torch.quantized_batch_norm — PyTorch 2.7 documentation (original) (raw)

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=x−E[x]Var[x]+ϵ∗γ+βy = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

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)