神经网络的全部结构以及每个层(node)的操作层等各种信息：

You 2023-12-31 00:28:57  64365

分类专栏：资讯

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  name: "torch-jit-export"
  initializer {
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    name: "2"
    raw_data: "\034

利用Pytorch导出

其中model为pytorch的模型，example为输入，export_params=True代表连带参数一并输出。

model = test_model()
state = torch.load('test.pth')
model.load_state_dict(state['model'], strict=True)

example = torch.rand(1, 3, 128, 128)

torch_out = torch.onnx.export(model,
                            example,
                            "test.onnx",
                            verbose=True,
                            export_params=True
                            )

已知一个由Pytorch导出ONNX模型的bug

使用view模拟flatten操作，但是导出的onnx的operator与预想的不一致：

https://pytorch.org/docs/stable/onnx.html#supported-operators

  # output = input.view(input.size(0), -1)         使用这个Pytorch操作层导出的onnx.flatten是错误的
  output = input.view([int(input.size(0)), -1])    # 一种暂时的解决方法
  output = input.flatten(1)

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神经网络的全部结构以及每个层(node)的操作层等各种信息：

利用Pytorch导出

已知一个由Pytorch导出ONNX模型的bug

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