Steps to Convert a Pytorch model to ONNX Format

Steps to Convert a Pytorch model to ONNX Format

Edited Version 2

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Here are the steps to convert a PyTorch model to ONNX format
1. Install the required packages

bash pip install torch pip install onnx
2. Load your PyTorch model using the `torch.load()` function

python import torch from torch import nn model = nn.Sequential( nn.Linear(784, 10), nn.ReLU(), nn.Softmax(dim=1) ) torch.save(model.state_dict(), 'model.pth')
3. Load the saved PyTorch model using the `torch.load()` function

python import torch from torch import nn model = nn.Sequential( nn.Linear(784, 10), nn.ReLU(), nn.Softmax(dim=1) ) torch.load('model.pth', map_location='cpu')
4. Convert the PyTorch model to ONNX format using the `torch.onnx.convert()` function

python import torch from torch import nn from torch.onnx import convert model = nn.Sequential( nn.Linear(784, 10), nn.ReLU(), nn.Softmax(dim=1) ) torch.save('model.pth') model_onnx = convert(model, torch.onnx.InferenceMode()) torch.save('model_onnx.onnx', model_onnx)
5. Verify the conversion by loading the ONNX model using the `onnxruntime.InferSession()` function

python import onnxruntime as rt session = rt.InferSession('model_onnx.onnx') input_data = np.array([[0, 0, 1, 0, 0, 0, 0, 0, 0, 0]]) output = session.run(None, {'input'
input_data}) print(output)
This should output the predicted class probabilities for the given input data.