Quantization produces large scale coffiecient, which pervents the model from being loaded

[gaikwadrahul8]

1. System information

Colab , as of 2023-10-23

2. Code

Please see the attached colab notebook here
https://colab.research.google.com/drive/1yUD0nDu8oeeDtQBa7xCbQWx_w8PxS4UC?usp=sharing
to reproduce the issue. It loads a pre-trained resnet18 from pytorch, converts it to onnx, converts it to tensorflow, and then exports it to tf-lite. ( The process is a bit convoluted, but I need a pretrained resnet18, and didn't find it in the tensorflow orbit so I used torchvision, hope that's ok.)

If you download the generated model (model_int8.tflite) and open it in netron.app and click on the first MaxPool2D op, you can see that the quantization scale is 1.3344405750530544e+36. See the attached image.

This scale parameter itself is of course implausible (impossible), but loading the model also produces an error here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/internal/quantization_util.cc#L117

Does anybody know why the quantization parameter is that high, and what can be done to fix it? Furthermore, can I let the quantization fails explicitly when it generates such high values?

[gaikwadrahul8]

This issue originally reported by @FabianSchuetze has been moved to this dedicated repository for ai-edge-torch to enhance issue tracking and prioritization. To ensure continuity, we have created this new issue on your behalf.

We appreciate your understanding and look forward to your continued involvement.

[pkgoogle]

Still awaiting this PR: tensorflow/tensorflow#62605