ONNX Version Converter
ONNX provides a library for converting ONNX models between different opset versions. The primary motivation is to improve backwards compatibility of ONNX models without having to strengthen the spec for ONNX backends. This allows backend developers to offer support for a particular opset version and for users to write or export models to a particular opset version but run in an environment with a different opset version. Implementation wise, the library leverages the in-memory representation that is much more convenient to manipulate than the raw protobuf structs, and converters to and from the protobuf format which were developed for the ONNX Optimizer.
You may be interested in invoking the provided op-specific adapters, or in implementing new ones (or both). Default adapters only work in the default domain, but can be generalized to work cross-domain or utilizing new conversion methods, dependent on the nature of relevant breaking changes.
Invoking The Version Converter
The version converter may be invoked either via C++ or Python.
The Python API is described, with example, here.
The C++ API consists of a single function
ModelProto ConvertVersion( const ModelProto& mp_in, const OpSetID& initial_version, const OpSetID& target_version);
which accepts an input
ModelProto, the initial opset version of the model,
and the target opset verison, and which returns a new
is the result of apply all relevant adapters between initial_version and
target_version. For a list of available passes, see
You can implement a new adapter by subclassing
Adapter, and registering
your new adapter with
VersionConverter::registerAdapter(). Adapters operate
on an in-memory graph representation defined in ir.h.
There are a number of examples in the adapters
directory. Please ensure that all adapters convert from opset version i to i + 1
or i - 1, i.e. from Version 6 to Version 5 or vice versa, even if the 2 versions
being converted between are Version 1 and Version 6.
If your adapter applies in the default domain, please consider adding it to the core ONNX repository