Applies batch normalization on a 4D (NCHW) quantized tensor.
y = x − E [ x ] V a r [ x ] + ϵ ∗ γ + β y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta y=Var[x]+ϵ x−E[x]∗γ+β
>>> qx = torch.quantize_per_tensor(torch.rand(2, 2, 2, 2), 1.5, 3, torch.quint8)
>>> torch.quantized_batch_norm(qx, torch.ones(2), torch.zeros(2), torch.rand(2), torch.rand(2), 0.00001, 0.2, 2)
tensor([[[[-0.2000, -0.2000],
[ 1.6000, -0.2000]],
[[-0.4000, -0.4000],
[-0.4000, 0.6000]]],
[[[-0.2000, -0.2000],
[-0.2000, -0.2000]],
[[ 0.6000, -0.4000],
[ 0.6000, -0.4000]]]], size=(2, 2, 2, 2), dtype=torch.quint8,
quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=2)
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