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# torch.fake_quantize_per_tensor_affine

torch.fake_quantize_per_tensor_affine(input, scale, zero_point, quant_min, quant_max) → Tensor

  Returns a new tensor with the data in `input` fake quantized using `scale`, `zero_point`, `quant_min` and `quant_max`.

  output=(m i n(quant_max,m a x(quant_min,std::nearby_int(input/scale)+zero_point))−zero_point)×scale\text{output} = ( min( \text{quant\_max}, max( \text{quant\_min}, \text{std::nearby\_int}(\text{input} / \text{scale}) + \text{zero\_point} ) ) - \text{zero\_point} ) \times \text{scale} output=(min(quant_max,ma x(quant_min,std::nearby_int(input/scale)+zero_point))−zero_point)×scale
  Parameters:

    - input (Tensor) – the input value(s), `torch.float32` tensor

    - scale (double scalar or `float32` Tensor) – quantization scale

    - zero_point (int64 scalar or `int32` Tensor) – quantization zero_point

    - quant_min (int64) – lower bound of the quantized domain

    - quant_max (int64) – upper bound of the quantized domain

  Returns:

    A newly fake_quantized `torch.float32` tensor

  Return type:

    Tensor

  Example:

      >>> x = torch.randn(4)
      >>> x
      tensor([ 0.0552,  0.9730,  0.3973, -1.0780])
      >>> torch.fake_quantize_per_tensor_affine(x, 0.1, 0, 0, 255)
      tensor([0.1000, 1.0000, 0.4000, 0.0000])
      >>> torch.fake_quantize_per_tensor_affine(x, torch.tensor(0.1), torch.tensor(0), 0, 255)
      tensor([0.1000, 1.0000, 0.4000, 0.0000])

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