Returns a new tensor with the data in input fake quantized using scale, zero_point, quant_min and quant_max.
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
input (Tensor) – the input value(s), torch.float32 tensor
scale (double scalar or float32 Tensor) – quantization scale
zero_point (int64 scalar or int32 Tensor) – quantization zero_point
quant_min (int64) – lower bound of the quantized domain
quant_max (int64) – upper bound of the quantized domain
A newly fake_quantized torch.float32 tensor
Example:
>>> x = torch.randn(4) >>> x tensor([ 0.0552, 0.9730, 0.3973, -1.0780]) >>> torch.fake_quantize_per_tensor_affine(x, 0.1, 0, 0, 255) tensor([0.1000, 1.0000, 0.4000, 0.0000]) >>> torch.fake_quantize_per_tensor_affine(x, torch.tensor(0.1), torch.tensor(0), 0, 255) tensor([0.1000, 1.0000, 0.4000, 0.0000])
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