Can an ONNX network be incompatible with onnxruntime?

Question

I am having trouble running inference on an ONNX model, either by making (tiny) adjustments to this Windows ML tutorial, or by implementing my own ONNX Runtime code following their MNIST Tutorial. As I understand it, Windows ML makes use of ONNX Runtime, so both efforts probably end up in the same place... and probably generating the same underlying exception for the same reason.

The exceptions thrown are either unintelligible (a second exception thrown during exception handling by the looks...) or not identified at all. It makes me wonder if the network itself is faulty or incompatible in some sense. The network was produced by taking a saved Tensorflow/Keras model and running this conversion:

python -m tf2onnx.convert --saved-model MyNet --output MyNet.onnx --inputs-as-nchw mobilenetv2_1_00_224_input:0

The result is a network that is rendered by Netron with the following input & output stages:

Is there anything about this network that is obviously incompatible with ONNX Runtime? Any suggestions on how to push past either/both of these exceptions?

Answer 1

Turns out that in my attempt to adapt Windows ML example, I had the output shape wrong - in that example, the output shape is 1 x 1000 x 1 x 1. I had copied/pasted this, and just modified the 1000 to suit. Clearly the network above needs a 1 x 10 shape....