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From Zhao Wu <notificati...@github.com>
Subject Re: [dmlc/tvm] [RFC][Quantization] Support quantized models from TensorflowLite (#2351)
Date Wed, 29 May 2019 02:55:38 GMT
@anijain2305 

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. see
TFLite's `CalculateActivationRangeUint8` function.

>From my experience, we needn't `q_relu`. But we need `q_add` / `q_concate` and so on.
I suggest we use `MobilenetV2` quant model for example, which is used very widely and have
common ops we should consider. For example, `depthwise convolution / add / pool and so on`.

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