BatchNormInference

The formula is the same as Batch Normalization primitive :ref:`batch_normalization-label like below.

\[\dst(n, c, h, w) = \gamma(c) \cdot \frac{\src(n, c, h, w) - \mu(c)} {\sqrt{\sigma^2(c) + \varepsilon}} + \beta(c),\]

where

• \(\gamma(c), \beta(c)\) are required scale and shift for a channel,

• \(\mu(c), \sigma^2(c)\) are mean and variance for a channel, and

• \(\varepsilon\) is a constant to improve numerical stability.

Operation Attributes

Attribute Name	Description	Value Type	Supported Values	Required or Optional
epsilon	A number to be added to the variance to avoid division by zero	f32	A positive float value	Required
data_format	Controls how to interpret the shape of src and dst.	string	NCX, NXC (default)	Optional

Execution Arguments

The inputs and outputs must be provided according to the below index order when constructing an operation.

Inputs

Index	Argument Name	Required or Optional
0	src	Required
1	gamma	Required
2	beta	Required
3	mean	Required
4	variance (\(\sigma^2\))	Required

Outputs

Index	Argument Name	Required or Optional
0	dst	Required

Supported Data Types

BatchNormInference operation supports the following data type combinations.

Src / Dst	Gamma / Beta / Mean / Variance
f32	f32
bf16	f32, bf16
f16	f32