1 背景描述

在部署模型时，如果某些模型中或者前后处理中含有rot90算子，但又希望一起和模型导出onnx时，可能会遇到如下错误（当前使用环境pytorch2.0.1，opset_version为17）：

import torch
import torch.nn as nnclass RotModel(nn.Module):def forward(self, x: torch.Tensor):x = torch.rot90(x, k=1, dims=(2, 3))return xdef main():print("pytorch version:", torch.__version__)model = RotModel()with torch.inference_mode():x = torch.randn(size=(1, 3, 224, 224))torch.onnx.export(model,args=(x,),f="rot90_counterclockwise.onnx",opset_version=17)if __name__ == '__main__':main()

torch.onnx.errors.UnsupportedOperatorError: Exporting the operator ‘aten::rot90’ to ONNX opset version 17 is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues.

简单的说就是不支持导出该算子，包括在onnx支持的算子文档中也找不到rot90算子，onnx官方github链接：
https://github.com/onnx/onnx

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2 等价替换

导不出咋办，那就想想旋转矩阵的原理，以及如何使用现有支持的算子替换。

2.1 rot90替换(NCHW)

废话不多说，rot90度（以逆时针为例）可以使用翻转和转置实现。具体代码如下，使用torch自带的rot90与自己实现的对比，通过torch.equal()来对比两个Tensor是否一致，结果一致，不信自己试试。

import torchdef self_rot90_counterclockwise(x: torch.Tensor):x = x.flip(dims=[3]).permute([0, 1, 3, 2])return xdef main():print("pytorch version:", torch.__version__)with torch.inference_mode():x = torch.randn(size=(1, 3, 224, 224))y0 = torch.rot90(x, k=1, dims=[2, 3])y1 = self_rot90_counterclockwise(x)print(torch.equal(y0, y1))if __name__ == '__main__':main()

2.2 rot180替换(NCHW)

rot180度（以逆时针为例）可以使用翻转实现。具体代码如下：

import torchdef self_rot180_counterclockwise(x: torch.Tensor):x = x.flip(dims=[2, 3])return xdef main():print("pytorch version:", torch.__version__)with torch.inference_mode():x = torch.randn(size=(1, 3, 224, 224))y0 = torch.rot90(x, k=2, dims=[2, 3])y1 = self_rot180_counterclockwise(x)print(torch.equal(y0, y1))if __name__ == '__main__':main()

2.3 rot270替换(NCHW)

rot270度（以逆时针为例）可以使用翻转和转置实现。具体代码如下:

import torchdef self_rot270_counterclockwise(x: torch.Tensor):x = x.flip(dims=[2]).permute([0, 1, 3, 2])return xdef main():print("pytorch version:", torch.__version__)with torch.inference_mode():x = torch.randn(size=(1, 3, 224, 224))y0 = torch.rot90(x, k=3, dims=[2, 3])y1 = self_rot270_counterclockwise(x)print(torch.equal(y0, y1))if __name__ == '__main__':main()

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3 rot导出ONNX

这里以rot90度（以逆时针为例）结合刚刚的等价实现来导出ONNX：

import torch
import torch.nn as nnclass RotModel(nn.Module):def forward(self, x: torch.Tensor):# x = torch.rot90(x, k=1, dims=(2, 3))x = x.flip(dims=[3]).permute([0, 1, 3, 2])return xdef main():print("pytorch version:", torch.__version__)model = RotModel()with torch.inference_mode():x = torch.randn(size=(1, 3, 224, 224))torch.onnx.export(model,args=(x,),f="rot90_counterclockwise.onnx",opset_version=17)if __name__ == '__main__':main()

使用netron打开生成的rot90_counterclockwise.onnx文件，如下所示：