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BatchNormInference

General

The formula is the same as Batch Normalization primitive 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 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

BatchNormForwardTraining BatchNormTrainingBackward