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  1. Q

    TF Lite runtime

    According to my current knowledge, the float32 or float16 TF Lite model should run faster than the uint8 model on a GPU device. So there is no need to perform int8 quantization for image denoising task which running on Exynos mail GPU. Is this correct? How about the image super-resolution task...
  2. Q

    TF Lite quantization

    Sounds great. Thanks.
  3. Q

    TF Lite quantization

    1. TF2.3 2. Yes. I found the problem and it seems "depth_to_space" op doesn't support uint8 input quantization. But I can feed float32 (0~255) input to the quant model. But it is extremely slow on PC.
  4. Q

    TF Lite quantization

    I used this example to quantize my model. However, after I print( input_details[0]['dtype'] ), the input type is still float32. So how to get uint8 input type? Thanks.
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