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Hey @waytrue17 , Thanks for submitting the PR
CI supported jobs: [unix-cpu, clang, unix-gpu, edge, windows-gpu, windows-cpu, website, sanity, centos-cpu, centos-gpu, miscellaneous] Note: |
python/mxnet/onnx/README.md
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By setting up optional flags in export_model API, users have the control of partially/fully dynamic shape input export. The flag `dynamic` is set to switch on dynamic shape input export, and `dynamic_input_shapes` is used to specify which dimensions are dynamic (None for dynamic shape). | ||
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```python | ||
# None indicating dynamic shape at a certain dimension |
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here the dynamic shape can also be something like ('batch', 'seq_length') i.e. using strings rather than None
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Updated the description
I think we might also touch on dynamic shapes in the tutorial as this is a very common use case e.g. different image sizes or sequence lengths. Also I think we can add a section to introduce onnx-simplifier as it can significantly simplify the onnx graph in many cases. CC the author of onnxsim @daquexian: we are releasing mx2onnx in the upcoming mxnet 1.9 release : ) |
Added dynamic shape input in the tutorial, and a link reference to onnx-simp |
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LGTM!
Description
Update ONNX tutorial and doc
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Essentials
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