Various runtimes might have bugs or don't fully support the onnx spec.
For that reason tensorflow-onnx has a --target option which triggers workarounds for issues we are aware of.
You find supported targets by calling python -m tf2onnx.convert without arguments. The usage will contain the possible targets, like:
[--target {rs4,rs5,rs6,caffe2}]
You can combine multiple targets as comma separated list.
Currently we support:
--target caffe2 - workarounds for missing ops and broadcast semantic. This was for older versions of caffe2 before it was merged into pytorch.
--target rs4 - rs4 targets the pre release for winml on windows, workarounds for missing ops and broadcast semantic
--target rs5 - rs5 targets winml in the october-2018 windows update, workarounds for missing type support
--target rs6 - rs6 targets winml in next windows update, workarounds for missing type support
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