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# FakeQuantize

class torch.ao.quantization.fake_quantize.FakeQuantize(observer=<class 'torch.ao.quantization.observer.MovingAverageMinMaxObserver'>, quant_min=None, quant_max=None, **observer_kwargs)

  Simulate the quantize and dequantize operations in training time. The output of this module is given by:

      x_out = (
        clamp(round(x/scale + zero_point), quant_min, quant_max) - zero_point
      ) * scale

  - `scale` defines the scale factor used for quantization.

  - `zero_point` specifies the quantized value to which 0 in floating point maps to

  - `fake_quant_enabled` controls the application of fake quantization on tensors, note that statistics can still be updated.

  - `observer_enabled` controls statistics collection on tensors

  - `dtype` specifies the quantized dtype that is being emulated with fake-quantization,

      allowable values are torch.qint8 and torch.quint8.

  Parameters:

    - observer (module) – Module for observing statistics on input tensors and calculating scale and zero-point.

    - observer_kwargs (optional) – Arguments for the observer module

  Variables:

    activation_post_process (Module) – User provided module that collects statistics on the input tensor and provides a method to calculate scale and zero-point.

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