torch.quantized_batch_norm#

torch.quantized_batch_norm(input,weight=None,bias=None,mean,var,eps,output_scale,output_zero_point)→Tensor#

Applies batch normalization on a 4D (NCHW) quantized tensor.

$$y=\frac{x-\mathrm{E}[x]}{\sqrt{\mathrm{V}\mathrm{a}\mathrm{r}[x]+ϵ}}\ast \gamma +\beta$$y=Var[x]+ϵ​x−E[x]​∗γ+β

Parameters

• input (Tensor) – quantized tensor

• weight (Tensor) – float tensor that corresponds to the gamma, size C

• bias (Tensor) – float tensor that corresponds to the beta, size C

• mean (Tensor) – float mean value in batch normalization, size C

• var (Tensor) – float tensor for variance, size C

• eps (float) – a value added to the denominator for numerical stability.

• output_scale (float) – output quantized tensor scale

• output_zero_point (int) – output quantized tensor zero_point

Returns

A quantized tensor with batch normalization applied.

Return type

Tensor

Example:

>>>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)