Tian Jin

@fumihwh we did consider onnx -> tensorflow -> tf serving path, that is why we have `export_graph` in our API, @nmakhotkin unfortunately as @fumihwh pointed out, max_pool is a very...

@nmakhotkin can you provide me with the onnx model generated by torch?

@nmakhotkin would you like to try again with this PR https://github.com/onnx/onnx-tensorflow/pull/171/files ? You can check out a different branch as well (https://github.com/onnx/onnx-tensorflow/tree/fix-pool).

@nmakhotkin to put it shortly, PyTorch's ResNet implementation is **incorrect** or more precisely, **not faithful** to the original paper. This might be unbelievable to you, but let me point you...

@nmakhotkin as a result, there is no semantic preserving AND serializable workaround. But we can try to give you an option to slightly alter the semantics of max pool so...

Hi, absolutely, but we might have other priorities in the meantime, like supporting onnx v1.2; sorry for the delay, my estimate is that it'll be there before the end of...

@inakinavarro @nmakhotkin hi, a tentative PR to address this issue has been created https://github.com/onnx/onnx-tensorflow/pull/212. @inakinavarro I've modified ur original script to use non-strict mode: ``` tf_backend.prepare(model, strict=False) ``` It seems...

@asarah-github the PR has not made its way into any of our existing releases yet. It won't be there if you install a release version of onnx-tensorflow (I'm not sure...

The model is not compliant because the model claims to have onnx opset version 9, and in that version of the spec, ONNX reshape operation must specify shape in int64...

@caoimhinuibrian that was my suggestion. We shouldn't care which datatype shapes are specified in.