OnnxToTorch lowering resize op (cherry-picks from upstream) (CR-1196776) #168
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nod-ai/SHARK-ModelDev#358 adds a lowering from onnx to linalg for bilinear and nearest resize with support for using scales or sizes to get resize shape. uses coordinate transform half pixel for bilinear mode and asymmetrical for nearest mode. See https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize. Added two passes -- one for bilinear and the other for nearest.
…ering (llvm#3351) Addresses [Shark-Turbine Related tracker [Shark-Turbine Related onnx.Resize issues [Shark-Turbine
The old lowering only had logic for 2d (i.e. images). this patch allows interpolation for n spatial dims, which is required for some 3d vision models such as - onnx/models/pytorch-3dunet_vaiq_int8 which successfully compiles and runs with this patch.
Updates: - some unsupported modes are now going to report a match failure for unsupported coordinate transformation modes. - fixes a bug that was introduced in the last patch for resize (my bad...) - uses actual x and y coordinates for computing weights in bilinear interpolation (rather than eps modified values) - slightly simplifies the bilinear interpolation payload for readability and performance - passes coordinate transformation mode information from an onnx.Resize op to the mode string for the aten._interpolate op. This allows us to perform custom logic in the torch->linalg lowering to support onnx.Resize options without losing the default behaviors of the interpolate op.
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This are the commits from upstream required to lower onnx.Resize to linalg.
In addition, I fixed the default nearest_mode to be round_prefer_floor as specified by ONNX, and implemented coordTfMode half_pixel.