scripted_model = torch.jit.script(model, input_image_pytorch.to(device).detach())
torch.onnx.export(scripted_model.to(device), # model being run
input_image_pytorch.to(device), # model input (or a tuple for multiple inputs)
"scripted_monodepth2.onnx", # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
input_names = ['input'], # the model's input names
output_names = ['output'], # the model's output names
)
gives the following error
OnnxExporterError Traceback (most recent call last)
Cell In[8], line 26
24 model = QuantizedDepth(encoder=encoder, decoder=depth_decoder, for_quantization=False).eval().to(device)
25 scripted_model = torch.jit.script(model, input_image_pytorch.to(device).detach())
---> 26 torch.onnx.export(scripted_model.to(device), # model being run
27 input_image_pytorch.to(device), # model input (or a tuple for multiple inputs)
28 "scripted_monodepth2.onnx", # where to save the model (can be a file or file-like object)
29 export_params=True, # store the trained parameter weights inside the model file
30 # opset_version=11, # the ONNX version to export the model to
31 # do_constant_folding=True, # whether to execute constant folding for optimization. buggy in torch == 1.12
32 input_names = ['input'], # the model's input names
33 output_names = ['output'], # the model's output names
34 # dynamic_axes={'input' : {0 : 'batch_size'}, # variable length axes
35 # 'output' : {0 : 'batch_size'}}
36 )
37 print("Size (MB):", os.path.getsize("traced_monodepth2.onnx")/1e6)
38 print("Size (MB):", os.path.getsize("monodepth2.onnx")/1e6)
File ~/miniconda3/envs/inference/lib/python3.8/site-packages/torch/onnx/utils.py:504, in export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, custom_opsets, export_modules_as_functions)
186 @_beartype.beartype
187 def export(
188 model: Union[torch.nn.Module, torch.jit.ScriptModule, torch.jit.ScriptFunction],
(...)
204 export_modules_as_functions: Union[bool, Collection[Type[torch.nn.Module]]] = False,
205 ) -> None:
206 r"""Exports a model into ONNX format.
207
208 If ``model`` is not a :class:`torch.jit.ScriptModule` nor a
(...)
501 All errors are subclasses of :class:`errors.OnnxExporterError`.
502 """
--> 504 _export(
505 model,
506 args,
507 f,
508 export_params,
509 verbose,
510 training,
511 input_names,
512 output_names,
513 operator_export_type=operator_export_type,
514 opset_version=opset_version,
515 do_constant_folding=do_constant_folding,
516 dynamic_axes=dynamic_axes,
517 keep_initializers_as_inputs=keep_initializers_as_inputs,
518 custom_opsets=custom_opsets,
519 export_modules_as_functions=export_modules_as_functions,
520 )
File ~/miniconda3/envs/inference/lib/python3.8/site-packages/torch/onnx/utils.py:1529, in _export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, fixed_batch_size, custom_opsets, add_node_names, onnx_shape_inference, export_modules_as_functions)
1526 dynamic_axes = {}
1527 _validate_dynamic_axes(dynamic_axes, model, input_names, output_names)
-> 1529 graph, params_dict, torch_out = _model_to_graph(
1530 model,
1531 args,
1532 verbose,
1533 input_names,
1534 output_names,
1535 operator_export_type,
1536 val_do_constant_folding,
1537 fixed_batch_size=fixed_batch_size,
1538 training=training,
1539 dynamic_axes=dynamic_axes,
1540 )
1542 # TODO: Don't allocate a in-memory string for the protobuf
1543 defer_weight_export = (
1544 export_type is not _exporter_states.ExportTypes.PROTOBUF_FILE
1545 )
File ~/miniconda3/envs/inference/lib/python3.8/site-packages/torch/onnx/utils.py:1115, in _model_to_graph(model, args, verbose, input_names, output_names, operator_export_type, do_constant_folding, _disable_torch_constant_prop, fixed_batch_size, training, dynamic_axes)
1112 params_dict = _get_named_param_dict(graph, params)
1114 try:
-> 1115 graph = _optimize_graph(
1116 graph,
1117 operator_export_type,
1118 _disable_torch_constant_prop=_disable_torch_constant_prop,
1119 fixed_batch_size=fixed_batch_size,
1120 params_dict=params_dict,
1121 dynamic_axes=dynamic_axes,
1122 input_names=input_names,
1123 module=module,
1124 )
1125 except Exception as e:
1126 torch.onnx.log("Torch IR graph at exception: ", graph)
File ~/miniconda3/envs/inference/lib/python3.8/site-packages/torch/onnx/utils.py:663, in _optimize_graph(graph, operator_export_type, _disable_torch_constant_prop, fixed_batch_size, params_dict, dynamic_axes, input_names, module)
660 _C._jit_pass_onnx_set_dynamic_input_shape(graph, dynamic_axes, input_names)
661 _C._jit_pass_onnx_lint(graph)
--> 663 graph = _C._jit_pass_onnx(graph, operator_export_type)
664 _C._jit_pass_onnx_lint(graph)
665 _C._jit_pass_lint(graph)
File ~/miniconda3/envs/inference/lib/python3.8/site-packages/torch/onnx/utils.py:1899, in _run_symbolic_function(graph, block, node, inputs, env, operator_export_type)
1894 if symbolic_fn is not None:
...
590 value,
591 )
--> 592 raise errors.OnnxExporterError(message)
OnnxExporterError: Unsupported: ONNX export of operator interpolate (with scales), missing input shape. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues
The model contains a torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest"), which causes the error. Works without JIT Scripting