使用Pytorch导出自定义ONNX算子的示例代码

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
from torch.onnx import OperatorExportTypes
class CustomAffineGrid(Function):
    @staticmethod
    def forward(ctx, theta: torch.Tensor, size: torch.Tensor):
        grid = F.affine_grid(theta=theta, size=size.cpu().tolist())
        return grid
    @staticmethod
    def symbolic(g: torch.Graph, theta: torch.Tensor, size: torch.Tensor):
        return g.op("AffineGrid", theta, size)
class MyModel(nn.Module):
    def __init__(self) -> None:
        super().__init__()
    def forward(self, x: torch.Tensor, theta: torch.Tensor, size: torch.Tensor):
        grid = CustomAffineGrid.apply(theta, size)
        x = F.grid_sample(x, grid=grid, mode="bilinear", padding_mode="zeros")
        return x
def main():
    with torch.inference_mode():
        custum_model = MyModel()
        x = torch.randn(1, 3, 224, 224)
        theta = torch.randn(1, 2, 3)
        size = torch.as_tensor([1, 3, 512, 512])
        torch.onnx.export(model=custum_model,
                          args=(x, theta, size),
                          f="custom.onnx",
                          input_names=["input0_x", "input1_theta", "input2_size"],
                          output_names=["output"],
                          dynamic_axes={"input0_x": {2: "h0", 3: "w0"},
                                        "output": {2: "h1", 3: "w1"}},
                          opset_version=16,
                          operator_export_type=OperatorExportTypes.ONNX_FALLTHROUGH)
if __name__ == '__main__':
    main()

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
from torch.onnx import OperatorExportTypes
class CustomRot90AndScale(Function):
    @staticmethod
    def forward(ctx, x: torch.Tensor):
        x = torch.rot90(x, k=1, dims=(3, 2))  # clockwise 90
        x *= 1.2
        return x
    @staticmethod
    def symbolic(g: torch.Graph, x: torch.Tensor):
        return g.op("Rot90AndScale", x, k_i=1, scale_f=1.2, clockwise_s="yes")
class MyModel(nn.Module):
    def __init__(self) -> None:
        super().__init__()
    def forward(self, x: torch.Tensor):
        return CustomRot90AndScale.apply(x)
def main():
    with torch.inference_mode():
        custum_model = MyModel()
        x = torch.randn(1, 3, 224, 224)
        torch.onnx.export(model=custum_model,
                          args=(x,),
                          f="custom_rot90.onnx",
                          input_names=["input"],
                          output_names=["output"],
                          dynamic_axes={"input": {2: "h0", 3: "w0"},
                                        "output": {2: "w0", 3: "h0"}},
                          opset_version=16,
                          operator_export_type=OperatorExportTypes.ONNX_FALLTHROUGH)
if __name__ == '__main__':
    main()