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Set exclude_outside=1 on Resize for antialiased upsample #2916

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Set exclude_outside=1 on Resize for antialiased upsample #2916

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14 changes: 14 additions & 0 deletions onnxscript/function_libs/torch_lib/ops/nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2330,6 +2330,7 @@ def _aten_upsample_output_size(
coordinate_transformation_mode: str,
antialias: int = 0,
cubic_coeff_a: float = -0.75,
exclude_outside: int = 0,
) -> TReal:
batch_and_channel = op.Shape(self, end=2, start=0)
# When output_size is passed in as a list of integers, the torch.onnx
Expand All @@ -2348,6 +2349,7 @@ def _aten_upsample_output_size(
cubic_coeff_a=cubic_coeff_a,
nearest_mode="floor",
antialias=antialias,
exclude_outside=exclude_outside,
)


Expand All @@ -2358,6 +2360,7 @@ def _aten_upsample_scales(
coordinate_transformation_mode: str,
antialias: int = 0,
cubic_coeff_a: float = -0.75,
exclude_outside: int = 0,
) -> TReal:
return op.Resize(
self,
Expand All @@ -2371,6 +2374,7 @@ def _aten_upsample_scales(
cubic_coeff_a=cubic_coeff_a,
nearest_mode="floor",
antialias=antialias,
exclude_outside=exclude_outside,
)


Expand Down Expand Up @@ -2410,13 +2414,18 @@ def aten__upsample_bicubic2d_aa(
coordinate_transformation_mode = _get_upsample_align_corners_mode(align_corners)
# PyTorch uses cubic_coeff_a=-0.5 (Keys interpolation, PIL-compatible) when
# antialias=True, as opposed to -0.75 (OpenCV-compatible) for the non-antialias case.
# exclude_outside=1 matches PyTorch's antialias kernel, which drops out-of-bounds
# samples from the filter window and renormalizes the remaining weights so they
# sum to 1. Without it the ONNX Resize keeps phantom out-of-bounds weight in the
# denominator and produces values that differ from eager near the boundary.
return _aten_upsample_output_size(
self,
output_size,
mode="cubic",
coordinate_transformation_mode=coordinate_transformation_mode,
antialias=1,
cubic_coeff_a=-0.5,
exclude_outside=1,
)


Expand Down Expand Up @@ -2495,12 +2504,17 @@ def aten__upsample_bilinear2d_aa(
# NOTE: Based on experimentation, scales_h and scales_w are always ignored in PyTorch,
# unless when align_corners is True, in which case we do not know what is going on.
coordinate_transformation_mode = _get_upsample_align_corners_mode(align_corners)
# exclude_outside=1 matches PyTorch's antialias kernel, which drops out-of-bounds
# samples from the filter window and renormalizes the remaining weights so they
# sum to 1. Without it the ONNX Resize keeps phantom out-of-bounds weight in the
# denominator and produces values that differ from eager near the boundary.
return _aten_upsample_output_size(
self,
output_size,
coordinate_transformation_mode=coordinate_transformation_mode,
mode="linear",
antialias=1,
exclude_outside=1,
)


Expand Down
14 changes: 0 additions & 14 deletions tests/function_libs/torch_lib/ops_test_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1784,13 +1784,6 @@ def _where_input_wrangler(
TorchLibOpInfo(
"ops.aten._upsample_bilinear2d_aa",
nn_ops.aten__upsample_bilinear2d_aa,
# ONNX and PyTorch use different anti-aliasing algorithms, so numerical results differ.
# However, the implementation is verified correct because:
# 1. The function correctly passes antialias=1 to ONNX Resize operation
# 2. Shape validation ensures the operation works correctly
# 3. Additional validation in test_aa_upsample_validation.py confirms correctness
# Shape-only comparison is the appropriate testing approach for this case.
compare_shape_only_for_output=(0,),
),
TorchLibOpInfo("ops.aten.upsample_bilinear2d.vec", nn_ops.aten_upsample_bilinear2d_vec),
TorchLibOpInfo(
Expand All @@ -1805,13 +1798,6 @@ def _where_input_wrangler(
TorchLibOpInfo(
"ops.aten._upsample_bicubic2d_aa",
nn_ops.aten__upsample_bicubic2d_aa,
# ONNX and PyTorch use different anti-aliasing algorithms, so numerical results differ.
# However, the implementation is verified correct because:
# 1. The function correctly passes antialias=1 to ONNX Resize operation
# 2. Shape validation ensures the operation works correctly
# 3. Additional validation in test_aa_upsample_validation.py confirms correctness
# Shape-only comparison is the appropriate testing approach for this case.
compare_shape_only_for_output=(0,),
),
TorchLibOpInfo("ops.aten.upsample_bicubic2d.vec", nn_ops.aten_upsample_bicubic2d_vec),
TorchLibOpInfo(
Expand Down