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# torch.quantize_per_tensor

torch.quantize_per_tensor(input, scale, zero_point, dtype) → Tensor

  Converts a float tensor to a quantized tensor with given scale and zero point.

  Parameters:

    - input (Tensor) – float tensor or list of tensors to quantize

    - scale (float or Tensor) – scale to apply in quantization formula

    - zero_point (int or Tensor) – offset in integer value that maps to float zero

    - dtype (`torch.dtype`) – the desired data type of returned tensor. Has to be one of the quantized dtypes: `torch.quint8`, `torch.qint8`, `torch.qint32`

  Returns:

    A newly quantized tensor or list of quantized tensors.

  Return type:

    Tensor

  Example:

      >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8)
      tensor([-1.,  0.,  1.,  2.], size=(4,), dtype=torch.quint8,
             quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10)
      >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8).int_repr()
      tensor([ 0, 10, 20, 30], dtype=torch.uint8)
      >>> torch.quantize_per_tensor([torch.tensor([-1.0, 0.0]), torch.tensor([-2.0, 2.0])],
      >>> torch.tensor([0.1, 0.2]), torch.tensor([10, 20]), torch.quint8)
      (tensor([-1.,  0.], size=(2,), dtype=torch.quint8,
          quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10),
          tensor([-2.,  2.], size=(2,), dtype=torch.quint8,
          quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=20))
      >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.tensor(0.1), torch.tensor(10), torch.quint8)
      tensor([-1.,  0.,  1.,  2.], size=(4,), dtype=torch.quint8,
         quantization_scheme=torch.per_tensor_affine, scale=0.10, zero_point=10)

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