/usr/local/lib64/python3.6/site-packages/torch/onnx/__pycache__
Edit: /usr/local/lib64/python3.6/site-packages/torch/onnx/__pycache__/utils.cpython-36.pyc (35888B)
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The torch.onnx module contains functions to export models into the ONNX
IR format. These models can be loaded with the ONNX library and then
converted to models which run on other deep learning frameworks.
N)string_classes)_unique_state_dict) ONNX_ARCHIVE_MODEL_PROTO_NAME ExportTypesOperatorExportTypes TrainingMode)ListType OptionalType"_propagate_and_assign_input_shapes_check_onnx_proto)ListTupleUnionFc @ s e Zd ZdS )ONNXCheckerErrorN)__name__
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| | |||||dS )NzN'f' is deprecated and ignored. It will be removed in the next PyTorch release.z_'_retain_param_name' is deprecated and ignored. It will be removed in the next PyTorch release.)r) rj r, r- r+ )r r! _export_to_pretty_string)r$ r4 r5 r6 r7 r r8 r9 r' export_typer* google_printerr( r: r, r- rj r) r+ r r r export_to_pretty_string7 s
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This diagnostic tool runs your model with operator_export_type set to
OperatorExportTypes.ONNX_FALLTHROUGH once in order to get a list of
all the ops that are not supported/implemented by the current exporter
operator_export_type is set to OperatorExportTypes.ONNX_FALLTHROUGH by default
OperatorExportTypes.ONNX_FALLTHROUGH: If an op is not supported
in ONNX, fall through and export the operator as is, as a custom
ONNX op. Using this mode, the op can be exported and implemented by
the user for their runtime backend.
Example graph::
graph(%0 : Float(2, 3, 4, strides=[12, 4, 1], requires_grad=0, device=cpu)):
%6 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
%4 : None = prim::Constant()
%5 : Float(2, 3, 4, strides=[12, 4, 1], requires_grad=0, device=cpu) = aten::cumsum(%0, %6, %4) # main.py:6:0
return (%5)
is exported as::
graph(%0 : Float(2, 3, 4, strides=[12, 4, 1], requires_grad=0, device=cpu)):
%6 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
%4 : None = prim::Constant()
%5 : Float(2, 3, 4, strides=[12, 4, 1], requires_grad=0, device=cpu) = aten::cumsum(%0, %6, %4) # main.py:6:0
return (%5)
In the above example, aten::cumsum in not implemented in opset 9, hence exporter falls
through and provides a list of unsupported ops, the result being:
Unsupported ops : [aten:cumsum]
r )r r N:r/ prim)r/ r )
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Create an ONNX operator "opname", taking "args" as inputs and attributes
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(see the `outputs` keyword argument for multi-return nodes).
The set of operators and the inputs/attributes they take
is documented at https://github.com/onnx/onnx/blob/master/docs/Operators.md
This function is monkey-patched onto Graph.
Args:
opname (string): The ONNX operator name, e.g., `Abs` or `Add`.
args (Node...): The inputs to the operator; usually provided
as arguments to the `symbolic` definition.
kwargs: The attributes of the ONNX operator, with keys named
according to the following convention: `alpha_f` indicates
the `alpha` attribute with type `f`. The valid type specifiers are
`f` (float), `i` (int), `s` (string) or `t` (Tensor). An attribute
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list of floats (e.g., you would say `dims_i` for a `dims` attribute
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outputs (int, optional): The number of outputs this operator returns;
by default an operator is assumed to return a single output.
If `outputs` is greater than one, this functions returns a tuple
of output `Node`, representing each output of the ONNX operator
in positional.
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