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From Animesh Jain <notificati...@github.com>
Subject Re: [dmlc/tvm] [RFC][Quantization] Support quantized models from TensorflowLite (#2351)
Date Wed, 29 May 2019 15:49:59 GMT
> For the `q_conv2d`, we will add two more arguments.
> 
> ```python
>   output_min=0, 
>   output_max=0
> ```
> 
> These will be used for restrict the output range, which could be calculated previously.

I see what you are saying, but I am not sure if this is the right approach. In my opinion,
it will be better to put it out of conv. The reason we have these 2 extra min/maxes is because
of fused activation in TFLite. It seems better to keep it separate so that both MxNet and
TFLite can share quantized_conv2d. In case of TFLite, when we see a fused conv, we can add
one more clamp operator in the sequence of ops at the end.

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