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Showing content from https://docs.pytorch.org/docs/stable/generated/torch.fake_quantize_per_channel_affine.html below:

torch.fake_quantize_per_channel_affine — PyTorch 2.8 documentation

Returns a new tensor with the data in input fake quantized per channel using scale, zero_point, quant_min and quant_max, across the channel specified by axis.

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,max(quant_min,std::nearby_int(input/scale)+zero_point))zero_point)×scale

>>> x = torch.randn(2, 2, 2)
>>> x
tensor([[[-0.2525, -0.0466],
         [ 0.3491, -0.2168]],

        [[-0.5906,  1.6258],
         [ 0.6444, -0.0542]]])
>>> scales = (torch.randn(2) + 1) * 0.05
>>> scales
tensor([0.0475, 0.0486])
>>> zero_points = torch.zeros(2).to(torch.int32)
>>> zero_points
tensor([0, 0])
>>> torch.fake_quantize_per_channel_affine(x, scales, zero_points, 1, 0, 255)
tensor([[[0.0000, 0.0000],
         [0.3405, 0.0000]],

        [[0.0000, 1.6134],
        [0.6323, 0.0000]]])

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