from torch.autograd import Variable
import torch.onnx

Load the trained model from file

net_save = SentimentRNN(vocab_size, output_size, embedding_dim, hidden_dim, n_layers)

trained_model = net_save
trained_model.load_state_dict(torch.load('sentiment.pth'))

Export the trained model to ONNX

dummy_input = Variable(torch.randn(1, 1, 28, 28)) # one black and white 28 x 28 picture will be the input to the model
torch.onnx.export(trained_model, dummy_input, "sentiment.onnx")

//error TypeError Traceback (most recent call last)
in ()
10 # Export the trained model to ONNX
11 dummy_input = Variable(torch.randn(1, 1, 28, 28)) # one black and white 28 x 28 picture will be the input to the model
---> 12 torch.onnx.export(trained_model, dummy_input, "sentiment.onnx")

/usr/local/lib/python3.6/dist-packages/torch/onnx/init.py in export(*args, **kwargs)
25 def export(*args, **kwargs):
26 from torch.onnx import utils
---> 27 return utils.export(*args, **kwargs)
28
29

/usr/local/lib/python3.6/dist-packages/torch/onnx/utils.py in export(model, args, f, export_params, verbose, training, input_names, output_names, aten, export_raw_ir, operator_export_type)
102 operator_export_type = OperatorExportTypes.ONNX
103 _export(model, args, f, export_params, verbose, training, input_names, output_names,
--> 104 operator_export_type=operator_export_type)
105
106

/usr/local/lib/python3.6/dist-packages/torch/onnx/utils.py in _export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, example_outputs, propagate)
279 training, input_names,
280 output_names, operator_export_type,
--> 281 example_outputs, propagate)
282
283 # TODO: Don't allocate a in-memory string for the protobuf

/usr/local/lib/python3.6/dist-packages/torch/onnx/utils.py in _model_to_graph(model, args, f, verbose, training, input_names, output_names, operator_export_type, example_outputs, propagate)
222 raise RuntimeError(''forward' method must be a script method')
223 else:
--> 224 graph, torch_out = _trace_and_get_graph_from_model(model, args, training)
225 params = list(_unique_state_dict(model).values())
226

/usr/local/lib/python3.6/dist-packages/torch/onnx/utils.py in _trace_and_get_graph_from_model(model, args, training)
190 # training mode was.)
191 with set_training(model, training):
--> 192 trace, torch_out = torch.jit.get_trace_graph(model, args, _force_outplace=True)
193
194 if orig_state_dict_keys != _unique_state_dict(model).keys():

/usr/local/lib/python3.6/dist-packages/torch/jit/init.py in get_trace_graph(f, args, kwargs, _force_outplace)
195 if not isinstance(args, tuple):
196 args = (args,)
--> 197 return LegacyTracedModule(f, _force_outplace)(*args, **kwargs)
198
199

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
--> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torch/jit/init.py in forward(self, *args)
250 try:
251 trace_inputs = _unflatten(all_trace_inputs[:len(in_vars)], in_desc)
--> 252 out = self.inner(*trace_inputs)
253 out_vars, _ = _flatten(out)
254 torch._C._tracer_exit(tuple(out_vars))

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
485 hook(self, input)
486 if torch._C._get_tracing_state():
--> 487 result = self._slow_forward(*input, **kwargs)
488 else:
489 result = self.forward(*input, **kwargs)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _slow_forward(self, *input, **kwargs)
475 tracing_state._traced_module_stack.append(self)
476 try:
--> 477 result = self.forward(*input, **kwargs)
478 finally:
479 tracing_state.pop_scope()

TypeError: forward() missing 1 required positional argument: 'hidden'