pytorch tril 用法并导出onnx demo

import torch

import torch.nn as nn

import onnxruntime as ort

import numpy as np

def create_tril_onnx():

    class SimpleNet(nn.Module):

        def __init__(self):

            super(SimpleNet, self).__init__()

            self.data1 = torch.ones((2,3), dtype=torch.bool)

        def forward(self, x):

            tril_x = torch.tril(x)

            tril_x = tril_x.float()

            x1 = x.float()

            return tril_x+x1

    model = SimpleNet()

    data = torch.ones((2,3), dtype=torch.bool)

    output = model(data)

    print("output:")

    print(output)

    torch.onnx.export(model, data, "tril.onnx", input_names=["input"], output_names=["output"])

def inference_onnx():

    model = ort.InferenceSession("tril.onnx", provider=["CPUExecutionProvider"])

    outputs = model.run(["output"], {"input":np.random.randn(2,3).astype(np.bool_)})

    print("outputs:", outputs)

def my_tril():

    key_size = 5

    data = torch.ones((key_size,key_size), dtype=torch.bool)

    for i in range(key_size):

        print("\n")

        print(i)

        print(data[i,i+1:])

        data[i,i+1:] = False

        print(data)

    print(data)

def main():

    # create_tril_onnx()

    # inference_onnx()

    my_tril()

if __name__ == "__main__":

    main()

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导出onnx如下：