files changed, 199 insertions(+), 23 deletions(-)
diff --git a/python/tvm/relax/frontend/onnx/onnx_frontend.py
b/python/tvm/relax/frontend/onnx/onnx_frontend.py
index 21b8f22a0c..fd883e3d4a 100644
--- a/python/tvm/relax/frontend/onnx/onnx_frontend.py
+++ b/python/tvm/relax/frontend/onnx/onnx_frontend.py
@@ -2632,6 +2632,105 @@ class Identity(OnnxOpConverter):
return inputs[0]
+def _onnx_resize_spatial_roi_vector(roi_full: relax.Expr, rank: int) ->
relax.Expr:
+ """Map ONNX ROI [starts..., ends...] to TOPI spatial ROI (drop N/C
axes)."""
+ return relax.op.concat(
+ [
+ relax.op.strided_slice(roi_full, axes=[0], begin=[2], end=[rank]),
+ relax.op.strided_slice(roi_full, axes=[0], begin=[rank + 2],
end=[2 * rank]),
+ ],
+ axis=0,
+ )
+
+
+def _topi_resize3d_roi_from_onnx_ncdhw_spatial(roi_spatial: list[float]) ->
list[float]:
+ """Reorder spatial ROI for NCDHW ONNX layout to TOPI resize3d convention.
+
+ ONNX spatial slice after dropping N/C is ordered (D, H, W) for starts then
ends.
+ TOPI ``resize3d`` with layout NCDHW expects
+ ``(start_w, start_h, start_d, end_w, end_h, end_d)`` (see
topi/image/resize.py).
+ """
+ if len(roi_spatial) != 6:
+ return roi_spatial
+ d0, h0, w0, d1, h1, w1 = roi_spatial
+ return [w0, h0, d0, w1, h1, d1]
+
+
+def _emit_resize_topi_dynamic_roi(
+ bb: relax.BlockBuilder,
+ data: relax.Expr,
+ roi_spatial_vec: relax.Expr,
+ sizes_spatial: list,
+ rank: int,
+ topi_mode: str,
+ coord_mode: str,
+ rounding_method: str,
+ cubic_coeff_a: float,
+ exclude_outside: int,
+ extrapolation_value: float,
+) -> relax.Expr:
+ """Lower Resize with runtime ROI via TOPI, which supports Expr ROI."""
+ if rank == 3:
+
+ def resize1d_dyn(d, r, s0):
+ return topi.image.resize1d(
+ d,
+ (r[0], r[1]),
+ [s0],
+ "NCW",
+ topi_mode,
+ coord_mode,
+ rounding_method,
+ cubic_coeff_a,
+ exclude_outside,
+ extrapolation_value,
+ )
+
+ return bb.emit_te(resize1d_dyn, data, roi_spatial_vec,
sizes_spatial[0])
+
+ if rank == 4:
+
+ def resize2d_dyn(d, r, s0, s1):
+ return topi.image.resize2d(
+ d,
+ (r[0], r[1], r[2], r[3]),
+ (s0, s1),
+ layout="NCHW",
+ method=topi_mode,
+ coordinate_transformation_mode=coord_mode,
+ rounding_method=rounding_method,
+ bicubic_alpha=cubic_coeff_a,
+ bicubic_exclude=exclude_outside,
+ extrapolation_value=extrapolation_value,
+ )
+
+ return bb.emit_te(resize2d_dyn, data, roi_spatial_vec,
sizes_spatial[0], sizes_spatial[1])
+
+ def resize3d_dyn(d, r, s0, s1, s2):
+ # r is ONNX order (D,H,W) x2; TOPI expects (W,H,D) x2.
+ return topi.image.resize3d(
+ d,
+ (r[2], r[1], r[0], r[5], r[4], r[3]),
+ (s0, s1, s2),
+ layout="NCDHW",
+ method=topi_mode,
+ coordinate_transformation_mode=coord_mode,
+ rounding_method=rounding_method,
+ bicubic_alpha=cubic_coeff_a,
+ bicubic_exclude=exclude_outside,
+ extrapolation_value=extrapolation_value,
+ )
+
+ return bb.emit_te(
+ resize3d_dyn,
+ data,
+ roi_spatial_vec,
+ sizes_spatial[0],
+ sizes_spatial[1],
+ sizes_spatial[2],
+ )
+
+
class Resize(OnnxOpConverter):
"""Converts an onnx Resize node into an equivalent Relax expression."""
@@ -2654,9 +2753,9 @@ class Resize(OnnxOpConverter):
# Unpack inputs.
x = inputs[0]
- roi = get_constant(inputs[1], params)
- scales = get_constant(inputs[2], params)
- sizes = get_constant(inputs[3], params)
+ roi = get_constant(inputs[1], params) if len(inputs) > 1 and inputs[1]
is not None else None
+ scales = get_constant(inputs[2], params) if len(inputs) > 2 else None
+ sizes = get_constant(inputs[3], params) if len(inputs) > 3 else None
ndims = len(x.struct_info.shape)
assert ndims in (3, 4, 5), "Only resize1d/resize2d/resize3d are
supported."
@@ -2664,26 +2763,29 @@ class Resize(OnnxOpConverter):
"Only one of scales and sizes can be provided in Resize."
)
- # Define relax implementation.
+ # ROI can be a static list (for relax.image.resize*) or dynamic tensor
(TOPI path).
+ roi_static: list[float] | None = None
+ roi_dynamic_vec: relax.Expr | None = None
if roi is not None:
if isinstance(roi, relax.Constant):
- roi = roi.data.numpy().tolist()
- if len(roi) == 2 * ndims:
- roi = roi[2:ndims] + roi[ndims + 2 : 2 * ndims]
- elif len(roi) == 0:
- roi = [0.0] * (2 * (ndims - 2))
+ roi_np = roi.data.numpy().tolist()
+ if len(roi_np) == 2 * ndims:
+ roi_static = roi_np[2:ndims] + roi_np[ndims + 2 : 2 *
ndims]
+ elif len(roi_np) == 0:
+ roi_static = [0.0] * (2 * (ndims - 2))
+ elif len(roi_np) == 2 * (ndims - 2):
+ # Some exporters already provide spatial-only ROI.
+ roi_static = roi_np
+ else:
+ roi_static = roi_np
else:
- roi = relax.op.concat(
- [
- relax.op.strided_slice(roi, axes=[0], begin=[2],
end=[ndims]),
- relax.op.strided_slice(roi, axes=[0], begin=[ndims +
2], end=[2 * ndims]),
- ],
- axis=0,
+ roi_dynamic_vec = bb.normalize(
+ _onnx_resize_spatial_roi_vector(roi, ndims)
)
- # TODO The backend C++ func resize2d does not support dynamic
ROI for now.
- raise NotImplementedError("Dynamic ROI is not supported in
resize for now.")
else:
- roi = [0.0] * (2 * (ndims - 2))
+ roi_static = [0.0] * (2 * (ndims - 2))
+
+ use_dynamic_roi = roi_dynamic_vec is not None
# Convert scales to sizes if needed.
if scales is not None:
@@ -2692,7 +2794,7 @@ class Resize(OnnxOpConverter):
elif isinstance(scales, relax.expr.ShapeExpr):
scales = [int(val.value) for val in scales.values]
else:
- assert f"Type {type(scales)} for scale is currently
unsupported."
+ raise ValueError(f"Type {type(scales)} for scale is currently
unsupported.")
sizes = []
for i, dim in enumerate(x.struct_info.shape):
@@ -2704,13 +2806,28 @@ class Resize(OnnxOpConverter):
elif isinstance(sizes, relax.expr.ShapeExpr):
sizes = [int(val.value) for val in sizes.values][2:]
else:
- assert f"Type {type(sizes)} for size is currently unsupported."
+ raise ValueError(f"Type {type(sizes)} for size is currently
unsupported.")
+
+ if use_dynamic_roi:
+ return _emit_resize_topi_dynamic_roi(
+ bb,
+ x,
+ roi_dynamic_vec,
+ sizes,
+ ndims,
+ topi_mode,
+ coord_mode,
+ rounding_method,
+ cubic_coeff_a,
+ exclude_outside,
+ extrapolation_value,
+ )
if ndims == 3:
return bb.emit_te(
topi.image.resize1d,
x,
- roi,
+ roi_static,
sizes,
"NCW",
topi_mode,
@@ -2724,7 +2841,7 @@ class Resize(OnnxOpConverter):
return relax.op.image.resize2d(
x,
size=relax.ShapeExpr(sizes),
- roi=roi,
+ roi=roi_static,
layout="NCHW",
method=relax_mode,
coordinate_transformation_mode=coord_mode,
@@ -2734,10 +2851,11 @@ class Resize(OnnxOpConverter):
extrapolation_value=extrapolation_value,
)
else: # ndims == 5
+ roi3d = _topi_resize3d_roi_from_onnx_ncdhw_spatial(roi_static)
return relax.op.image.resize3d(
x,
size=relax.ShapeExpr(sizes),
- roi=roi,
+ roi=roi3d,
layout="NCDHW",
method=relax_mode,
coordinate_transformation_mode=coord_mode,
diff --git a/tests/python/relax/test_frontend_onnx.py
b/tests/python/relax/test_frontend_onnx.py
index 621ce43379..ab3a5c5148 100644
--- a/tests/python/relax/test_frontend_onnx.py
+++ b/tests/python/relax/test_frontend_onnx.py
@@ -3268,6 +3268,64 @@ def test_resize(with_roi, roi_list, with_constant):
check_correctness(model)
+def test_resize_dynamic_roi_tf_crop_and_resize():
+ """ROI is a graph input (not initializer), lowered through TOPI
dynamic-ROI path."""
+ resize_node = helper.make_node(
+ "Resize",
+ ["X", "roi", "scales"],
+ ["Y"],
+ mode="linear",
+ coordinate_transformation_mode="tf_crop_and_resize",
+ )
+ graph = helper.make_graph(
+ [resize_node],
+ "resize_dynamic_roi",
+ inputs=[
+ helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 32,
32]),
+ helper.make_tensor_value_info("roi", TensorProto.FLOAT, [8]),
+ ],
+ initializer=[
+ helper.make_tensor("scales", TensorProto.FLOAT, [4], [1.0, 1.0,
2.0, 2.0]),
+ ],
+ outputs=[
+ helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 64,
64]),
+ ],
+ )
+ model = helper.make_model(graph, producer_name="resize_dynamic_roi")
+ check_correctness(model, atol=1e-5)
+
+
+def test_resize_dynamic_roi_3d_tf_crop_and_resize():
+ """5-D NCDHW: ROI is a graph input; covers dynamic-ROI TOPI resize3d
path."""
+ resize_node = helper.make_node(
+ "Resize",
+ ["X", "roi", "scales"],
+ ["Y"],
+ mode="linear",
+ coordinate_transformation_mode="tf_crop_and_resize",
+ )
+ graph = helper.make_graph(
+ [resize_node],
+ "resize_dynamic_roi_3d",
+ inputs=[
+ helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 3, 4,
5]),
+ helper.make_tensor_value_info("roi", TensorProto.FLOAT, [10]),
+ ],
+ initializer=[
+ helper.make_tensor("scales", TensorProto.FLOAT, [5], [1.0, 1.0,
2.0, 2.0, 2.0]),
+ ],
+ outputs=[
+ helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 6, 8,
10]),
+ ],
+ )
+ model = helper.make_model(graph, producer_name="resize_dynamic_roi_3d")
+ # Use a valid full-tensor ROI so ORT and TOPI agree on tf_crop_and_resize
(random ROI
+ # can hit extrapolation / numerical differences across runtimes).
+ x_np = rg.standard_normal((1, 1, 3, 4, 5)).astype(np.float32)
+ roi_np = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], dtype=np.float32)
+ check_correctness(model, opset=18, atol=1e-5, inputs={"X": x_np, "roi":
roi_np})
+
+
def test_resize_nd_sizes():
cases = [
("resize1d", [1, 1, 4], [1, 1, 7]),
