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torch.quantize_per_channel#

torch.quantize_per_channel(input,scales,zero_points,axis,dtype)→Tensor#

Converts a float tensor to a per-channel quantized tensor with given scales and zero points.

Parameters

  • input (Tensor) – float tensor to quantize

  • scales (Tensor) – float 1D tensor of scales to use, size should matchinput.size(axis)

  • zero_points (int) – integer 1D tensor of offset to use, size should matchinput.size(axis)

  • axis (int) – dimension on which apply per-channel quantization

  • 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

Return type

Tensor

Example:

>>>x=torch.tensor([[-1.0,0.0],[1.0,2.0]])>>>torch.quantize_per_channel(x,torch.tensor([0.1,0.01]),torch.tensor([10,0]),0,torch.quint8)tensor([[-1.,  0.],        [ 1.,  2.]], size=(2, 2), dtype=torch.quint8,       quantization_scheme=torch.per_channel_affine,       scale=tensor([0.1000, 0.0100], dtype=torch.float64),       zero_point=tensor([10,  0]), axis=0)>>>torch.quantize_per_channel(x,torch.tensor([0.1,0.01]),torch.tensor([10,0]),0,torch.quint8).int_repr()tensor([[  0,  10],        [100, 200]], dtype=torch.uint8)