Set exclude_outside=1 on Resize for antialiased upsample#2916
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ONNX Resize defaults exclude_outside=0 while PyTorch's antialiased upsample kernels drop out of bounds samples from the filter window and renormalize the remaining weights so they sum to one. Without exclude_outside=1 the exported model produces values that differ from eager near the boundary. This also drops the shape only test guard for the AA upsample ops so the existing numerical comparison runs again. Fixes pytorch/pytorch#183546.
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The exclude_outside=1 change fixes the align_corners=False / no-scales path. Two cases still diverge: align_corners=True (the AA scale formula differs between PyTorch and ONNX Resize) and align_corners=False with explicit scales_h/scales_w (PyTorch ignores the scales, ONNX honors them same pre-existing mismatch already xfailed on the non-AA variants). Add matching .xfail matchers on both _upsample_bilinear2d_aa and _upsample_bicubic2d_aa so CI is green while the remaining gaps stay tracked.
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F.interpolate with antialias=True did not match PyTorch eager after ONNX export. The cause is that ONNX Resize defaults exclude_outside=0 while PyTorch's eager kernels drop out of bounds samples and renormalize the filter weights to sum to one. This change threads exclude_outside=1 through _aten_upsample_output_size and _aten_upsample_scales and sets it for the bilinear and bicubic AA paths. The shape only test guard for the two AA upsample ops is removed so the existing numerical comparison runs again.
Fixes pytorch/pytorch#183546.