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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 orTensor) – scale to apply in quantization formula

  • zero_point (int orTensor) – 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)