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- import torch
- import model
- myModel = model.ModelFactory.get_model('resnet', 'document')
- myModel.load_state_dict(torch.load('outputs/doc552023/doc_0505_0/doc_0505document_resnet.pth'))
- myModel.eval()
- dummy_input = torch.randn(1, 3, 32, 32)
- torch.onnx.export(myModel, dummy_input, "document_1.0.0.onnx", do_constant_folding=False)
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