QueensGambit commented on issue #17827: [Numpy] Bugfix of slice operator export (MXNet to ONNX) URL: https://github.com/apache/incubator-mxnet/pull/17827#issuecomment-615874810 Hello @RuRo, great to see that you have been working on the shape inference issue. Surprisingly, I didn't receive any notification for your PR and only noticed it after you mentioned it. Passing `graph_shapes` as a dict is a working solution but is cumbersome for deep models with changing shape sizes. I noticed that there is existing functionality for inferring the out-shapes of a symbol object. For instance you can plot the model structure including all shapes of each layer like this: ```python display(mx.viz.plot_network( symbol, shape={'data':(input_shape)}, node_attrs={"shape":"oval","fixedsize":"false"} )) ``` The corresponding code for inferring looks like this: ```python internals = symbol.get_internals() input_name = "data" _, out_shapes, _ = internals.infer_shape(**{input_name: input_shape}) ``` This way you would only require `input_name` as an additional parameter which could be set to `"data"` by default. Do you want to update your code or should I do it?
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