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Looks that it is very rarely seen.
s_scale is a floating pointed number, it generally does not become 0 during training.
I will try to add an eps, when I have spare time. (it need some experiments)
Looks that it is very rarely seen. s_scale is a floating pointed number, it generally does not become 0 during training. I will try to add an eps, when I have spare time. (it need some experiments)
Thanks for your the implementation!
In LsqQuan.
lsq-net/quan/quantizer/lsq.py
Line 54 in 2c24a96
If s_scale contains 0, there will be Divided by 0 Error, and the training loss will become nan.
Maybe eps should be used.
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